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Impact of rapid transit stations on land use changes in their proximity Baross, Paul P. 1972

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THE IMPACT OF RAPID TRANSIT STATIONS ON LAND USE CHANGES IN THEIR PROXIMITY; TOWARDS A MODEL by Paul P. Baross B.A. L. Arch., University of Budapest, 1967 and Robert R. Stiissi H B.Sc, Institute of Technology, Zurich, 1968 A THESIS SUBMITTED IN PARTICAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in the School of Community and Regional Planning We accept th i s thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA May, 1972 In p r e s e n t i n g t h i s t h e s i s in p a r t i a l f u l f i l m e n t o f the requirements fo r an advanced degree at the U n i v e r s i t y of B r i t i s h Columbia , I agree that the L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r reference and s tudy. I f u r t h e r agree tha t pe rmiss ion for e x t e n s i v e copying o f t h i s t h e s i s for s c h o l a r l y purposes may be granted by the Head of my Department or by h i s r e p r e s e n t a t i v e s . I t i s understood that copying o r p u b l i c a t i o n o f t h i s t h e s i s f o r f i n a n c i a l ga in s h a l l not be a l lowed wi thout my w r i t t e n p e r m i s s i o n . Department o f The U n i v e r s i t y o f B r i t i s h Columbia Vancouver 8, Canada In present ing th i s thes i s in p a r t i a l f u l f i lmen t of the requirements fo r an advanced degree at the Un iver s i t y of B r i t i s h Columbia, I agree that the L ib ra ry sha l l make i t f r ee l y ava i l ab le for reference and study. I fu r ther agree that permission for extens ive copying o f t h i s thes i s f o r s cho la r l y purposes may be granted by the Head of my Department or by his representat ives . It i s understood that copying or pub l i c a t i on of th i s thes i s fo r f i nanc i a l gain sha l l not be allowed without my wr i t ten permiss ion. Depa rtment The Un ivers i ty of B r i t i s h Columbia Vancouver 8, Canada - i i -abstract F o r n e a r l y one h u n d r e d y e a r s , f r o m a p p r o x i m a t e l y t h e 1830-s t o t h e e a r l y d e c a d e s o f t h e t w e n t i e t h ) ; c e n t u r y , t h e f o r m o f many N o r t h A m e r i c a n c i t i e s was d o m i n a t e d b y t h e p a t t e r n o f mass t r a n s p o r -t a t i o n r o u t e s . E a c h s u c c e s s i v e f o r m o f t r a n s p o r -t a t i o n f r o m t h e h o r s e d r a w n o r a n i - b u s t o t h e e l e c t r i c s t r e e t r a i l w a y s , h a d v i s i b l e e f f e c t s u p o n t h e g r o w t h , s h a p e and i n t e r n a l o r g a n i z a t i o n o f u r b a n a g g l o m e r a t i o n s . A f t e r f i f t y y e a r s o f a l m o s t s o l e r e l i a n c e on p r i v a t e t r a n s p o r t a t i o n , t h e l a s t d e c a d e h a s w i t n e s s e d a s i g n i f i c a n t l y i n c r e a s e d i n t e r e s t i n r a i l r a p i d t r a n s i t w i t h a n o f t e n c l a i m e d , b u t r a r e l y * t h o r o u g h l y a n a l y z e d e x p e c t a -t i o n , t h a t t h e r e v i t a l i z e d and i m p r o v e d mass t r a n s p o r t a t i o n r o u t e s w i l l u l t i m a t e l y p i e c e t h e f r a g m e n t e d e n v i r o n m e n t o f t o d a y ' s m e t r o p o l i s e s i n t o a m a n a g e a b l e w h o l e a g a i n . T h i s s t u d y t r e a t s one a s p e c t o f t h e m u l t i -d i m e n s i o n a l i n t e r a c t i o n b e t w e e n t h e i n t r o d u t i o n / ABSTRACT - i i i -operation of rapid t r a n s i t l i n e s and subsequent restructuring of the s p a t i a l d i s t r i b u t i o n of a c t i v i t i e s i n the urban f i e l d * the rate of devel-opment of areas i n the proximity of rapid t r a n s i t stations. Explanations f o r the aparent difference i n the rate of new construction around various stations i s sought not i n terms of the t r a d i t i o n a l a c c e s s i b i l i t y concept, but rather i n the environ-mental context within which each station i s placed. Drawing from a rather d i s t i n c t subdivision of urban research and extensive data analysis, the components of the environmental context and t h e i r r e l a t i v e importance i n exerting influence on the s p a t i a l d i s t r i b u t i o n of new construction were i d e n t i f i e d . During the course of the study a simple simulation model was developed i n order to capture the dynamics of changes within the envir-onmental context and consequently to a s s i s t i n a n t i c i p a t i n g the s p a t i a l d i s t r i b u t i o n of new constructions or replacements of e x i s t i n g p h y i s i c a l stock i n the v i c i n i t y of rapid t r a n s i t stations. The emphasis i s placed on these s p e c i f i c s t r u c t u r a l changes because the consequences of l o c a t i o n a l and investment decisions that r e s u l t i n s i g n i f i c a n t a l t e r a t i o n or renewal of buildings represent a more substantial modification i n the i n t e r n a l organization of the c i t y than those r e s u l t i n g - i v -from the continuous shifting and f i l t e r i n g of ac t i v i t i e s within the standing stock. _ v -contents CHAPTER TITLE PAGE 1. Prologue 1 2. Introduction 11 3. Conceptual Background 18 3.1 3.2 3-3 3.4 Location Theory Highway Impact Studies Rapid Transit Impact Studies Summary 19 37 47 70 Environment as an Input 82 4.1 4.2 The n&ohc&jpp.of&Environment General Hypothesis Elements of the Environmental* Context 83 89 90 5. Study Design 116 5.1 5.2 Methodology Limitation of the Study 117 121 6. Empirical Background 129 6.1 6.2 6.3 Metropolitan Toronto Data Description Preparation of Input f o r simulation 130 140 166 - v i -CHAPTER PAGE 7. Simulation model I89 7.1 Model Description 196 7.2 Model Ca l i b r a t i o n 21k 7.3 S e n s i t i v i t y Analysis 230 7.4 Conclusions on the Simulation 2kl 8. Synthesis 2k6 8.1 Implication f o r Planning 2^7 8.2 Directions f o r Further Research 252 A. Appendices A.a Technical Notes A.b Data A.c S t a t i s t i c a l Analysis A.d Simulation - v i i -list of figures FIGURE PAGE NUMBER TITLE NUMBER 1-1 Metropolitan Population Increase Trends since 1800 A.D. 2 3.1-1 A c c e s s i b i l i t y Cone 22 3.1-2 Transportation Function 23 3.1-3 S p a t i a l Structure of Position Rents 23 3.144 Unit Rent and Density P r o f i l e s with Distance 24 3.2-1 Freeway Network, L o u i s v i l l e , Kentucky, U.S.A. 42 3.2-2 Land Use as a Function of Land Value and A c c e s s i b i l i t y 43 3.2-3 Timing of Transportation Impact (Tappan Zee Bridge, Hudson River, Rockland County, N.Y.) 44 3.3-1 London 'Underground* I863 51 3-3-2 Rapid Transit Networks of London, Paris, Moscow, New York, and Hong Kong 53 3.3-3 The Radial Corridor Plan f o r the Metropolitan Washington 59 4.3-1 Sub-Hypothesis #1 93 4.3-2 Sub-Hypothesis #2 96 v i i i FIGURE PAGE NUMBER TITLE NUMBER 4 . 3 - 3 Sub-Hypothesis # 3 9 9 4 . 3 - 4 Sub-Hypothesis # 4 1 0 1 4 . 3 - 5 Sub-Hypothesis # 5 1 0 3 4 . 3 - 6 Sub-Hypothesis # 6 1 0 6 4 . 3 - 7 Sub-Hypothesis #7 1 0 9 5 . 2 - 1 The Street Mapiof Metropolitan Toronto 1 2 2 6 . 1 - 1 The Growth of the City of Toronto 1 3 3 6 . 1 - 2 Growth of the Built-up area, Metropolitan Toronto I 9 5 3 - I 9 6 7 1 3 3 6 . 1 - 3 Population Growth of the City, of Toronto 1 8 3 4 - 1 9 8 1 1 3 4 6 . 1 - 4 Changes in the Population of Metropolitan Toronto Planning Areas 1 3 5 6 . 1 - 5 Value of Building Permits 1 9 5 0 - 1 9 7 0 - in millions of dollars 1 3 6 6 . 1 - 6 Metropolitan Toronto Subway, New Line Stages of Construction 1 3 8 6 . 1 - 7 Subway Passengers Carried 1 3 9 6 . 2 - 1 Cumulative Distribution of Apartment Building Size -1 9 5 9 - 1 9 6 4 144 6 . 2 - 2 Cumulative Distribution of Apartment Building Size -1 9 6 5 - 1 9 7 0 144 6 . 2 - 3 Future Distribution of Apartment Units by Planning Districts 147 6 . 2 - 4 Topography of the Metropolitan Toronto Site 149 - i x -FIGURE PAGE NUMBER TITLE NUMBER 6.2-5 Proposed Water Supply, and Sewers 153 6.2-6 Commercial Areas of Toronto, 1966 158 6.2-7 D i s t r i b u t i o n of Public Open Space 5 160 6.2- 8 Zoning system, Toronto 162 6.3- 1 Deductive Analysis 166 6.3-2 Inductive Analysis 16? 6.3-3 Inductive-Deductive Analysis 168 6.3-4 Concept of S t a t i s t i c a l Analysis 170 6.3-6 Sequence of Analysis 172 6.3-7A Tablefunction Lotsize -Normalized a t t r a c t i v i t y Scores, unweighted 174 6.3-7B Tablefunction Lotsize -Weighted A t t r a c t i v i t y Scores 174 6.3-8 Construction of New Apartments 177 6.3-9 Technological Constraints 177 6.3-IO Available Land f o r New Construction 177 6.3-11 Vacant Land 178 6.3-12 Building Age Mixture 178 6.3-13 Neighborhood Quality 178 6.3-14 Average Lot Size 179 6.3-15 Proximity to Major Open Space 179 6.3-16 Surface A c c e s s i b i l i t y 179 - X -FIGURE PAGE NUMBER TITLE NUMBER 6.3-I7 Measurement of Nodality 180 6.3-I8 Zoning 180 6.3-19 Commercial Development 180 6.3-20 Undesirable Conditions 181 6.3=21 Time Periods of Analysis of Apartment Development Size Distribution 182 6.3-22 Apartment Development Size Function per Station Sub-area 1959-1970 I83 6.3-23 Apartment Development Size Function per Station Sub-area 1959-1964 184 6.3-24 Apartment Development Size Function per Station Sub-area 1965-1970 184 6.3-25 Apartment Development Size Function per Station Sub-area 1959-1962 185 6.3-26 Apartment Development Size Function per Station Sub-area I963-I966 185 6.3-27 Apartment Development Size Function per Station Sub-area 1967-1970 186 6.3-28 Apartment Development Size Function per Station Sub-area 1971-1985 186 7-1 Subway Lines 191 7-2 Station Sub-area Code 192 7-3 Station Code 193 7-4 Moving Averages for Model Evaluation 195 7.1-1 General Model Structure 198 - xi -FIGURE PAGE NUMBER TITLE NUMBER 7.1-2 Specific Model Structure 199 7.1-3 Dimensions of Model 203 7.1-4 Elements and Relationships of the Model 207 7.1- 5 General Program Flowchart 208 7.2- 1 Simulated Apartment Growth, Subway Line Yonge 218 7.2-2 Scattergram 223 7.2-3 Scattergram 224 7.2-4 Scattergram 225 7.2-5 Scattergram 226 7.2-6 Scattergram 227 7.2-7 Scattergram 228 7.2- 8 Scattergram 229 7.3- 1 Designated Areas for High Density Residential Development 235 7.3-2 Simulated Cumulative Apartment Growth Subway Line Yonge 1970-1986 238 7.3-3 Simulated Cumulative Apartment Growth Subway Line BWO, BWN 1970-1986 239 7.3-4 Simulated Cumulative Apartment Growth Subway Line BEO, BEN 1970-1986 240 - x i i -list of tables TABLE PAGE NUMBER TITLE NUMBER 3.3-1 Changes i n Realty Tax Assess-ments, City of Toronto, 1950-1959 62 3.3-H D i s t r i b u t i o n of Redevelopment Construction, City of Toronto, I959 - I 9 6 3 64 6.3-1 Weights of Environmental Factors 176 7.1-1 Dimension Limits of the Model 205 7.1- II Dimensions of the Toronto Subway System 206 7.2- 1 Refined Weights of Environ-mental Factors 216 7.2-II Comparison of E f f e c t i v e and Simulated Apartment Growth 220 7.2-111 Comparison of Actual and Simulated Apartment Growth 221 x i i i This thesis was a joint venture. It was a very rich experience for the authors who had a close working relationship with each other and many other persons involved in the project. Gratitude i s owed to many. Several persons reviewed an earlier draft and their detailed comments set guidelines for this f i n a l version. In this regard a special note of appreciation i s extended to Paul Roer, Professor of Transportation Planning at the School of Community and Regional Planning, who acted as * f i r s t advisor* for both of us; to Michael Goldberg, Director of the UPS-Project and Professor of Economicsi and to Michael Seeling, Professor of Planning at the School of Community and Regional Planning, who were our second advisors. We are particularly grateful to Dennis W. Pervis, §rt5graramer» Computing Center, University of British x i v Columbia, f o r h i s invaluable support. He wrote the program f o r the simulation model and gave us many-hel p f u l suggestions. In t h i s regard, we also received considerable help and advice from our fri e n d Hirotaka Koike. We are indebted to the many peoplefdn government and planning agencies who assisted us i n our data c o l l e c t i o n . Special thanks to Susan, Allan and Henry who helped us to overcome our language d i f f i c u l t i e s . Our wives did everything else. Government and planning agencies who supported the data c o l l e c t i o n : City of Toronto, Planning Board R.F. Cohen B. Cook G. Moravec A. Murray J. Rey J. Warden Metropolitan Toronto Planning Board B. Ellwood E. Scholl Planning Department, Borough of Etobicoke J. Sinnott Centre f o r Urban and Community Studies L.S. Bourne D. Soberman - 1 -Clearly, one of the most outstanding phenomena of our time i s the accelerating rate of urbaniz-ation. The gravitation of rural population toward large urban centers has occurred irrespec-tive of national boundaries or political/ideolog-i c a l systems and has been manifested, with minor variations, in both the developed and modernizing countries. Since the turn of the nineteenth century the total number of metropolitan status c i t i e s i n the world has grown from 25 to 90* Their population has increased from 11 to 173 million or about sixteenfold,awhile during the same period the world population increased only 2 . 4 times. 1 (Figure 1-1) The annual rate of horizontal expansion of these metropolises has been even more dramatic, often exceeding twice their population growth rate. URBANIZATION PAST AND PERSPECTIVE 1. Papageorgion, (1971) P«4 -See also Davis(1955) - 2 -500 03L 1S00 1 / / i / i i H— / / s. \ / / f \ 1 t i i ** r 1 . —•• / 1 1 _— IB50 — — — world population • '• population of metropolises' - total —i population of metropolises - grade A — - population of metropolises - grade 8 population of metropolises - grade C — population of metropolises-grade 0 population of Athens ' FIGURE 1-1 METROPOL-ITAN POPU-LATION INCREASE TRENDS SINCE 1800 A.D. Source; Papageorgion, "A Comparative Analysis of F i f t e e n Metropolises", E k i s t i e s , Vol. 32, No. 188, p.k. Current population projections f o r the United States and Canada predict further rapid expansion of urban agglomerations. Pickard has estimated that by the year 2000 the American population 1. i n Manners, (19^9) P»57 - 3 -w i l l be 320 m i l l i o n and almost two-thirds of these people w i l l be concentrated i n the north-east (from the A t l a n t i c to the Great Lakes), C a l i f o r n i a r and F l o r i d a . The Lithwick report, analyzing pres-ent trends and projections i n the Canadian urban development scene, foresees that 73$ of the Canadian population w i l l probably be l i v i n g within 12 major urban centers at the beginning of the twenty-first century. 1 The notions of megal-opolis and ecumenopolis, that i s the urbanized world, may seem to have the f u t u r i s t i c overtone of the next century, yet t h e i r crowded, polluted n u c l e i already ex i s t i n North America, Japan, and 2 Western Europe. The s p a t i a l d i s t r i b u t i o n , i n t e r n a l structure and growth of these future c i t i e s can not be divorced from the economic, s o c i a l , technological, and p o l i t i c a l context within which i n d i v i d u a l decision-making units (firms, households) operate. These "context components" are increas-i n g l y regarded as parts of a "whole" exhibiting system c h a r a c t e r i s t i c s . The whole i s the s p a t i a l pattern a r i s i n g from the accumulating r e s u l t of STRUCTURAL GROWTH AND THE ROLE OF TRANS-PORTATION 1. Lithwick, (1971) p.146. 2. Papaioannov, (1970). - 4 -large numbers of individual "firm" and "house-hold" location decisions and transportation choices. The role of transportation choice within this framework i s of particular interest, for any inquiry into a phenomenon which has apatial dimensions necessitates the appraisal of the linkage system that f a c i l i t a t e s flow among i t s discrete points. Indeed the interrelationship between the available forms of communication/transportation and the location, distribution and forms of settlement growth has been extensively studied i n the past from the points of view of a variety 1 of disciplines. Yet, partly because of the diversity and uncoordinated nature of the i n -quiry, and partly because; of the complexities and the large number of variables involved, a systematic theory has been slow to evolve. It i s pertinent at this point to quote Britton 2 Harris* comment on the state of the art. "No general, quantitative laws emerged that are applicable to c i t i e s of large variety of sizes, functions and locations and over long periods 1. Morrill, (1970) 2. Harris. (1961) - 5 -of time with s h i f t i n g technological and economic conditions." Although the profession s t i l l lacks powerful, r e l i a b l e predictive techniques and models that would a s s i s t i n forecasting the impact of large-scale transportation investments on the future growth pattern and s p a t i a l reorganization of a c i t y i n any s i g n i f i c a n t d e t a i l , considerable l i t e r a t u r e i s available that convincingly r e l a t e s h i s t o r i c a l urban developments to contemporary transportati on/communi c a t i on forms. Mo r r i l l ' explored the profound e f f e c t s of water •features - r i v e r s , lakes, estuaries - on the s p a t i a l d i s t r i b u t i o n of settlements at the time when water transportation was the unifying i element and p r i n c i p a l means of communication. The dynamic role of r a i l transportation i n the regional development of the United States i s well documented, and today increasingly more attention i s directed toward assessing the impact 2 of the rapidly-expanding a i r transportation. On the c i t y scale, many scholars found explan-ations f o r the emergence of various settlement REGIONAL SCALE CITY SCALE 1. M o r r i l l (1970) p. 10 2. Taafee (1959) - 6 -structures and t h e i r density gradients i n the con-temporary transportation technology. H a l l s ' reasoning may be cited as the paradigm on which, with some variations, these speculation are based: "At any point i n the c i t y * s development, i t s form affected the available choice of transportation: but then, the available transportation affected the subsequent growth.fi Accordingly, the mutual re l a t i o n s h i p between TRANSPOR-TATION AND transportation systems and the pattern of urban CHANGING URBAN FORM land use can be demonstrated i n various stages of 2 c i t y development. In the "pre-public transpor-t a t i o n c i t y " there was a dense concentration of people and a c t i v i t i e s within walking distance from the center. The early "public transportation c i t y " depending upon the horse, buse and tram, exhibited tentacular growth along major a r t e r i a l roads. By the 1930 ,s the "mature transportation c i t y " evolved* formed by the e l e c t r i c t r a i n and motor bus, with an o v e r a l l spread of medium density housing, but employment was s t i l l concen-trated at the center or i n well-defined factory and warehouse areas. The erosion of the r e l a t i v e (and often absolute) importance of the cen t r a l 1. H a l l (1969) p. 409 2. Fagin (1962): Smerk (I967) - 7 -areas of most c i t i e s and the rapid expansion of the low-density urban fringe, marks the most recent tendency which i s manifested i n many North American metropolitan structures. The changing c h a r a c t e r i s t i c s of central c i t i e s and suburbs may be attributed to a number of factors of varying importance: mass production, improved packing and handling techniques, innovations i n commun-i c a t i o n and data processing technology, etc. However, the role that the automobile and i t s supporting f a c i l i t i e s (roads, highways, parking) played seems to be of major importance. "The lamentable consequences of the fragmen-t a t i o n of man-made environment - lack of focus, s p e c i f i c i t y and i d e n t i t y - are increasing .... The private motor vehicle makes provision f o r consumer f a c i l i t i e s at random, because i t has uninterrupted access everywhere. Location p r i o r i t y then becomes based on the automobile, and generates a s p a t i a l organization i n an ever more chaotic mosaic." 1 Many of those who believe that the indiscrim-inate accommodation of the automobile ultimately leads to an undesirable c i t y advocate innovation and expansion of public t r a n s i t f a c i l i t i e s . They argue that rapid t r a n s i t can be used as"a " t o o l i n reshaping urban areas towards a more orderly 1. Chermayeff, et a l . , (1971) p.9^ - 8 -and better form of urban developments." 1 Both statements quoted above reveal t h e i r bias -the former by giving a negative score to the urban structure which resulted from the influence of the private car, and the second by assuming that rapid t r a n s i t can piece the "fragmented" environment into a manageable whole. There has been vehement discussion i n recent years regarding the influence of the automobile and mass t r a n s i t on urban l i f e and form. I t i s not the intention of the authors to take a stand on what is the desirable c i t y and on the d e s i r a b i l i t y of certa i n transportation modes per se. But i t i s our b e l i e f that urban l i f e - and therefore the well-being of i n d i v i d u a l s and s o c i a l groups - i s influenced, f o r better or worse, by the form and structure of the urban set t i n g . Form and structure are c l o s e l y related to l o c a t i o n and communication. To investigate the f i e l d of t h i s problem, and to obtain an understanding of the i n t e r r e l a t i o n s h i p between the s i g n i f i c a n t environment and human well-being, i s therefore not merely a technocratic exercise. URBAN LIFE AND URBAN FORM 1. Meyer, et a l . , (1969) P»3 - 9 -Knowledge of the significance of environment w i l l provide people with the opportunity to change i t according to t h e i r desires. - 10 -Chermayeff, S. and Tzonis, A., (1971), Shape of  Community, Penguin Books Inc., Baltimore, Maryland. Davis, K., (1955)* "The Origin and Growth of Urbanization i n the World", American Journal of Sociology, Vol. 60, No. 3» March 1955» P«429-37. Fagin, M., (1962), "Transportation Systems Planning as an Influence on Urban Land Uses", Proceedings? The^Dynamics of Urban Transpor- tati o n , Detroit, Automobile Manufacturers Association. H a l l , P., (1969)» "Transportation", Urban  Studies, Vol. 6, No. 3, p.408-435. Harris, B., (1961), "Some Problems i n the Theory of Intraurban Location", Operations Research 9» 1961, p.695-721. Lithwick, N.H., (1970), Urban Canada, Problems  and Prospects. Manners, G., (1965)* "Urban Expansion i n the United States", Urban Studies, Vol. 2, No. 1, p. 51-66. Meyer, J.R., et a l . , (1969), The Urban Trans-portation Problem, Harvard University Press, Cambridge, Massachusetts. M o r r i l l , L.R., (1970), The S p a t i a l Organization  of Society, Wadsworth Publishing Company, Inc., Belmont, C a l i f o r n i a . Papageorgion, (1971), "A Comparative Analysis of F i f t e e n Metropolises", E k i s t i c s , Vol. 32, No. 188, July, 1971, p.4-11. Also Figure 1-1. Papaioannov, J., (1970), "Future Urbanization Patterns: A Long-Range World-Wide View"', E k i s t i c s , Vol. 31, No. 175, June 1970. Smerk, G.M., (1967), "The Streetcar: Shaper of American C i t i e s " , T r a f f i c Quarterly, Vol. 21, No. 3, October, 1967. p. 569-584. Taffe, E.J., (19^9)* " A i r Transportation and United States Urban D i s t r i b u t i o n " i n Mayer, M.H., and Kohn, C F . Readings i n Urban  Geography, University of Chicago Press, Chicago. introduction - 11 -Much of the debate concerning the role and function of urban transportation originates from i t s dual character. On the one hand, transpor-t a t i o n serves the metropolis as i t evolves, by-f a c i l i t a t i n g the flow of people and goods around i t s various areas. On the other hand, by the mere provision of f a c i l i t i e s that handle the flow, the transportation network shapes the metropolis. H i s t o r i c a l l y , t h i s dichotomy has tended to polarize the approach taken by plan-ners and engineers towards urban transportation problems. Pr a c t i t i o n e r s , emphasizing the service aspect, have focused on t r i p generation, modal s p l i t , flow capacities, engineering e f f i -ciency, etc., and have paid l i t t l e or no attention to the land-use changes that followed the introduction of new f a c i l i t i e s or transpor-t a t i o n p o l i c i e s . This view implies that land use changes autonomously i n response to consumer INTERACTION BETWEEN URBAN DEVELOPMENT AND TRANS-PORTATION - 12 -demands, investment decisions and other, non-transportation factors, and that the transpor-t a t i o n demand which i s produced hy the new con-f i g u r a t i o n and density of land uses i s balanced with the provision of new/improved transpor-t a t i o n f a c i l i t i e s . 1 Accordingly, transportation planning i s seen as a process of forecasting land uses and designing a system that best serves the future land-use pattern. "Given a p a r t i c u l a r pattern of r e s i d e n t i a l and non-residential uses, the transportation planner i s to design the best possible transportation system. Ideally t h i s could be accomplished i f two conditions are met* (1) accurate information on the performance of any proposal could be obtained, (2) appropriate c r i t e r i a to evaluate that information were agreed upon." 2 However, as widened a r t e r i a l roads and multi-land urban freeways have become congested well before they were expected to reach t h e i r saturation point, the confidence i n t h i s narrowly techno-c r a t i c transportation planning practice has gradually eroded. Almost ten years ago the Penn-Jersey Transportation Study set out to test the r e c i p r o c a l proposition that the transportation linkage system plays a decisive role i n the evolution of various urban land-use patterns. 1. M i t c h e l l and Parkins, (1954) 2. C a r o l l , (19^2) p.3 - 13 -Professor Fagin, one of the chief architect s of the study, summarized the rationale behind the proposition i n the following ways "Let us assume that we could provide a transpor-t a t i o n system so evenly spread and so speedy and e f f i c i e n t that urban complexes would remain permanently below the c r i t i c a l size beyond which time and cost do become s i g n i f i c a n t factors i n determining loca t i o n . Let us further assume that the advantages of concentration and area s p e c i a l i z a t i o n having been n u l l i f i e d , the various places of work, residence, recreation, education and l i k e would become evenly spread. In short, l e t us assume the attainment of the very con-d i t i o n s just c i t e d that tend to prevent any s i g n i f i c a n t impact of the transportation system on changing patterns of land use. Have we, by these assumptions, proved that transportation decisions do not a f f e c t the evolving patterns of land use? Quite the contrary! We have merely shown that the deliberate development of one p a r t i c u l a r kind of transportation system i s conducive to one p a r t i c u l a r type of urban pattern." 1 The gradual switch of emphasis, from land-use  projection as a basis f o r transportation planning to transportation systems as a p o t e n t i a l means of promoting a desired pattern of urban development, has resulted i n an unprecedented wave of metropolitan transportation research. Hoping to expose the mutual i n t e r r e l a t i o n s h i p between t r a f f i c patterns and land-use patterns, researchers have attempted to draw t h e i r evidences INTERACTIVE URBAN MODELS 1. Fagin, p.3 - 14 -from two, somewhat i n t e r r e l a t e d , sourcesJ i . Theoretical speculations on the r e l a t i o n s h i p between transportation networks ( a c c e s s i b i l -i t y ) and land use (location rent). i i . Empirical studies related to the impact of large-scale transportation investments (usually freeways) on the evolving config-uration of urban land uses and d e n s i t i e s . The common element i n both types of investigations i s the concept of "featureless p l a i n " ( " a l l land i s of equal quality, ready f o r use without further improvements, surrounding the c e n t r a l l y located, single *market-place* ") that discounts any i n t r i n s i c or gained quantitative differences which may e x i s t among the various points on the possible l o c a t i o n surface. This concept i s e x p l i c i t i n most t h e o r e t i c a l models where the property of l o c a t i o n A d i f f e r s from the property of l o c a t i o n B i f , and only i f , t h e i r a c c e s s i b i l i t y from area G (or areas C i , 03, .... Cn), to which t h e i r connec-tions have been agreed to be of some importance, i s d i f f e r e n t . Since most of the empirical impact studies were conducted during the era when the main mass of urban development occurred outside the c i t y core, consuming large amounts of v i r t u a l l y f l a t , undifferentiated fringe land, the featureless p l a i n concept was i m p l i c i t l y incorporated into - 15 -t h e i r research methodology. The usefulness of t h i s concept, however, i s severely limited when a new transportation network (rapid t r a n s i t , f o r example) i s superimposed on an e x i s t i n g and well-developed urban area. Here, physical structures sheltering e x i s t i n g a c t i v -i t i e s , l e g a l subdivision of land, sentiments attached to s p e c i f i c areas and other, non-trans-portation components r e f l e c t the "optimal" d i s t r i -bution of land uses based on the former accessi-b i l i t y surface, and can be expected to play a role of varying importance i n the change and evolution of new land use and density configurations. The incorporation of the influence of past develop-ments on future l o c a t i o n a l choice c a l l s f o r a new dimension that would endow locations A and B with addi t i o n a l properties. These properties should not be derived only from the new network, but also  from the past commitments which were made to  u t i l i z e t h e i r p o s i t i o n i n previous transportation  networks. In short, the authors propose the replacement of t h e . s t e r i l e notion of featureless p l a i n with a more d i f f e r e n t i a t e d and r e a l i s t i c notion of "environment" i n transportation impact research. Thus t h i s thesis i s an attempt to elaborate on the CONTENT OF THE THESIS - 16 -proposed new dimension, and i t s relevance to the planning of rapid t r a n s i t l i n e s and t h e i r s t a t i o n l o c a t i o n . The thesis i s organized into s i x major parts. Chapter three gives a b r i e f review of relevant l i t e r a t u r e on the theory of location of a c t i v i t i e s i n an urban se t t i n g . This l i t e r a t u r e review i s supplemented with an appraisal of empir-i c a l impact studies related to both urban freeways and to rapid t r a n s i t l i n e s . These findings are evaluated as to the extent the observations can be r a t i o n a l i z e d i n terms of the location theories previously discussed. At the end of the chapter some conclusions are drawn regarding the s i m i l a r -i t i e s and differences between the two types of transportation systems, and the extent and import-ance of the "environmental context" within which t h e i r operations should be analyzed. Chapter four exposes the concept of "environment" and i t hypothesizes on the impact i t s various aspects may have on the l o c a t i o n a l choice of d i f f e r e n t a c t i v i t i e s . Special attention i s paid to the difference between a c t i v i t i e s (land use) and the physical stock necessary to accommodate these a c t i v i t i e s . Based on t h i s conceptual departure, the research methodology i s outlined i n chapter f i v e . - 17 -In chapter s i x the Toronto rapid t r a n s i t system as a case study i s analyzed. The r e s u l t s of the empirical research explain how the i d e n t i f i e d environmental components seem to influence the transformation of various areas a f t e r the rapid t r a n s i t l i n e s were introduced. On the basis of empirically defined parameters and some • i n t e l l i -gent 1 speculations, where no adequate data was available, the formal structure of a simulation model i s presented i n chapter seven. The model i s tested under several d i f f e r e n t assumptions and po l i c y interventions, and the r e s u l t s are then compared and analyzed. The thesis concludes with some observations pertaining to what the previous analysis suggested i n terms of sta t i o n location and planning implications of rapid t r a n s i t pro-j e c t s . F i n a l l y , d i r e c t i o n s f o r further research on the subject are indicated. - 18 -C a r r o l l , D.J., (1962), F i t t i n g Transportation Systems Plans to Urban Land-Use Projections", The  Dynamics of Urban Transportation, National Symposium sponsored by the Automobile Manufact-urers Association Inc., I962. Fagin, H., (I962), "Transportation Systems Planning as an Influence on Urban Land Uses", Proceedings* The Dynamics of Urban Transpor-tat i o n, Detroit, Automobile Manufacturers Association. BIBLIO-GRAPHY -CHAPTER 2 M i t c h e l l , R.B. and Parkins, C , (1954), Urban  T r a f f i c ; A Function of Land Use, Columbia University Press, New York. 1 concptual background 3.1 Location Theory 3.2 Highway Impact Studies 3.3 Rapid Transit Impact Studies 3.4 Summary - 19 -Location theories are usually understood as con-ceptual tools explaining the s p a t i a l d i s t r i b u t i o n of land development i n urban/regional areas. Most activity-distribution/growth/planning models, p o l i c y programs f o r development, etc., incorporate some aspects of l o c a t i o n theory into t h e i r theor-e t i c a l basis. A b r i e f review of l o c a t i o n theories i s therefore fundamental f o r the construction and evaluation of the growth a l l o c a t i o n model devel-oped i n t h i s t h e s i s . To the extent that space (area) i s a f a c t o r i n location, i t must have a price (or cost) and vary with loc a t i o n . To the extent that space i s not a f a c t o r i n location of a c t i v i t i e s , they can be arranged and rearranged i n space without conse-i quence. The cost of space arises from the trans-actions necessary to overcome the distance between 1. R a t c l i f f , (1957), Goldberg, (1970) - 20 -s p a t i a l l y separated a c t i v i t i e s (cost of f r i c t i o n or transportation cost). Usually these trans-actions are thought of as transportation of goods and people, although communication i n general should be considered i n the theory of urban loca-t i o n . 1 Thus transportation - the movement of people and goods - has usually been taken as the quantifiable manifestation of the cost of f r i c t i o n i n the abstract structure of loc a t i o n theory. A firm or household that requires transportation inputs can obtain them eithe r by purchasing trans-portation services or by purchasing location, or a combination of the two, f o r they are substitut-a b l e . 3 1. Means of communication other than moving people, i . e . , telecommunications are only to a c e r t a i n degree a substitute f o r a l l trans-actions as i s shown, e.g., i n Meier's attempt to explain urban growth with communication theory (Meier, 1962). 2. There i s a negative good (distance) with a positi v e costs (commuting costs); or, conversely, a posit i v e good ( a c c e s s i b i l i t y ) with negative costs (saving i n commuting), (Alonso, i960, p.1^9). 3. "When a purchaser acquires land, he acquires two goods (land and location) i n only one transaction, and only one payment i s made f o r the combination." (Alonso, i960, p.150). - 21 -The competition f o r l o c a t i o n i s handled through the theory of r e n t . 1 Under the assumptions of: i ) single market place; i i ) i n f i n i t e , homogeneous p l a i n ; i i i ) production per u n i t of output are everywhere equal i n the p l a i n ; i v ) transportation cost per u n i t i s a l i n e a r function of distance; a rent cone i s derived which expresses the value of each l o c a t i o n r e s u l t i n g from i t s a c c e s s i b i l i t y to the market. Because s i t e s c loser to the market are more p r o f i t a b l e , owners of these s i t e s can charge more f o r t h e i r use up to the producers' surplus at each l o c a t i o n . The diameter of the cone i s determined by the distance at which trans-portation costs equal possible p r o f i t . This means that near the center the price paid f o r access-i b i l i t y ( s i te rent) i s higher and decreases with distance from the center. (Figure 3«1-1) As the e f f i c i e n c y of the transportation networks increas, rents l i k e l y would decrease (because p r o f i t s decrease as more land i s opened and com-2 p e t i t i o n increases). 1. The roots of t h i s theory are to be found i n Von Thunen's e l a s s i c work. His theory of a g r i c u l t u r a l land was l a t e r expanded and applied to urban land use by Hurd and Haig. Von Thunen, (1825); Hurd, (1903); Haig, (1926). 2. Goldberg, (1970), p.160. - 22 -MARKET PRICE MARKET VALUE AMOUNT OT ACCESSBUJTY PURCHASED I Source* Hutchinson, B.G., "An Approach to the Economic Evaluation of Urban Trans-portation Investments", Highway Research  Record, No. 316, 1970, p.7°. Von Thunen showed how d i f f e r e n t a g r i c u l t u r a l production a c t i v i t i e s would form into rings around the market, depending on which type of production could afford to pay higher rent i n a p a r t i c u l a r l o c a t i o n . A s i m i l a r extension of the above model i s to allow output per unit of land to vary (inten-s i t y of land use, non-land inputs of productivity increase, s i m i l a r to density increase i n housing) which also gives the rent curve a concave shape. FIGURE 3.1-1 ACCESSIBIL-ITY CONE Wingo uses the same concept, i s o l a t i n g the trans-portation function as shown i n the following figure as a key feature of an urban transportation system that influences the d i s t r i b u t i o n of house-1. Von Thunen*s theory on a g r i c u l t u r a l land use i n H a l l , (1966). 2. Wingo, (1961); also Alonso, (1964). - 23 -holds i n an urban region. The t r a v e l time or cost increases with distance from the center, as depic-ted i n Figure 3,1,-2, However, an improved trans-portation system lowers t h i s cost. FIGURE 3.1.-2 TRANSPOR-TATION FUNCTION DISTANCE FROM CITY CENTRE Source: Wingo, L., Transportation and Urban Land, Baltimore, The John Hopkins Press, 1961. From the transportation function shown i n the l a s t figure, Wingo derived a s p a t i a l structure of pos i t i o n rents, as shown i n Figure 3.1 . - 3 * ^.CONSTANT LOCATION COST POSITION RENT / ^ • ' 'TRANSPORT COSTS 1 m •DISTANCE FROM CITY CENTRE Source: Wingo, L., Transportation and Urban Land, Baltimore, The John Hopkins Press, I96I. The notion embodied i n the above figure i s that the householder located at " i " enjoys a premium i n transportation costs with respect to a house-FIGURE 3 . 1 . - 3 SPATIAL STRUCTURE OF POSITION RENTS - 24 -hold located at the margin V , This location premium i n v i t e s competition from a l l householders located at a greater distance than " i " , because a household at the margin can o f f e r a p o s i t i o n rent f o r " i " equal to the difference i n transportation costs, R^  . In t h i s way a loe a t i o n a l equilibrium i s established where each household's lo e a t i o n a l costs are constant. Wingo has then demonstrated how density and unit rent p r o f i l e s of the type shown i n Figure 3.1.-4 may be derived from c e r t a i n assumptions about space consumption and the rent r e l a t i o n of Figure 3.1.-3« Changes i n the density and unit rent p r o f i l e s r e s u l t i n g from changes i n the transpor-t a t i o n function are shown i n the broken l i n e s i n Figure 3.1.-4. Source: Wingo, L., Transportation and Urban Land, Baltimore, The John Hopkins Press, 1961. FIGURE 3.1.-4 UNIT RENT AND DENSITY PROFILES WITH DISTANCE x DENSITY DISTRIBUTION RENT DISTRIBUTION DUE TO NEW TRANSPORT SYSTEM DISTANCE - 25 -The theory as formulated so f a r assumes not only that transportation cost and rent are substitut-able, but that t h e i r r e l a t i o n s h i p i s known. However, l i t t l e empirical evidence i s available at present to allow the rent surface to be defined. Most e a r l i e r attempts to explain the r e l a t i o n s h i p between land values and distance from the c i t y have f a i l e d to l i n k them to a reasonable measure-ment of a c c e s s i b i l i t y . The major studies under-taken to determine the rent surface as a function i of a c c e s s i b i l i t y were made by Kain i n Detroit and Chicago, where he found a l i n e a r land value/dist-ance relationship, and by Berry , who derived a negative exponential function. Related works^have o i f been completed by Harris^ and by Robinson. Alonso modified the site-rent/^transportation cost model by introducing two additional concepts. F i r s t , the trade-off a household or a firm makes i n seeking a location f a r t h e r from the center depends on the amount of land i t consumes.-* Second, i n the ease of a r e s i d e n t i a l l o c a t i o n decision, the cost function of the firm i s repla-ced by a u t i l i t y function. 1. Kain, (I962) and (1965) 2. Berry, et a l . , (I963 3. Harris, (I966) k. Robinson, et a l . , (I965) 5. See also Goldberg, (1970), p.159-160 - 26 -"The household d i f f e r s from the farmer and the urban firm i n that s a t i s f a c t i o n rather than p r o f i t s i s the relevant c r i t e r i o n of optional l o c a t i o n . A consumer, given h i s income and his pattern of tastes, w i l l seek to balance the costs and bother of commuting against the advantages of cheaper land with increasing distance from the center of the c i t y and the s a t i s f a c t i o n of more space f o r l i v i n g . " 1 RESIDENTIAL LOCATION The notions of l o c a t i o n theory discussed so f a r are commonly referred to as concept of access-i b i l i t y , i . e . , the r e l a t i o n s h i p between transpor-t a t i o n cost and s i t e rent or land value. However, a further l i n k between land uses and a c c e s s i b i l i t y has now to be established. The question to be answered i s : what land uses seek.a given l o c a t i o n with a given a c c e s s i b i l i t y , i . e . , a location with a p a r t i c u l a r combination of s i t e rent/transpor-t a t i o n cost. There are two alternative ways to formulate these i n t e r r e l a t i o n s h i p s : i . Access i s the major determinant of land value, or rent, and the amount of rent that each type of firm or household could pay at each s i t e could be determined. This would suggest that firms and households would be c l a s s i f i e d according to the  amount of rent they can pay and t h i s c l a s s i f i c a -t i o n could be c a l l e d land use. (Such a land-use ACCESSIBIL-ITY AND LAND USE 1. Alonso, (I960), p.154. - 27 -c l a s s i f i c a t i o n obviously would d i f f e r from the conventional one, i n that i t would be more d i s -aggregated and d i f f e r e n t i n rank, i . e . , a commer-c i a l use could rank before single family use, but a f t e r high density or luxury apartment use i n a b i l i t y to pay, e t c . ) . This approach, however, has some d i f f i c u l t i e s because the rent paid i s not the s i t e or access rent but the aggregate rent which i n addition values i m p l i c i t l y a l l those l o e a t i o n a l factors which are access independent, i i . Another formulation of the i n t e r r e l a t i o n s h i p between land use and a c c e s s i b i l i t y d i r e c t l y uses the access-using c h a r a c t e r i s t i c s of firms and households. A measurement f o r that i s the t r i p generation of a cer t a i n land use. However, t r i p s should be weighted. Household t r i p s to work, shopping and recreation might be of varying impor-tance to the i n d i v i d u a l . Also a c c e s s i b i l i t y to the labour pool has a d i f f e r e n t significance to the firm than i t s access to the market or the raw materials. Therefore, again, the conventional land-use c l a s s i f i c a t i o n would have to be refined. I t i s l i k e l y that such a breakdown would follow c l o s e l y the ability-to-pay categories. 1. Evidence f o r t h i s i s given - e.g., i n a Chicago study, where the same land uses at d i f f e r e n t distances from CBD generate v a s t l y d i f f e r e n t t r i p numbers.(Creighton, 1970). - 28 -In summary, the location theory seeks to explain EVALUATION OF the d i s t r i b u t i o n of a c t i v i t i e s or land uses as a LOCATION THEORY function of a c c e s s i b i l i t y , that i s as a trade-off between transportation cost and p o s i t i o n rent. A prerequisite f o r the assignment of land uses corresponding to a given rent or a c c e s s i b i l i t y surface i s the knowledge of the r e l a t i o n s h i p between land use and a c c e s s i b i l i t y which can be expressed by the a b i l i t y - t o - p a y rent, or by the t r i p generation of land uses, or by any other substitute measurement. Adjustments must be made fo r varying densities, i . e . , f o r the land consum-ption per person or a c t i v i t y . This concept of lo c a t i o n theory using the a c c e s s i b i l i t y determin-ant r e s u l t s i n a twofold s t r a t i f i c a t i o n of land uses. F i r s t , a c t i v i t i e s requiring high access-i b i l i t y and able to pay f o r i t w i l l be located cl o s e r to centers of high a c c e s s i b i l i t y (CBD or sub-centers). Second, cost of high cost land close to the center may be balanced by high density uses. Therefore, the density gradient declines from centers of high a c c e s s i b i l i t y towards the fringe area. This concept of explaining land-use d i s t r i b u t i o n i s rather mechanistic and does not allow f o r many i r r e g u l a r i t i e s and l o c a l deviations from the - 29 -predicted pattern. This follows necessarily from the f a c t that l o c a t i o n a l choice i s not only a function of a c c e s s i b i l i t y . Bourne concludes, f o r example, from his studies i n Toronto: " . . . i t must also be concluded, from the f a c t o r loadings as well as the c o r r e l a t i o n matrices, that distance to either the commercial or geo-graphic center does not o f f e r nearly as substan-t i a l explanatory power i n understanding the r e l a t i v e dimensions of urban land use as might be expected. Variations among land-use types, between these types and the indices of density of a c t i v i t y and a c c e s s i b i l i t y are too complex to i s o l a t e by two distance-decay functions alone." 1 Swerdloff, who investigated the r e s i d e n t i a l density structure of smaller sized urban areas i n North Carolina, arrives at s i m i l a r conclusions. "The u t i l i t y of distance gradients as e f f e c t i v e representations of the density surface quite l i k e l y diminishes as geographic analysis becomes f i n e r . At gross l e v e l s of analysis, r e s i d e n t i a l density patterns are apparently well correlated with distance outward from the c i t y ' s centers; however, there exists an underlying pattern of small area heterogeneity superimposed on t h i s growth pattern of exponential decay." 2 Then, Swerdloff makes a very s i g n i f i c a n t state-ment. "...distance gradients are quite useless i n reproducing the l i k e l y f l u c tuations i n residen-t i a l development compactness r e s u l t i n g from 1. Bourne, (1970), p.20. 2. Swerdloff, (I967), p.20 - 30 -alternations i n one or a number i n the socioecon-omic character of the population. Only through the^development of sound and l o g i c a l models which simulate these i n t e r r e l a t i o n s h i p s can such planning f l e x i b i l i t y be established." 1 This means that even though i n the past transpor-t a t i o n projects may have played a major role i n shaping the c i t i e s , combined with much les s control of land development by government, i n the future the incremental improvement of the trans-portation network i n a highly evolved c i t y w i l l cause less disequilibrium i n the a c c e s s i b i l i t y -land-use i n t e r a c t i o n . In addition, i t was commonly assumed that the location of the work-place i s a major determinant of r e s i d e n t i a l l o c a t i o n (and vice verse, the labour-intensive firm locates close to the labour shed). Present studies also indicate a change i n lo e a t i o n a l behaviour towards a higher emphasis on environ-mental q u a l i t i e s trading o f f lower a c c e s s i b i l i t y 2 3 4 (longer commuting). Kain explained part of the deviation from the t h e o r e t i c a l prediction of r e s i d e n t i a l d i s t r i b u -1> Swerdloff, (1967), p.20. 2. Clawson, (1965). 3. Lowenstein, (1969). 4. Shapiro, (1959). - 31 -t i o n by h i s empirical work done i n Chicago and Detroit. " I t seems probable that a surface of location would be very complex and location rent surfaces might d i f f e r f o r various types of accommodations (those of varying quality, density, age, e t c . ) . The quasi-rents obtainable i n one sub-market defined by, say, q u a l i t y differences, might d i f f e r s u b s t a n t i a l l y from those obtainable i n another. Market disequilibrium may well be the rule rather than the exception." 1 Kain documents with h i s findings the e f f e c t s of r a c i a l discrimination, of trade-offs between transportation cost to work and housing cost as a function of high density work places and the housing cost savings as a function of the amount of r e s i d e n t i a l land consumed. He also demon-strated that these trade-offs are a function of c i t y s i z e . In small c i t i e s , where within a given time t r a v e l distance a higher percentage of a l l r e s i d e n t i a l places are located; furthermore, transportation costs are on the average smaller, hence they play a l e s s s i g n i f i c a n t r o l e i n the l o c a t i o n decision. This finding, although i t does not contradict the l o c a t i o n theory, i s very important, since t h i s l i m i t a t i o n of the theory i s disregarded i n numer-1. Kain, (1965)* p.248. 2. Kain, (1965), p.256, pp.262-274. - 32 -ous cases. The gravity model, f o r instance, i s applied often f o r c i t i e s i n which the transpor-t a t i o n cost d i f f e r e n t i a l s are too small to j i e l d s i g n i f i c a n t r e s u l t s . I f time cost i s used, an additional uncertainty i s introduced through the valuation of work and l e i s u r e time. The location theory, since i t assumes that the rent paid consists of s i t e rent and transporta-t i o n cost and therefore f o r a user with a p a r t i c u l a r a b i l i t y - t o - p a y i s constant over the c i t y , i s i n i t s nature economic. Harris pro-vides an economic explanation f o r deviations from the t h e o r e t i c a l prediction. EXTERNAL-ITIES " E x t e r n a l i t i e s take the form that c e r t a i n types of land uses, f o r example, are e i t h e r mutually supporting or mutually r e p e l l i n g . These e x t e r n a l i t i e s lead to economics of scale and economics of agglomeration, and they have extremely important consequences f o r analysis and model b u i l d i n g . " 1 However, other authors 2 question the predomin-antly economic approach to l o c a t i o n theory and expect a better explanation of l o e a t i o n a l choice i f additional variables are included i n SOCIAL CRITERIA 1. Harris, (I96I), p.711. 2. See, f o r example: Chapin, (1968); Berman, (I96I). - 33 -the model. "These theories (of location) a l l place almost exclusive emphasis on economic variables, l i k e relevant prices and costs or proxies f o r costs such as * elapsed t r a v e l time* f o r access to places of work or other centers. I t would seem highly probable that a number of s o c i o l o g i c a l variables, l i k e those commonly encountered i n cross-section consumer budget studies, are required f o r a r e a l l y adequate empirical explan^ ation of l o c a t i o n a l choices." 1 I t i s of importance to appreciate that whatever conscious or subconscious c r i t e r i a of l o c a t i o n a l choice i n d i v i d u a l s or firms have; be they access, economic, s o c i a l , or whatever, t h e i r e f f e c t i v e s e l e c t i o n of a l o c a t i o n i s dependent upon two f a c t o r s . F i r s t the decision i s not made by the consumer (demand) or the producer (supply) alone. The market mechanism, operating within the con-s t r a i n t s set by public policy, i s the medium i n which lo c a t i o n behaviour responds to the given conditions and selects among available loca-p t i o n s . Secondly neither the consumer nor the producer have perfect knowledge of t h i s market s i t u a t i o n . 1. Meyer, (1963)t P.^6. 2. See also Manual f o r Market Analysis; C r i t e r i a f o r the Evaluation of Location  Choices of Firms and Households, M i l l e r , (1971). CONSUMER AND PRODUCER OF LOCATION IMPERFECT MARKET KNOWLEDGE - 3^ -"From the beginning of the process, when land must be released by w i l l i n g s e l l e r s i n d i f f e r e n t sections of the metropolitan community, through the entire development process involving d i f f e r i n g behaviors o f ; r e a l estate men, developers, mortgage fin a n c i e r s , and builders, the combination of possible outcomes m u l t i p l i e s r a p i d l y . Next, according to the opportunities emerging from t h i s part of the development process, households make t h e i r l o e a t i o n a l decisions, some taking up re n t a l accommodations, some acquiring l o t s and negotiat-i n g f o r a house through an ar c h i t e c t or bu i l d i n g contractor, and some buying the complete shelter package. Because of imperfect knowledge that both producers and consumers have of t h i s process and the possible v a r i a b i l i t y entering into decisions along the way, the outcomes are not easy to fore-cast."! I t i s evident from the c r i t i c i s m of the quoted THE ROLE OF LOCATION authors and t h e i r suggestions, that l o c a t i o n THEORY theory i s very much i n a state of development and expansion. There i s also a trend from merely t r y i n g to predict with l o c a t i o n theory the s p a t i a l  d i s t r i b u t i o n of land use to an attempt to r a t i o n -a l i z e why land development takes place. Although i t i s only a matter of drawing the border l i n e between the two aspects, the d i s t i n c t i o n i s impor-tant i f we are to relate the role of loc a t i o n theory i n explaining the structure of c i t i e s . Chapin o f f e r s an excellent conceptualization of t h i s problem. 2*^ He proposed three steps of 1. Chapin, (1965a), p.121. 2. Chapin, (1965b), p.4. 3. Chapin and Thomas, (1969). - 35 -analysis necessary to capture and explain e x i s t i n g and changing land-use pattern. He c a l l s them f i r s t , second and third-order areas of concern i n an a n a l y t i c a l framework. The f i r s t - o r d e r concern r e f e r s to the (i) value system derived from man's experience with his environment. The second-order of analysis focuses on ( i i ) behaviour  patterns which Chapin defines as "the various kinds of human actions involved i n c i t y l i f e which have become s u f f i c i e n t l y routinized to take the form of d e f i n i t e patterns". Chapin distinguishes two major patterns - patterns of s p a t i a l d i s t r i -bution of a c t i v i t i e s , and patterns of time a l l o -cation to a c t i v i t i e s . The study of these patterns requires the investigation of antecedent values associated with them, the values with respect to "environmental q u a l i t i e s " of a l o c a t i o n and the values placed on " a c c e s s i b i l i t y as i t i n h i b i t s or f a c i l i t a t e s the c a p a b i l i t y of an i n d i v i d u a l to engage i n a c t i v i t i e s " . Value systems and a c t i v i t y patterns of people generate ( i i i ) l o c a t i o n  decisions, the third-order area of concern i n the study framework. The important conclusion to be drawn from Chapin's proposed framework i s that l o c a t i o n theory w i l l never allow f o r more than the description of the - 36 -land use patterns and f o r the formulation of some "laws" to predict future land uses as long as the f i r s t and second-order phenomena are not f u l l y understood. In other words, most location models are able to predict, rather than to explain. However, the ultimate objective of t h i s important branch of urban research i s c e r t a i n l y to under-stand the influence that changed land-use patterns w i l l have on people's behaviour and values f o r i t i s ihe r e s p o n s i b i l i t y of the agent of change - the planner or p o l i t i c i a n , or whoever - to be aware of the consequences h i s decisions w i l l bear. Hamburg, Creighton and Scott formulate t h i s concern c l e a r l y : "Evaluating alternative land-use patterns must be based on the impact that differences i n c i t y form and composition have on the goal structure of society. To attack the problem of evaluation therefore requires (a) a d e f i n i t i o n and a means of measuring land-use patterns (form and composition), (b) a compilation and measurement of relevant goals, and (c) the i d e n t i f i c a t i o n and measurement of the impact of differences i n land-use patterns on s o c i e t a l goals." 1 1/ Hamburg, Creighton, Scott, (1967), p.231. - 37 -3.2 It i s often suggested that the impact of freeways HIGHWAY IMPACT and rapid t r a n s i t i s comparable - to a c e r t a i n STUDIES extent. F i r s t , both systems have s i m i l a r charac- j t e r i s t i c s insofar as they are limited access trans-portation channels. The system can only be entered/ departed at s p e c i f i c points. This channelizes the transportation flow and concentrates the impact of 1 2 the f a c i l i t y i n the area of access points. ' Following the "freeway boom" of the l a s t two decades, a great number of impact studies were undertaken to evaluate the consequences of free-3 ways. The early impact studies concentrated on economic aspects (land values), ^»5»6,7»8 o n 1. T h i e l , (1965). 2. Bardwell, (i960).. 3. See Highway Research Report No. 16, (1963), No. 75» (1965), No. 149, (1966), and Highway Research B u l l e t i n No. 268, (i960). 4. Cribbins, (1965). 5. Ryan, (1959). 6. Adkins, (1959). 7. Pendleton, (I963). 8. M i l l e r , (1971). - 38 -the changes i n land uses.*» 2»3 More recently, s o c i a l consequences to communities passed or crossed by freeways are being studied,^ since the negative reaction of neighborhoods and whole c i t i e s towards freeways increasingly influenced the p o l i t i c a l decision-makers concerned with trans-portation investment decisions. This i s evidenced, f o r example, i n Toronto, Vancouver and numerous United States c i t i e s . Mass t r a n s i t , which had been very slow to evolve i n the same time span and only i n recent years obtained increased attention, has not yet produced a s i m i l a r amount of empirical research on i t s impact on people and a c t i v i t i e s . Since the object of t h i s thesis i s the evaluation of the impact of rapid t r a n s i t l i n e s on land use, highway impact studies w i l l not be reviewed i n d e t a i l . However, because the authors do not accept the conclusion that substantial inferences can be made from the freeway experience to explain and forecast the influence rapid t r a n s i t i s l i k e l y to have on urban l i f e and urban structure, highway impact studies are discussed only b r i e f l y to the 1. Lemly, (1959). 2. Campbell, (I969). 3. Davis, (1963). 4. T h i e l , (1965). ... 3 9 -extent necessary to demonstrate the difference of impacts. The basic differences of the two trans-portation systems and t h e i r impacts are considered to be i n i . the s p a t i a l extent of impact, ' i i . the state (environmental conditions) of the impact area at the time the system i s introduced, and i i i . the type of land uses changed or generated. S p a t i a l l y , three areas of impact may be d i s t i n - SPATIAL EXTENT OF guished. F i r s t , the area p h y s i c a l l y affected, IMPACT which includes not merely the right-of-way, but more important, the b a r r i e r - e f f e c t d i v i d i n g e x i s t i n g communities and communications (cross-roads) and the environmental consequences (noise/ a i r / v i s u a l p o l l u t i o n , e t c . ) . I f we compare a subway l i n e (the concern of t h i s study) with a freeway, the differences of impact and impact area* are self-evident. Second, we can delineate an area of "user bene-f i t s " - the area defined by the residence of the user of the transportation system ("user shed").* In the case of t r a n s i t , t h i s area can be s t r a t i f i e d by users who walk to the station ( c o l l e c t i o n - 40 -p o i n t ) , 1 and by users who use the feeder system or car to reach the transfer point. The user shed i n the case of a freeway corresponds to the l a t t e r and i s s u b s t a n t i a l l y larger then f o r a t r a n s i t system, 2 as evidenced by the lower r e s i d e n t i a l d e n s i t i e s . F i n a l l y , we can di s t i n g u i s h a t h i r d area of con-cern, which i s d i r e c t l y affected by the system. This area includes the non-users of the system. The impacts are mainly s h i f t s i n land values, land uses, tax base, job opportunities, acquaintances, etc. This area i s adjacent to the access-points and d i f f e r s f o r the two transportation systems considerably i n i t s q u a l i t i e s . i The most s i g n i f i c a n t difference i n impacts of the two transportation systems r e s u l t s from the state and type of development of the land at the time of introduction of the new system. The mass t r a n s i t l i n e , because of i t s nature as a high performance transportation system, requires great r i d e r s h i p ENVIRON-MENTAL " CONDITIONS OF IMPACT AREA 1. Co l l e c t i o n point i s defined as a s t a t i o n i n which more people are entering than leaving the station i n the morning rush hour. 2. Compare r e s i d e n t i a l densities around freeway interchanges as reported by Thi e l , (I965), with those adjacent to the subway stations i n Toronto, presented i n Chapter 5* - 41 -numbers to j u s t i f y the investment. These passenger frequencies i n turn can only be achieved i n r e l a -t i v e l y high density areas. That i s to say, rapid t r a n s i t l i n e s are usually introduced i n already built-up areas and predominantly i n higher density, r e s i d e n t i a l areas. Freeways, on the other hand, are often designed to open up new areas of mostly undeveloped and vacant land, as Figure 3*2.-1 demonstrates. Of course, the costs of producing new housing vary considerably from one l o c a t i o n to another. The greatest differences are due to variations i n land costs, and the greatest of these are between the costs of vacant and nonvacant land. Site costs of developed s i t e s are equal to the discounted value of the income streams of e x i s t i n g proper-t i e s plus demolition costs. Thus i t i s hardly surprising that demolition i s seldom carried out by the private market except to provide s i t e s f o r very high-density and high-quality apartment developments i n areas where there i s substantial excess demand f o r them, or to provide s i t e s f o r i n d u s t r i a l or commercial use. 1 A further difference i s found i n the type of land use generated by the new transportation system. Figure 3.2.-2 shows land-use changes as a function of land values and amount of new land supplied by the transportation improvement. Curve "A" resem-bles the e f f e c t of the introduction of a t r a n s i t l i n e , which opens up l e s s land and a t t r a c t s mostly 1. Kain, (1965),, p. 254. - kz -Existing Freeways _ Proposed Freeways Sources Schimpeler, C.C., Grecco, W.L., "The Community Systems Evaluations An Approach Based on Community Structures and Values", Highway Research Report, No. 238, 1968, p.150. medium and high-density r e s i d e n t i a l land uses. Curve "B" demonstrates the impact of a freeway, which opens up more land with higher portions of low-density and i n d u s t r i a l land uses generated. - ^3 -Type of Use t Commercial Kultiple-Eesidential Single-Family Residential T Agricultural-Pasture Lov Density Residential •1* 6 8 10 12 . ih 16 Supply of land i n thousands of acres Source* Wendt, P.F. "Influence of Transportation Changes on Urban Land Uses and Values", Highway Research B u l l e t i n No. 268, (I960), p.100. F i n a l l y , the development processes and changes of land display a t y p i c a l pattern over time. The changes are i n i t i a t e d already i n the planning stage of a f a c i l i t y and d e f i n i t e l y when a project i s authorized. The rate of change increases thereafter over time, decreasing usually some time a f t e r the project i s implemented. Herr made detailed studies on t h i s subject and Figure 3 . 2 - 3 1 gives a sample of his findings. 1. See also: Goldberg, (1971). FIGURE 3 . 2 - 2 LAND USE AS A FUNCTION OF LAND VALUE AND ACCESSIBIL-ITY TIME LAG OF TRANSPOR-TATION IMPACT - 44 -« 3% 5! •ti C S & ° ca K c 1 O rt 0 % —5 — r YEAR OPENING + 5 1 rr SURGE IMPACT ^ P R O B A B L E " N O R M A L . R A T E iS5o '956 i960 Source: Herr, P.B., "The Timing of Highway Impact", T r a f f i c Quarterly, 1964, p.284. As a consequence of the d i f f e r e n t s p a t i a l expan-sion of the impact area, the nature of the trans-portation f a c i l i t y (public transport/car) and the environmental conditions of the impact area, s i g n i f i c a n t l y d i f f e r e n t land developments are observed around access points of freeways and rapid t r a n s i t l i n e s . In the case of the freeway, a considerable part of the land i s undeveloped. As found by C r i b b i n s , 1 and by Raup,2 an acceler-ated process of "ripening*'for development can be observed i n newly opened areas. However, 1. Cribbins, (1965). 2. Raup, (1959)* P.84, FIGURE 3.2-3 TIMING OF TRANSPOR-TATION IMPACT (Tappan Zee Bridge, Hudson River, Rockland County, N.Y.) GENERATED AND CHANGED LAND USES - -Goldberg 1 notes, "that freeways do not usually lead to immediate land development, e s p e c i a l l y i n the urban-rural f r i n g e . " However, he states that land i n t h i s state shows normally the highest percen-tage appreciation i n value.^'^ In the case of t r a n s i t l i n e s , land i s developed to a great extent, representing a commitment, which w i l l not, f o r economic reasons, e a s i l y change. I f i t changes, the vacant and the more depreciated land w i l l be used f i r s t . Most of the studies on the timing of land develop-ment were made f o r freeways and on a rather aggregated l e v e l . This i s j u s t i f i e d by the r e l a -t i v e uniformity of land opened by a freeway. Development p r i o r i t i e s i n t h i s case are mainly determined by a c c e s s i b i l i t y rather than by the q u a l i t i e s of the land. This does not hold f o r rapid t r a n s i t l i n e s . The a c c e s s i b i l i t y d i f f e r e n -t i a l along the l i n e i s usually much smaller, but the variance i n environmental q u a l i t y i s much large r because of the development stage ( f i l t e r i n g ) of the land. Therefore, changes i n land use over O 1. Goldberg, (1971). p.135. 2. Goldberg, (1971), p.175. 3. Bardwell, (i960,). - 46 -time are expected not to be uniform f o r t r a n s i t stations. In order to capture and explain the differences i n time lags and at what point an i n d i v i d u a l s t a t i o n i s " r i p e " f o r development, the analysis must take place on a much more disaggre-gated l e v e l and must d i f f e r e n t i a t e among environ-mental f a c t o r s . Both these points are taken care of i n the model presented i n t h i s t h e s i s . In summary, both transportation improvements - new freeways and rapid t r a n s i t l i n e s - cause changes i n the scale of land use. However, the nature, extent and timing of t h e i r impact i s d i f f e r e n t . Whereas i n the case of freeways considerable empirical evidence exists on these impacts, the experience derived from rapid t r a n s i t developments i s rather scarce. Knowledge on the consequences of invest-ments i n mass t r a n s i t can not be gained by i n d i s -criminantly t r a n s f e r r i n g the findings of freeway impact studies. A separate framework of analysis needs to be developed. - 4? -3.3 Rapid t r a n s i t i s a p a r t i c u l a r type of mass t r a n s i t RAPID TRANSIT system, generally defined as a method of transpor- IMPACT STUDIES t i n g large numbers of people and t h e i r i n c i d e n t a l baggage i n vehicles operating on exclusive r i g h t s of way within an urban area. Although great v a r i a t i o n exists among present rapid t r a n s i t ser-vices regarding vehicle and roadbed configura-t i o n , 1 capacity, 2 network layout3 and l e v e l of automation, etc., a r e l a t i v e l y simple way of c l a s s i f i c a t i o n i s adopted f o r the purpose of thi s study i ( i ) Rapid t r a n s i t systems employing vehicles capable of leaving t h e i r designated r i g h t s of way and operating on c i t y streets (bus rapid t r a n s i t ) , ( i i ) Rapid t r a n s i t systems employing vehicles operating on, and incapable of leaving, 1. F e r r e r i , (1970) 2. Young et a l . , (1969) 3. Tass,. (1971) - 48 -specialized tracks ( r a i l rapid t r a n s i t ) . In the past, the differences between these two types of t r a n s i t systems have been considered generally i n terms of t h e i r r e l a t i v e capital/oper-ating expenses and f l e x i b i l i t y . The construction costs of a grade separated r a i l t r a n s i t track often greatly exceed the cost of constructing an addi-t i o n a l freeway or redesigning an e x i s t i n g freeway lane to accommodate bus t r a n s i t . * Similar cost r e l a t i o n s h i p exists i n general between the r o l l i n g stock of the two systems. On the other hand, r a i l rapid t r a n s i t systems are more r e a d i l y adaptable to automation. The prospect of reducing opera-t i o n a l expenses - of which often more than 60% i s i labour cost - i s less promising f o r bus rapid t r a n s i t systems. Capital and operating cost considerations are v i t a l points i n the selection of mass transporta-, t i o n hardware, f o r planners are i n e v i t a b l y con-fronted with the problem of how to finance any transportation improvement. Yet these consider-ations reveal l i t t l e about the interactions between the transportation system and the environ-ment within which i t i s placed. The above c l a s s -1. Robinson, (1970), p.10 - V e r -i f i c a t i o n , however, i s s u f f i c i e n t l y f l e x i b l e to take into account other differences between the two types of rapid t r a n s i t systems. Thus i t can y i e l d operational advantages pertinent to t h i s study when another inherent c h a r a c t e r i s t i c of rapid t r a n s i t systems i s considered - that i s the ' l e v e l of commitment.• E a r l i e r i n t h i s chapter when impact studies i n general were discussed, an important r e l a t i o n s h i p was noted between the information available and/or the l e v e l of commitment to a p a r t i c u l a r transpor-t a t i o n investment and r e a l estate speculations, leading to change i n land value patterns along the corridor. M i l l e r has demonstrated that as uncer-t a i n t y decreases through the steps oft (i) announ-cing the intentions of the authorities to construct a freeway, ( i i ) a c q u i s i t i o n of land along the proposed rig h t s of way, ( i i i ) beginning and (iv) completion of construction, the rate of land value/ use/density change increased accordingly. 1 THE ROLE OF COMMITMENT Interpreting M i l l e r ' s observation i n a more generalized manner, the interchangeable notions of decreasing uncertainty and increasing commitment 1. M i l l e r , (1971) - 50 -seems to be a useful concept to predict the impact of the two types of rapid t r a n s i t systems on land value/use/density changes. In the case of the bus rapid t r a n s i t system, both the vehicle and the reserved land can be 're-used 1; the commitment to a p a r t i c u l a r route i s re v e r s i b l e . I f demand f o r the service drops d r a s t i c a l l y or s h i f t s s p a t i a l l y , the service can be abandoned or re-routed without much d i f f i c u l t y . In the case of the r a i l rapid t r a n s i t system, neither can the vehicle leave the track i t was designed for, nor can the track be used to accommodate other, no n - r a i l , rapid t r a n s i t c a r r i e r s . The commitment to both the vehicle and the track i s i r r e v e r s i b l e . Consequently, i f rapid t r a n s i t systems do have any impact on the evolu-t i o n of land use/value/density along t h e i r route, t h i s impact can be expected to be more evident along r a i l rapid t r a n s i t corridors than along bus rapid t r a n s i t corridors. Hence i n the following discussion the focus i s placed on r a i l rapid t r a n s i t impact studies and the general conclusions drawn at the end of t h i s chapter should not be interpreted as having either e x p l i c i t or i m p l i c i t reference to bus rapid t r a n s i t systems. The f i r s t underground railway service was inaug-urated i n London i n I863. Although the o r i g i n a l function of the three and a half-mile service was HISTORICAL DEVELOPMENT - 51 -to f a c i l i t a t e the transportation of goods, i t soon became the railway carrying almost exclusively 1 passengers. Probably the only comparable fea-tures of the 'underground* that are exhibited by present t r a n s i t systems are those of grade separ-ation, and the fa c t that i t was f u l l y underground. (Figure 3*3-1)• The success of these features, however, led to the construction of new sections "* Source» Tass, L., Modem Rapid Transit, Carleton Press, Inc., New York, 1971, p.180. 1. Tass, (1970). FIGURE 3 .3 .-1 LONDON •UNDERGROUND* I863 - - LONDON M E T R O P O L I T A N B O R O U G H S <= RAILWAY STATION S C A L E s * 5 0 U N D E R G R O U N D RAILWAY 1863 t -! - 52 -and extensions i n the following years. This marked the beginning of major developments i n con-t i n u a l l y improving rapid t r a n s i t services, which are now operating i n a number of metropolises. ^Figure 3.3-2). On the North American continent, r a i l rapid t r a n s i t systems are i n operation i n seven c i t i e s : New York, Chicago, Philadelphia, Boston, Cleveland Toronto and Montreal. A r a i l system i s soon to be opened i n the San Francisco region (BART) and i n Washington, D.C. construction has already begun on the f i r s t stage of the proposed 28 mile rapid t r a n s i t network. R a i l t r a n s i t proposals were recently approved i n Atlanta and Baltimore, while several other c i t i e s are examining t h e i r mass t r a n s i t systems and studying r a i l t r a n s i t as a p o t e n t i a l means of a l l e v i a t i n g transportation problems. These include Buffalo, Detroit, Houston Kansas City, L o u i s v i l l e , Miami, Minneapolis-St. Paul, New Orleans and Pittsburg i n the U.S.1 and Edmonton and Vancouver i n Canada. It was noted e a r l i e r that central to the recent i n t e r e s t i n rapid t r a n s i t has been the increasing 1. Wermers, (1970) p.49. - 53 -Ropid Transit Line Shore Line SCALE IN MILES 0 -2 4 6 8 10 12 t H pf WrmW 1- 1 Sourcet Taylor, S.P.', "Urban Transport - A World-Wide Problem", Institute of Transport  Journal, July, 1970, p.497. - 54 -recognition of the need to shape urban and region-a l growth. Credible l i t e r a t u r e on the actual impact of l i n e s , which have been or are to be constructed, on the growth/redevelopment process along t h e i r route i s rather l i m i t e d . To be sure, t r a n s i t trade associations and large corporations with a vested i n t e r e s t i n urban t r a n s i t hardware do produce volumes of testimony on the impact rapid t r a n s i t has on the nature of subsequent land use, but the examination of t h i s material often reveals more rhetoric than f a c t . 1 Commenting on the apparent difference between Toronto, where cl u s t e r i n g of high density development i s observ-able around 'specific stations, and Cleveland, where r e l a t i v e l y l i t t l e such development has occurred i n association with the system, Thomas Deen warns against instantaneous generalizations! I t appears there are times and conditions when t r a n s i t can have an impact and others when i t has r e l a t i v e l y l i t t l e impact. Research needs to be conducted that w i l l help confirm the conditions that are required to bring about desirable urban land-use development goals.2 Since no paradigm has yet evolved that r e l a t e s land value/use/density structure to rapid t r a n s i t 1. Jernstedt, (1970) p.3-7 2. Deen, (1970), p.11 - 55 -systems i n a systematic and comprehensive frame-work, the writers drew the l i t e r a t u r e review from a variety of sources. These have included schol-a r l y works, professional opinions, and appraisals conducted by various c i t y planning departments and consultants. As a r e s u l t , two aspects of these studies need to be treated with some caution - the academic rigour with which the conclusions were formulated, and the scale of s p a t i a l aggregation employed to derive the findings. The e f f e c t of a rapid t r a n s i t system on the c i t y ' s growth pattern i s probably the most debated issue i n the l i t e r a t u r e . To i l l u s t r a t e the range within which professional opinions d i f f e r , the opposing propositions of Lash and Heenan may be c i t e d . "By the time a metropolitan area begins seriously to consider adding a rapid t r a n s i t system, much of i t s transportation system, i n the form of an extensive network of roads and streets, i s well established. ..... Thus the new network may be less of a c o n t r o l l i n g influence i n determining the form of urban development than i s sometimes imagined . . . "1 On the other hand,. Heenan claims that the f i r s t l e g of Toronto's rapid t r a n s i t l i n e has attracted so much new r e a l estate development to the c i t y CITY SCALE 1. Lash, (1967) p.193. - 56 -that, "... i f urban rapid t r a n s i t system never earned a dime, i t would pay f o r i t s e l f many times over through i t s b e n e f i c i a l impact on r e a l estate values and increased assessments." 1 Whether the new rapid t r a n s i t system leads to greater population growth and a t t r a c t s additional investment i s highly debatable. Theoretically, assuming unitary price e l a s t i c i t y demand f o r land, improvements i n the transportation system r e s u l t i n decline of land prices and rents, f o r with the increase i n the supply of land the competition among land and land service s e l l e r s becomes greater. However, as Goldberg demonstrated, the price e l a s t i c i t y of demand f o r land does not appear to be unitary, and i n growing urban regions with expanding transportation networks the o v e r a l l impact, at i t s best, would be only a slower rate of increase i n land prices than without the trans-portation improvements. To i n t e r p r e t the slower rate of land price increase on the aggregate l e v e l as a major fac t o r f o r a t t r a c t i n g a d d i t i o n a l growth to the c i t y , as Heenan*s statement seems to imply, i s to assign a decisive role to the marginal differences of aggregated land prices among var-ious urban regions f o r interurban l o e a t i o n a l 1. Heenan, (1968) p.213 2. Goldberg, (19?0). - 57 -decisions. This proposition, i n the l i g h t of the the o r e t i c a l and empirical research on in t e r r e g -i o n a l l o e a t i o n a l behaviour, i s rather ambitious, i f not misleading. Indeed, Conway's analysis of four metropolises with extensive and well developed rapid t r a n s i t networks— Boston, Chicago, Philadelphia, New York - finds no apparent pattern i n the population growth rate or s h i f t s i n population density d i s -t r i b u t i o n which would d i s t i n g u i s h these c i t i e s from those with no rapid t r a n s i t l i n e s . In summarizing the findings of the extensive urban transportation research study carried out by the RAND Corporation, Meyer arrived i n more general terms at si m i l a r conclusions. "An array of technological, economic and s o c i a l forces has altered the structure and character of American c i t i e s i n recent decades. The p a r t i c u l a r form, mode or even presence or absence of public t r a n s i t i s only one of these factors and apparently one of limi t e d importance. In fac t , the patterns of land use, population growth, employment locations and r e s i d e n t i a l choices recorded i n recent years by the most t r a n s i t oriented American c i t i e s have e s s e n t i a l l y mirrored those of other c i t i e s with very strong highway o r i e n t a t i o n . " 2 1. Conway, ( 1 9 6 8 ) 2 . Meyer, ( 1 9 6 9 ) p. 3 ^ 0 . - 58 -However, both Conway's and Meyer's arguments are based on highly aggregated data and on the s p a t i a l d i s t r i b u t i o n of growth i n c i t i e s where the rapid t r a n s i t system was b u i l t before the second World War and not r a d i c a l l y modernized or expanded since. As i n a l l of these c i t i e s - s i m i l a r to most other North American c i t i e s - attempts to improve the q u a l i t y of urban transportation were almost exclu-s i v e l y l i m i t e d to highway and road improvements i n the l a s t two decades, Conway's and Meyer's con-clusions are neither surprising, nor, f o r that matter, conclusive. Stockholm i s probably the most commonly used example where a deliberate attempt has been made to use the density generating e f f e c t of rapid t r a n s i t systems to channel "predesigned" growth. To avoid disorganized sprawl a f t e r e s s e n t i a l l y a l l land i n the central c i t y was used, some 18 sate-l l i t e c i t i e s were created, each centering around a rapid t r a n s i t station. Tass uses the examples of Madrid and Hamburg to demonstrate the same point. "One l i n e i n Hamburg was b u i l t f o r the sole purpose of developing the adjacent area. Consequently, the population of Hamburg's northern d i s t r i c t s t r i p l e d while no other d i s t r i c t showed s i m i l a r r e s u l t s . " 1 1. Tass, (197D p. 81 - 59 -On the North American continent the only e x p l i c i t attempt to use rapid t r a n s i t to encourage and di r e c t the orderly growth of a ei t y , and i n the process "to contribute to an improved way of l i f e f o r i t s residents", i s the Radial Corridor Plan of Washington, D.C. I t proposes 5 corridors of urban growth, based on transportation spines r a d i a t i n g 1 from downtown Washington. The plan c l e a r l y resembles the structure of Stockholm and i t s s a t e l l i t e c i t i e s . (Figure 3.3-3)• Source: Metropolitan Washington, Council of Governments, Vol.12, No. 5* 1971* p.l« 1. C.O.G., (1971). FIGURE 3.3-3 THE RADIAL CORRIDOR PLAN FOR THE METROPOL-ITAN WASHINGTON - 60 -I t may be argued, however, that the examples pre-sented i n the previous paragraph cannot be i n t e r -preted as evidence which contradicts the conclu-sions of Lash, Conway and Meyer. In the f i r s t place, these examples represent experiences i n a non-North American context (different l i f e s t y l e , lower car ownership, e t c . ) . In the second place, the s p e c i f i c growth patterns recorded i n these c i t i e s were brought about by the presence of a rapid t r a n s i t system, i n conjunction with a d e l i b -erate planning e f f o r t such as zoning, taxation, public land assembly, and bonus. This dichotomy leaves ample opportunities to speculate on the magnitude of influence attributable to the; d e l i b -erate planning rather than to the presence of a rapid t r a n s i t system. To t h i s end, the c r u c i a l role that meticulous planning and public a s s i s t -ance can play i n the development of high density nodes around rapid t r a n s i t stations was specif-i c a l l y stressed at a recent conference on urban transportation sponsored by the U.S. Department of Housing and Urban Development. "Most respondents believed that high concen-t r a t i o n of f a c i l i t i e s around t r a n s i t nodes would occur only i f zoning, taxation, and other public powers were used to reinforce the transportation advantages of the nodes ... William Wheaton noted that the scale and density of development foreseen was r e a l i s t i c and even conservative f o r a 30-^0 year period, but f i n a n c i a l incentives such as - 61 -public underwriting of private nodal development r i s k , would be necessary." 1 Favourable p o l i c y devices can undeniably enhance the attractiveness of rapid t r a n s i t corridors f o r s p e c i f i c a c t i v i t i e s . However, examples from North American c i t i e s have indicated that even where rapid t r a n s i t was intended to be nothing more than a means to r e l i e v e congestion, changes i n land values and the rate of development have occurred along the t r a n s i t corridors which are quite d i f f -erent from those i n other parts of these c i t i e s . In Toronto, f o r example, a study was undertaken by the Toronto Transit Commission i n 1959 to i s o l a t e properties considered to be within the sphere of influence of the Yonge Street subway l i n e and to compare land value increases (as measured by r e a l t y tax assessment) recorded within these selected areas with land value changes elsewhere i n the c i t y . For taxation purposes the City of Toronto i s divided into 40 ward subdivisions, 14 of which are adjacent to the Yonge Street rapid t r a n s i t l i n e . From the following figures taken from the study i t i s evident that property values adjacent to the l i n e increased at a greater rate 1. U.S. Department of Housing and Urban Development (1968) p.158. - 62 -than elsewhere i n the c i t y . 1» < i (Table 3*3-1)• ( a l l figures i n thousands) Year Total City Adjacent to Yonge Subway* Increase Increase * 1950-53 1954-56 1957-59 $101,426 127,721 212,523 7.5 8.5 13.5 $ 48,557 69,846 121,521 9.2 12.1 18.8 $441,670 32.8 $239,924 45.4 1 * Opened 1954. Sources Kearns, J.H., The Economic Impact of Yonge Subway, (T.T.C. Toronto, 1964, p.6) According to Heenan's estimates i n the f i v e year period between 1959-63, 48.5$ of a l l highrise apartment development and 90$^ of a l l new o f f i c e 1. Kearns,(1964). 2. Unfortunately, the study has two s i g n i f i c a n t shortcomingss f i r s t , the increase of land values adjacent to the l i n e was compared to the t o t a l c i t y increase which obviously i n -cluded the former one, and second, no pro-v i s i o n was made to discount the fa c t that Yonge Street has always been one of the main business d i s t r i c t s of Toronto, thus land prices along the street might have been h i s t -o r i c a l l y r i s i n g more rapidl y than elsewhere. 3. This number may look le s s impressive i f i t i s noted that Heenan generously included i n t h i s figurefeall^the CBD o f f i c e development i n t h i s time period. The t o t a l Toronto Downtown area l i e s within 5 minutes walking distance from the Yonge-University subway l i n e . - 63 -construction i n Toronto had occurred within 5 minutes walking distance from the Yonge Street subway l i n e . 1 Larry Bourne's analysis of the private redevelop-ment process that has taken place between 1959-63 e s s e n t i a l l y confirmed Heenan's findings. The s p a t i a l pattern of new construction a c t i v i t i e s i n Toronto was lar g e l y l i m i t e d to f i v e areas - the central business d i s t r i c t , a l i m i t e d sector of the north of the CBD, and three outlying concentrations of o f f i c e and apartment development. These f i v e concentrations accounted f o r over kOfo of a l l f l o o r areas added by new construction, 83$ of a l l new o f f i c e s and 51$ of a l l apartments. A l l but one of these areas are adjacent to the Yonge Street rapid t r a n s i t l i n e . (Table 3»3-H)« Anderson documented s i m i l a r tendencies i n downtown San Francisco, Oakland, Berkley and the suburban areas of Contra Costa and Allamando Counties, through which the BART rapid t r a n s i t l i n e , expected 3 to be i n f u l l operation by 1972, passes. The • o f f i c e boom1 i n the Bay area i s limited s p a t i a l l y almost e n t i r e l y to the proximity of the subway. 1. Heenan, (1968) p.217 2. Bourne, (1970) 3. Anderson, (1970) - 64 -% of Floor Area Added General % Land ' i ATCH Total Offices Aparfar.onts Commercial Aren AITcctcdJ (Districts) Total Downtown (C.B.D.) 16.0 44.0 15.3 5.5 Uptown (Bloor) 11.1 19.8 10.4 17.5 10.2 i Eglinton-Yonge 9.7 13.0 11.4 7.4 8.0 St. Clair-Yonge 8.6 6.6 18.9 0.2 2.6 Pavkdale 5.9 16.0 3.6 0.2 i Totals 51.3 83.4 56.7 44.0 26.5 TABLE 3 . 3 - I I DISTRIBUTION OF REDEVEL-OPMENT CONSTRUCTION, CITY OF TORONTO, 1 9 5 9 - 6 3 Sources Bourne, L.S., "Trends i n Urban Develop-ment - the Implication f o r Urban Form", The Appraisal Journal, January, 1 9 7 0 , p. 3 0 . Furthermore, private developers are often w i l l i n g to pay the cost of the construction of d i r e c t access from t h e i r building to the rapid t r a n s i t 1 system's station mezzanines. There are no com-parative analyses available which relate, i n any d e t a i l , the growth pattern of the c i t i e s of Cleveland and Montreal to the land value changes and development process that has taken place along t h e i r respective rapid t r a n s i t l i n e s . However, some descriptive investigations do confirm the hypothesis that developers of new o f f i c e and high 1 . Metropolitan, ( 1 9 7 1 ) . - 65 -density apartment construction tend to seek loca-tions i n proximity to rapid t r a n s i t lines. 1 , 2»3 Based oh the previous discussion and the evidence published i n the reviewed l i t e r a t u r e , i t i s d i f f -i c u l t to he conclusive about whether rapid t r a n s i t l i n e s s i g n i f i c a n t l y increase or decrease land values, growth or rate of redevelopment of c i t i e s at the aggregate l e v e l , or whether t h e i r presence merely r e d i s t r i b u t e s the changes that would have taken place i n the c i t y , i r r e s p e c t i v e of the presence or absence of the system. Yet i t i s evident that rapid t r a n s i t l i n e s do tend to create well defined corridors of intensive commercial and r e a l estate development. This phenomenon cannot be captured adequately by the scale of aggregation used by Meyer and Conway. Simi l a r l y , studies that confirmed the existence of r e l a t i v e l y higher rates of development growth along the t r a n s i t corridors have done so by employing data aggregated to the whole corridor. However, these studies f a i l to recognize and to inquire into the apparent heter-ogeneity of growth that exists among s p e c i f i c sections of a l i n e . 1. Hyde, (1967) p.171 2. Herbert (1969) p.^7 3. Mathias (I965). - 66 -One of the s p e c i f i c operational c h a r a c t e r i s t i c s of rapid t r a n s i t i s that, while i t i s physically-l i n e a r , often extending over many miles, access to the system i s r e s t r i c t e d to s p e c i f i c points (stations). Thus the notion of a corridor, f r e -quently related to the s p a t i a l pattern of develop-ment along the l i n e , i s somewhat misleading. While the impact of rapid t r a n s i t may to some les s e r extent reverberate throughout the entire urban region, i t originates from and can be expected to 1 be the most dramatic around the stations. During the analysis of the impact of Toronto's Yonge Street l i n e on new real-estate developments, Dawson noticed that construction was sharply focused around stations. The heights of buildings (density) and the^range of commercial development 1. In the case of surface or elevated l i n e s such factors as noise, p o l l u t i o n , v i s u a l and physical b a r r i e r s can have a blightening influence on adjacent properties as has been the case i n Boston, New York and more recently i n Montreal. The adverse a f f e c t s of the exposed r i g h t s of way, however, w i l l not be considered i n t h i s thesis, p a r t l y because they are poorly documented, and p a r t l y because i t i s assumed that i n the future deliberate e f f o r t s w i l l be made to avoid such consequences by building the l i n e below surface when severe impact i s expected. - 67 -declined r a p i d l y as the distance from stations increased. The main development appeared to have taken place within 5 minutes walking radius, and he observed not only the absence of dynamic changes of land uses i n sections between stations, but even the vacating of some premises as t h e i r 1 trade was attracted to the station. Davis has attempted to esta b l i s h the station's range of influence by analyzing land value changes around i t . Taking the BART*s Glen Park Station s i t e (situated within a r e s i d e n t i a l area charac-te r i z e d by private homes and small apartment buildings), he documented r e a l estate price trends to four d i s t i n c t rings* one block radius and adjacent properties to the station s i t e , two ^lock, s i x block and the remainder of the area encompassed by an approximately sixteen block radius. Comparing the average annual percentage increases of r e a l estate sales within the four rings, Davis found that the f i r s t two rings exper-ienced an increase s i g n i f i c a n t l y above the average of the other two rings and concluded "that the price trend i s subs t a n t i a l l y the d i r e c t r e s u l t of the Bay Area Rapid Transit System's Glen Park Station Location." 1. Dawson, (1968) pp.91-100. 2. Davis, (1970) p.568. - 68 -No comparative study has yet analyzed whether factors such as the station's p o s i t i o n within the urban f i e l d , v a r i a t i o n i n the station size, nature and q u a l i t y of the feeder system, or the difference i n spacing of stations modify the stationl-s range of influence. As a r e s u l t , f o r p r a c t i c a l purposes one to three blocks, or the f i v e minute walking distance, has generally been accepted as the area where the a c c e s s i b i l i t y advantage can be regarded as being homogeneous. Numerous reports prepared by l o c a l planning agencies and private consultants •t on the anticipated impact of BART (Bay area) and 2 Metro (Washington) , J stations based t h e i r inves-t i g a t i o n on t h i s radius. New development tends to locate near the a c t i v i t y points served by t r a n s i t . The most immediate area of impact i s represented by a c i r c l e around the entrance. This distance constitutes a four to f i v e minutes walk. Land values can be expected to increase i n t h i s zone, since regional a c c e s s i b i l i t y combined with increased exposure to r i d e r s improves development pote n t i a l f o r commercial, o f f i c e and r e s i d e n t i a l uses.^ 1. Liskamm, (1968), Okamoto, (1966), Development Research Associates, (196?), Oakland Planning Department, (1969)» Stratford Research Inst i t u t e , (1970). 2. Washington, D.C. 3. Alexandria Department of Planning, (1969). 4. D i s t r i c t of Columbia, (1971) p.11. - 69 -In Washington, D.C. a s p e c i f i c plea has been made to reduce the extensive area o r i g i n a l l y assigned f o r parking around outlying stations so "the opportunities to re l a t e development d i r e c t l y to the stations would not be l o s t . " 1. Transit Development Team, (1971) p.8. - 70 -In t h i s chapter an attempt was made to expose and synthesize the relevant t h e o r e t i c a l and empirical l i t e r a t u r e on which t h i s thesis i s b u i l t . I t was also intended to provide a point of conceptual departure toward a more r e a l i s t i c framework within which the impact of a s p e c i f i c transportation system - r a i l rapid t r a n s i t - on the s p a t i a l pattern of growth and development of urban areas may be understood. It was noted that t r a d i t i o n a l location theories tend to explain the s p a t i a l d i s t r i b u t i o n of urban a c t i v i t i e s through the i n t e r r e l a t e d concepts of transportation costs, a c c e s s i b i l i t y , land rent and land use. One of the e s s e n t i a l assumptions that enabled scholars to compose t h e i r t h e o r e t i c a l speculations into elegant mathematical models was the proposition that a l l land to be allocated f o r various a c t i v i t i e s i s of equal q u a l i t y . Since i n the l a s t three decades most urban transportation - 71 -investments have been lim i t e d to the construction of freeways of various sizes, t h i s assumption has not been seriously challenged; f o r freeways often open up vast areas of fringe land - land that f o r p r a c t i c a l purposes could indeed be regarded from the point of view of urban use as being homogen-eous. But even i f some heterogeneity did exis t among parcels of land (slope, vegetation, cover-age, etc.), the f a c t that highways tended to at t r a c t the whole spectrum of urban land uses with extensive land consumption, t h i s heterogen-e i t y could be absorbed with minor s h i f t s i n the configuration of land uses. I t was also estab-l i s h e d from the reviewed impact studies that freeways tend to induce new growth - that i s , the conversion of a g r i c u l t u r a l or vacant land to urban  uses - rather than redevelopment. Since the middle of the f i f t i e s a number of North American c i t i e s have b u i l t and many others are ac t i v e l y considering building rapid t r a n s i t l i n e s . Proponents of the system use many reasons to j u s t i f y t h i s rather expensive transportation investment, not the lea s t being the proposition that i t can be used as a t o o l to create a more desirable pattern of urban land uses than that r e s u l t i n g from the sole reliance on the automobile. - 7 2 -However, empirical or t h e o r e t i c a l research that would substantiate t h i s assumption i s rather l i m i t e d . Furthermore, studies that are available on s p e c i f i c aspects of t h i s phenomenon document the i n t e r r e l a t i o n s h i p on p a r t i c u l a r l e v e l s of s p a t i a l aggregation which may be inappropriate or non-existent at any other l e v e l . Despite these d i f f i c u l t i e s , a number of 'tendencies* have been traced i n the l i t e r a t u r e on rapid t r a n s i t impact which can provide a base to challenge the sugges-tions of some authors that meaningful inferences can be drawn from highway impact studies to anticipate the impact of rapid t r a n s i t , 1 and to provide a rationale f o r the need to relax the assumption of the featureless p l a i n i n transpor-t a t i o n research. 1. H i s t o r i c a l l y rapid t r a n s i t l i n e s were b u i l t within the c i t i e s * most densely developed areas. Consequently, the economic u t i l i z a t i o n of the improved access around stations involves land  redevelopment rather than the simple r u r a l to urban conversion process. CHARACTER-ISTICS OF RAPID TRANSIT LINES 2 . Rapid t r a n s i t l i n e s tend to a t t r a c t s p e c i f i c 1 . T h i e l , ( 1 9 7 0 ) . - 73 -rather than the whole range of urban a c t i v i t i e s . These a c t i v i t i e s include medium and high density-r e s i d e n t i a l , r e t a i l , o f f i c e , i n s t i t u t i o n a l and service industry uses, a l l of which can be charac-terized by t h e i r intensive land u t i l i z a t i o n and t h e i r strong orientation toward people (labour, or consumer) rather than goods. 3. Most ex i s t i n g and proposed rapid t r a n s i t l i n e s expand r a d i a l l y from the CBD. Consequently, there are a number of stations which are situated at approximately equal time distance from the center. 4. Past experience indicates that the most dram-a t i c impact of rapid t r a n s i t i n terms of land value changes and i n t e n s i f i e d redevelopment occurred within the area of four to f i v e minutes walking distance around stations. This evidence substan-t i a t e s the assumption that increased a c c e s s i b i l i t y i s s p a t i a l l y limited to r e l a t i v e l y small areas within which a c c e s s i b i l i t y advantages can be regarded as being homogeneous. The l a s t two observations imply that there are a number of loeational choices available f o r a c t i v -i t i e s seeking location i n the proximity of rapid t r a n s i t l i n e s - choices that o f f e r s i m i l a r advan-tages both i n terms of CBD related a c c e s s i b i l i t y - ?4 -and within the stations* sphere of influence. However, experience i n Toronto, as w i l l he demon-strated i n a l a t e r part of t h i s thesis, indicates that impressive developments occur around some stations while the area around others, located at a s i m i l a r distance from the CBD, remains v i r t u a l l y unchanged. Furthermore, the s p a t i a l pattern of growth i n the proximity of stations which do at t r a c t growth i s s e c t o r i a l rather than concentric. In the following chapter some hypotheses w i l l be put forward which attempt to o f f e r explanations/® r f o r t h i s phenomenon. - 75 -Adkins, W.G., (1959). "Land Value Impacts of Expressways i n Dallas, Houston and San Antonio, Texas", Highway Research Board, B u l l e t i n 227, 1959, Washington, D.C. Alexandria, Department of Planning, (I969), Rapid  Transit Expected Impact, Report No. 19. Alonso, W., (i960), "A Theory on the Urban Land Market*, Papers and proceedings of the  Regional Science Association, Volume 6, pp. 149-157. Alonso, W., (1964), Location and Land Uses Toward  a General Theory" of Land Rent, 1964, Harvard University Press, Cambridge, Massachusetts. Anderson, A.C, (1970), The E f f e c t of Rapid Transit on Property Values", The Appraisal  Journal, January, 1970, p. 59-67. Bardwell, G.E., and Merry, P.R., "Measuring the Economic Impact of a Limited-Access Highway on Communities, Land; Use, and Land Value", Highway Research Board, B u l l e t i n 268, i960, pp. 37-73. Barraciough, R.E., (I967), "Information f o r Land Use Models", Highway Research Record No. 194, pp. 1-14. "BART Booms Building, Land Values", (1971), Metropolitan, March/April, 1971, pp. 17-19. Berman, B., (1961), "Analysis of Urban Problems -Discussion", American Economic Review No. 51, pp. 299-300. Berry, B.J., (1963), "Urban Population Densities* Structure and Change", Geographic Review, Vol. 53, 1963. Bourne, L.S. (1970), "Trends i n Urban Redevelop-ment - The Implication f o r Urban Form", The  Appraisal Journal, January, 1970, p.24-36. Brand, D., Barber, B., Jacobs, M., (1967), "Technique f o r Relating Transportation Improvements and Urban Development Patterns", Highway Research Report No. 207, pp. 53-64. - 76 -Brodsky, H., (1970), "Residential Land and Improvement Values i n a Central City", Land  Economics, November, 1970, pp. 220-247. Burns, L.S., Mittelbach, F.G., (1964), "Location -Fourth Determinant of Residential Value", The Appraisal Journal, Vol. XXXII, No. 2, A p r i l , 1964. Campbell, E.W., "An Evaluation of Alternative Land Use and Transportation Systems i n the Chicago Area", Highway Research Record  No. 238, pp. 103-122. Carter, C.B., (1971), "Urban Growth Models and Washington P o l i t i c s : An Unlikely Combination - Or Is It?", Conference Paper, AID Confer-ence, San Francisco, C a l i f o r n i a , pp.1-22. Chapin, F.S., Jr., (1965a), "A Model f o r Simulating Residential Development", Journal of the  American Institute of Planners, May, 1965, pp. 120-125. Chapin, F.S., Jr., (1965b), Weiss, S.F., Donnelly, T.G., "Some Input Refinements f o r a Residen-t i a l Model", Center f o r Urban and Regional  Studies, University of North Carolina. Chapin, S.F., (1968), " A c t i v i t y Systems as a Source of Input f o r Land Use Models", Highway Research Record, 1968. Chinitz, B., (1966*), " W i l l Model Building and the Computer Solve Our Economic Forecasting Problems?", Highway Research Record No. 149, 1968. C.O.G. Metropolitan Washington, (1971), Regional  Report, Vol. 12, No. 5» 1971. Conway, T., (1968), "Rapid Transit Must Be Improved to A l l e v i a t e Congestion", T r a f f i c  Quarterly, March, 1968, pp. 103-118. Creighton, R.L., (1970), Urban Transportation  Planning, Chicago, University of I l l i n o i s . Cribbins, P.D., H i l l , W.T., and Seagraves, H.D., (1965), "Economic Impact of Selected Sections of Interstate Route on Land Value and Use", Highway Research Record 75, 1965, pp. I-31. - m -Davis, J.T., (1963), "Parkways, Values, and Development i n Washington Metropolitan Region", Highway Research Record 16, 1963, pp. 32-43. Davis, F.W., (1970), "Proximity to Rapid Transit Stations as a Factor i n Residential Property Values", The Appraisal Journal, October, 1970, pp. 554-572. Dawson, I, (1968), Rapid Transit and Land Use -The Example of Toronto, Unpublished Masters Thesis, University of Atlanta. Deen, T.B., (1970), "Mass Transportation Research The Basic Issues", Highway Research Record No. 318, 1970, pp. 1-11. Development Research Associates, (1967), Berkeley  Transit Route. D i s t r i c t of Columbia, (1971), Metro Impact, Washington. Echenique, M., (1969), "A Spatial Model of Urban Stock and A c t i v i t y " , Regional Studies, Vol. 3, 1969, pp.281-312. E l l i s , R.H., (1967), "Modeling of Household Location:^ A S t a t i s t i c a l Approach", Highway  Research Record No. 207, pp. 42-51. Goldberg, H.A., (1969), The Use of Land Develop-ment Simulation Models m Transportation  Planning, Centre f o r Real Estate and Urban Economics, Print No. 59, 19^9, Berkeley, C a l i f o r n i a . Goldberg, M.A., and Heaver, T.D., (I97O), "A Cost Benefit Evaluation of Transportation Corridors", Highway Research Record 305, 1970, pp. 28^fr5: Goldberg, M.A., (1970), "Transportation, Urban Land Values and Renti A Synthesis", Land  Economics, Vol. 44, No. 2, May, 1970, pp. 153-162. Gwilliam, K.M., (1970), "The Indirect E f f e c t s of Highway Investment", Centre f o r Transport  Studies, University of Leeds, pp. 167-176* Haig, Robert M., (1927), "Toward an understanding of the metropolis", Quarterly Journal on  Economics, XL: 3, May 1926, and Regional  Survey of New York and i t s Environments. N.Y.: New York City Plan Commission, 1927. H a l l , P., (1966), Von Thumen*s Isolated State, Oxford, Pergamon. Hamburg, J.R., Creighton, R.L., Scott, R.S., (I967), "Evaluation of Land Use Patterns", Highway Research Record No. 207, 1967. Hansen, W.G., (1967), "How A c c e s s i b i l i t y Shapes Land Use", Journal of the American I n s t i t u t e of Planners. 1967. Harris, B., (I96I), "Some Problems i n the Theory of Intraurban Location", Operations Research 9, 1961, pp. 695-721. Harris, B., (1966), Basic Assumptions f o r a'Simu-l a t i o n of the Urban Residential Housing and  Land Market, In s t i t u t e of Environmental Studies, University of Pennsylvania, Philadelphia. leenan, W.G., (1965), "The Economic E f f e c t of Rapid Transit on Real Estate Development", The Appraisal Journal, A p r i l , I965, p.213-224. Herbert, L., ( I 9 6 9 ) , Community Consequences of  Rapid Transit, Unpublished Masters Thesis, University of B r i t i s h Columbia. Hoover, E.M., (1948), The Location of Economic  A c t i v i t y , Englewood C l i f f s , New Jersey. Hoover, E.M., (1968), "The Evolving Form and Organization of therMetropolis", i n Harvey, S. (ed.), Issues i n Urban Economics, Resources f o r the Future, Inc., Baltimore, pp. 237-284. H.U.D. (1968), Conference on New Approaches to  Urban Transportation, U.S. Department of Housing & Urban Development. Hurd, R.M., (1903), " P r i n c i p l e s of City Land ValueSj,1 N.Y.", The Record and Guide. - 79 -Hutchinson, B.G., (1970), "An Approach to the Economic Evaluation of Urban Transportation Investments", Highway Research Record, No. 316, 1970, pp. 72-86. Hutchinson, B.G., (I969), "Interim Report on the Formulation of an Economic Evaluation Frame-work For P r o v i n c i a l Highway Investments", Department of C i v i l Engineering, University  of Waterloo, pp. 1-60. Hyde, D.C, (I967), "Case Study: Mass Transit Planning i n an Active Operation (Example: Cleveland)", i n Homberger, W.S. (ed.), Urban  Mass Transit Planning, pp. 152-177. Jernstedt, G.W., Robinson, J.S., Skorpic, R.E., (1970), "Rapit Transit - A Prescription f o r Urban Growth", Westinghouse Engineer, January, 1970, pp. 3-7* Kain, J.F., (1962), " Multiple Equation Model of Household Location and Trip-making Behaviour?, RAND Corp. RM 3086-FF, Santa Monica, C a l i f o r n i a , 1962. Kain, F.K., (I965). "The Commuting and Residential Decisions of Central Business D i s t r i c t Workers", Transportation Economics, published by NBER, New York. Kearns, J.H., (1964), The Economic Impact of the  Yonge Street Subway, T.T.C., Toronto, 1964. Lash, M., (1967), "Exploring the Benefits of Improved Mass Transit", i n Homburger, W.S. (ed.), Urban Mass Transit Planning, University of C a l i f o r n i a , pp. 87-195. Lee, D.B., (I97I), BART Impact Studies: Transpor-t a t i o n and Land"""Use, Research Design f o r the  Analysis of BART Impact, University of C a l i f o r n i a . Lemly, J.H., (1959), "Changes i n Land Use and Values Along Atlanta's Expressways", Highway  Research Board B u l l e t i n 227, 1959, PP« 1-20. Leven, C.L., (1968), "Determinants of the Size and Sp a t i a l Form of Urban Areas", Regional Science  Association, Papers XXLL, Budapest Conference, pp. 8-28. - 80 -Liskamm, W.H., (I968), "Transportation i n i t s Environment", Fourth International Conference  on Urban Transportation, Pittsburgh, Penn» Mathias, P., (I965), "Rapid Transit Pays f o r I t s e l f i n Many Ways", The F i n a n c i a l Post, March 20, 1965. Meier, L., (1962), Communications Theory of Urban* Growth, Cambridge, M.I.T. ?Press. Meyer, J.R., (1963), "Regional Economics: A Survey", American Economic Review, Vol. 5 3 , pp. 1 9 - 5 ^ . M i l l e r , S.F., (1971), " E f f e c t s of Proposed Highway Improvements on Property Values", Highway  Research Report No. 114, 1971. Nidercorn, J.M., Kain, J.F., (1962), "Changes i n the Location of Food and General Merchandise Stores Employment Within Metropolitan Areas -1948-1958", Western Economics Association  Meeting. Oakland City Planning Department, (19^9), BART  Impact. Okamoto & Liskamm, (1966), Richmond Rapid Transit  Station. R a t c l i f f , R.U., (1957), "Commentary: on Went's Theory of Land Values", Land Economics, November, 1957. Pendleton, W.C, ( 1 9 ^ 3 ) , "Relation of Highway A c c e s s i b i l i t y to Urban Real Estate Values", Highway Research Record 16, 1963, pp. 14-24. Raup, P.M., (1959), "The Land Use Map Versus the Land Value Map - A Dichotomy?", Highway  Research Board B u l l e t i n 227, pp. 83-88. Robinson, I.M., (1965), Wolfe, H.B., and Barfinger, R.L.,."A Simulation Model f o r Renewal Planning", Journal American In s t i t u t e of Planners, Vol. 31, 1965. Robinson, J.S., Skorpil, R.E., (1970), "The Cost of Expanding Urban Transportation - Highways Versus Rapid Transit", Westinghouse Engineer, January, 1970, pp. 9-14. - 81 -Ryan, F.E., (1959), "A Method of Measuring Changes i n the Value of Residential Properties", Highways Research Board B u l l e t i n 232, 1959, pp. 79-83. Shapiro, I.D., (1959), "Urban Land Use C l a s s i f i -cation", Land Economics, May, 1959, pp. 1^9-155. Stratford Research In s t i t u t e , (19?0), Transit Im-pact Study of the Lafayette BART Station Area. Tass, L., (1971), Modern Rapid Transit, Carlton Press, Inc., New York. Taylor, S.R., (1970), "Urban Mass Transport - A World Wide Problem", I n s t i t u t e of Transport  Journal, July, 1970, pp. 485-502. T h i e l , F.I., (19^5), "Highway Interchange Area Development", Highway Research Record No. 96, 1965, pp. 24-45. T h i e l , F., (1970), "Highway Studies Relevant to Analysis of Rapid Transit", Highway Research  Board Special Report I I I , 1970, pp. 33-42. Transit Development Team, (1971), Parking at Metro  Stations, A p r i l , 1971. Von Thunen, J., (I863), "Der i s o l i e r t e Staat i n Beziehung auf Landwirtschaft und National-ekonomie", 1st. v o l . and new edition, 1863. Wermers, L.G., (1970), "Urban Mass Transportation Planning", Journal of the Urban Planning and-" Development Di v i s i o n , Proceedings of the American Society of C i v i l Engineers, March, 1970. Weiss, S.F., Kaiser, E.J., (I968), "A Quantitative Evaluation of Major Factors Influencing Urban Land Development i n a Regional Cluster", T r a f f i c Quarterly, Vol. XXII, No. 1, pp. 109-121. Werner, C , (1970), "Formal Problems of Transpor-t a t i o n Impact Research", Annals of Regional  Science, December, 1970, pp. 134-49. Wingo, L., (1961), Transportation and Urban Land, Baltimore, The Johns Hopkins Press.» I - 82 -Wolforth, J.L., (1965), Residential Location and  the Place of Work, Vancouver, Tantalus Research Limited. Worrall, R.D., (1967), "The Urban Panel as a Longitudinal Data Source", Highway Research  Record No. 194, pp. 62-67. Young, A.P., Maltby, D., Constantine, T., (1969), "Urban Transit Systems", O f f i c i a l Architec-ture and Planning, Vol 32, No. 12, I969, December, pp. 1454-1461. environment as an input 4.1 The Concept of Environment 4.2 General Hypothesis 4.3 Elements of the Environmental Context - 83 -4.1 In general, models derived from research i n urban THE CONCEPT OF ENVIRON-s p a t i a l structure are b u i l t upon three basic MENT componentst (i) the two-dimensional land-surface; ( i i ) urban a c t i v i t i e s , u t i l i z i n g some areas of the surface; and ( i i i ) linkage systems, f a c i l i t a t i n g the flow of i n t e r a c t i o n among associated a c t i v i t i e s . However, i t i s evident i n the l i t e r a t u r e discussed i n the previous chapter that, i n practice, often only two of these components have been treated as variables i n explaining how the s p a t i a l structure of an urban setting evolves - the a c t i v i t i e s and the linkage systems. The t h i r d element - the featurl e s s , two-dimensional p l a i n - has served merely to represent the t o t a l supply of possible l o c a t i o n s i t e s to be assigned to a c t i v i t i e s bidding f o r space, and to provide a series of reference - a p -points from which the cost of i n t e r a c t i o n can he measured. The points chosen, and the amount of area consumed on t h i s two-dimensional surface, are determined by the bidding power of alternative a c t i v i t i e s and the cost of overcoming 'the f r i c t i o n of space* as defined by the available communicat-ion/transportation linkage systems. In a more formal presentation the components may be characterized by the following expressions: (i) P^. (C) Bidding Power f o r the composite 1 cost of s i t e rent and transpor-t a t i o n cost - of a c t i v i t y i n time period t . t = Time Period (t=l,2,...,p) A^= A c t i v i t y i (i=l,2,...,q) (land use) ( i i ) C T. (d) Cost of overcoming distance d 1 using the communication/trans-portation system i n time period t . T^ = Transportation/Commun-i c a t i o n system i (i=l, 2, • • •, r) ( i i i ) Q^=^,q|i Total Supply of Land i n time X J J period t . q^. = Sites at l o c a t i o n i , j i n J time period t . (i=l, 2, •..,n and j=l, 2,... ,m) The f a c t that these models f a i l to incorporate the processes by which various a c t i v i t i e s adopt s i t e s f o r t h e i r successful operation and thus discount - m -the impact that past commitments may have on loca-t i o n a l behaviour, has been one of the p r i n c i p a l c r i t i c i s m s l e v e l l e d against such models. Bourne notes that one of the c r i t i c a l differences between r u r a l and urban land economics i s that ...to earn income from urban land, i t i s usually necessary to construct a b u i l d i n g . Pendelton, i n his generally favorable c r i t i q u e of Alonso*s "Location and Land Use", argues that he can put l i t t l e f a i t h i n a theory of urban residen-t i a l s p a t i a l structure which completely ignores 2 the standing stock of buildings. In more general terms, Margolis expresses s i m i l a r concern! One of the major impediments of s i t e adjustment i s the long l i f e of buildings, streets, u t i l i t i e s , parks, e t c . . . . I t i s d i f f i c u l t to v i s u a l i z e a reasonable approximation model f o r an urban area which does not concede the long continuing i n f l u -ence of old technologies and past a l l o c a t i o n of-c a p i t a l , land and population to previous forms.-' The formulation of the general concept of environment, on which t h i s thesis i s b u i l t , repre-sents an outgrowlth of the above c r i t i c i s m . I t i s argued that urban a c t i v i t i e s do not merely occupy s i t e s on the two-dimensional, featureless surface, but through the process of adjusting to the 1. Bourne, (1967) P» 3 9 » 2 . Pendelton, ( 1 9 6 5 ) 3 . Margolis, ( 1 9 6 7 ) p . 2 3 5 -- 86 -s p e c i f i c needs of t h e i r operation, they s i g n i f i -cantly a l t e r these s i t e s . Through the process of adjustment and use, s i t e s 'acquire* a number of c h a r a c t e r i s t i c s - c h a r a c t e r i s t i c s which can be expected to play roles of varying importance on t h e i r transformation to other uses. Further, because of i t s fixed location, every piece of land occupies a unique position among other p a r c e l s . 1 Thus, the prospects f o r transformation of a part-i c u l a r area are modified not only by the acquired c h a r a c t e r i s t i c s , but also by the general make-up of the surrounding area. As i t i s implied i n the above proposition, land surface u t i l i z e d by various a c t i v i t i e s i s no longer treated as a homogeneous pl a i n , but rather, incorporated into the model as a multifacetted environment, representing a quant-i f i a b l e input f o r the explanation of l o c a t i o n a l behaviour. In t h i s model, the a t t r a c t i o n of any s i t e f o r a p a r t i c u l a r a c t i v i t y i s defined i n terms of the r e l a t i v e difference of cost/inconvenience (monetary and intangible) involved i n a l t e r i n g i t s environmental c h a r a c t e r i s t i c s to s u i t the oper-a t i o n a l needs of that a c t i v i t y , as compared to a l l other s i t e s . 1. Weimer and Hoyt, (1960)p.l0. 2. Bourne, (1967) p.26. - 8 0 -,To contrast the variables composing the proposed model with those c r i t i c i z e d e a r l i e r , a formal description i s presented belowi (i) p j * (C) Bidding Power of A c t i v i t y Aj 1 f o r the t o t a l cost of s i t e rent, transportation cost and the cost of a l t e r a t i o n of the Environmental Characteristics of the s i t e . ( i i ) c£. (d) Cost of overcoming distance d 1 using the communication/ transportation system Tj. i n time period t. T^ = Transportation/Communi-cation system i ( i = 1,2, ...,r) ( i i i ) Q t =.H. Ei,q|, Total Supply of Land i n time i j fc K - L - i period t . Ej^q^j = Sites with Environ-mental Charact e r i s t i c s Ej£ at l o c a t i o n i , j i n time period t. In the following, a general hypothesis i s put forward which, upon v e r i f i c a t i o n , i s intended to e s t a b l i s h the existence of the influence that environmental c h a r a c t e r i s t i c s have on the change i n s p a t i a l d i s t r i b u t i o n of urban a c t i v i t i e s . This general hypothesis i s supplemented with a number of sub-hypotheses, each suggesting a separate element of the environment and speculating on the magnitude of impact various elements may exhibit. I t was noted e a r l i e r that both the magnitude of impact and the r e l a t i v e importance that i s attributable to - 88 -each element i s expected to vary with regard to d i f f e r e n t a c t i v i t i e s , and over time. Thus i n the formulation of sub-hypotheses, the shape of graphs, showing the re l a t i o n s h i p between s i t e a t t r a c t i o n and quantitative change i n the environ-mental element, represents a general approximation of the phenomenon, and i t i s subject to modifica-t i o n when related to s p e c i f i c a c t i v i t i e s . - 8§> -4.2 When a new rapid t r a n s i t network i s introduced into an urban setting, the s p a t i a l d i s t r i b u t i o n of redevelopment taking place along the l i n e and i n the proximity of rapid t r a n s i t stations cannot be accounted f o r so l e l y i n terms of the s h i f t i n g a c c e s s i b i l i t y surface. The explanation must be sought i n the additional influence exerted by the environmental context within which each station i s placed. GENERAL HYPOTHESIS 4.3 There are a number of ways i n which the environ-mental context may be described and i t s components grouped. The following c l a s s i f i c a t i o n system adopted f o r the presentation of sub-hypotheses i s suggested p a r t l y because i t c l e a r l y expresses the character of various environmental elements and hence they can e a s i l y be related to the type of action needed to be taken to a l t e r them; and p a r t l y because t h i s c l a s s i f i c a t i o n y i e l d s some operational advantages f o r the simulation model presented l a t e r . The i n t r i n s i c physical features of an area play a central role i n a g r i c u l t u r a l and regional l o c a t i o n theories. The d i s t r i b u t i o n of natural resources, land f e r t i l i t y , topographic and c l i m a t i c charac-t e r i s t i c s explain the s p a t i a l d i s t r i b u t i o n of a substantial amount of economic a c t i v i t y , e i t h e r because the a c t i v i t y i s technologically t i e d to the resource input, or because costs are minimized ELEMENTS OF THE ENVIRON-MENTAL CONTEXT PHYSICAL - 91 -"by a location i n the resource area. Although, as Hoover pointed out, f o r some urban a c t i v i t i e s (airport, recreation) the presence of c e r t a i n topographical or other natural s i t e features are 2 e s s e n t i a l , the impact that slope, s o i l conditions, bedrock, water table l e v e l , vegetation coverage and the l i k e may have on the s p a t i a l d i s t r i b u t i o n of a c t i v i t i e s i n an urban area i s generally -a assumed to be l e s s s i g n i f i c a n t . I t i s argued that within the scale of an urban agglomeration the difference i n physical c h a r a c t e r i s t i c s among s i t e s i s n e g l i g i b l e as compared wit the variance that occurs on a regional scale, and further, that advanced engineering and b u i l d i n g construction technology makes i t possible to overcome the d i f f -erence that does e x i s t with r e l a t i v e l y l i t t l e a d d i t i o n a l cost. (The mutilated natural environ-ment within and around our c i t i e s presents ample evidence to v e r i f y at l e a s t the second part of that statement.) However, as public awareness increases and techniques l i k e those developed by McHarg,1* H i l l s , ^  and Lewis ^ gain professional acceptance i n planning, more emphasis can be 1. Stabler, (1968) 2. Hoover, (1968) p.239 3. Hoch, (1969) 4. McHarg, (I969) 5. H i l l s , (1966) 6. Lewis, (1965) - 92 -expected to be placed i n the future on the preser-vation and sympathetic incorporation of the physical c h a r a c t e r i s t i c s of the land when s i t e s are developed. This process w i l l tend to reduce the amount of available land f o r development within a fixed geographic area. The concept of available land as opposed to the homogeneous p l a i n has already been incorporated into Lowry'ffi urban growth model f o r the Pittsburg Metropolitan Area. In h i s approach, however, a l l available land could be assigned f o r suitable a c t i v i t i e s and the amount of available land l e f t f o r the successive year i n the simulation process represented no additional input. I t i s proposed i n sub-hypothesis #1 that the at t r a c t i o n of an area f o r suitable a c t i v i t i e s changes considerably when available land (as derived from the physical constraints) assumes values between the minimum and average amounts needed as s i t e requirements f o r these a c t i v i t i e s . (Figure 4.3-1)* This proposition i s derived from the observation that when a c i t y block or an entire neighborhood i s redeveloped, the process r a r e l y involves a l l the properties i n that area. 1. Lowry, (1964) - 93 ->> -p •H > •H •P O rt p -p rt • - - - — — - - — -i J 3 KSSC. available land This phenomenon can be r a t i o n a l i z e d either by speculating that during the land assembly process some undesirable fragmentation occurred and the configuration of •left-over" properties cannot be economically u t i l i z e d , or by suggesting that d r a s t i c increases i n land prices following the i n i t i a l stages of redevelopment forced prospective developers to seek alternative locations. The successful operation, and indeed existence, of most urban a c t i v i t i e s depend on the existence, q u a l i t y and quantity of a number of technological supporting systems. The powerful role played by the communication/ transportation linkage system i n f a c i l i t a t i n g the flow of i n t e r a c t i o n among associated a c t i v i t i e s FIGURE 4.3-1 SUB-HYPOTHESIS #1 TECHNOL-OGICAL plays on the evolution of urban s p a t i a l structure i s well researched, documented and f a i r l y well understood. However, i t was not u n t i l the P o l i s h and successively the English economic school had developed the threshold theory of urban development that the significance of other supporting systems (water, sewer, gas, e l e c t r i c i t y ) was widely recog-n i z e d , 1 although the writers are not aware of any urban simulation model that incorporates as inputs the influence of these factors. For the purpose of t h i s study, i t i s proposed to include among the technological components of the environmental con-text such * non-technological* elements as school capacity, available r e c r e a t i o n a l space, and other s o c i a l services, i n addition to the t r a d i t i o n a l public u t i l i t y systems. There are three charac-t e r i s t i c s of the technological components that i n v i t e s p e c i a l attentions i . Variables of t h i s type exhibit threshold  behaviour and the option to s h i f t the thres-hold value often l i e s outside the power of the i n d i v i d u a l decision-making units (firms, households) u t i l i z i n g locations served by the f a c i l i t i e s . 1. Koslowski, (1971). i ' i . These variables are not area but density- sensitive (number of people times consumption/ production m u l t i p l i e r ) ; thus, while serving a general area, t h e i r capacity can be exhausted by u t i l i z i n g any amount of land within t h i s area. i i i . The cost of a l t e r a t i o n (capacity increase) i n these variables are generally not d i r e c t l y levied against a c t i v i t i e s u t i l i z i n g locations i n the general area which i s served by the f a c i l i t i e s , but i s rather carried by the urban region as a whole. Sub-hypothesis #2 implies that technological ele-ments exert a changing influence on the a t t r a c t -i v i t y of an area when t h e i r value exceeds the optimum capacity l e v e l and reaches the maximum capacity load. (Figure 4.3-2). This proposition i s based on the observation that authorities responsible f o r the supporting systems are re l u c -tant to permit further development or density increased unless the load that the new development produces can be accommodated within the system*s e x i s t i n g capacity, or comparable new capacity construction occurs simultaneously. I t should be noted that while various a c t i v i t i e s depend upon a number of d i f f e r e n t supporting - 9.6 -systems, i t i s the c r i t i c a l rather than the compo-s i t e values of the technological component which are measured. This proposition i s the l o g i c a l extension of Leibnitz's minimum nourishment analogy f o r plant growth which states that when the growth of an entity E depends upon factors a,b,c, ....n, i t s growth w i l l be r e s t r i c t e d by that f a c t o r which has the minimum value. -p •H > •H • P O d • P •p A" — — — — i PI H il 1 N fh ] . « -in -p 1 p HI II It I** §_ SSSSs .a s5sv technological constraints I t i s generally agreed that a l l the monetary and intangible costs which can be s p e c i f i c a l l y related to p a r t i c u l a r locations, land values, building and land assembly costs are the most v i s i b l e ones when transportation expenditures are held constant. Of course, within the framework of the t r a d i t i o n a l FIGURE 4.3-2 SUB-HYPOTHESIS #2 ECONOMIC - 97 -location theories, none of these factors can be treated as additional inputs i n location choice?,;, assuming s i m i l a r transportation costs, no d i f f e r -ence i n land values would occur since the composite of the two i s assumed to be constant, and neither the physical stock nor the fragmentation of land has been e x p l i c i t l y incorporated into these models. In an e a r l i e r part of the thesis, some reference hM has already been made to c r i t i c s of the t r a d i t i o n a l l o c ation theories who suggested that land i n urban use should be treated as a three-dimensional resource. Their argument i s based on the observ-ation that i n r e a l i t y the majority of urban a c t i v -i t i e s are accommodated within some kind of physical  shelter; hence the presence, q u a l i t y and s u i t a b i l -i t y of the building stock i n alternative, otherwise favourable locations can be expected to play an important role i n the decision process as to which loc a t i o n w i l l ultimately be chosen. Two often i n t e r r e l a t e d and r e i n f o r c i n g processes characterize the building stock i aging, and technological obsolescence. 1 Aging implies phys-i c a l deterioration, increasing maintenance costs and ultimately demolition and replacement. Tech-1. R a t c l i f f , (19^9) p.356 n o l o g i c a l obsolescence r e s u l t s from changes i n the a c t i v i t i e s * requirements f o r the i n t e r n a l organ-i z a t i o n of buildings and building complexes i n the •firm' sector, or changes i n l i f e s t yle or taste i n the 'household' sector. Because of the dura-b i l i t y of buildings and the often available option of f l e x i b i l i t y that accommodates minor alterations, a large segment of a c t i v i t i e s seeking and changing locations i n the already built-up areas i n the c i t y i s accommodated within the e x i s t i n g stock. Thus the rate of change and replacement i n the physical stock of the c i t y i s considerably slower than that of the a c t i v i t i e s that use these structures. However, when a new transportation investment or p o l i c y s i g n i f i c a n t l y a l t e r s the a c c e s s i b i l i t y p o t e n t i a l of various areas, the economic u t i l i z -ation of the new p o t e n t i a l often cannot be accom-modated within the e x i s t i n g building stock. In sub-hypothesis #3 the building age as a proxy variable f o r the economic l i f e of the e x i s t i n g physical stock (value remaining i n the properties) i s related to the a t t r a c t i v i t y of an area f o r p o t e n t i a l redevelopment. (Figure 4.3-3). 1. Lowry, (i960) - p •H > •H •P O a u - p - p a ~ 1 N -ra i S Ij a % a vl 6» IS S P d d 0 . sis m -a building age In addition to the e x i s t i n g stock of buildings, another inherence from the past a l l o c a t i o n of a c t i v i t i e s i s the l e g a l subdivision of land into various sizes of parcels. The size of these par-cels, as Alonso postulated, i s the d i r e c t r e s u l t of previous transportation networks as a c t i v i t i e s , to substitute f o r the cost of access, a l t e r not only t h e i r l o c a t i o n but also the amount of space consumed at any locati o n . Consequently the s i t e size requirement of a c t i v i t i e s seeking l o c a t i o n i n areas where the a c c e s s i b i l i t y p o t e n t i a l r e s u l t i n g from the new transportation investment increases, can be expected to be d i f f e r e n t from that provided FIGURE 4.3-3 SUB-HYPOTHESIS #3 LAND FRAG-MENTATION 1. Alonso, (1964). -100 -by e x i s t i n g subdivision of properties. In fact, Bourne observed that, although the r a t i o of land u n i t per f l o o r space decreases as high density/ i n t e n s i t y a c t i v i t i e s replace lower ones through the redevelopment process, the t o t a l l s i t e require-ment of new developments, nevertheless, exceeds the previous one because of s h i f t s i n the economics of s c a l e . 1 In sub-hypothesis #4 the e x i s t i n g fragmentation of land as measured by a proxy variable of l o t size and the a t t r a c t i v i t y of area f o r redevelopment, i s postulated. (Figure 4.3-4). As the number of continuous parcels needed to be consolidated increases, the assembly process becomes increas-i n g l y cumbersome, slow and costly. The l a s t parcels obtained are often more expensive per u n i t than the f i r s t ones as owners •hold out* f o r higher 2 p r i c e s . I t i s expected that developers would be i n c l i n e d to seek out those areas among a l l other-wise suitable ones where either l a r g e . i n d i v i d u a l l o t s are dominant or where consolidation has already taken place f o r some other purposes. 1. Bourne, (196?) p.90. 2. Davis and Whinston, (1966). - 103: ->> -P •H > •H -P O cd u •p -p a I *>, T~ t m a g •& . . . - -i AS | V IS •! 1 I 3 4 *f 35 8 4* & Si 5& l o t size The ' l i k e seeks l i k e ' theory - that i s , the tenden-cy toward area s p e c i a l i z a t i o n and segregation through the cl u s t e r i n g of i d e n t i c a l or s i m i l a r a c t i v i t i e s - has gained support from the empirical 1 works of both urban economists and geographers. This apparent cohesion of various establishments i s generally i n i t i a t e d by the opportunity to share some common advantages - an es p e c i a l l y suitable labour pool, a variety of specialized business services, or the concentration of potential customers seeking to compare a variety of o f f e r -ings. The most often c i t e d i l l u s t r a t i o n s related the urban scale include restaurant and entertain-ment centers, f i n a n c i a l and o f f i c e d i s t r i c t s , FIGURE 4.3-4 SUB-HYPOTHESIS #4 AGGREGATIVE 1 . Harris and Ullman, ( 1 9 6 7 ) - 102 -specialized wholesale aggregations, or apartment complexes. Thorngern argued that one of the reasons f o r the slower rate of o f f i c e decentral-i z a t i o n from the CBD may he the absence of support-ing a c t i v i t i e s i n the suburban environment. 1 By the same token, Bourne observed that the agglomer-ative e f f e c t of ex i s t i n g concentrations of a c t i v i t i e s i s a r e l a t i v e l y powerful in d i c a t o r i n ant i c i p a t i n g the s p a t i a l d i s t r i b u t i o n of future 2 apartment growth. Sub-hypothesis #5 r e l a t e s the a t t r a c t i v i t y of an area f o r a p a r t i c u l a r a c t i v i t y to the amount of si m i l a r or supporting land uses e x i s t i n g i n that area. The quadratic type of graph implies that some c r i t i c a l mass of ex i s t i n g c l u s t e r i n g i s needed to influence favourable further agglomeration, and when the size of clust e r s exceeds an optimum l e v e l , l i t t l e additional a t t r a c t i v i t y difference exists among alternative c l u s t e r s . . (Figure 4.3-5). This proposition i s e s s e n t i a l l y the l o g i c a l extension of Perroux*s concept of 'development poles* i n the 3 regional context. 1. Thorngern, (1967) 2. Bourne, (1968) 3. Perroux, (1970). - 103 --p > •H •P O cd f-. •P -p cd / _ _ J 5 s a assess: la •a id assays 122 S i ! c l u s t e r size One of the major ' e x t e r n a l i t i e s * i n the operation of the private land market i s the i n s t i t u t i o n a l i z e d aspect of the environment which f a c i l i t a t e s public control over the a l l o c a t i o n , use and p r o f i t a b i l i t y of land. In general, there are two areas within which t h i s influence i s exercised: i . public construction and investment . 1 i i . zoning. The impact that the provision of public u t i l i t i e s and s o c i a l services may have on a l t e r i n g the loca-t i o n a l preferences of various a c t i v i t i e s has already been accounted f o r i n sub-hypothesis #2. Simil a r l y , i t i s argued that the e f f e c t building FIGURE 4 . 3 - 5 SUB-HYPOTHESIS #5 INSTITU-TIONAL 1. Tiebout, (1971) - 104 -construction a c t i v i t y of public agencies (govern-ment o f f i c e s , low cost housing, etc.) may have on the l o c a t i o n a l choices of other land users w i l l be manifested through the s p a t i a l d i s t r i b u t i o n of public construction - that i s , the creation or reinforcement of c l u s t e r s - and t h i s influence has e s s e n t i a l l y been incorporated i n sub-hypothesis #5» Probably the most comprehensive and the most widely used i n s t i t u t i o n a l i z e d public control over the s p a t i a l d i s t r i b u t i o n of a c t i v i t i e s i s exercised through zoning. H i s t o r i c a l l y , the concept of zoning grew out of the concern f o r protecting the enjoyment of private property from a n t i - s o c i a l use by neigh-bouring parcels - that i s , separating 'incompatible* a c t i v i t i e s - but more recently t h i s power has been increasingly applied either to protect and s t a b i l -i z e property values, or to s p a t i a l l y channel future 1 development. Despite the long history and increas-ing sophistication of zoning practices, t h e i r effectiveness on changes i n land-use pattern and urban development/redevelopment i s not very well 2 understood. 1. Marcus and Groves, (1970) 2. Probably the c r i t i c a l variable, and most l i k e l y the least r e a d i l y measurable one, i s the degree of enforcement exercised on the part of the m u n i c i p a l i t i e s . ZONING -105 -The great number of 'spot zoning' cases evident i n most large c i t i e s seems to indicate that when the economic pressure i s high enough to replace one type of a c t i v i t y with another or to increase density, t h i s pressure ultimately receives the blessing of the zoning a u t h o r i t i e s . Alonso argues that i n the long run zoning exerts l i t t l e influence on the price of i n d i v i d u a l properties and Yeates* empirical analysis on the e f f e c t of zoning on the s p a t i a l variations i n land values e s s e n t i a l l y con-2 firms Alonso*s proposition. On the other hand, Fisher argues at great length that the obsolete, r i g i d zoning practices contributed s i g n i f i c a n t l y to the inner c i t y b l i g h t and he proposes a new zoning concept which would be ". . . l e s s stringent as f a r as the use to which i n d i v i d u a l properties may be put but more s t r i c t i n regulating the c o l l a t e r a l e f f e c t s upon the neighborhood."3 The only aspect of zoning that has produced some consensus i n the l i t e r a t u r e i s i t s tendency to r e s t r a i n the metamorphosis of areas when there i s a pressure f o r change. Sub-hypothesis #6 i s b u i l t upon t h i s consensus. I t i s proposed that the time 1. Alonso, (1964), p.117 2. Yeates, (1964) 3. Fisher, (1963) p. 18. - 106 -delay and the cost of l e g a l procedure involved when changes i n zoning are needed to accommodate the new or the same a c t i v i t y at a s i g n i f i c a n t l y higher density l e v e l , w i l l tend to force developers to seek out those areas where permissive zoning already exists, or the necessary changes are minimal. (Figure 4 . 3 - 6 ) . -p > •H • P O a u •p -p rt A — — • — — — 1 i % 1 V. 1 --/ 1 1 i as '#1 1 f i J mm m 22: mm 3 8 zoning "permissiveness" The i n t e r r e l a t i o n s h i p between the socio-demographic character of an area (number, age, family size, income, ethnic o r i g i n of i t s population) and i t s propensity to change (transformation) has trad-i t i o n a l l y been i n the focus of urban s o c i o l o g i c a l research. Many of the early studies were essen-t i a l l y l imited to the v a r i a t i o n of Burgess* 1 and 1. Burgess, ( 1 9 2 5 ) . FIGURE 4 . 3 - 6 SUB-HYPOTHESIS #6 SOCIAL - 107 -Hoyt's 1 c l a s s i c a l works and i t was only a f t e r Hoover and Vernon's empirical research i n New York that a new concept - ?The Stage Theory of Urban 2 Growth" - evolved. Hoover and Vernon's theory, and the subsequent research which attempted to 3 v e r i f y t h e i r proposition i n other urban areas, related the evolutionary process of physical devel-opment experienced by p a r t i c u l a r 'neighborhoods' to t h e i r socio-demographic character. The central theme of the stage theory i s that, as neighborhoods undergo transformation from the i n i t i a l development to the f i n a l stage of renewal, corresponding changes occur i n the income, family size, ethnic composition, etc., i n t h e i r population. Sub-hypothesis #7 sets out the r e c i p r o c a l proposi-t i o n . It i s suggested that not only the physical c h a r a c t e r i s t i c s of an area - building type, density, use, age - define i t s population, but the population i n turn has an impact on the rate by which the corresponding area passes through the various devel-opment stages. I t i s argued that the rate of tran-sformation becomes slower i n poor and high income neighborhoods r e l a t i v e to 'average* lower income 1. Hoyt, (1939) 2. Hoover and Vernon, (1959) p. 190-209 3. Birch, (1971). - 108 -neighborhoods. (Figure 4.3-7). Because of the generally unattractive physical and s o c i a l envir-onment i n poor neighborhoods, new a c t i v i t i e s are often forced to create t h e i r own •environment', necessitating a greater scale i n the project, which i n turn increases the r i s k f actor. On the other hand, i n prestige neighborhoods, i n d i v i d u a l and community power that exists among i t s r e s i -dents can e f f e c t i v e l y block changes that would a l t e r the character of the area. To substantiate the above argument, Bourne's analysis of the s p a t i a l d i s t r i b u t i o n of land-use changes i n Toronto may be c i t e d : " . . . i n t e r e s t i n g are those types of areas which have not changed. These are 1) c e r t a i n high income inner c i t y r e s i d e n t i a l areas which are generally low density ...3) older working class r e s i d e n t i a l neighborhoods ....In many instances the l a t t e r are densely occupied by recent immigrant groups and thus tend to be unattractive to developers f o r e i t h e r o f f i c e or apartment constructions.... Zoning r e s t r i c t i o n s p a r t i c u l a r l y i n apartment development have been considerably more r i g i d i n higher income neighborhoods than i n other r e s i d e n t i a l areas within the central c i t y . " l 1. Bourne, (1970) p.7-8. - 109 -FIGURE 4.3-7 neighborhood q u a l i t y The main concern of the previous discussion was to THE DYNAMICS OF THE i s o l a t e the various components of the proposed ENVIRONMENT environmental surface, and when one attempts to divorce parts from a complex phenomenon, t h i s endeavour in e v i t a b l y introduces some s t a t i c fea-tures into the analysis. To some extent, a l l sub-hypotheses r e f l e c t these shortcomings, f o r t h e i r formulations were based on the permissible, but ra r e l y adequate, proposition of 'everything else being constant*. Thus sub-hypothesis #4, f o r example, i s interpreted ass i f i t i s possible to fi n d two locations where a l l other environmental factors are i d e n t i c a l , then i t i s expected that the area where the average l o t size i s larger w i l l be chosen by a p a r t i c u l a r a c t i v i t y requiring a minimum l o t size greater than the largest i n d i v i d u a l l o t available i n eithe r l o c a t i o n . - 110 -However, since the multitude of forces that create, a l t e r and formulate the various components of the environment are dynamic processes, the r e s u l t i n g environmental surface exhibits changing complex-i t i e s . The picture i s further complicated when various a c t i v i t i e s are matched with the environ-mental surface. Here the importance that d i f f e r e n t a c t i v i t y types attach to i n d i v i d u a l environmental components i s not the only f a c t o r expected to vary - (a 10$ slope may represent an i n h i b i t i n g expense f o r large scale i n d u s t r i a l land develop-ment, whereas the sloping t e r r a i n may be conceived as an additional benefit f o r single family housing location) - but also the way a c t i v i t i e s i nterpret the significance of various components as compared to one another. Neighborhood q u a l i t y or prestige l o c a t i o n may become the most important l o c a t i o n a l factors f o r some apartment or o f f i c e development, while other a c t i v i t i e s may place l i t t l e importance upon these. To analyze the dynamics of t h i s complexity i n any d e t a i l i s c l e a r l y beyond the scope of t h i s thesis as well as the i n t e l l e c t u a l resources of the authors at t h i s point. However, as a f i r s t attempt to make some inference from the separated environ-mental components to the complexity of t h e i r - I l l -t o t a l i t y , three observations are offered, i . While the t o t a l i t y of the environmental components impinge upon any i n d i v i d u a l s i t e , not a l l environmental components necessarily originate from the s i t e . The physical char-a c t e r i s t i c s , zoning, the e x i s t i n g physical structure, or the actual size, can s p e c i f i c -s', a l l y be related to i n d i v i d u a l l o t s ; neighbor-hood q u a l i t y or the capacity of the support-ing technological services are environmental components which can only be derived from the general area within which the s i t e i s located, i i . A c t i v i t i e s have an option to a l t e r only a limited number of environmental components. For example, developers cane-change some physical c h a r a c t e r i s t i c s or have the option to increase the size of a s i t e through land consolidation processes, but to modify zoning or the capacity of the supporting u t i l i t i e s often l i e s outside the realm of t h e i r power. This d i v i s i o n of opportunities i n the a l t e r -ation of undesirable or l i m i t i n g elements also implies that although a l l changes repre-sent some measure of 'penalty* (cost, time delay, etc.) f o r the a c t i v i t y , t h i s penalty does not necessarily equal the actual cost involved i n changing a p a r t i c u l a r environ-- 112 -mental component. Time delay and l e g a l costs involved i n changing zoning regulations carried by the developer are often greater than the cost of actually changing the zoning ordinance which requires one public hearing. Alterna-t i v e l y , as i t was noted i n sub-hypothesis #2, the f u l l cost of modifying the technological l i m i t i n g factors i s r a r e l y passed on to the developers. i i i . There i s a great time variance within which various components of the environment change, or can be changed. The neighborhood character often remains stable over 20-50 years and strong sentiments attached to p a r t i c u l a r areas could p r e v a i l long a f t e r i t s o r i g i n a l charac-te r has changed. Other elements of the environment can be modified within a r e l a t i v e -l y short period of time (demolition of b u i l d -ings, land assembly, etc.), while some compon-ents can be changed i n s t a n t l y (zoning). The above observations s t i l l f a l l short i n capturing a s i g n i f i c a n t part of the dynamic i n t e r r e l a t i o n s h i p between the l o c a t i o n a l choices and a c t i v i t i e s and the environmental surface. However, i t was f e l t that by incorporating these observations into a model which attempts to simulate the a t t r a c t i v i t y of areas around rapid t r a n s i t stations f o r various - 113 -developments, some progress may be achieved i n a n t i c i p a t i n g the impact rapid t r a n s i t has on a l o c a l scale. - 114 -Alonso, W., (1964), Location and Land Use: Toward  a General Theory of Land Renty. Harvard University Press, Cambridge, Massachusetts. Birch, D.L., (1971), /'Toward a Stage Theory of Urban Growth: A Case Study of New Haven", Ekistics, Vol. 32, No. 188, July, 1971, pp. 85-91. Bourne, L.S., (1967), Private Redevelopment of the  Central City, Public Litho Service Inc., Chicago. Bourne, L.S., (1968), "Market, Location and Site Selection i n Apartment Construction", Canadian Geographer, Vol. 7, No. 4, 1968, pp.211-226. 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Lewis, P.H., (1965), "Environmental Design Concepts for Open Space Planning in Minneapolis and i t s Environs", University of I l l i n o i s : Parks  and Recreation in Minneapolis, Vol. 3, Minneapolis Board of Park Commissioners, 1965. Lowry, I.S., (i960), "Filtering and- Housing Standards", Land Economics, Vol. 36, No. 4, November, I960, pp. 362-370. Lowry, I.S., (1964), Model of Metropolis, Rand Corporation Memorandum, RM-4053-R.C., Santa Monica. Marcus, N., and Groves, M., (1971), (eds) The New  Zoning: Legal, Administrative and Economic  Concepts and Techniques, Pragerer Publishers, New York. McHarg, I.L., (1969), Design with Nature, Natural History Press, Garden City, New York. Margolis, J., (1967), "Discussion", American  Economic Association, Vol. 57, No. 2, May, 1967, p.235. Pendleton, W.C., (1965), "Review of W. Alonso, 'Location and Land Use' ", Journal of the American Institute of Planners, Vol. 31, No. 1, February, 1965, p. 78-79. - 116 -Perroux, F., (1970), "Note on the Concept of Growth Poles", i n McKee, L.D. (ed.) Regional Econ-omics s Theory and Practice, Free Press, New York, pp. 91-93. Ratcliff, R.V., (1949), Urban Land Economics, McGraw-Hill Co., New York. Stabler, J.C., (1968), "Exports and Evolutions The Process of Regional Change", Land Economics, Vol. 44, No. 1, February, I968, pp. 11-23. Thorngern, B., (1967), "External Economics of the Urban Core", in Van Hulten, M.H. (ed.) Urban  Core and Inner City, Leiden, Netherlands, pp. 413-430. Tiebout, CM., (1971), "Intra-Urban Locational Problemss An Evaluation", in Bourne, L.S. (ed.) Internal Structure of the City, Oxford University Press, Inc., Toronto, pp.492-496. Weimer, A.M., and Hoyt, H., (i960), Principles of  Real Estate, New Yorks Ronald Press. Yeates, M.H., (1964), "An Estimation of the Effect of Zoning on the Spatial Distribution of Land Values in Rogers Park, Chicago, i960." Paper presented at the 60th Annual Meeting of the Association of American Geographers, Syracuse, New York. study design 5.1 Methodology 5.2 Limitations of the Study - 117 -5.1 To test the v a l i d i t y of the conceptual expectations formulated i n the previously presented sub-hypo-theses, a two-step analysis was pursued. F i r s t , some variables were selected to approximate the components of the proposed environmental context and by using a 'broad-brush' s t a t i s t i c a l analysis the i n t u i t i v e l y defined and generalized 'table functions' - i n t e r r e l a t i o n s h i p s between s i t e a t t r a c t i v i t y and components of the environmental context - were modified. This analysis also pro-vided some i n s i g h t as to how a p a r t i c u l a r a c t i v i t y - new apartment construction - assigned r e l a t i v e importance to the various environmental components i n s i t e s e l e c t i o n . Second, based on the adjusted table functions and weighting-scale, a simulation model was constructed. With the model, the s p a t i a l d i s t r i b u t i o n of new apartment construction i n the proximity of rapid t r a n s i t stations was simulated f o r the period of 1959-1970 and compared with the actual development which had taken place METHODOLOGY - 118 -over the same time period. When a reasonable ' f i t ' was achieved, the d i s t r i b u t i o n of future apartment development along the rapid t r a n s i t l i n e was simu-lated and the effectiveness of options currently-available to planning a u t h o r i t i e s f o r channeling development was tested. For those who are accustomed to the rigorous s t a t -i s t i c a l procedures designed to analyze i n t e r r e l a -tionships among phenomena, model building may be a questionable t o o l f o r v e r i f y i n g hypotheses. In a simulation model a large number of factors and complex i n t e r r e l a t i o n s h i p s are formulated. While these models e x p l i c i t l y reveal both the l o g i c a l structure of the postulated i n t e r r e l a t i o n s h i p s among factors and the value range within which factors are assumed to operate, the empirical v a l i d i t y of i s o l a t e d i n t e r r e l a t i o n s h i p s between two factors, or the value range of any i n d i v i d u a l f a c t o r within the model cannot be tested. That i s , when system *A* i s constructed with properties Pl» P2» • • • Pn "k° m o d e l system *B*, the l a t t e r not necessarily consists of properties pj_, P2...p n, but rather of some other properties - q^, q2»..q n, which, however, exhibit a re l a t i o n s h i p with one another s i m i l a r to the re l a t i o n s h i p that exists among the properties of model 'A*. - 119 -...models are isomorphs of one another. Both systems have the same structure, i n the sense that whenever a r e l a t i o n holds between elements of one system, corresponding r e l a t i o n holds between the corresponding elements of the other system. The systems need not stand i n any causual connection, f o r what i s required i s only that the r e l a t i o n s correspond.1 Thus the v e r i f i c a t i o n of t h e o r e t i c a l proposals (hypotheses) through a simulation model i s quite simple; the pattern or order of the phenomen being simulated must resemble the pattern or order of th phenomenon i n r e a l i t y , within l i m i t s established by the academic and professional community. As to whether the so v e r i f i e d model can be used as a forecasting t o o l , and i f so, f o r how long a time period, s o l e l y depends upon the s t a b i l i t y of i n t e r r e l a t i o n s h i p s which were thought to be explan atory i n the system*-s behaviour. There are two reasons why the writers took the simulation approach to explain the s p a t i a l d i s t r i -bution of developments along rapid t r a n s i t lines s one i s methodological, the other s t r a t e g i c a l . I t was noted e a r l i e r that the factors underlying the s p a t i a l patterns and relationships i n l o c a t i o n a l choices were expected to be complex and dynamic, inv o l v i n g n o n - l i n e a r i t i e s , time lags and feedbacks In t h i s s i t u a t i o n the s t a t i s t i c a l analysis of pro-1. Kaplan, (1964) p.263 2. Harris, (1968) p.407 - 120 -cesses i s extremely d i f f i c u l t , i f not impossible. Substantial sets of data are needed which are comparable over a f a i r l y long time period and i n order to capture both the feedback and time lag elements, the s t a t i s t i c a l analysis must be per-formed repeatedly. In a simulation model most of these problems can be dealt with i n a very e f f i -cient way and the trade-off between the mathemat-i c a l refinement of most s t a t i s t i c a l / a n a l y t i c a l t o o l s and the 'improficiency* of a simulation model ultimately pays o f f . Since the scope of t h i s study goes beyond a purely descriptive analysis, indeed i t was hoped that in s i g h t would be gained from the understanding of past patterns as to how future developments w i l l be s p a t i a l l y d i s t r i b u t e d along rapid t r a n s i t l i n e s s t r a t e g i c a l l y the' simulation model appears to be a more meaningful predicting t o o l than any other technique currently a v a i l a b l e . The model can e a s i l y handle changes i n the importance that various a c t i v i t i e s may place on the components of the environmental context over time and vice-versa Further, since there are a number of manipulative p o l i c y variables b u i l t into the model, i t enables the user to select and evaluate the actions needed i f one i s to influence the s p a t i a l d i s t r i b u t i o n of future growth. - 121 -5.2 Before the selection of the variables, description LIMITATIONS OF THE STUDY of data collection and analysis and a formal des-cription of the model are presented, i t i s appro-priate to consider the range of limitations within which the following discussion should be inter-preted. These limitations are grouped and elaborated on under the three headings of: spatial, dimensional and theoretical limitations. i . Due to the complexity of information necessary SPATIAL LIMITATIONS to analyze the spatial distribution of changes along rapid transit lines over a considerable period of time, the data used to construct and test the simulation model i s limited to one city -Toronto. (Figure 5»2-l). This city was selected for the case study for several reasons. - 122 -FIGURE 5.2-1 THE STREET MAP OF METRO-POLITAN TORONTO Sourcet Bain, R.P., and McMurray, A.L., Toronto; An Urban Study, Clarke Irwin, 1970, p.22. a. Metropolitan Toronto currently has 20 miles of subway in service, with 45 stations. 1 Because this system has been built i n three stages since 1954, i t offers a unique opportunity to observe the time lag that may exist between the construction of a line and the new development around i t s stations. b. In recent years a number of large scale studies have been undertaken on transpor-tation, land use, population changes in 1. Toronto Transit Commission, (1971), p.21. - 123 -both the c i t y and the metropolitan area of Toronto. The wealth of t h i s research pro-vided a broad empirical background from which ample data could be drawn, c. The writers had an intimate knowledge of the c i t y . This personal experience was extremely h e l p f u l f o r general orientation and i n the in t e r p r e t a t i o n of the data when there was some inconsistency i n the l e v e l of aggregation, i i . Both the model and the data analysis were limited to stations situated outside Toronto CBD. The exclusion of the core area, however, was only p a r t l y due to data and time l i m i t a t i o n s . I t i s argued that while the CBD i n general undoubtedly benefits from the rapid t r a n s i t l i n e , neither the growth nor i t s s p a t i a l d i s t r i b u t i o n can be derived s o l e l y from the system. For most a c t i v i t i e s l o c a t i n g i n the CBD, the at t r a c t i o n of the core area i s attributable e i t h e r to i t s maximum o v e r a l l a c c e s s i b i l i t y within the urban agglomeration, or to the external economics of cl u s t e r s . I t was noted i n the. summary of the l i t e r a t u r e review that there i s ample empirical evidence DIMENSIONAL LIMITATIONS 1. Hoover, (19&9) p.2^0. - 124 -suggesting that rapid t r a n s i t stations tend to a t t r a c t high density land uses, such as apartments, o f f i c e s and some types of i n s t i t u t i o n a l and speci-f i c r e t a i l uses. As the mixture of l o c a t i o n a l determinants i s quite d i f f e r e n t f o r each of these categories, the incorporation of a l l of these a c t i v i t i e s into the simulation model was impossible within the time and budgetary constraints of t h i s t h e s i s . Thus, f o r the purpose of the detailed analysis, the s p a t i a l d i s t r i b u t i o n of only one land use category - apartment - was selected. There were two reasons f o r the choice of t h i s p a r t i c u l a r land use category. F i r s t , detailed data on the l o c a t i o n and the amount of new apartment construc-t i o n were r e a d i l y available f o r the period of I959-I97O. Second, 60% of the t o t a l f l o o r area added by new construction along the Yonge subway l i n e between 1952-1962 was apartment. Although no comparable analysis was available f o r a l a t e r period, a v i s u a l survey indicated that the apart-ment share of the t o t a l growth had remained f a i r l y stable, or possibly even increased. Further, because new o f f i c e constructions - the other major land use category - were limited to two s p e c i f i c locations outside the CBD, i n the 1. Bourne, (1970) p.36. - 125 -St. C l a i r and Eglington station areas, t h e i r possible influence on the locations of apartment developments could be e a s i l y and accurately gener-ated exogeneously to the model. i . The model i s e s s e n t i a l l y a growth d i s t r i b u t i o n model. The focus of t h i s study was not to deter-mine the amount of growth rapid t r a n s i t corridors capture from the t o t a l new development i n the c i t y , but rather to evaluate the propensity of i n d i v i d u a l stations to a t t r a c t part of the growth assigned to the l i n e . Thus the amount of new apartment con-st r u c t i o n anticipated to be attracted to the corridors i s provided exogeneously to the model and i s derived from h i s t o r i c a l trends i n the c i t y . This approach represents some ove r - s i m p l i f i c a t i o n of the phenomenon. Clearly, some mutual i n t e r -relationships e x i s t between the a t t r a c t i v i t y of s i t e s adjacent to rapid t r a n s i t stations as com-pared to other s i t e s i n the c i t y , and the amount of growth channeled to the corridor. It i s suggested, however, that the p r i n c i p a l a t t r a c t i v i t y of the corridor f o r apartment development l i e s i n i t s high a c c e s s i b i l i t y p o t e n t i a l to the CBD, and the t o t a l number of apartment uni t s located i n the proximity of rapid t r a n s i t stations r e f l e c t s the aggregate l o c a t i o n a l choices of households, which THEORETICAL LIMITATIONS - 126 -f o r some reasons (work, shopping, etc.) place high p r i o r i t y on the ease of access to the core area. The number of these households i n turn w i l l be some function of jobs and shopping opportunities a v a i l -able i n the CBD, and change i n the l a t t e r can be expected to r e s u l t i n corresponding changes i n the former. i i . I t i s assumed that the l o c a t i o n a l choice of a l l new apartment construction i n the proximity of rapid t r a n s i t stations can be attributed to the presence of the system. To empirically substan-t i a t e t h i s proposition, a large scale study would be required to analyze the t r a v e l patterns of households^dwelling i n these apartment developments and p a r t i c u l a r l y t h e i r reliance on the rapid tran-s i t system. However, t h i s assumption can be supported with an i n d i r e c t argument. A developer, by choosing a s i t e f o r the l o c a t i o n of his project, performs a c o l l e c t i v e decision on behalf of a l l prospective households of his building and the t o t a l cost of his l o c a t i o n a l choice w i l l ultimately be passed on to the tenants. It was demonstrated e a r l i e r that land prices i n the v i c i n i t y of rapid t r a n s i t stations (one to three blocks) tend to be r e l a t i v e l y higher than f o r s i t e s farther away. Thus, i f only the l o c a t i o n of the general area within the c i t y was assessed to be valuable, the - 127 -developer would secure sites outside of the higher price range, assuming some rational behaviour on his part. When development takes place within the stations' range of influence, the premium paid for sites can only be rationalized in terms of some additional benefits to be gained by building within walking distance from the station, i i i . It i s assumed that a l l stations enjoy similar accessibility to the CBD - that i s , no appreciable additional benefit can be derived from locating around one station as opposed to another one withs regard to the ease of access to the core area. Conceptually accessibility, as i t i s commonly used in urban research, refers to some measure of dis-tance and spatial association. 1 Although there i s some variation in how i t i s measured, the concept generally includes two components: points of interest and their relative importance, and the •cost' of overcoming the f r i c t i o n of space between these points. This 'cost* element in turn i s measured either by physical distance, time dis-tance, the actual money cost paid by the user of the transportation/communication system, or by some combination of a l l these elements.2 1. Wilson, (1970) 2. Ingram, (1971). - 128 -In Toronto, the 'point of interest' has been assumed to be the CBD for a l l subway stations. Of course, there are a great number of people located along the line who pursue activities in areas other than the CBD served by the transit network. How-ever, the I966 analysis of passenger flow indicated that their number accounted for less than 25$ during the peak period, and this proportion i s not 1 expected to change u n t i l I 9 8 O . The other two elements that tend to equalize the CBD related accessibility from station-points are the similar-i t i e s in the level of service to a l l directions and the cost of using the system (fares). Thus, i n fact, the only variable held constant despite empirical evidence of i t s variation i s the travel time needed to reach the CBD from various stations. This varies between 2 and 14 minutes along the lines. However, the lack of research on how the importance of travel time i s perceived in trips on subways and the fact that the changing waiting time during the day further modifies the actual travel time, necessitated the exclusion of this variable from the analysis. 1. Vorhees and Associates, ( 1 9 6 8 ) , p.21 - 129 -Harris, B., (I968), "Quantitative Models of Urban Development: Their Role in Metropolitan Policy Making", in Perloff, H.S., and Wingo, L. (ed.) Issues in Urban Economics, The Johns Hopkins Press, Baltimore, pp. 363-412. Ingram, D.R., (1971), "The Concept of Accessibility: A Search for An Operational Form", Regional  Studies, Vol. 5, No. 2, March, 1971, p.101-107. Kaplan, A., (1964), The Conduct of Inquiry, Chandler Publishing Company, Scranton, Pennsylvania. Toronto Transit Commission, (1971), Transit in  Toronto, Toronto. Voorhees and Associates, (1968), Central Area Trans-portation Study, Toronto. Wilson, A.G., and Hayes, M.C., (1970), Spatial Interaction, Centre for Environmental Studies, London, England, Working Paper 57, 1970. CHAPTER V -BIBLIOGRAPHY empirical background 6.1 Metropolitan Toronto 6.2 Data Description 6.3 Preparation of Inputs f o r Simulation - 130 -6.1 In urban research the unique characteristics of individual c i t i e s often play an important role when general theories and propositions are tested on special urban aggomerations. Even in the North American context, where a number of common denom-inators characterizing the spatial patterns and processes of urban growth in many metropolitan areas have been identified, the geographical loca-tion and histori c a l evolution of various c i t i e s often act to modify the operation of general forces. To place the following data and analysis i n perspective, a brief history of Toronto*s urbanization and transportation development i s presented. 2 METROPOLITAN TORONTO 1. Kain, J.P. (1962). 2. For a more comprehensive discussion of Toronto's growth and the factors manipulating i t , see Kerr and Spelt, (1965); Brain and McMurray, (1970); and Kirkup, (1969). - 131 -Since the incorporation of the City of Toronto in POPULATION, AREA AND I839 with a total population of 9000 persons, the DEVELOPMENT GROWTH provincial capital of Ontario has been one of the most vigorously growing eon-urbations in North America. The f i r s t era of rapid development began in the early 1850s. With the construction of several railway lines, Toronto became the com-mercial distribution-centre for the rich farm lands of the province and a jumping-off point for north-ern development. By I89O i t s population had risen to 180,000, partly through the annexation of several suburban communities, but mainly as a result of tremendous growth? in I 8 9 I i t s 2400 industries employed 26,000 workers. Toronto reached metropolitan status in the early decades of the twentieth century. Its area doubled between 1900 and 1920, and by the time of the First World War, nearly a l l the nearby small villages and towns had been incorporated into the city. By that time i t s population had passed the half-million mark and i t s downtown skyscrapers had begun to appear. Man- . ufacturing continued to prosper and the built-up area began to expand from the downtown core along the main arte r i a l roads. The great influx of people to the ci t i e s following the end of the Second World War changed the face of Toronto pro-foundly. As the extensive suburbanization repre-- 132 -sented an increasing burden to the 12 suburban municipalities, a need for close cooperation be-tween the City of Toronto and the adjacent munici-palities arose. As a result, in 195^ a new p o l i t i c a l unit - the Municipality of Metropolitan Toronto - was formed. There are many indeces to i l l u s t r a t e the magnitude of the recent growth that turned Toronto into the premier trade, industrial and financial capital of Canada. However, probably none would be as powerful and representative of the *Torontonian' attitude as the introductory para-graph of Kirkup's book. "Toronto, the capital of the province of Ontario, i s the most dynamic city on the earth. Metropol-itan Toronto i s boomtown. Metro's per capita value of construction i s greater than that of any major city on this earth. Building permits in the metro area now total well over one b i l l i o n dollars - second only to New York which has four times Toronto's population." 1 To approximate graphically some dimensions of the emergence of Toronto from a small military fort to a metropolis, the following illustrations are offered. 1. Kirkup, (1969), P.l. - 133 -1850 1920 Sources The Changing City, The City of Toronto Planning Board, 1959, p.10. FIGURE 6.1-1 THE GROWTH OF THE CITY OF TORONTO FIGURE 6.1-2 GROWTH OF THE BUILT-UP AREA METROPOLITAN TORONTO 1953-1967 Sources Metropolitan Toronto, 1970, Metropolitan Toronto Council, p.6. - 134 -800 700 600 500 400 300 200 100 l l II 11 II II II 1 T l J J 1 +1 | | III T l | | • • i l l I I I • 1 - . J J FIGURE 6.1-3 POPULATION GROWTH OF THE CITY OF TORONTO 1834-1981 year Source 1 Proposed Plan for Toronto. City of Toronto Planning Board, I967, p.22. - 135 -FIGURE 6.1-4 CHANGES IN THE POPULA-TION OF METROPOLITAN TORONTO PLANNING AREAS Source: Toronto: An Urban Study. R.P. Baine and A.L. McMurray, 1970, p.66. - 136 ro 51 52 53 54 SS 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 0 0 Source: Annual Report 1970, Department of Buildings, City of Toronto, p.9. Despite a number of reports dating back to 1910, studying the f e a s i b i l i t y of introducing rapid transit lines to alleviate the severe t r a f f i c congestion that accompanied Toronto's rapid rate of FIGURE 6.1-5 VALUE OF BUILDING PERMITS 1950-1970 - in millions of dollars. RAIL RAPID TRANSIT 1 1. The discussion in this chapter i s based upon the following data sources: Boorse, (1968); Tass, (1970): T.T.C., (1971). - 137 -urbanization, i t was not u n t i l 1947 that the citizens of Toronto voted 9 to 1 in favour of the public referendum authorizing the Toronto Transit Commission (T.T.C.) to build the f i r s t rapid transit line i n Canada. One of the primary design objectives was that the subway would go where the need was greatest, despite the obvious high cost of building a line through the heart of the downtown area. 1 The need was the greatest along Yonge Street and the f i r s t 4.6 miles of Toronto's rapid transit opened in 1954. 2 The Yonge Street line connects Union Station to Eglington and has twelve stations. The need for an east-west route was apparent even before the Yonge subway was opened, but instead of constructing the Queen Street subway line which was originally conceived to be the f i r s t extension, an alternative route was decided upon, adjacent to Bloor Street and Danforth Avenue - the city's major East-West t r a f f i c artery. Further, to provide additional service to the downtown business dis-t r i c t , the extension of the Yonge subway line under University Avenue was proposed. The ten-mile 1. T.T.C. (1971), p.21. 2. For reference on the technical description of the route alignment, track, r o l l i n g stock, etc., of the system, see Boorse, (1968), T.T.C., (1971). - 138 -system has been constructed in three stages: the University section from Union Station to St. George Street, 5 stations - completed in I963; the eastern section from St. George to Woodbine, 9 stations; and the western section from St. George to Keel, 10 stations. The entire Bloor-Danforth line was opened for passenger t r a f f i c in 1966. In 1969, almost two years before the scheduled completion date of the Bloor subway, the extension of the line both eastward and westward was approved. Thus, in I968 nine additional stations were opened - three to east and six to west. (Figure 6.1-6). This subway system carried over 400,000 people per day, or approximately 50$ of a l l transit trips, i n I968, and s t i l l continues to attract new riders. (Figure 6.1-7). Metropolitan Toronto Subway LEGEND -Opened March 30,1964 -Opened Febfu&ry 28, 1963 -Opened February 26,1966 • Opened Mav IV 1968 •Now under construction FIGURE 6.1-6 METROPOLITAN TORONTO SUBWAY, NEW LINE STAGES OF CONSTRUCTION Source: Development Follows Toronto Subway, Toronto Transit Commission, 1969. - 139 -M I L L I O N S 1 5 7 , 6 0 0 , 0 0 0 1 4 5 1 3 0 1 1 5 1 0 0 8 5 7 0 " 5 5 1 9 5 5 1 9 7 0 FIGURE 6.1-7 SUBWAY PASSENGERS CARRIED Source: TTC 50 Years!, Toronto Transit Commission, 1971. - 140 -To construct and evaluate the simulation model two basic sets of data were required» the amount and s p a t i a l d i s t r i b u t i o n of land use to be simulated i n the model, and the description of variables needed to approximate various components of the environ-mental context. Although the Metropolitan Toronto Planning Board had already assembled a large amount of information on variables pertinent to t h i s thesis f o r the years 1963, 1966, 1968, the use of t h i s computerized information was not f e a s i b l e . 1 Thus f o r the study the major data source was the review of a l l zoning change applications f o r the area within s i x blocks of the corridor containing three 'legs* of the Toronto subway system, from i960-1970. This data was supplemented by i n f o r -1. This data was related to i n d i v i d u a l l o t s and the Planning Board i n s i s t e d that the writers had no 'authority* to obtain information on i n d i v i d u a l properties. The transformation of t h i s data to the next l e v e l of aggregation -c i t y block - represented i n h i b i t i n g expenses. - 141 -mation obtained from various planning d i s t r i c t reports, studies and personal interviews. One of the most recent features of the North APARTMENT REDEVELOP-Ameriean urban landscape i s the upsurge of highrise MENT apartment developments. While in the United States only 17?o of a l l dwelling units were apartments in 1950, by 1965 this portion was increased to kofo,1 The trend has been similar in Canada - the share of apartment dwelling units from the total new residen-t i a l construction increased from l6fo to 52$ by 1969. 2 As,is expected, the tendency for apartment dwellings to dominate the provision of new r e s i -dential units i s even more pronounced in metropol-itan areas; i n 1967 i n Toronto, for example, 73% of a l l residential units added to the existing stock of buildings were apartments. Extensive research has revealed a complexity of factors both on the supply and the demand side of the housing market leading to the accelerating trend towards apartment construction. Although there i s some dispute over the future behaviour of this trend, the Toronto Planning Department fore-casts a large amount of new apartment constructions 1. Neutze, (1968) p.9. 2. Nader, (1971) p.308. 3. Bourne, (1968) p.12. - 142 -for the next fifteen years within the metropolitan region. 1 For the construction and calibration of the simu-lation model, the following 'apartment' data was required: i . Number and spatial distribution of apartment units built before 1970. i i . Land consumption rate ofAan average apartment unit, i i i . The amount of future apartment construction that could be assigned to the transit corridor for 1970-1985. Information for the f i r s t input was gathered from the Toronto Planning Board's 1966 apartment survey, which i s updated yearly. This survey contains information on the number of buildings, their location, and the units and storeys of a l l apart-ment developments since 1958 on a yearly basis. While the study gives similar information on the apartment construction which took place before 1958, i t does not indicate in which year they were bui l t . From the survey information, the following data 1. Metropolitan Toronto Planning Board, (1967) - 143 -inputs were compiled! i . The t o t a l number of apartment un i t s b u i l t within two-year periods along each •leg* of the subway l i n e between 1959 and 1970 (Appendix A.B. - 2 , 3 , 4 ) , For the i n i t i a l simulation process t h i s data was externally provided f o r the model as •the number of u n i t s to be d i s t r i b u t e d f o r each time period*. i i . Total number of apartment un i t s b u i l t i n each st a t i o n sub-area within two-year time periods. 1 These data were used f o r two purposes« f i r s t , as a proxi-variable f o r the aggbmerative component of the environmental context - the influence of the s p a t i a l d i s t r i b u t i o n of past developments - and second, as a base f o r comparing the amount and s p a t i a l d i s t r i b u t i o n of simulated growth with that which ac t u a l l y took place. i i i . The analysis of average apartment building size was necessary to learn how the economics of scale of apartment construction changes over time. (Appendix A.S.-l). The cumulative d i s t r i b u t i o n of apartment bu i l d i n g and development size were com-p i l e d f o r various time periods. For those years within which the scale economics shifted consider-1. Reasons f o r subdividing the network into three sub-sections, each station into four substation areas and the simulation time in t o two-year time periods w i l l be given i n the next chapter. - 144 -ably, d i f f e r e n t d i s t r i b u t i o n curves were used i n the apartment assignment process. (Figures 6.2-1 and 6.2-2). ro to -P M C C <D «H O •« d) MH m o> > -P •H C -P a> cd 6 H -P 3 U & ct a _ in 0.0 80.0 160.0 1 240.0 I 320.0 Apartment Building Size A (Number of Dwelling Units) 8000 Apartment Building Size A (Number of Dwelling Units) FIGURE 6.2-1 CUMULATIVE DISTRIBUTION OF APARTMENT BUILDING SIZE -1959-1964. FIGURE 6.2-2 CUMULATIVE DISTRIBUTION OF APARTMENT BUILDING SIZE -1965-1970. - 145 -In order to be able to deplete the available land f o r new apartment construction when, through the model, additional units are assigned to the area, some measure of land consumption rate had to be established. This rate e s s e n t i a l l y depends upon two factors: the size of average apartment units (square feet) and the permitted density. Apartment sizes vary from 850 square feet i n the core area where most buildings are composed of one or two bedroom units, to approximately 1100-1200 square feet i n suburban locations where, catering to a d i f f e r e n t family size structure, most b u i l d -ings have two, three and four bedroom dwelling u n i t s . However, f o r reasons of s i m p l i c i t y an average apartment u n i t size of 1000 square feet was used uniformly i n the model. The other land con-sumption rate modifying f a c t o r also varies i n the c i t y . ^ However, i t was evident from the reviewed zoning application approvals that f o r most apartment developments located near rapid t r a n s i t stations some density •bonus* was given the average 1. Neutze, (I968), p.24. 2. The 1000 square feet size was derived from averaging the actual apartment unit sizes of three large scale development projects located near the core (St. James Town), i n midtown ( D a v i s v i l l e ) , and i n suburbia (Warden. 3. Metropolitan Toronto Planning Board, (1967). - 146 -density could be treated as stable ( 2 . 5 times the land area) throughout the c i t y . From these two figures, the average land consumption rate of apartment un i t s was calculated to be . 0 1 acres. The amount of future apartment growth to be d i s t r i -buted along the l i n e was derived by employing a 1 r e l a t i v e l y simple formula. F i r s t , the t o t a l amount of apartment growth i n those i n d i v i d u a l planning d i s t r i c t s which were adjacent to the rapid t r a n s i t l i n e were compared to the r e l a t i v e growth that had been attracted to the v i c i n i t y of subway stations. Then, the Metropolitan Planning Board's apartment construction forecast f o r each of these planning d i s t r i c t s were related to the l i n e , assuming that the r e l a t i v e a t t r a c t i v i t y of the system remains constant throughout the simulation period (Figure 6 . 2 - 3 ) . 1. The shortcoming of t h i s process has already been discussed and acknowledged. 2 . Metropolitan Toronto Planning Board, ( 1 9 6 7 ) . - 147 -FIGURE 6.2-3 FUTURE DIS-TRIBUTION OF APARTMENT UNITS BY PLANNING DISTRICTS Sourcet Metropolitan Apartment Development Control Policy. Metropolitan T o r o n t o P l a n n i n g Board, I967. - 148 -To approximate the role of the environmental context DATA BASE FOR ENVIRONMENTAL i n s i t e s e l e c t i o n f o r apartment development, VARIABLES thi r t e e n variables have been incorporated i n the model. Data f o r these variables were selected from a variety of sources and l e v e l s of aggregation. The following section gives a.brief account of how the various proxi-variables were measured and expressed i n operational terms f o r the simulation model. This general discussion i s supplemented by tables i n which the value and the frequency d i s t r i b u t i o n of each variable i s tabulated, both f o r those s t a t i o n sub-areas that had attracted some apartment growth, and f o r those that had not, between I959-I970. When maps were available to i l l u s t r a t e the occur-rence of various variables on the c i t y or metropol-i t a n scale, these figures are also presented. In sub-hypothesis #1 i t was suggested that the PHYSICAL influence physical constraints exert on s i t e l o c a t i o n should be measured through t h e i r role i n reducing the t o t a l geographical surface t h e o r e t i -c a l l y available f o r development to a smaller area where the development costs are to l e r a b l e . In Toronto topographic c h a r a c t e r i s t i c s - the harbour, lake front, ravines, the Lake Plain, the shoreline of the Lake Iroquois and the Scarborough B l u f f s -had played a very important role i n the development - 149 -of the city. (Figure 6.2-4). However, since the rapid transit line passes through mostly already built-up areas, where the replacement of one Sourcet Proposed Plan for Toronto. City of Toronto Planning Department, 1967, p.103, FIGURE 6.2-4 TOPOGRAPHY OF THE METROPOL-ITAN TORONTO SITE * 150 -structure with another i s not expected to involve major d i f f i c u l t i e s , the role of physical constr a i -nts i s rather n e g l i g i b l e . Thus, f o r a l l station sub-areas where no other obvious constraints existed (cemetery, public open space, flood p l a i n , school playground, church, e t c . ) , the whole sub-area (20 acres of land) was assigned as input. However, since the model does not generate indigenously the land comsumption of competing land uses with a higher a b i l i t y to pay f o r s i t e s , a l l areas which had been zoned f o r high-density commercial or s p e c i f i c i n s t i t u t i o n a l uses (university, h o s p i t a l , f o r example) were deducted. (Table 6 . 2 - I ) 1 The name of the variable was abbreviated as LANDAV i n the model. I t s dimension i s "acres". To thoroughly assess and measure the value of a l l public u t i l i t i e s and s o c i a l services covering the area i n the v i c i n i t y of the rapid t r a n s i t l i n e s was c l e a r l y beyond the available resources of the 1. For a l l the following tables (Table 6 . 2 - 1 to 6.2-XI) the figures i n the upper portion are representative f o r those s t a t i o n sub-areas which attracted growth ( 2 5 ) , whereas i n the lower part those s t a t i o n sub-areas are grouped which have not ( 1 0 3 ) , between 1 9 5 9 - 1 9 7 0 . Under the column-heading of "absolute frequency' the number of station sub-areas exhibiting the value* of the ^ environmental"!*variable i s given i n the fcolumn of adjusted frequency (the absolute frequency i s converted into percentage). TECHNOLOGICAL - 151 -VLASD AVAILABLE (IN ACBES) VALUE ABSOLUTE ADJUSTED CUHUL AT IV E' FREQUENCY FR F.2 UE NC ¥ AD J PREQ (PERCENT) (PERCENT) NO LA ND AVAILABLE 0 1 U. 0 1. 0 1- 5 5 2 8. 0 12.0 6- 10 10 3 12. 0 21 . 0 1 1- 15 15 2 8. 0 32 .0 16-20 20 11 56. 0 88. 0 21-25 25 1 U. 0 92 .0 MORE THAN 25 26 2 8. 0 100. 0 TO 25 100. 0 100. 0 NO LAND AVAILABLE 0 12 11.7 11.7 1- 5 5 12 11.7 23.3 6- 10 10 11 10. 7 tn. o 11-15 15 10 9. 7 U3.7 16-20 20 58 56. 3 100.0 TO 103 100. 0 100. 0 TABLE 6.2-1 writers. However, two sources provided inform-ation on areas where shortages r e s t r i c t i n g further growth existed: the reviewed zoning applications, where the r e j e c t i o n was j u s t i f i e d by the lack of service i n the general area (school, open space, sewer), and the appraisals of various planning d i s t r i c t s . Consequently, unlimited supply was assigned to those st a t i o n sub-areas where no apparent liimitation existed, or where no informa-t i o n was available; and r e s t r i c t i o n s were maintain-ed f o r those sub-areas where i t was documented. In these l a t t e r cases, some i n d i c a t i o n was often given as to when new services were expected to be i n s t a l l e d ( D a v i s v i l l e , Spadina, Main). (Figure - 152 -6.2-5)• Thus during the simulation period these r e s t r i c t i o n s were relaxed at the appropriate time. The name of the variable was abbreviated to TECHNC i n the model. I t s dimension i s : apartment u n i t . To approximate the influence of land fragmentation on apartment s i t e selection, the average size of i n d i v i d u a l properties was measured. In station sub-areas where l o t sizes were not uniform, some adjustments were made: when the difference among l o t s i z e s was small, simply the average was calcu-lated; i n areas where one l o t size was dominant, but a r e l a t i v e l y small area was comprised of considerably d i f f e r e n t l o t sizes, t h i s difference was ignored. In cases where a larger area had already been assembled f o r some other purposes, the amount of t h i s land was c l a s s i f i e d as *assembled?• However, an area, to be c l a s s i f i e d as •assembled*, had to be greater than or equal to the minimum l o t size needed f o r average apartment s i t e development. (Table 6.2-II and Figure 6.2-6). The problem that i n d i v i d u a l l o t s are often further fragmented through the l e g a l d i v i s i o n of ownership i s acknowledged here. However, f o r the purpose of t h i s study, t h i s complication was ignored. The name of the variable was abbreviated as LOTSIZ i n the model. I t s dimension i s "square f e e t " . ECONOMIC - 1 5 3 -Source: Towards a New Plan f o r Toronto, City of Toronto Planning Board, 1 9 & 5 , p.24. - 15£ -AVERAGE LOT SIZE (IN SCUAREFOOTS) VALUE ABSOLUTE FREQUENCY ADJUSTED FREQUENCY (PESCENT) 3UMULATI VF. ADJ FII EQ (PERCENT) LESS T. 2500 1 2 H. 0 8. 0 2500- 3000 2 2 8. 0 1 6. 0 3000- 3500 3 5 20. C 36. 0 3500- U500 u 1 U. 0 UO. 0 H500- 6000 5 6 2<l. 0 6U. 0 P10R E T. 6000 6 3 12. 0 76. 0 LAND ASSEMBLED 7 6 2H. C 1 00 .0 TO 25 100. 0 100 . 0 LESS T. 2 50 0 1 . 21 20. 14 20.a 2500- 3000 2 53 51.5 71. 0 3000- 3500 3 7 6. 8 7 8. 6 3500- USOO H 5 U. 9 83. 5 U500- 6000 5 2 1. 9 85.U HORE T. 6000 6 1 3. 9 89. 3 LAND ASSEMBLED 7 1 1 10. 7 100 . 0 TO 103 100. 0 100.0 TABLE 6.2-II AVERAGE LOT SIZE To account f o r the cost that occurs when one or a number of buildings are replaced with another, two proxi-variables were introduced* the age mixture of the e x i s t i n g stock, and the vacant land. During the preliminary i n v e s t i g a t i o n stage, i t was observed that apartments tended to replace low-density r e s i d e n t i a l structures. Since the economic l i f e of these buildings i s generally estimated to be approximately f i f t y years, the bui l d i n g age proxi-variable was expressed i n terms of the average percentage of the physical stock i n the sub-area b u i l t before 1920. (Table 6.2-III). - 155 -NUMBER OF HOUSES BUILT BEFORE 192 0 VALUE ADSCLUTE FREQUENCY ADJUSTED FREQUENCY (PERCENT) CUMULATIVE ADJ FR EQ (PERCENT) NONE 0 2 B. 0 8. 0 11-20 % 2 10 10. 0 18. 0 21-30 % 3 3 12. 0 60. 0 31-10 % a 3 12. 0 72 .0 51-60 % 6 5 20. 0 92 . 0 61-70 X 7 2 . 11. 0 100. 0 TO 25 100. 0 100. 0 NONE 0 12 11.7 11.7 0-10 % 1 3 2. 9 11.6 11-20 % 2 , 23 22. 3 36. 9 2 1-30 % 3 6 5. 8 1 2 . 7 31-10 X 1 12 11.7 51.1 1 1-50 % 5 1 1. 0 55.3 51-60 X 6 5 1. 9 60. 2 61-70 X 7 1 1 3 9. 8 100.0 TO 103 100. 0 100.0 D a t a f o r t h e v a r i a b l e w e r e i n f e r r e d f r o m maps i l l u s t r a t i n g t h e h i s t o r i c d e v e l o p m e n t o f T o r o n t o . The name o f t h e v a r i a b l e was a b b r e v i a t e d a s BUILDAG i n t h e m o d e l . I t s d i m e n s i o n is» p e r c e n t a g e . The v a r i a b l e o f v a c a n t l a n d was a d d i t i o n a l l y -i n t r o d u c e d t o r e p r e s e n t t h e e c o n o m i c b e n f i t t o be g a i n e d when no b u i l d i n g d e m o l i t i o n c o s t o c c u r s . S i m i l a r l y t o t h e v a r i a b l e o f a s s e m b l e d l a n d , t h e v a r i a b l e o f v a c a n t l a n d was a s s i g n e d t o a s t a t i o n s u b - a r e a o n l y i f i t s s i z e was g r e a t e r t h a n t h e s i t e r e q u i r e m e n t o f t h e minimum, e c o n o m i c a l l y f e a s i b l e d e v e l o p m e n t s i z e . ( T a b l e 6.2-IV). The name o f t h e TABLE 6.2-1II BUILDING AGE MIXTURE - 156 -variable was abbreviated as LANDVC i n the model. I t s dimension i s : acres. V A C A N T L A N D (IN A C R E S ) V A L U E A B S O L U T E A D J U S T E D C U H U L A T I V E F R E Q U E N C Y F R E Q U E N C Y A D J F R E Q ( P E R C E N T ) ( P E R C E N T ) NO L A N D V A C A N T 0' IH . 72.0 72.0 1- 5 5 tl 16.0 88.0 6- 10 10 12. 0 100. 0 TO 25 100.0 100.0 NO L A N D V A C A N T 0 10 1 9a. 1 98. 1 1- 5 5 1 1.0 9 9 . 0 6- 10 10 1 1.0 100.0 TO 101 100.0 100. 0 The e f f e c t of clu s t e r i n g , the presence of suppor-t i n g or the absence of incompatible land uses were measured by four variables: new construction, commercial development, proximity to major open space and lack of undesirable conditions. The influence of past apartment concentration on the s p a t i a l d i s t r i b u t i o n of future construction has already been discussed i n the previous section. Here only the variable abbreviation and i t s dimen-sion i s presented: NEWCONj apartment units. The amount, q u a l i t y and v i a b i l i t y of commercial development i s not generated within the model; thus, t h e i r influence on apartment s i t e s e l ection was assessed through the provision of an external TABLE 6.2-IV VACANT LAND AGGREGATIVE - 15? -v a r i a b l e . In most urban structure models commer-c i a l and r e t a i l development i s treated as a depen-dent variable, assuming that t h e i r l o c a t i o n g r a v i -tates towards e x i s t i n g or future concentration of the population they serve. The somewhat r e c i p r o c a l proposition implied here was based on the constant recurrence of reasonings, evident i n most reviewed zoning applications, claiming that e x i s t i n g or planned shopping f a c i l i t i e s could adequately serve the a d d i t i o n a l population increment r e s u l t i n g from new apartment development. To scale the qu a l i t y of the commercial strength of i n d i v i d u a l s t a t i o n sub-areas, three l e v e l s of commercial development were used« l o c a l , d i s t r i c t and o f f i c e center. (Figure 6.2-6; Table 6.2-V). The name of the variable was abbreviated as GOMDEV i n the model. Its dimension i s * dimensionless. C L A S S I F I C A T I O N OF V A L U E A B S O L U T E A D J U S T E D C U M U L A T I V E C O M M E R C I A L D E V E L O P M . . F R E Q U E N C Y F L U E N C Y A D J F R E O ( P E R C E N T ) ( P E R C E N T ) L O C A L 1 15 6 0 . 0 6 0 . 0 D I S T R I C T . , 2 5 2 0 . 0 8 0 . 0 O F F I C E 3 5 2 0 . 0 1 0 0 . 0 TO 25 1 0 0 . 0 1 0 0 . 0 L O C A L 1 . 85 8 2 . 5 8 2 . 5 D I S T R I C T 2 17 1 6 . 5 9 9 . 0 O F F I C E 3 1 1. 0 1 0 0 . 0 T O 103 1 0 0 . 0 1 0 0 . 0 TABLE 6.2-V COMMERCIAL DEVELOPMENT f - 158 -FIGURE 6.2-6 COMMERCIAL AREAS OF TORONTO, - 159 s The ava i l a b i l i t y of public open spaee within or adjacent to the sub-areas of individual stations was assessed to be important for two reasonsi i t provides a valuable recreational service and acts as a physical, visual buffer between low density areas and highrise apartments. The variable was treated as a binary choice, and assigned to the sub-areas only i f the size of the public open space was greater than five acres. (Figure 6.2-7; Table 6.2-VI). The name of the variable was abbreviated as PARKLD i n the model. Its dimension iss dimensionless. P R O X I M I T Y T O MAJOR V A L U E A B S O L U T E . A D J U S T E D O P E N S P A C E F R E Q U E N C Y F R E Q U E N C Y ( P E R C E NT) C U M U L A T I V E A D J FREQ ( P E R C E N T ) NO P A R K L A N D P A R K L A N D NO P A R K L A N D P A R K L A N D 0 1 0 TO 12 1.3 25 US. 0 52. 0 100. 0 U8. 0 100.0 100.0 0 77 1 26 TO 103 7U. 8 25. 2 100. 0 7U. 8 100. 0 100.0 TABLE 6.2-VI OPEN SPACE Similarly, the presence of land uses generally considered as incompatible with residential devel-opment was also measured as a binary choice. Industrial and some transportation land uses were classified as incompatible. (Table 6.2-VII). The name of the variable was abbreviated as UNDC0N - 160 -FIGURE 6.2-7 DISTRIBUTION OF PUBLIC OPEN SPACE Source t Towards a New Plan for Toronto. City of Toronto Planning Board, 1965, p.23. - I l l -i n the model. I t s dimension i s * dimensionless. U N D E S I R A B L E C O N D I T I O N S NON S O M E V A L U E A B S O L U T E A D J U S T E D C U M U L A T I V E F R E Q U E N C Y F R E Q U E N C Y A D J F R E Q ( P E R C E N T ) ( P E R C E N T ) 0 2 1 1 14 T O 25 B<4. 0 1 6 . 0 1 0 0 . C H14. 0 1 0 0 . 0 1 0 0 . 0 NON son E 0 H 9 1 1(1 T O 1 0 3 8 6 . « 1 3 . 6 roo. o 8 6 . 1 1 0 0 . o 1 0 0 . 0 To assess the importance that zoning exerts on apartment s i t e selection, the e x i s t i n g zoning i n stat i o n sub-areas was mapped.1 Since the model i s concerned mainly with apartment development, only the r e s i d e n t i a l zoning was d i f f e r e n t i a t e d accord-ing to the permissible density, and a l l other non-r e s i d e n t i a l zoning (commercial, i n s t i t u t i o n a l , i n d u s t r i a l ) was treated under one zoning category. (Table 6.2-VIII). The name of the variable was abbreviated as Z0N i n the model. I t s dimension i s : dimensionless. TABLE 6.2-VII UNDESIRABLE CONDITIONS INSTITUTIONAL 1. Figure 6.2-8 gives a generalized i n d i c a t i o n of the kinds of uses permitted i n each zoning. - 162 -Permissible Uses Residential Districts G R.l R.1A R.1F R.2 R.3 R.i R.4A Park—Playground o o 0 0 9 Q _9_ e Community Centre o o o o o o _?_ _o Church o o o o o 0 0 Detached Dwelling 0 O 0 0 0 0 o Doctor, Dentist o 0 o o o o o Ssmi-Detached Dwelling O 0 0 0 0 Duplei O O 0 0 0 0 Double Duplet O O 0 o 0 0 Triplex 0 o 0 0 Double Tripler o o o o Row House 0 0 o 0 Apartment House 0 0 o 0 0 Converted Dwelling O O O 0 0 o Boarding House o o 0 o Parking Station o o o o Nursing Home o o o Day Nursery o o o o Children's Hom» o o o Boys' Home o 0 0 Public School o o o o o o 0 Private School o o o o Public Hospital o 0 Private Club o o Fraternity House 0 0 Public Library 0 o YMCA, etc. o 0 Institutional Office o Professional Office o Administrative Office o Office Doiliing for above Olfices o Commercial Districts which ollow reside nti 3l US es CIA CIS Cl AC All Residential Buildings o <j ' Sc.-.o Residential Buildings O o o Public Buildings o o 0 o Institutions o 0 o Olfica Building o o o o Hospital o 0 o Bank o Q o Hotel o o Restaurant o o o o Theatre, Hall o G o Commercial Cluo 0 o o Place of Amusement o o Retail Store o 9 o Personal Service Shop o O o Bake-Shop o O o Repair and Service Shop o 0 o Studio, Custom Workshop o o o Commercial School 0 0 o Supermarket e 0 o Animal Hospital o o Private Parking Garage o e o Public Parking Garage o _o_ Service Station 0 o Used Car Lot o o O Permitted o Permitted subject to restrictions in By-Low Not oil uses are necessarily permitted in all locotions with designations as shown above; for specific exclusions reference should be made to sections 16 and 17 of the Zoning By-Law. Permissible Densities FIGURE 6.2-8 ZONING SYSTEM, TORONTO Zone 1 0.35 DO L 1 1.0 D D V 1 3.0 DH Z o n e D e s i g n a t i o n s Zone 2 Zone 3 0.6 D S L 2 2.0 D E I V 2 5.0 DE! 1 .ot D D L 3 V 3 7.0 DC Zone 4 Zone 5 2.0T 2.5f D H D E B L 4 (through) L 9 4.0 9.0 DE V4 12.0 DD: Dl t Bonuses up to a maximum of 1.35 in Zone 3, 2.5 in Zcr.e 4 and 4.375 in Zone 5 under special circumstances have been recommended by the Planning Board. Source:" Metropolitan Toronto Planning Board. - 1 6 3 -ZONING V A ABSOLUTE ADJUSTED CUMULATIVE FREQUENCY FREOUE NCY ADJ FR EQ (PERCENT) (PERCENT) LOW DENSITY, DD PL EX 2 16 6U. 0 6U.0 MED DENSITY,LOW RISE 3 U 16. 0 80. 0 HIGH DENSITY, HIGH BIS H 3 12. 0 92.0 NON RESIDENTIAL 5 2 rt. 0 100. 0 TO 25 100. 0 100.0 LOW DENSITY,SING FA M 1 20 19. ') 19.U LCW DENSITY,DUPLEX 2 60 5a. 3 77. 7 MEDIUM DENS,LOW RISE 3 H 7. 8 85. 1* HIGH DENS,HIGH RISE U 1 1. 0 86.U NON RESIDENTIAL 5 It 13.6 100.0 . TO 103 100. 0 100. 0 Since i t was expected that the influence of neigh-borhood q u a l i t y and cohesion on apartment s i t e s e l e c t i o n would be most apparent i n extremely low and high income neighborhoods, a r e l a t i v e l y crude process was followed to estimate the neighborhood q u a l i t y variable. By compiling the average income, the percentage of b l u e - c o l l a r workers and dominance of ethnic concentration, three generalized values were derived: poor, average and high. (Table 6.2-IX). The name of the variable was abbreviated as NEIGHQ i n the model. I t s dimension i s : dimensionless. NEIGBORH30D VALUE ABSOLUTE ADJUST EC CUMULATIVE QUALITY FREQUENCY FRE2UENCY ADJ FREQ (PERCENT) (PERCENT) PR EDOMINATLY NON 1ES. 0 1 4. 0 U.O LOW QUALITY 1 5 20. 0 2«.0 AVERAGE QUALITY 2 18 72. 0 96. 0 HIGH QUALITY 3 1 1). 0 100.0 TO 25 100. 0 100.0 PREDOMINANTLY NON RES. 0 12 11.7 11. 7 LOW QUALITY 1 62 60. 2 71.8 AVERAGE QUALITY 2 8 7. 8 • 79. 6 HIGH CUALITY 3 21 20.a 100.0 TO 103 100. fi 100.0 TABLE 6.2-VIII ZONING SOCIAL TABLE 6.2-IX NEIGHBORHOOD QUALITY - 162* -I t was previously discussed that the CBD access- NON-CBD ACCESS-i b i l i t y of a l l stations could be assumed to be IBILITY constant. To account f o r each s t a t i o n sub-area*s general l o c a t i o n within the larger urban setting, \ two ' a c c e s s i b i l i t y * variables were introduced: nodality and surface a c c e s s i b i l i t y . The former was derived from the number of feeder l i n e s converging to each subway sta t i o n (Table 6.2-X). Although some forms of addit i o n a l weight-ing, such as the length of the l i n e , or the t o t a l acreage of user shed, would undoubtedly have been b e n e f i c i a l , time and data l i m i t a t i o n s necessitated the omission of t h i s f urther refinement. The name of the variable was abbreviated as NODAL i n the model. I t s dimension i s : dimensionless. TABLE 6.2-X NUMBER OF FEEDER ABSOl ADJU CUM UMULATIVB . T _ _ v LINES (SURFACE) FREQUENCY FREQUENCY ADJ FR EQ (PERCENT) (PERCENT) 1 1 5 20. 0 20. 0 2 2 2 8 . 0 28. 0 li 14 8 32. 0 60. 0 5 5 1 H. 0 6U. 0 6 6 5 20. 0 ' 8 « . 0 9 9 14 16.0 100.0 . TO 25 100. 0 100. 0 0 0 20 1 9. U 19.tt 1 1 23 22. 3 <41.7 2 2 26 25. 2 67. 0 3 3 8 7. e 7tt. H U 14 16 ' 15. 5 90. 3 5 5 '• 3 • 2. 9 93.2 6 6 3 2. 9 96. 1 9 9 H 3. 9 100.0 TO 1 0 3. 100. 0 100.0 - 165 -The surface a c c e s s i b i l i t y variable approximates the ease of access from stations to the general metro-p o l i t a n area v i a non-rapid t r a n s i t . This proxi-variable was designed to account f o r the large number of shopping, 3social and recre a t i o n a l t r i p s which, because of t h e i r dispersed destinations, are made by ear. I t i s assumed that a l l stations have an average surface a c c e s s i b i l i t y and the 'above average* value was assigned only to those stations which were located on major a r t e r i a l roads having d i r e c t access to the c i t y ' s freeway system. (Table 6 . 2-XI). The name of the variable was abbreviated as SURACC i n the model. I t s dimension i s * dimensionless. SURFACE ACCESSIBILITY ABSOLUTE ADJUSTED FREQUENCY FREQUENCY (PERCE KT ) I CUMULATIVE ADJ FREQ (PERCENT) AVERAGE ABCV E AVERAGE AVERAGE ABCV E AVERAGE 0 5 1 20 TO 25 20. 0 80. 0 100. 0 20. 0 100.0 100.0 0 1 TO 75 28 103 72. 8 27. 2 •100. 0 72. 8 100.0 100.0 TABLE 6 .2-XI SURFACE ACCESSIBILITY - 166 -6.3 I n c h a p t e r f i v e , a r g u m e n t s w ere p r e s e n t e d a s . t o why t h e w r i t e r s c h o o s e t o c o n s t r u c t a s i m u l a t i o n m o d e l r a t h e r t h a n t o u s e s t a n d a r d s t a t i s t i c a l t e c h n i q u e s f o r p r e d i c t i o n o f l a n d u s e s a l o n g subway l i n e s . H o wever, i t was d e c i d e d t o i n c l u d e c e r t a i n s t a t i s -t i c a l m e t h o d s i n t h e a n a l y s i s , b e c a u s e i t was f e l t t h a t t h e two m e t h o d s c a n be c o m p l e m e n t a r y e v e n t h o u g h t h e i r c o n c e p t s and o b j e c t i v e s d i f f e r f r o m e a c h o t h e r . F i g u r e s 6.3-1 and 6.3-2 d e m o n s t r a t e t h e two a p p r o a c h e s o f a n a l y s i s . A d e d u c t i v e m e t h o d , a s f o r e x a m p l e s i m u l a t i o n , u t i l i z e s PREPARATION OF INPUT FOR SIMULATION REAL WORLD THEORETICAL ABSTRACTION SIMULATION FIGURE 6 .3.-1 DEDUCTIVE ANALYSIS - 16? -t h e o r e t i c a l abstrations from the r e a l i t y . Based on the l o g i c a l argument, the simulation model i s constructed and i t s r e s u l t s are compared with the r e a l world. The r e s u l t i n g contradiction or •error' (dashed edge of t r i a n g l e i n Figure 6.3-1) i s minimized by c a l i b r a t i o n of the model (dotted edge). REAL WORLD EXPERIMENTAL ABSTRACTION STATISTICAL INTERPRETATION FIGURE 6.3-2 INDUCTIVE ANALYSIS The inductive method employs experimental abstrac-t i o n from the r e a l world. The "experiments" are the various s t a t i s t i c a l standard analyses available. The r e s u l t s of the analysis are compared with the r e a l world. In t h i s case, the r e s u l t i n g tension (dashed edge, Figure 6.3-2) from the comparison can be reduced by se l e c t i o n of s t a t i s t i c a l l y d i f f e r e n t methods (dotted edge) because the stat-i s t i c a l inference i t s e l f allows f o r no modifica-tions once the method and c r i t e r i a f o r v a l i d i t y ( l e v e l of significance) i s chosen. - 168 -The approach adopted by the writers combines to a cer t a i n extent both types of analysis, as shown i n Figure 6 . 3 - 3 . The graph how has two ' f l e x i b l e ' sides. By adequate s e l e c t i o n of s t a t i s t i c a l tools, the construction of the simulation model can be improved. REAL WORLD THEORETICAL EXPERIMENTAL ABSTRACTION STATISTICAL INTERPRETATION SIMULATION FIGURE 6 . 3 - 3 INDUCTIVE-DEDUCTIVE ANALYSIS There are two reasons why s t a t i s t i c a l analysis was undertaken. F i r s t , i t was d i f f i c u l t to concept-u a l i z e a l l the data c o l l e c t e d . Therefore, s t a t i s -t i c a l analysis was used to comprehend the data, to obtain a broad understanding of the patterns, ranges, i n t e r r e l a t i o n s , etc. Second, i t was expected that the f i r s t approximation of the table functions (relationship between land use and envir-onmental variables) could be improved and better j u s t i f i e d . In addition, the apartment building and apartment development size functions were derived by s t a t i s t i c a l methods. Figure 6.3-4 dem-CONCEPT OF STATISTICAL ANALYSIS - 169 -onstrates where the s t a t i s t i c a l analysis f i t s into the general framework of t h i s study. Only f i v e variables selected to predict land uses CHARACTER-ISTICS AND have i n t e r v a l scale (NEWCON, TECHCON, LANDAV, DIMENSIONS OF THE DATA LANDVC, CEILCAP), i . e . , they have continuous values. The remaining 8 variables are of nominal scale, which means that, t h e i r values can only be ranked (1,2,3...., and 2 i s bigger than 1); and the i n t e r v a l between two values i s not measurable. However, the f a c t that these variables are of discrete nature does not diminish t h e i r value f o r the present analysis. The degree of information and the pr e c i s i o n i s s u f f i c i e n t f o r the variables under consideration, such as neighborhood qualit y , access to non-CBD areas, influence of parkland close to the station. They are only quantifiable within c e r t a i n ranges and cannot be encountered with precise values, which would imply higher accuracy than legitimately can be expected. How-ever, the handling of ordinal data makes s t a t i s -t i c a l analysis i n general more d i f f i c u l t . Measure-ments of central tendency and normality can only be determined within constraints. Another problem i s the non-linearity of the table functions. The a t t r a c t i v i t y scores, which are assigned to the environmental conditions found - 1?0 -FIGURE 6.3-4 CONCEPT OF STATISTICAL ANALYSIS - 171 -around the stations, decrease rapidly to zero when the characteristics are not favorable. If the conditions are favorable, the attractivity scores increase, but at a reduced rate. After a certain threshold, the value of the variable remains at the maximum level (because the sewer system, for example, i s limiting, i f the capacity i s exhaus-ted, but excess capacity of the system w i l l not attract more apartments). Again, most s t a t i s t i c a l analyses handle poorly non-linear data. Two c r i t e r i a were used i n selecting the s t a t i s t i c a l methods. First, the above-described characteris-t i c s of the data (non-linearity, ordinal scale, dynamic entities). Second, those methods were sought which yield most exp l i c i t l y the relation-ships among variables. That means, for example, that correlation does not satisfy these c r i t e r i a well, because the single indicator (correlation coefficient) gives only information on the " f i t " i n a highly abstracted manner. SELECTION OF STATISTICAL TECHNIQUES The flow chart i n Figure 6.3-6 shows the analytical methods chosen and the sequence of analysis. Most of the computer programs used are contained in the manual " S t a t i s t i c a l Package for Social Science" (SPSS).1 1. Nie, et.al., (1970). - 172 -t 1 TABULATIONS FIGURE 6.3-6 SEQUENCE OF ANALYSIS TABLE FUNCTION SHAPE OF TABLE FUNCTION rl RELATIVE WEIGHT OF ENVIRON-MENTAL FACTORS SIMULATION MODEL CROSS-TABULATION I 4 LOGICAL TREES 4 PEARSON CORREL. - 173 -The main objective of s t a t i s t i c a l analysis was to determine the shape and weight of the environ-mental fact o r s i n determining the number and lo c a t i o n of apartments to be b u i l t . The weights, i n d i c a t i n g the r e l a t i v e importance of environmental f a c t o r s are necessary because the a t t r a c t i v i t y scores i n the table functions are (for p r a c t i c a l reasons ) normalized - that means they a l l have the same weight. The r e l a t i v e importance of the environmental factors i s obtained by multiplying the table function values obtained f o r a given sta t i o n sub-area by the appropriate weight. OBJECTIVES OF STATIS-TICAL ANALYSIS Figures 6 . 3 - 7 A and B demonstrate the e f f e c t of the weight. The normalized environmental f a c t o r " l o t s i z e " i s "squeezed" by multiplying i t with i t s weight 0 . 2 (assumed weight). The s t a t i s t i c a l analyses were performed f o r each subway l i n e separately i n order to f i n d out whether d i f f e r e n t table functions should be applied to the d i f f e r e n t c o r r i d o r s . Based on the r e s u l t s i t was decided to use i n the present analysis the same functions f o r a l l l i n e s . However, RESULTS OF STATISTICAL ANALYSIS 1. In determining the values of a table function, i t i s d i f f i c u l t to comprehend at the same time shape and weight, because the weight varies f o r the variables and deforms the picture of the function. - 17^ -E N V I R O N M E N T A L 4 F R C T O R 5 , 0 a.D ENVIR2ONMENTRL4'FRCTOR e.D 8.0 FIGURE 6.3-7A TABLEFUNCTION LOTSIZE A NORMALIZED ATTRAC-TIVITY SCORES, UNWEIGHTED FIGURE 6.3-7B TABLEFUNCTION LOTSIZE B WEIGHTED ATTRACT-IVITY SCORES - 175 -a d i f f e r e n t i a t i o n could e a s i l y be introduced during simulation by multiplying the a t t r a c t i v i t y scores of the station sub-areas of each l i n e by c o e f f i -c i e n t s . Therefore any number of environmental fact o r s or t h e i r corresponding a t t r a c t i v i t y scores could be modified f o r d i f f e r e n t l i n e s or stations. C In Appendixes A.C-l through A.C-3 b r i e f descrip-tions of the s t a t i s t i c a l methods used and the summarized r e s u l t s are given. Based on these analyses both the f i r s t approximation of table functions presented i n Chapter 4.3 and the r e l a t i v e importance (weighty of each environmental f a c t o r were refined. Table 6.3-1 gives the l i s t of weights attributed to the environmental fac t o r s f o r the c a l i b r a t i o n of the simulation model. Figures 6.3-8 through 6.3-20 depict the refined table functions. As i t was noted e a r l i e r only f i v e of the selected proxi-variables (NEWCON, TECHCON, LANDAV, LANDVC, CEILCAP) have i n t e r v a l scale and thus can assume any value between zero and the indicated maximum on the X axis. For the remaining 8 variables with nominal scale the continuous l i n e of the graph i s somewhat meaningless, f o r the model inte r p r e t s only those predefined values which were used to "scale'* the proxi-variables. That i s 2.8, f o r example, f o r NEIGHQ does not correspond with any a t t r a c t i v i t y value on the Y axis since NEIGHQ - 176 -, waa defined as having a value on the nominal scale of 1,2,3,4 only. Weight Environmental Factor F i r s t Approx-imation from Literature and Empirical Evidence Weight Adjusted Afte r S t a t i s t i c a l Analysis Construction of new apartments 16 15 Technological constraints 3 2 Available land f o r new con-str u c t i o n 3 Vacant land 12 11 Building age mixture 5 k Neighborhood q u a l i t y 12 10 Average l o t size 12 13 Proximity to major open space 7 5 Surface access-i b i l i t y 5 8 Measurement of nodality 5 7 Zoning 9 10 C e i l i n g capacity 0 0 Commercial development 3 Undesirable ! conditions 8 8 100 100 TABLE 6.3-1 WEIGHTS OF ENVIRONMENTAL FACTORS - 177 -I—in (_) a. CL a 0 . 0 5 0 . 0 1 0 0 . 0 1 5 0 . 0 , ENVIRONMENTAL FACTOR C X 1 0 1 2 0 0 . 0 T 4 0 . 1 B 0 . E N V I R O N M E N T A L FACTOR 1 2 0 . 0 , C X 1 0 1 1 1 6 0 . 0 8 . 0 1 6 . 0 E N V I R O N M E N T A L FACTOR FIGURE 6.3-8 CONSTRUCTION OF NEW APARTMENTS FIGURE 6.3-9 TECHNOLOGICAL CONSTRAINTS FIGURE 6.3-10 AVAILABLE LAND FOR NEW CONSTRUCTION - 178 -FIGURE 6.3-11 VACANT LAND 0.0 8.0 16.0 24.0 32.G E N V I R O N M E N T A L FACTOR FIGURE 6.3-12 BUILDING AGE MIXTURE FIGURE 6.3-13 NEIGHBORHOOD QUALITY 0.0 1.0 2.0 3.0 4.0 E N V I R O N M E N T A L F A C T O R - 179 -a FIGURE 6.3-14 AVERAGE LOT SIZE FIGURE 6.3-I5 PROXIMITY TO MAJOR OPEN SPACE E N V . F A C T O R S FIGURE 6.3-16 SURFACE ACCESSIBILITY - 180 -a I—in ' ' a C J CO a: tx • a a ' 0 . 0 4 . 0 - B - 0 1 2 . 0 E N V I R O N M E N T A L FACTOR 1 6 . 0 FIGURE 6.3-17 MEASURMENT OF NODALITY FIGURE 6.3-18 ZONING E N V I R O N M E N T A L FACTOR - *81 -a FIGURE 6.3-20 UNDESIRABLE CONDITIONS - 182 -For the assignment of apartment growth to sta t i o n sub-areas, a series of cumulative apartment devel-opment size functions were calculated i n order to determine whether the magnitude of development sizes i s changing over the years. The analysis was f i r s t made f o r the apartment building size (dwellings per single apartment structure, see chapter 6.2). However, f o r the simulation, the apartment devel- opment size had to be analyzed - i . e . , the number of dwelling u n i t s b u i l t per station sub-area. This was necessary because growth i s assigned to sub-areas, and not to city-blocks or i n d i v i d u a l properties (see discussion on aggregation i n the next chapter). Figure 6.3-21 shows the time spans f o r which the apartment development functions were calculated. 1959/60 1961/62 1963/64 1965/66 1967/68 1969/70 86 APARTMENT DEVELOPMENT FIGURE 6.3-21 TIME PERIODS OF ANALYSIS OF APARTMENT DEVELOPMENT SIZE DISTRIBUTION - 183 -Figures 6.3-22 to 6.3-28 depict the apartment development size distribution. They clearly demon-strate the increasing scale of apartment develop-ments over time. The apartment development size increased at a higher rate than the apartment building size. For the simulation, four size functions were chosen - 1959-63, 1964-67, 1968-70 and 1971-86. 1 / 2 APARTMENT DEVELOPMENT S I Z E F U N C T I O N PER S T A T I O N S U B A R E A 1 9 5 9 - 7 0 ZD 0.0 40.o ao.o 120.0 A - D E V E L . S I Z E [ D W . U N I T S ] ( X 1 0 1 160.0 FIGURE 6.3-22 APARTMENT DEVELOPMENT SIZE FUNCTION PER STATION SUB-AREA 1959-1970 1. In addition, the simulation model contains an option which weights the development size functions according to the overall attractivity of the various subway lines i n each year (see Chapter 7.1). - 184 -RPRRTMENT DEVELOPMENT S I Z E F U N C T I O N PER S T A T I O N 5 U 8 R R E R 1 9 5 9 - 6 4 CL CL C 3 .0 20.D 40.0 SO.O t R=DEVEL.SIZEIDV.UNITS) (X101 80.0" a a . —^ C3 . a. cc a ZD 0.0 O RPRRTMENT DEVELOPMENT S I Z E F U N C T I O N PER S T A T I O N S U B A R E A 1 9 6 5 - 7 0 i i f— ~i 40.0 80.0 120.0 , 160, A - D E V E L . S I Z E ( D W . U N I T S ) CX10 1 ) FIGURE 6.3-23 APARTMENT DEVELOPMENT SIZE FUNCTION PER STATION SUB-AREA 1959-1964 FIGURE 6.3-24 APARTMENT DEVELOPMENT SIZE FUNCTION PER STATION SUB-AREA 1965-1970 - 185 -a o _ CL (X c C J APARTMENT DEVELOPMENT S I Z E F U N C T I O N PER S T A T I O N S U B A R E A 1 9 5 9 - 6 2 0.0 80.0 160.0 240. A = D E V E L . S I Z E C D V . U N I T S ) 320.0 a a — APARTMENT DEVELOPMENT S I Z E F U N C T I O N PER S T A T I O N S U B A R E A 1 9 6 3 - 6 6 ex cx 5° 0.0 20.0 40.0 - 60.0 , <-> A=DEVEL . S I Z E ( D W . U N I T S ) ( X 1 0 1 80.0 FIGURE 6.3-25 APARTMENT DEVELOPMENT SIZE FUNCTION PER STATION SUB-AREA 1959-1962 FIGURE 6.3-26 APARTMENT DEVELOPMENT SIZE FUNCTION PER STATION SUB-AREA I963-I966 - 186 -a a. <X Q_ CE c APARTMENT DEVELOPMENT S I Z E F U N C T I O N P E R S T A T I O N S U B R R E A 1 9 6 7 - 7 0 T 5° 0 .0 40 .0 80.0 120.0 , <-> A - D E V E L . S I Z E C D W . U N I T S ) C X 1 0 1 i 160.0 cr a. in LJJ a CE a RPRRTMENT DEVELOPMENT S I Z E FUNCTION PER STRTION SUBRRER 1971-85 1 1 1 " 1 O.D 50.0 100.0 150.0 , 200.0 R=DEVEL.S IZEtDW.UNITS) IX10 3 ) F I G U R E 6.3-27 A P A R T M E N T D E V E L O P M E N T S I Z E F U N C T I O N P E R S T A T I O N S U B - A R E A 1967-1970 F I G U R E 6.3-28 A P A R T M E N T D E V E L O P M E N T S I Z E F U N C T I O N P E R S T A T I O N S U B - A R E A 1971-1985 - 187 -Boorse, J.W., (1968), Rapid Transit i n Canada, Almo Press, Philadelphia. Brain, R., and McMurray, A.L., (1970), Toronto, An  Urban Study, Clarke, Irwin and Company Ltd., Toronto. City of Toronto Planning Board, (1959)» The  Changing City, Toronto. City of Toronto Planning Board, (19^7)» Proposed  Plan f o r Toronto, Toronto. Department of Buildings, Ci t y of Toronto, Annual  Report, 1970, Toronto. Kain, J.F., (1967)» Postwar Changes i n Land Use i n  the American City, Program on Regional and Urban Economics, Discussion Paper No. 24, Harvard University. Kerr, D., and Spelt, J., (I965), The Changing Face  of Toronto - A Study of Urban Geography, Menoir 11, Geographical Branch, Department of Mines and Technical Surveys, Queen's Printer, Ottawa. Kirkup, D.B., Boomtown Metropolitan Toronto, Metro Toronto News Company, Toronto, 1969. Nader, G.A., (1971)» "Some Aspects of Recent Growth and D i s t r i b u t i o n of Apartments i n the P r a i r i e Metropolitan Area", Canadian  Geographer, Vol. XV, No. 4, 1971, pp.307-317. Neutze, M., (1968), The Suburban Apartment Boom, Resources f o r the Future Incorporation; The Johns Hopkins Press, Baltimore. Nie, N.: Bent, D.H.i Hull, C.H., S t a t i s t i c a l  Package f o r S o c i a l Science, McGraw-Hill Books, 1970. Metropolitan Toronto Council, (1970), Metropolitan  Toronto 1970, Toronto. Metropolitan Toronto Planning Board, (1967), Metropolitan Apartment Development Control  Policy, Toronto. BIBLIOGRAPHY CHAPTER 6 Toronto Transit Commission, (1969), Development  Follows Toronto Subway, Toronto. - 188 -Toronto Transit Commission, (1971), Transit i n  Toronto, Toronto. Toronto Transit Commission, (1971), TTC 50 Years Toronto. simulation model -7.1 Model Description -7.2 Model Calib r a t i o n -7.3 S e n s i t i v i t y Analysis - 7*4 Conclusions on the Simulation - I 8 9 -This chapter presents i n i t s f i r s t part the model structure, the functioning of the model and the ch a r a c t e r i s t i c s of the program written f o r the simiaLlation. ..In the second part, the model c a l i -bration i s described. The t h i r d part presents the s e n s i t i v i t y analysis and forecast runs made with the model to tes t alternative p o l i c i e s , and the l a s t part summarizes and c r i t i c i z e s the modelling approach. A l i s t of abbreviations and codes used i n the model precedes t h i s chapter. Details of the model (the f u l l program and part of the results) are contained i n Appendices A.a-1 to A.a-7. The fourteen environmental factors are grouped under three headings* dynamic, s t a t i c and p o l i c y / intervention variables. The chosen taxonomy represents not i n a l l cases how the variable behaves i n ' r e a l i t y * . The c l a s s i f i c a t i o n rather indicates how the variables were treated i n the ABBREVIATIONS CODES SYMBOLS - 190 -present version of the model. Dynamic Variables! NEWCON = Construction of new apartments. TECHNC = Technological constraints. LANDAV «= Available land f o r new construction. LANDVC = Vacant land. S t a t i c Variables t BUILAG = Building age mixture. NEIGHQ = Neighborhood q u a l i t y . LOTSIZ = Average l o t s i z e . PARKLD = Proximity to major open space. Policy and Intervention Variables t SURACC = Surface a c c e s s i b i l i t y . NODAL = Measurement of nodality. ZON = Zoning. CEICAP = C e i l i n g capacity. COMDEV = Commercial development. UNDCON = Undesirable conditions. ENVIRONMENTAL FACTORS - 1 9 1 -CODE SUBWAY LINE SIMULATION PERIOD WHEN LINE CAME INTO OPERATION YONGE Yonge 1 BWO Bloor West Old BWN Bloor West New 5 BEO Bloor East Old BEN Bloor East New 5 • A Ul O z o >• • a a • a BWN BWO BEO • • o • a B E N SUBWAY LINES FIGURE 7 -1 SUBWAY LINES - 1 9 2 -Each Station Sub-Area has a code which indicates to which l i n e and station i t belongs. See Figures 7 - 2 and 7 - 3 . Station Sub-Area Number (X,Y,ST,SA) X Line (e.g. BLOOR) Y Subline (e.g. 'old' or *new*) ST Station SA Station Sub-Area Example 8 2 1 4 STATION SUB-AREA 3 , SE STATION NUMBER 4 NEW SECTION BLOOR LINE NW NE 2 4 s w SE STATION CODE FIGURE 7 - 2 STATION SUB-AREA CODE - 193 -Ul Z 1011 1012 1013 1014 FIGURE 7-3 STATION CODE BWN d • a • 2111 2112 2113 2114 BWO BEO 2011 3011 2012 3012 2013 3013 2014 3014 BEN • a a a • 3111 3112 » 3113 * 3114 - 1 9 4 -In a l l symbols, E indicates actual apartment growth, S indicates simulated apartment growth. E(X,Y) = Number of apartments a c t u a l l y b u i l t over X time periods with time period Y as center of the moving average (in dwelling u n i t s ) . See Figure 7 - 4 . S(X,Y) = Number of apartments simulated. X Number of time periods over which the moving average i s calculated (X can be 2 , 3 , or 5 , i n d i c a t i n g the moving average two, three and f i v e respect-i v e l y ) . Y Time period which i s the center of the moving average. 1 ACTUAL AND SIMULATED GROWTH Example t E ( 3 , 4 ) = Moving average 3 , i . e . number of dwelling units a c t u a l l y b u i l t over the three time periods 3 , 4 and 5 , 4 being the center of the average. MOVING AVERAGES FOR MODEL EVALUATI ON 1. For the moving average two, Y i s the s t a r t i n g period of summation. - 1 9 5 -TIME PERIOD E21 S21 E22 S22 E23 S23 E24 S24 E25 S25 3 4 E32 S32 E33 S33 E34 S34 E35 S35 E52 S52 E53 S53 E54 S54 E55* S55* BB9B3 n a m FIGURE 7-4 MOVING AVERAGES FOR MODEL EVALUATI ON * The moving averages 5 2 and 5 5 cover only four time periods; otherwise, however, they follow the conventions of the moving average 5 » Their center period i s 2 and 5 respectively. - 196 -7.1 The model i s described i n three stages: i . model structure i i . functioning of model i i i . program structure and c h a r a c t e r i s t i c s . Figure 7.1-1 i l l u s t r a t e s the general model structure with the f i r s t and second-order d i s t r i -bution process f o r land use. The f i r s t - o r d e r d i s t r i b u t i o n a l l o c a t e s land uses at a c i t y scale primarily as a function of the a c c e s s i b i l i t y surface. The second-order d i s t r i b u t i o n assigns land uses and a c t i v i t i e s to station sub-areas within the transportation c o r r i d o r based on the environmental conditions of the stations. The s p e c i f i c structure of the simulation model designed f o r t h i s thesis i s shown i n Figure 7.1-2. The model treats a single land use - high density r e s i d e n t i a l - defined f o r t h i s purpose as a high MODEL DESCRIPTION MODEL STRUCTURE - 197 -rise structure of more than k storeys and situated normally within a 2.5 density zoning area (for zoning definitions see chapter 6.2). The environ-mental factors determining the growth distrubution within the transportation corridor are grouped into three categories: i . dynamic variables (feedback) i i . static variables i i i . policy and intervention variables. The taxonomy chosen does not represent in a l l cases how the variables behave in 'reality*. As discussed below, some simplifications were made for the present version of the model. Therefore, the classification represents how variables were treated in the model. i . Dynamic variables alter as a function of the dependent variable (apartment growth) and therefore their contribution to the attractivity of a station sub-area changes whenever a station receives growth in a time period. The f i r s t variable, NEWCON, i s the cumulative number of apartments per station sub-area (including apartments built before 1959). The second variable, TECHNO, i s a summary variable for a l l technological constraints and reduces the attractivity of an area as soon as one of the constraints i s approached, i.e. i f the capacity DYNAMIC VARIABLES - .198 -FIGURE ?.1-1 GENERAL MODEL STRUCTURE projected growth f o r various land uses, c i t y scale t- . a c c e s s i b i l i t y surface growth assigned to the corridor t. . f • environmental variables - s t a t i c , - dynamic, - intervention/ p o l i c y * i allocationaof assigned growth to i n d i v i d u a l stations t . TIME SCALE t« t , I T ._ . I'1 - 199 -FIGURE 7.1-2 SPECIFIC MODEL STRUCTURE access-i b i l i t y new construction technical constraints land available vacant^^land^ m m m m  b u i l d i n g age mixture neighborhood q u a l i t y average l o t size proximity to major ?.?.e.n.. F.PS?.? surface a c c e s s i b i l i t y n o d ality zoning c e i l i n g capacity commercial development undesirable conditions projected apartment demand, • c i t y scale T apartment growth assigned to the corridor (dwelling units) a l l o c a t i o n of assigned apartment growth to i n d i v i d u a l s t a t i o n sub-areas 4 p o l i c i e s and external interventions! - 200 -i s used up. TECHNO i s expressed i n dwelling units ( i . e . , number of dwelling units which can be served by sewer, schools, e t c . ) . The t h i r d and fourth dynamic variables express the stock of available land (LANDAV, i n acres) and vacant land (LANDVC, i n acres) l e f t i n any time period f o r apartment development. i i . The s t a t i c variables - average l o t size (LOTSIZ), proximity to major open space (PARKLD), neighborhood q u a l i t y (NEIGHQ), and building age mixture (BUILAG) - although not changing over time, a l t e r t h e i r r e l a t i v e contribution to the station a t t r a c t i v i t y over time. Only the f i r s t two of these variables can i n f a c t be considered s t a t i c . In a r e l a t i v e highly devel-oped urban environment as one find s along the subway l i n e s , the average l o t size does not change before a developer moves i n f o r land assembly (at which time he i s already committed f o r develop-ment) and no major open space can be expected to be created i n the proximity of the stations. Neighborhood quali t y , which was f e l t to be a c r u c i a l variable f o r apartment development was nonetheless treated as s t a t i c . This was f o r the simple reason that the present state of the art i n s o c i a l and behavioural research allows hardly to q u a l i f y STATIC VARIABLES - 201 -and describe a thing c a l l e d *neighborhood qual-1 i t y * ; much l e s s can i t be predicted. The b u i l d i n g age mixture does not change over time, because t h i s variable applies only to the remaining land suitable f o r apartment development, therefore excluding the land area newly developed f o r apart-ments during simulation. (The influence of new apartment construction i s considered i n a separate variable NEWCON which expresses the pooling e f f e c t ) . I t would be desirable to include e x p l i c -i t l y the f i l t e r i n g process into the model because, although the bias possibly introduced i n the model i s a systematic one, i . e . applies to a l l stations, the f i l t e r i n g process exhibits a threshold behav-iour. That means that the a t t r a c t i v i t y scores of t h i s variable would diminish much l a t e r i n sta t i o n areas with generally young housing stock than i n old areas. • • • i n . Policy and intervention variables can both be altered externally to allow interaction with the development process. They can be changed during the run of the simulation at a computer terminal, i f desired, taking into account r e s u l t s INTERVENTION AND POLICY VARIABLES 1. For that reason, a very crude and i n t u i t i v e assignment of values f o r t h i s variable had to be applied, as discussed i n Chapter 6.2. - 202 -of the aprtment d i s t r i b u t i o n of previous time i periods. This has two purposes. Alternative p o l i c i e s can be tested as they are introduced by p o l i t i c i a n s , planners, etc., over time. Some of these decisions are not known at the outset of a simulation which covers a r e l a t i v e l y long time period because actions taken are influenced by the developments occurring subsequently. Other intervention variables, such as commercial develop-ment (COMDEV) and surface a c c e s s i b i l i t y (SURACC),2 serve i n addition as a corrective mechanism i n the present stage of the model. Because the model does not treat other land uses i n t e r n a l l y , feedback from developments i n other a c t i v i t y sectors must be entered into the model through intervention. C e i l i n g capacity i s a dummy variable and does not contribute d i r e c t l y to the st a t i o n a t t r a c t i v i t y . 'K I t serves to simulate p o l i c y decisions to improve 1. The necessary information i s printed out during the simulation, a f t e r each time period. 2. I t must be emphasized that t h i s variable expresses not the a c c e s s i b i l i t y to the downtown as t h i s i s already accounted f o r when apartment growth i s assigned to the subway corrido r s . The variable SURACC expresses the a c c e s s i b i l i t y to the res t of the metropolitan area, i . e . , f o r recreation, shopping, etc. Its values were estimated rather crudely, based on the present and future highway network and the a c t i v i t y centers. - 203 -or add services (schools, sewers, e t c ) . The addi t i o n a l capacity i s transferred by the model to the dynamic variable; technological constraint (TECHNC). The model has b a s i c a l l y three dimensions, as shown i n Figure 7»1-3S i i i Time dimension S p a t i a l dimension (subway l i n e s , stations, station sub-areas) i i i ; Environmental q u a l i t i e s . E N V I R O N M E N T FUNCTIONING OF THE MODEL FIGURE 7.1-3 DIMENSIONS OF MODEL However, because of the s t r a t i f i c a t i o n of the s p a t i a l dimension by l i n e , s t a t i o n and stat i o n sub-area, a five-dimensional array i s used i n the 1 model. When the model was designed, a reasonable 1; Or i n other words, sets of at le a s t f i v e nested do-loops were i n the program. - 204 -choice had to be made as to the maximum number of items to provide f o r i n each dimension. This choice was governed on the one hand by the desire to make the model generally applicable, i . e . to other c i t i e s with d i f f e r e n t number of l i n e s and stations, and on the other hand to allow f o r the in c l u s i o n of subway extensions (additional l i n e s and/or stations) over time. The upper l i m i t s of the dimensions were made by consideringIthe e f f i c i e n c y of programming (and therefore cost of model runs) and the expected dimensions of a future subway network may have. Table 7»1 - I indicates the upper l i m i t of items per dimension f o r the present model and Table f p . l - I I the dimen-sions of the simulated system i n Toronto. Figure 7*4-1 depicts the elements of the model, the r e l a t i o n s h i p s and feedback among them and the l o g i c a l structure. Figure 7*1-5 i l l u s t r a t e s the general (program structure. The functions and the calculations performed by the program and i t s subroutines are summarized b r i e f l y below. Addi-t i o n a l d e t a i l s and the f u l l program write-up i s contained i n Appendices A.a-l through A.a-5. - 205 -MAXIMUM NUMBER OF TABLE 7.1-1 DIMENSION ITEMS PER DIMENSION DIMENSION LIMITS OF THE MODEL Lines* 5 Stations per line 30 Sub-areas per 4 station Environmental 14 factors Time Periods** 14 * Lines can be extended i n two ways: they can receive additional, stations at the end of the line or can s p l i t into two or more branches. ** The length of the time periods can be assumed. For Toronto, two years were chosen which relate to the average apartment development cycle, i.e. the time between application for building permit and completioni,of construction. - 206 -DIMENSIONS SPATIAL NO. OP ITEMS City T O R O N T O Line . • 1 r~~i 3 | Y O N G E | B W N B W O B E O B E N Station 42 |E G L I N G T O N ] DAVISVILLE Sub-area 128 ~r s w i S E ENVIRONMENTAL 14 | N E W C 0 N | L A N D A V L TIME 14 I T " 1959/60 1961/62 * BWN and BEN we're not treated as new separate lines but as extensions of thelllines BWO and BWN respectively. That means that in future additional lines could be included i n the simulation. TABLE 7 . 1 -II DIMENSIONS OF THE TORONTO SUBWAY SYSTEM TECH MULTIPLIER! LAND MULTIPLIER H ENVIRONMENTAL FACTORS NEWCON »; TECHCN Z LANDAV *< o ...» LANDVC • • i BUILAG • • j u NEIGHQ • TATI LOTSIZ • *• in PARKLD >: z SURACC • o p z NODAL RVE ZON • INTE CEILCP • >-o COMDEV POLI UNDCON WEIGHT -OF ENVIRONMENTAL FACTORS | RANKING j ATTRACTIVITY : OVERALL MACHING RANKED SCORE : ATTRACTIVITY * ATTRACTIVITY FOR EACH SCORE FOR ..Y.» SCORES WITH ENVIRONMENTAL STATION RANKED APARTMENT FACTOR SUB AREAS DEVELOPMENT GROWTH ALLOCATION I • ..J TABLE FUNCTIONS t WEIGHT COEFFICIENT FOR DEVELOPMENT SIZE RANKING TOTAL APARTMENT GROWTH TO BE DISTRIBUTED FOR EACH SUBWAY CORRIDOR RANDOM SELECTION APARTMENT DEVELOPMENT SIZE FUNCTIONS I o I o w ^ w ^ > o Ui M C/3 8 ro - ra Iro (-9 I - 208 -CONTROL PROGRAM Starts the simulation, c a l l s f o r various sub-routines, communicates with the user, deter-mines length of simu-l a t i o n , terminates simulation. SUBROUTINE DATIN Reads i n a l l i n i t i a l data. SUBROUTINE GOGO Distributes apartment growth i n each time period  SUBROUTINE CONT Environmental c h a r a c t e r i s t i c s are updated as res u l t of the d i s t r i b u t i o n process SUBROUTINE INTER Governs the intervention? process during simulation SUBROUTINE SIGH Provides the f i n a l outputs of the simulation FIGURE 7.1-5 GENERAL PROGRAM FLOWCHART - 209 -CONTROL PROGRAM CONTROL PROGRAM i . The control program askes the user: - to enter a random number seed (for the selection of apartment development sizes), - how many time periods he wishes to simulate i n i t i a l l y , - i f he wants to adjust the apartment development sizes along each subway line as a function to the overall attractivity of that line (as compared to a l l other lines. See subroutine GOGO), - after each time period, i f he wishes to interact, - after the number of time periods i n i t i a l l y and subsequently specified, i f he wishes to continue the simulation and for how many periods, - when the user wishes to f i n i s h the simulation or after 14 time periods , and what version of output he wishes (see subroutine SIGH). This i s followed by the indication of the success-f u l end of the simulation. In addition, the control program governs the calling of the various sub-routines throughout the simulation. i i . SUBROUTINE DATIN. This routine reads the SUBROUTINE DATIN folliwing i n i t i a l data necessary for the simulation (from the input medium specified on the RUN command): - X-Values of the points defining the table functions (values of environmental factors). The X-values are subsequently normalized. - Y-Values (already normalized) for the table functions (i.e., attractivity scores assigned to specific environmental quali-ties? they assume values between 0 and 1). - 210 -The r e l a t i v e weight of each environmental f a c t o r . (The sum of the weights i s equal 100). Matrix with i n i t i a l environmental charac-t e r i s t i c s of a l l st a t i o n sub-areas. (The values must be smaller than the maximum X-value of the respective table function). Apartment growth to be di s t r i b u t e d i n each year and f o r each l i n e (number of dwelling u n i t s ) . Apartment development size functions (exactly four, each to be applied f o r a s p e c i f i c number of simulation periods. Each function can be defined by a maximum of 60 points. The subroutine signals many possible mistakes i n the data set-up by error messages (which leads to immediate termination of the simulation). For purposes of checking, a l i s t i n g of data can be obtained by choosing PAR=FULL. i i i . SUBROUTINE G0G0. This subroutine performs.the following c a l c u l a t i o n necessary to d i s t r i b u t e the apartment growth and to determine the development size i n each time period* - Normalizes the matrix of environmental c h a r a c t e r i s t i c s (environmental conditions regarding a l l environmental factors of each st a t i o n sub-area). - Calculates the a t t r a c t i v i t y score f o r each value of the above matrix (according to the table functions). - Weights the a t t r a c t i v i t y scores according to the r e l a t i v e importance of each environmental f a c t o r . SUBROUTINE G0G0 - 211 -- Sums the a t t r a c t i v i t y scores f o r each stat i o n sub-area. This sum represents the composite or t o t a l a t t r a c t i v i t y f o r each sub-area. I t assumes values between 0 and 1. - Selects randomly the number and size of apartment developments necessary to provide f o r the spe c i f i e d growth of the given time period and l i n e , using the apartment size function applicable i n t h i s time period. - Adjusts the development size f o r each l i n e , i f chosen so by the user at the beginning of the simulation. The adjust-ment i s made as a function of the o v e r a l l a t t r a c t i v i t y of a given l i n e as compared to a l l other l i n e s . (The a t t r a c t i v i t y scores of each sta t i o n sub-area i s summed f o r each l i n e and divided by the number of sub-areas of the respective l i n e . The r e s u l t i n g value of each l i n e i s then divided by the biggest X which y i e l d s the adjustment c o e f f i c i e n t . This c o e f f i c i e n t assumes values smaller than 1 f o r a l l but the l i n e with the highest o v e r a l l a t t r a c t i v i t y , f o r which i t has the value 1 . - Ranks the resultant apartment develop-ments according to t h e i r sizes and assigns them i n order of the highest to lower t o t a l a t t r a c t i v i t y to the station sub-areas, checking whether the land available at the st a t i o n sub-area i s s u f f i c i e n t to accommodate the assigned growth. (Otherwise the growth i s assigned to the area with the next lower a t t r a c t i v i t y s c o r e ) . 1 i v . SUBROUTINE CONT. This subroutine at;the end of each time period adjusts the environmental c h a r a c t e r i s t i c s of the dynamic variables NEWCON, LANDVC, TECHNO, LANDAV. I f any of l i In t h i s case, the stat i o n does compete f o r the remaining smaller apartment developments to be assigned i n t h i s or any following time period. - 212 -the l a t t e r two variables f o r any sub-area reaches the value when they begin to l i m i t growth ( i . e . , not enough land available f o r future apartment development or services are inadequate, e.g., exhausted sewer capacity), the respective s t a t i o n sub-area i s no longer available f o r apartment assign-ments i n consecutive time periods. 1 I f the user chooses i n the RUN command PAR=FULL, then most of the r e s u l t s of the calculations i n subroutine CONT and GOGO are printed out a f t e r each simulation period. A sample of t h i s i s given i n Appendix A.a-3» v. SUBROUTINE INTER. This subroutine governs the in t e r a c t i o n process. Afte r each time period, the user has the option of i n t e r a c t i n g with any one of the six p o l i c y variables f o r any of the sta t i o n sub-areas. The subroutine asks the respective questions and replaces the newly entered environmental character-i s t i c s i n the matrix. The user i s informed, f o r each i n t e r a c t i o n , of the old value of the variable he intends to change and the range of values he may choose to enter. Any 1. I f by i n t e r a c t i o n i n a l a t e r time period ad d i t i o n a l services are provided, the sta t i o n can enter the competition f o r growth again. - 213 -mistake during the i n t e r a c t i o n i s countered by an error message, a f t e r which the cor-r e c t i o n may be entered. An example of the i n t e r a c t i o n process i s given i n Appendix A.a-4. The remainder, more s p e c i f i c c h a r a c t e r i s t i c s of the program (input/output units, RUN command, cost of simulation, error messages, etc.) are described i n Appendix A.a-1. - 214 -7.2 There are two prerequisites for a successful application of a simulation model. i . The model must be calibrated for a situation for which the outcoming, in this case the actual apartment growth, i s known. i i . The sensitivity of the model has to be tested i n order to allow a judgement on the extent to which the model can be applied to other cases (cities) and other time periods (length of forecast period). In order to calibrate a model, the results of the model must be evaluated. This evaluation requires analytical tools which allow a comparison between reality and simulation and c r i t e r i a of success, i.e., i t has to be decided what degree of repro-duction of reality i s necessary and succifient as to accept the model. The following tools of MODEL CALI-BRATION AND EVALUATION TOOLS OF MODEL ANALYSIS - 215 -evaluation were chosen; they a l l compare the outcome of the simulation with the actual apartment growth in Toronto for the years 1959-1970 (6 simu-lation periods). i . Graphical presentation of the results in histograms. i i . Comparison of the difference in percentage. i i i . Correlation analysis. iv. Graphical presentation of correlation in scattergrams. This set of evaluations were performed three times. After each evaluation, the model parameters were changed. It should be noted that the f i r s t run achieved f a i r l y good results. The changes necessary during calibration were the following: - changes in the table function for zoning, - changes in the relative weights for five environmental factors. The reason why only minor .changes were necessary probably l i e s in the extensive s t a t i s t i c a l analysis which preceded the simulation and prepared the model inputs. The following Table 7«2-I summarizes the changes in weights. For a growth distribution model, the following CRITERIA OF MODELLING c r i t e r i a might be used for the evaluation of SUCCESS success: the quality of the distribution, i.e., i f the "right" stations received growth, the quality of allocation of growth, i.e., i f the i - 2 1 6 -F i r s t Approximation from Literature and Empirical Evidences Weight Envi ronmental Variable F i r s t Approx. Adjusted a f t e r s t a t i s . analysis Adjusted during model c a l i b r . Construction of new apartments 1 6 1 5 1 5 Technological constraints 3 3 2 1 Available land f o r new con-st r u c t i o n 3 4 3 Vacant land 1 2 11 1 1 Building age mixture 5 4 2 Neighborhood quality-1 2 1 0 1 2 Average Lot size 1 2 1 3 x 3 Proximity to major open space 7 5 1 Surface access-i b i l i t y 5 8 8 Measurement of nodality 5 7 7 Zoning 9 1 0 1 1 C e i l i n g capacity 0 0 0 Commercial development 3 4 4 Undesirable conditions 8 8 1 2 TOTAL 1 0 0 1 0 0 1 0 0 TABLE 7 . 2 - 1 REFINED WEIGHTS OF ENVIRONMENTAL FACTORS - 217 " r i g h t " amount of apartments were assigned to s t a t i o n sub-areas, and the timing of allocation? A l l four evaluation tools l i s t e d above provide answers to both questions, however to a d i f f e r e n t extent. Each of the methods w i l l be described b r i e f l y i n the l i g h t of these differences. The r e s u l t s of the l a s t (third) mode c a l i b r a t i o n are given, p a r t l y i n t h i s section, p a r t l y i n Appendices A.d-1 to A.d-3. The histograms allow a quick inspection of the r e s u l t s . As the sample i n Figure 7*2-1 demonstrates information i s provided on which stations received how much growth (expressed i n number of dwelling un i t s and as a percentage of the t o t a l growth i n a s p e c i f i c subway corridor and time period). The corresponding figures are given f o r the actual growth. Appendix C contains the f u l l set of histograms and Appendix A the program f o r the p l o t t i n g of the histograms. - 218 -S I H O L A I E D A P A R T f l E N T G S t O I T H SOBWAI CORRIDOR T 0 I G E TEAR 1 9 6 3 / 1 9 6 * T I B S PERIOD J 0 X 10 X 2 0 1 E G L I K Nil GTOa NE I l l l X I I I I I I I U I I l I I X X I t l l t t sti SE m i l s sv f ILLS HE S V X I I I I I I I I I X I I X X 1 I I X S E i n i i i u i i u i i S T . RM C t l l R >C I 1 I I I X I I I I I 5« S E X l X I I I I I I l I X I X Z I I I X I I I t l X I I X I sonnc »» R H I L L RE S » SE ROSE NR DALE NE SW SE II T i n E P E R I O D 3 THE StJBWHT I I I E I 0 H C 8 BHD 5 S T A T I O N S » I 1 R 20 S T A T I O N S U B - A R E A S . 1 « S 1 I S TH B TOTAL I O f A P A R i n E S T S B U I L T I N T I N E P E B I O D 10 Or APARTHEHTS ABSOLUT X • 0 X 5 0 0 0 389 2 6 0 0 0 0 0 0 0 0 281 19 198 13 0 0 1S8 10 0 0 » 2 S 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T O T A L 1 ( 5 1 1 0 0 It was neither expected nor does i t seem essential that the timing of allocation, within limites, has to he very precise. That i s , i f a station sub-area receives i t s growth i n one period "too early or too late", i t i s less important than i f the proper stations received a reasonably accurate amount of apartment growth. Therefore, the results were not only compared for each time period, but the moving averages over 2, 3 and 5 time periods were com-pared. The moving averages are defined at the beginning of this chapter. They are the sum of apartment growth over 2, 3 and 5 time periods (both for actual and simulated growth). A l l further analysis was made for the f u l l set of moving averages and on a time period by time period basis. FIGURE 7.2-1 TIMING OF ALLOCATION - 219 -The difference of simulated and actual growth was calculated as a percentage of simulated growth by the following formula: Percentage E(X,Y) - S(X.Y) * .100 Difference- v s(X,Y) ~ E = E f f e c t i v e growth) ,, S = Simulated g r o w t h ) a l 1 m o v i n * averages Cases where no growth was simulated and e f f e c t i v e growth took place are indicated by a "999" i n the percentage column; cases where growth was simulated where no development took place, by a "444". Table 7»2-II gives a sample of the percentage com-parison. The f u l l set of comparisons are contained i n Appendix A.d-2. PERCENTAGE DIFFERENCE (1) Correlation analysis provides only a measurement f o r the q u a l i t y of allotment. The q u a l i t y of d i s -t r i b u t i o n can not be compared, because a l l data-pai r s used i n a c o r r e l a t i o n must have non-zero values ( i . e . , cases where growth took place but was not simulated and vice versa are not included i n the a n a l y s i s ) . Again the analysis was performed CORRELATION ANALYSIS 1. The same applies i n the case where the simulated figure was l e s s than one-third of the e f f e c t i v e f i g u r e . Otherwise the percentage figure would increase exponentially because of the structure of formula (1). - 220 -TABLE 7.2-II C O M P A R I S O N O F E F F E C T I V E A M D S I M U L A T E D A P A R T M E N T G R O W T H DIFFERENCE EXPRESSED AS PERCENTAGE OF SIMULATED GROWTH 999 SIMULATED GROWTH WAS LESS THAN 1/3 OF THE ACTUAL GROWTH 444 GROWTH WAS SIMULATED WHEN SO GROWTH ACTUALLY TOOK PLACE E EFFECTIVE GROWTH (IN DWELLIMG UNITS) S SIMULATED GROWTH (IN DWELLING UNITS ) P PERCENTAGE DIFFERENCE ( (E-S)/S* 100) APPLICABLE TO LINE YONGE YONGE STATION AREA TIME PERIOD 2 r i H E PERIOD i 3 E52 S52 P52 E5 3 S53 P5 3 EGLINGTON 1011 0 0 0 168 0 999 EGLINGTON 1012 1118 1771 -37 1369 2408 -43 EGLINGTON 1014 602 154 999 97 1 1 54 999 DAVISVILLE 1023 333 4 37 -24 333 437 -24 DAVISVILLE 1024 1 100 5 36 105 1745 1613 8 ST.CLAIR 1031 427 476 -10 427 476 - 10 ST.CLAIR 10 32 0 312 444 0 312 444 ST.CLAIR 1034 1422 1730 -18 2170 2493 -13 APPLICABLE TO LINE YONGE YONGE STATION AREA TIME PERIOD • 4 TIME PERIOI ! 5 E54 S54 P54 E55 S55 P55 EGLINGTON 1011 168 24 4 -31 168 244 -31 EGLINGTON 1012 1459 2135 -32 1 204 1976 -36 EGL INGTON 1014 1061 4 20 153 902 266 999 DAVISVILLE 1021 0 246 444 0 246 444 DAVISVILLE 1023 153 4 37 -65 0 281 444 DAVISVILLE 1024 2528 2393 6 2408 22 3 8 7 ST.CLAIR 1031 427 268 59 212 0 999 ST.CLAIR 10 32 311 9 38 -67 311 784 -50 ST.CLAIR 1034 2525 2296 10 2 30 3 2139 7 - 221 -for a l l moving averages. Table 7 . 2-III shows the correlation matrix. E ACTUAL APARTMENT GROWTH (in dwelling units) S. SIMULATED APARTMENT GROWTH (in dwelling units) The correlation analysis includes a l l stations which received actual and simulated growth for a l l lines which were in operation in the respective time periods. MOVING AVERAGE 2 TIME PERIOD CORRELATION BETWEEN CORR. COEPF. SIG. LEVEL NO. OF CASES 2 E22 - S22 .64 .088 6 3« E23 - S23 NA 3 4 E24 - S24 . 7 9 . 0 3 2 6 5 E 2 5 - S25 .77 .001 16 MOVING i AVERAGE 3 TIME PERIOD CORRELATION BETWEEN CORR. COEFF. SIG. LEVEL NO. OF CASES 2 E32 - S32 .87 .012 6 3 E33 S33 .74 .047 6 4 E34 - S34 NA - 3 5 E 3 5 - S35 .71 .021 10 MOVING AVERAGE 5 TIME PERIOD CORRELATION BETWEEN CORR. COEFF. SIG. LEVEL NO. OF CASES 2 E52 - S52 .78 . 0 3 4 6 3 E53 - s53 . 8 3 .021 6 4 E54 - S 5 4 . 9 0 .001 8 :•; 5 E55 - S55 .86 .014 6 TABLE 7 . 2-III - 22& -The scattergrams give the most complete information for evaluation and are at the same time easily comprehendable. They represent the correlation between actual and simulated growth. The correla-tion i s good i f the points l i e close to a line through the origin of the coordinate system in the following figures. 1 Points along the X and Y-axis indicate cases where either the actual or simulated growth was zero when the simulated and effective growth were not zero respectively. Figures 7*2-2 to 7.2-6 show the comparison of actual and simu-lated growth for a l l moving averages 2, 3 and 5 for each time period. Figures 7.2-6 to 7«2-8 give an overall indication of the quality of simulation in regard to the sliding averages 2, 3 and 5 respect-ively (for a l l time periods together). Therefore they indicate for each of the three levels of significance (accepted deviation of simulation results from reality) the performance of the simulation. SCATTERGRAMS 1. The line has not to be the 45-degree line because the two axes are differently scaled. - 223-CM CORRELATION BETWEEN ACTUAL AND SIMULATED APARTMENT GROWTH ERROR 2 / 3 / 5 PERIOD 1961 /1962 ERRORS IN DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-flXIS INDICATE THAT NO GRDVTH VAS SIMULATED WHEN GROWTH ACTUALLY TOOK PLACE AND VS FOR THE Y-AXIS 2 = ERROR Z 3 = ERROR 3 5 = ERROR 5 a to. a x a O QC CD c • d j UJaa I— CL I ZD x; >—i<-> to 3 ••s 3 <?•'• 0.0 —I 1 1 40.0 80.0 120.0 ACTUAL GROWTH (X10 ] ) 160.0 FIGURE 7.2-2 SCATTERGRAM - 224 -CORRELATION BETWEEN ACTUAL AND SIMULATED APARTMENT GROWTH ERROR 2 / 3 / 5 PERIOD 196 3 /1 9 6 4 FIGURE 7.2-3 SCATTERGRAM ERRORS IM DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-flXIS INDICATE THAT NO GRDVTH WAS SIMULATED WHEN GROWTH ACTUALLY TOOK PLACE AND VS FOR THE Y-AXIS a a — , CM a a x a m- 1 a LD a Qd_| ZD x: 2 = ERROR 2 3 = ERROR 3 S - ERROR 5 3 ZV * A [5 / 3 S 2 S 5 3 5 3 2 / i i i i 0.0 50.0 100.D 150.0 200.0 ACTUAL GROWTH tXICM ) - 225 -CORRELATION BETWEEN ACTUAL AND SIMULATED APARTMENT GROWTH ERROR 2 / 3 / 5 PERIOD 1965 /1966 ERRORS JN DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-RXIS INDICATE THAT NO GROWTH WAS SIMULATED WHEN GRDWTH ACTUALLY TOOK PLACE AND VS FOR THE Y-AXIS FIGURE 7 .2-4 SCATTERGRAM • 2 = ERROR 2 „• 3 = ERROR 3 5 = ERROR £ CM 1 to _ « - a a ~ - N C M _ J r— I* Q Q£ CD a • a . UJCO r— CL _ J 3 a 5 2 9 i y ' 1 2 / 2 3 / / B3 2 5 2 / 3 2 I 1 1 1 0.0 50.0 1D0.0 150.0 200.0 ACTUAL GROWTH C X 1 0 r ) - 226 -C O R R E L A T I O N B E T W E E N A C T U A L AND S I M U L A T E D A P A R T M E N T GROWTH ERROR 2 / 3 / 5 P E R I O D 1967 /1968 FIGURE 7.2-5 SCATTERGRAM ERRORS IN DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-BXJS INDICATE THAT NO 6P0VTH WAS SIMULATED WHEN GROWTH ACTUALLY TOOK PLACE AND VS FDR THE Y-AXIS a a CM a a •—I a — « x .—CM. O CH CD a LUOQ I— CL I ZD — « a " V a 2 = ERROR 2 3 - ERROR 3 5 3 5 5 8 5 / 5 = ERROR 5 p 5 V 3 55 / 3 / 3 3 2/ —I 1 1 1 0.0 50.0 100.0 150.0 200.0 A C T U A L GROWTH CX10 1 ) - 22? -C O R R E L A T I O N BETWEEN A C T U A L AND S I M U L A T E D A P A R T M E N T GROWTH A L L E R R O R S 2 A L L T I M E P E R I O D S ( I N D I C A T I O N FOR T H E Q U A L I T Y OF T H E S I M U L A T I O N IN REGARD TO T H E ERROR 2 FIGURE 7.2-6 SCATTERGRAM ERRORS IN DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-AXIS INDICATE THAT NO GROWTH WAS SIMULATED WHEN GROWTH ACTUALLY TOOK PLACE AND VS FOR THE Y-AXIS a a C 3 . 2 = ERROR 2 a to _| a —-1 X — a a a CD a R a j (— CC —"a CO* 27 2 / • • • q. ^2 22 2 2 =4 .•2 ? 2 r z 2/ 2 * Z z 2 2/ "1 1 1 — I 0.0 50.0 100.0 150.0 200.0 A C T U A L GROWTH CX10r ) - 228 -C O R R E L A T I O N B E T W E E N A C T U A L AND S I M U L A T E D A P A R T M E N T GROWTH A L L E R R O R S 3 A L L T I M E P E R I O D S ( I N D I C A T I O N FOR T H E Q U A L I T Y OF T H E S I M U L A T I O N IN R E G A R D TO T H E ERROR 3 FIGURE 7.2-7 SCATTERGRAM ERRORS IN DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-BXIS INDICATE THAT NO GROWTH WAS SIMULATED WHEN 6R0WTH ACTUALLY TOOK PLACE AND VS FDR THE Y-AXIS O a CD -—i X a a ce CD a a a " _ i LUao I— CC I Z D s: *—ir-j a a" 3 = ERROR 3 3 3 3 3 / 3 3 >* B / 3 ! 3 / 3 3 / 3 3 >4 333 0.0 3 / / 3 3/ / 3 "! 1 1 1 50.0 100.0 1,50.0 200.0 A C T U A L GROWTH (X10 ] ) - 229 -CORRELATION BETWEEN ACTUAL AND SIMULATED APARTMENT GROWTH ALL ERRORS 5 ALL TIME PERIODS (INDICATION FOR THE QUALITY OF THE SIMULATION IN REGARD TO THE ERROR 5 ERRORS IN DIAGONAL INDICATE GOOD CORRELATION ERRORS ALONG X-AXIS INDICATE THAT NO GROWTH WAS SIMULATED WHEN GROWTH ACTUALLY TOOK PLACE AND VS FOR THE Y-AXIS FIGURE 7.2-8 SCATTERGRAM S = ERROR 5 5 5 5 / 5 5 / 5 5 55 5^5 5/ / 5 1 1 1 1 0.0 50.0 100.0 150.0 200.0 ACTUAL GROWTH (XlO 3 ) - 230 -7.3 B a s i c a l l y , there are three areas of i n t e r e s t i n analyzing the s e n s i t i v i t y of simulation models f o r changes i n parameters, that i s the Constances and table functions that describe r e l a t i o n s h i p s within the systems i . does modification of table functions r e s u l t i n changes i n the performance of the model? i i . are these parameters to which the model exhibits strong s e n s i t i v i t y c ontrollable through planning p o l i c i e s ? i i i . does the replacement of one proxi-variable measuring a parameter with another r e s u l t i n any s i g n i f i c a n t changes i n the performance of the model? In the f i r s t area of i n t e r e s t the s e n s i t i v i t y analysis i s aimed to gain some understanding, whether quantitative changes i n the postulated parameters a f f e c t any aspect of the system. When 1. Forester, 19^9 • SENSITIVITY ANALYSIS - 231 -s e n s i t i v e elements of the model are i d e n t i f i e d , f u r t h e r and more e x t e n s i v e r e s e a r c h can be conduc-ted f o c u s s i n g on these p a r t i c u l a r r e l a t i o n s h i p s i n o r d e r to improve the model's p r e d i c t i n g a b i l i t y . In the second a r e a of i n t e r e s t the emphasis i s p l a c e d on those parameters t h a t do e f f e c t the system's c o n d i t i o n . Here those s e n s i t i v e v a r i a b l e s are i d e n t i f i e d which can be changed or c o n t r o l l e d through a c t u a l p l a n n i n g i n t e r v e n t i o n s . The t h i r d purpose of the s e n s i t i v i t y a n a l y s i s i s to i d e n t i f y whether the employment of a l t e r n a t i v e p r o x i - v a r i a b l e s measuring p a r t i c u l a r parameters would e s s e n t i a l l y produce i d e n t i c a l outcomes i n the l o n g run. That i s to gather d e t a i l e d informa-t i o n on age, f a m i l y s t r u c t u r e , d i s p o s a b l e income, e t c . to d e s c r i b e the neighborhood q u a l i t y para-meter of our model may not be n ecessary i f an a l t e r n a t i v e p r o x i - v a r i a b l e , for>example the per-centage of blue c o l l a r workers, s u f f i c i e n t l y d e s c r i b e s the parameter. Thus e x t e n s i v e e v a l u a t i o n of the system's s e n s i -t i v i t y t o changes i n i t s parameters i s an i n t e g r a l p a r t of model b u i l d i n g and indeed o f t e n the u l t i m a t e g o a l of the e x e r c i s e . However, the pro-cedure of s e t t i n g up e l a b o r a t e runs w i t h i n which - 232 -various parameters are held constant and evaluate each experiment i s time consuming and expensive. The budgetary and time limitations within which this work was undertaken necessitated a drastic reduction in the experimental runs. The performance and sensitivity of the calibrated model was tested for three different sets of con-ditions. Simulation * A* was carried out by incor-porating the general policies l a i d down by the o f f i c i a l plan-for the spatial confinement of various land uses for the next thirty years i n Toronto. (See Figure 7»3 -D» ' Here the technological con-straints for station sub-areas were sequentially relaxed i n accordance with scheduled construction of new water mains, sewer lines, and school space. For sub-areas where the o f f i c i a l plan designated high-density residential uses, the zoning variable was changed to assume the value of 4 (zoned for high density residential) and similarly the inter-vention variable for the external generation of commercial development was modified where the plan envisaged future r e t a i l and office space develop-ment. The variable of undesired conditions was changed for the Eglington, Davisville, Summerhill and Islington station areas where plans existed for the elimination or covering of open railway lines FORECAST 1971-1986 - 233 -in the near future. On the basis of information received 'during a personal interview with T.T.C. o f f i c i a l s , the proposed extension of the feeder system was incorporated into the simulation through the alteration of the nodality variable. However, two changes envisaged in the o f f i c i a l plan were omitted in this run. First, no modification was entered in surface accessibility variables since the proposed Spadina Express Way i s not expected to be constructed. Similarly, the impact of the future extension of the subway line in the median strip of the freeway was discounted i n light of the high uncertainty (level) that this expansion w i l l ever take place. Simulation • B* tested the influence of alternative grouping of the interactive policy variable to achieve different spatial distribution of future apartment development. Here the objective was to create a smaller number of, but more intensive, nodal developments and thus to investigate whether there i s any ground for the fears of many munici-pa l i t i e s that rapid transit merely improves the strength of the CBD at the expense of development elsewhere, or alternatively, i f with good and vigorous policy interventions this trend can be reduced. However, the comparison of the two runs - 234 -cannot be ex p l i c i t l y related to one another, since a number of nodal developments was proposed for stations where some apartment development was assigned by the o f f i c i a l plan as well. Simulation *C tested the influence of non-policy variables. Conceptually, i t wculd have been desirable to treat a l l fourteen variables as •interactive*, but this treatment would have resulted in a more expensive and a more trouble-some manipulation. Thus a number of variables (neighborhood quality, lot size, etc.) which were not expected to be sensitive for alternative policies were sunk into the model as non-inter-active. Consequently, changing the values of these variables necessitated the alterations of the i n i t i a l conditions. For the purpose of this simulation run, these changes were made quite arbitrarily, as the objective of the run was not to achieve any spatial distribution of apartment construction, but rather to test the influence of policy versus the non-interactive variables on the evolving pattern. Here three sets of changes were-introduced. First, the policy variables reflecting the objectives of the o f f i c i a l plan were maintained; second, similar policy variables were introduced to a number of other stations; and f i n a l l y , the non-interactive variables of neighborhood quality and - 235 -FIGURE 7.3-1 DESIGNATED AREAS FOR HIGH DENSITY RESIDENTIAL DEVELOPMENT Sourcei Proposed Plan for Toronto, Toronto Planning Department, 1967, p.105 - 236 -lot size were favorably changed for a third set of stations, but no policy variable other than the relaxation of technological constraints was modified. The results of the three simulation runs were compiled in three maps for comparison. (Figure 7.3-2,3,4). From the f i r s t inspection of these maps, i t i s evident that through the policies adopted by the Metropolitan Planning Board, future apartment development can be channeled to the predesignated areas (Simulation 'A'). The fact that not a l l of these areas received growth during the simulated period may be due to either the too small assignment of total growth to the corridor, or that the o f f i c i a l plan envisaged a time period for the growth of these station areas longer than the simulated time. When the results of Simulation *A* are contrasted with the development patterns simulated by the alternative grouping of policy variables (Simu-lation 'B*), only limited improvement i s evident. Although concentrated nodal development i s apparent at the Islington, Broadview-Chester and Jane Street subway sections at the expense of Pape and Dufferin stations, the second simulation run reproduced essentially similar growth in sub-areas - 237 -around Eglington, Davisville, St. Claire, High Park, and Main Street subway stations. These results gave rise to speculations that policy interventions have a more moderate impact on channeling development than was previously expected. Thus the reasonable f i t between the spatial d i s t r i -bution of simulated growth and the location of apartment construction envisaged i n the general plan of Toronto i s rather attributable to the correct anticipation of the attractivity of other components of the environmental context than the effectiveness of currently available policy devices. The distribution pattern produced by the third simulation run essentially confirmed the above assumption. The spatial concentration of new apartments became less accentuated and more disper-sed. Areas with favorable neighborhood quality and large lot sizes diverted growth from areas which received concentrated apartment growth in Simu-lation *A*, despite the fact that the policy variables were identical in both sets of station sub-areas. Furthermore, those stations also attracted some limited development where the other-wise favorable non-interactive variables were not reinforced by policy variables encouraging concen-tration. - 238 -SIMULATION RUNS C B A A B C -EGLINGTON DAVISVILLE ST. CLAIR SUMMERHILL ROSEDALE BLOOR STREET FIGURE 7.3-2 SIMULATED CUMULATIVE APARTMENT GROWTH SUBWAY LINE YONGE 1970-1986 Apartment growth less than 1000 units Apartment growth more than 1000 units - 239 -SIMULATION RUNS C B A A B C • • • • • • r J \ • • • c ) • c ) • • • c ) • • • c ) c ) s • • r ; \ r ) • ( ) • • ) c ) • r ) r ) -e ) T-• • • "ISLINGTON ROYAL YORK -OLD MILL JANE RUNNYMEDE -HIGH PARK •KEELE DUNDAS WEST -LANDSDOWNE -DUFFERIN OSSINGTON CHRISTIE -BATHURST •SPADINA •ST. GEORGE Apartment growth less than 1000 units ^Apartment growth more than 1000 units FIGURE 7.3-3 SIMULATED CUMULATIVE APARTMENT GROWTH SUBWAY LINE BWO, BWN 1970-1986 - 240 -SIMULATION RUNS C B A A B C • • • ( \ • • c ) \ • • f ) • • • • \ ( • • • • ( ) • ( ) \ \ / ) \ • K ( ) • K ( > \ • ( \ • ( ) \ • • K ( ) \ • • • • SHERBOURNE •CASTLE FRANK "BROADVIEW -CHESTER -PAPE -DONLANDS -GREENWOOD •COXWELL -WOODBINE "MAIN STREET -VICTORIA PARK -WARDEN Apartment growth less than 1000 units Apartment growth more than 1000 units - 241 -74 As evident from this chapter, the usefulness of the simulation model can he concluded from two sets of considerations. F i r s t l y , the model c a l i -bration which indicates how well the model "works", i.e. i f i t i s able to reproduce the past; secondly, the quality of the model i n regards to sensitivity analysis, to test alternative policies and to forecast their consequences. The f i r s t condition -that the model works - i s of course a prerequisite for the second. However, i t has to be ascertained as to what extent the quality of the results of calibration i s a consequence of imperfect data or the model i t s e l f . CONCLUSIONS ON THE SIMULATION The results of the model calibration are generally satisfactory. Three c r i t e r i a of success were established to measure the model quality; perform-ance in regard to apartment distribution, a l l o t -ment and timing. RESULTS OF CALIBRATION - 242 -Of the 128 station sub-areas, 22 actually received growth i n the time period 1959-1970.1 In 19 of those cases the model predicted growth for the respective areas. The model simulated growth for one additional station, where i n actuality no growth took place. Therefore the distribution achieves favourable results. As previously discussed, the model was not expected to predict growth precisely in the time period in which the actual apartment development took place. During the calibration period 59 apartment devel-opments occurred. In only $0% of the cases did the simulation allocate growth in the proper time period. To measure the quality of timing, the 2, 3 and 5-year moving averages were introduced. The results indicate that in r80$ of-tha-cases the timing was satisfactory when the moving average 2 was used, i.e. eight out of ten developments were predicted not more than one time period too early or too late. In other words, 50$ were predicted i n the right time period, 30$ with a deviation of plus/minus one period and 20$ with a greater 2 deviation. 1. This number deviates from the figure 25 used i n the s t a t i s t i c a l analysis in chapter 6.3. The s t a t i s t i c a l analysis included three developments along the Bloor line which took place shortly before the subway was introduced. 2. The moving average of 3 and 5 did not improve the results beyond 80$. - 243 -The allocation of growth, i.e. the actual size of predicted apartment developments was less accurate. The percentage differences between actual and simu-lated growth l i e s generally in a range of plus/ minus 30 to 5®%* some of them reaching up to 80 to 100$ (see Table 7.2-2 and Appendix A.d-2).* However, this difference i s much smaller i f the moving averages are compared, where the difference i s usually less than plus/minus 30% or i f the cumula-tive total of predicted and simulated apartment growth i s compared at the end of the 11-year calibration period. That indicates that although the individual development sizes were rather inaccurately predicted, the amount forecasted over a longer time period i s much closer to reality. The most l i k e l y reason for this i s the fact that the analysis was made on a station sub-area basis rather than using city-blocks. This aggregation levels out many environmental differences. In addition, the apartment development size i s much greater for a sub-area than the apartment building size for a block or an individual property as demonstrated i n chapter 6.2 and 6.3. That indicates that the random distribution process of apartment l i The reasons for this limitation in the present analysis are discussed in Chapter 6.1. - 244 -sizes ""bigger" mistakes in the ease of sub-area aggregation. As discussed in chapter 7«3» the model does allow RESULTS OF SENSITIVITY for sensitivity analysis and simulation of alter- AND FORECAST ANALYSIS native policies. However i t i s in this area where further work i s required. 1 Additional sensitivity analysis i s necessary to analyze precisely which combination of environmental factors influence the apartment growth. As of now, the sensitivity of factors i s expressed in their weight and i n the shape of the table functions. In the range where the gradient of the function i s steep, conditions are unstable and attractivity scores change sig-nificantly i f the environmental conditions change slightly. The sensitivity of not only isolated factors but also of the concerted influence of any number and combination of factors can only be -tested in a carefully designed sequence of simulation runs. This could at present only be done to a limited 1. In the present analysis, some of the sensi-t i v i t y analysis and policy testing runs were combined for budget reasons. The extensive print outs of of intermediate results made i t possible to trace and separate the two processes and their results even i f they are combined in one run. - 245 -extent f o r time and e s p e c i a l l y f o r money reasons. The s e n s i t i v i t y analysis provides f o r two possi-b i l i t i e s . F i r s t l y , data c o l l e c t i o n f o r an improved simulation f o r Toronto or f o r any other c i t y may now be lim i t e d to the important, i . e . high weight f a c t o r s . This allows f o r a reduction i n data c o l l e c t i o n and to allocat e more funds to achieve a higher q u a l i t y of the data basis. Secondly, and more importantly, the s e n s i t i v i t y analysis i s o l a t e s those fac t o r s and groups of factors which are suitable and successful i n achieving desired changes, i . e . those which provide information f o r the planning and decision-making processes. These general implications of the model are now d i s -cussed i n more d e t a i l i n the following and con-cluding chapter. - 246 -Foster, J.W., (1969), Urban Dynamics, The M.I.T. Press, Cambridge, Massachusetts. Metropolitan Planning Department, Proposed Plan  for Toronto, Toronto, 1967. synthesis 8.1 Implications f o r Planning 8.2 Directions f o r further research. - 247 -8.1 Planning implies the rational ordering of the environment to suit proitious events of man and society. This ordering i s achieved by employing a number of tools and policies and by choosing among options of commitments which are at the disposal of society as a whole and of planners in particular. While i t has been repeatedly empha-sized that tools, policies and commitments must not be treated as ends i n themselves, but rather as appropriate actions designed to serve most f u l l y the society's present and future needs, there i s s t i l l less than sufficient attention paid to understand and ultimately anticipate the objective consequences of various actions. When a downtown office tower i s built, i t not merely accommodates a particular activity, but defines the spatial concentration of a number of people at specific times, alters the locational choices of a number of other activities, represents some loading on related transportation/communication channels and IMPLICATIONS FOR PLANNING - 248 -i t "becomes a landmark to be proud or ashamed of. Thus to conceive the downtown office tower as a specific commitment serving a specific goal (that i s the sheltering of commercial activities) means to ignore the ramifications of the project within the larger context of urban environment. One of the most powerful tools currently available for planners to influence the spatial evolution of c i t i e s i s the transportation/communication net-work. This network f a c i l i t a t e s and defines the ease of interaction among various members of the community and by doing so, i t bridges the resources and the opportunities of the city. Since the need for easy interaction gave birth to the whole pro-cess of urban agglomeration and remained the single most important force underlying i t s rapid growth, the thorough understanding of the consequences of transportation investments remains of major impor-tance, i f we are to master the quality of our urban environment and the level of opportunities within i t . In recent years one particular transportation system, the rapid transit, has received consider-able attention despite the fact that the last two decades witnessed a marked decrease i n mass trans-portation patronage. Partizans for the r e v i t a l -- 249 -ization of rapid transit often use emotionally charged arguments to prove the superiority of the system over the automobile for specific purposes. To describe the various points of these arguments would be a repetition of what has already been said in previous chapters, yet to provide a proper perspective for the planning implications of this study one point of the reasoning i s repeated here. Large segments of the city population are denied convenient access to urban opportunities, such as employment, education, recreation, medical care, etc. i n the automobile oriented city, because they cannot afford to buy a car, or simply are unable to drive. Mass transportation coupled with a high level of service can lower the barrier to urban opportunities by offering an improved personal mobility to the disadvantaged. However, this argument implicitly assumes the simultaneous occurrence of two favorable conditions. First, that residential areas where the economically disadvantaged are concentrated remains essentially unchanged after the rapid transit station i s introduced, and second, that there i s a s i g n i f i -cant concentration of urban ac t i v i t i e s attracted to somewhere along the line and which can now be reached more quickly by those from whom the con-venient access to urban services and f a c i l i t i e s - 250 -have been previously denied. When either, or both of these conditions are missing the social objec-tive of the transportation investment cannot be achieved. I n i i t s present form the model i s designed to give answers within some range of limitations to the f i r s t problem, that i s whether or not the f i r s t people to be replaced by the residential redevel-opment triggered by the introduction of rapid transit lines, are those for whom the system originally was designed. If the model i s extended to simulate the spatial distribution of other ac t i v i t i e s attracted to the line, the second con-dition can also be tested. A further application of the model within the planning process i s i t s capacity to evaluate the relative attractivity of various stations for capturing some portion of the future development growth. The concentration of high density r e s i -dential, employment, shopping and entertainment centers i s essential for the economical and successful operation of rapid transit. In fact, one of the basic rationale behind the introduction of rapid transit i s to f a c i l i t a t e the spatially and temporally confined high density travel trips within an urban area. Thus i t i s ironic that - 251 -whereas the relative density of residential devel-opment has been rapidly increasing over the last decade, and the absolute growth of the core area as the employment center i s s t i l l considerable, the patronage of rapid transit in most North American c i t i e s has relatively declined during the same time period. One explanation for this phen-omenon could be sought in the relative attractivity of environments within which rapid transit stations are located. In Toronto, for example, 22 of the 128 station sub-areas were identified as not capable of attracting future development. Thus with the model i t i s possible not only to test new development stimulus potential of various network layouts, but also to alter the magnitude of this stimulus by placing stations in alternative envir-onmental context, or with vigorous planning policies creating new ones. - 252 -8.2 It i s often argued that the significance of scien- DIRECTIONS FOR FURTHER t i f i c investigations should not be evaluated RESEARCH solely in terras of the answers given to specific problems, but rather in terms of the new questions which those answers generated. The theoretical limitations of this study enumerated i n chapter 5.2; the evaluation of the simulation model in chapter 7.4 and the c r i t i c a l remarks throughout the thesis- discussing additional problems encountered during the course of analysis and model building, suggest several lines of further research. The model i t s e l f could be profitably extended in two directions. First, the spatial distribution of a wider range of activ i t i e s such as office, r e t a i l , other commercial (theatres, stadiums, exhibition parks) and some institutional, a l l of which require the concentration of a significant number of people rather than goods for the economic scale of their operation, could be included i n the - 253 -model. The incorporation of competing land uses which have been treated as exogenous variables i n this study should increase the dynamics of the model and ultimately portray a more adequate picture of changes taking place after the rapid transit lines are introduced. The addition of this new dimension does not require conceptual changes i n the structure of the model, however i t would require some modifications of program. New land uses would be determined by a specific subset of a l l environmental factors. 1 The spatial d i s t r i -burion of additional land uses may be influenced partly by the same, partly exclusive environmental variables. In any case, the weight configuration would be different for each land use. Further, the development size functions would have to be specified for each use. The feedback among land uses would be reflected i n the change of those environmental factors which are common to two or more land uses. The output of the program would consist of the amount of growth for each land use. 2 1. The fourteen presently used environmental variables might have to be expanded. 2. In the case of limited budget and time, the model could be run for each land use separately, with a different data f i l e which would include environmental factors applicable to the respec-tive land uses. However, the feedback among land uses would have to be entered externally by interaction, as i t was done for the present analysis. - 254 -Second, further research i s required to test the model* s reproducing and forecasting a b i l i t y in other c i t i e s as well. Although i t i s suggested that both the table functions and the relative importance of environmental components represent an important input i n loeational choices, the universal applicability of these table functions and weighting scales could be validated only when additional information i s available from other ci t i e s , with other rapid transit network configur-ations and different stages of development. Both the preliminary s t a t i s t i c a l analysis and the model essentially focus on those loeational shifts which involved new constructions of replace-ments of existing physical structures. The emphasis on these specific changes, however,was only partially due to the time limitation on data collection. The consequences of loeational and investment decisions that result in significant alteration or renewal of theophysical stock repre-sent more substantial changes in the spatial structure of the city than those resulting from the continuous shifting and f i l t e r i n g of a c t i v i -ties within the standing stock. Yet, conceptually the two processes cannot be divorced entirely from one another, for changes in the rate of f i l t e r i n g - 255 -(e.g. the conversion of single family houses to renting accommodation)^ultimately lead to changes in the rate of replacement. Thus further research concerning the magnitude and spatial distribution of changes within the existing buildings could add an important dimension to the model. Furthermore, the literature reveals insufficient information as to how the total amount of new construction in the city can be related to the amount of development which locates near to rapid transit stations. The method the writers employed to assign new apartment units to the line admit-tedly represents a rather crude estimation pro-cedure, although i t i s believed that i t could serve as a reasonable assumption on which addition-al research can be based. There are at least two reasons for continued interest in the impact of rapid transit stations on the spatial distribution of activities within the urban f i e l d . F i r s t , there i s a need to under-stand how high density development can be channeled to become spatially associated with the network, for the economically successful operation of transit lines the interaction of high density nodes i s necessary. Second, the rate of urbaniza-tion, predicted to culminate by the end of the - 256 -century, w i l l r e s u l t i n a further s p a t i a l expansion of urban agglomerations. I f the objective conse-quences of rapid t r a n s i t i s recognized i t can be used as a t o o l to c a t a l i z e and integrate future development into high density f u n c t i o n a l nodes. These nodes, i n turn, could become organic nuclei g i v i n g structure to the otherwise disintegrated expansion of metropolises. - 2 5 7 -bibliography Adkins, W.G., "Land Value Impacts of Expressways i n Dallas, Houston and San Antonio, Texas", Highway Research Board, B u l l e t i n 2 2 7 , 1 9 5 9 » Washington, D.C. Alexandria, Department of Planning, Rapid Transit  Expected Impact, Report No. 1 9 , May, I969. Alonso, W., "A Theory on the Urban Land Market", Papers and proceedings of the Regional Science Association, Volume 6, i960," pp. 149 - 1 5 7 . Alonso, W., Location and Land Use: Toward a  General Theory of Land Rent, Harvard University Press, Cambridge, Massachusets, 1 9 6 4 . 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Young, A.P., Maltby, D., Constantine, T., "Urban Transit Systems", O f f i c i a l Architecture and  Planning, Vol. 32, No. 12, 1969, December, pp. 1454 - 1461. appendices Key f o r Station Locations TECHNICAL NOTES A. a Model Characteristics A.a-1 Sample Output Par Pull A.a-2 Sample Output of Interaction Process A.a-3 Sample Outprint of Error Messages A.a-4 Program of Simulation A»a-5 Program for Histogram Plotting A.a-6 Program for Scattergram Plotting A. a-7 DATA A.b Apartment Developments along Subway Corridors A«.b-1 Apartment Growth, Incremental A.b-2 Apartment Growth, Cumulative A.b-3 Apartment Growth, Cumulative A.b-4 STATISTICAL ANALYSIS A.c~ Crosstabulation and Correlation Analysis A.c-l Logical Tree Analysis A.c-2 Guttman Table and Guttman Scale Analysis A.c-3 SIMULATION A.d Control Measurement for Moving Averages A.d-1 Calibration, Histograms A.d-2 Calibration, Percentage Difference A.d-3 Calibration, Correlation analysis A.d-4 Testing of Alternative Policies, Sample Outprint of Results A.d-5 keymap technical notes (See Chapter 7.1) In Chapter 7*1 the general program structure was discussed. The specific program character-i s t i c s are described in this appendix, providing sufficient information to run the simulation model. The following sample illustrates the RUM command. RUN COMMAND $RUN MODEL.0 4=*SING* 5=DATA 6=PRINT 7=CFILE GUSER=INP PAR=FULL In this case, the object program (compiled program) i s i n a f i l e called MODEL.0, the interaction i s displayed on a terminal. The input data are on a f i l e called DATA and a l l outputs come from the line*printer except the output for further use, which w i l l be entered into the f i l e FILE. PAR=FULL i s optional and indicates that the f u l l set of outputs i s desired; GUSER indicates that the user w i l l respond to the questions asked by the model during simulation. The format of the DATA f i l e i s described at the end of this appendix. The program was written in FORTRAN IV and requires seven routines from the UBC Program Library (DATE, PAR, FINDC, FINDST, INFREE, RAND and FRAND). Subroutine SIGH activates the outputs chosen by the LOGICAL OUTPUT UNITS user on the RUN command. The following outputs AND FORMAT are provided by the model on logical units 4 - Unit 4 i s used to interrogate the user about various aspects of the model and the output required. Responses to these requests are made via GUSER. 6*- Two types of output are provided through this unit. 1. If PAR=FULL i s given, then a considerable amount of intermediate data i s provided as the model procedes (see sample, Appendix a.a-2). 2, When simulation i s complete and regard-less of the parameter, a f i n a l l i s t i n g of incremental and cumulative apart-ments for each substation in eacy year i s provided in a form to f i t on 8 | " x l l " paper (see sample, Appendix a.dr»4). 7 - If desired (depending upon the response to a question) a f i l e may be prepared via this unit which contains the incremental and cumulative apartments for each substation i n each year in a form which may be easily used as input by other programs. In particular, this format i s used for the evaluation of the model calibra-tion (Chapter 7«2) and for the graphical pre-sentation (histograms, scattergrams). The cost of the runs varies with the amount of COST OF SIMULATION interaction and the outputs desired, and can be up RUNS to $30 for a 14 time period run. Howeveu the cost can be reduced drastically, i f PAR=FULL i s not chosen (PAR=FULL i s mainly a device for the calibration of the model) or i f : . i t i s chosen, the results can be written on a tape and afterwards printed at a lower rate factor (batch or over-night). The cost was reduced further by using the FORTRAN H. compiler. In addition, the whole simu-lation can be run on the batch, provided a l l answers to the questions during the simulation are compiled properly. The average cost of a f u l l run was therefore reduced to approximately $6-8 on terminal, $4-5 on batch. 1 If PAR=FULL, the printing of the results requires an additional $6-8. The 1. Or $2-3 for overnight runs. model then, in i t s present state, i s extremely-economical. The program provides an exhaustive l i s t of error-messages (see sample in Appendix A). This has two advantages. First, i f the model i s applied to any other city or subway network, the data base has to be established and provided for, in the format required by the simulation. However, any mistake made in the preparation of the data inputs i s commented on by error-message, which guides the user in his corrections. Second, any mistake made during the simulation, and especially during the interaction, i s answered by an error-message which allows the user to repeat his commant. This pre-vents the abortion of the simulation and therefore considerably reduces the cost. A sample of error-messages i s given in Appendix A.a-4 for a l l those errors which do not cause immediate termination of the simulation. FORMAT OF THE DATA FILE The data f i l e i s div ided into 5 l og i ca l sec t ions : 1. Raw X " tab le" funct ion values. 2. Normalized Y " tab le " funct ion values 3. I n i t i a l s ta t ion cha rac te r i s i t cs 4. Total apartment numbers 5. Pro ject s i ze funct ions. 1. Raw X "Table" Function Values There are exact ly 14 records in th is sec t i on , one record for each table funct ion ( c h a r a c t e r i s t i c ) . FORMAT ( I I , 2(1X12), F3 .0J0F5.0) Column Contents 1 1 2 Blank 3-4 The number of the table funct ion to which the values refer (1 to 14) 5 Blank 6-7 The number' of points which define the function (Max. = 10) 8-10 The weight to be associated with the charac te r i s i t c when summing the pa r t i a l a t t r a c t i v i t y scores. 11-15 ~N • 16-20 J . 21-25 / / The raw X values of the funct ion. Up to 10 X values may be defined 26-30 > but in a pa r t i cu la r funct ion there must be exact ly the number of 31-35 points spec i f i ed in columns 6-7. 36-40 I • 41-45 \ 46-50 51-55 56-60 J No $ENDFILE record i s to fo l low th is data. 2. Normalized Y Values of the "Table Functions" There are exact ly 14 records in th is sec t i on , one record fo r each table funct ion ( c h a r a c t e r i s t i c ) . F0RMAT(I1,2(1X,I2),3X,10F5.0) Column Contents 1 2 2 Blank 3-4 The number of the " tab le" funct ion to which the values refer (1 to 14) 5 Blank 6-7 Number of points which define the func t ion , (max.=10) 8-10 Blank Normalized Y values of the funct ion. Up to 10 Y values may be defined but in a pa r t i cu la r func t ion , there must be exact ly the number of points spec i f i ed in columns 6-7. 3. I n i t i a l Stat ion Charac ter is t i cs The model i s represented by up to 5 rapid t rans i t l ines made up of stat ions with 4 sub-areas each. The charac te r i s t i cs of the sub-areas are read in from up to 5 sets of records, each record representing a sub-area. There may be up to 120 sub-areas per l i n e . I f there are less than 120 sub-areas • on a l i ne then a $ENDFILE must fo l low the l as t substation of that l i n e . I f there are exact ly 120 sub-areas no $ENDFILE record is required. I f there are no substat ions on the l i n e ( i . e . the l i ne does not ex is t ) then only a $ENDFILE must be inc luded. Each data record w i l l have the fo l lowing format: FORMAT ( I I , I4 ,1X,2F6.0,2F5.1,10X,4F2.0,8X,3F2.0,4X,3F2.0,1X,I2) Column Contents 1 2-5 6 7-12 13-18 19-23 24-28' a number which designates the sub-area (they must be in numerical order) Blank Number of apartments i n i t i a l l y in the sub-area The e f fec t of technological cons t ra in ts . Total land ava i lab le ( inc lud ing vacant land) Vacant land 29 -38 Blank 39 -40 Bui ld ing age mix 41 -42 Neighbourhood qua l i t y 43--44 Lot s i ze 45--46 Park land a v a i l a b i l i t y 47 -54 Blank 55 -56 Surface access to central area 57 -58 Nodal i ty 59 -60 Zoning 61 -64 Blank 65 -66 Ce i l i ng Capacity 67 -68 Commercial development 69 -70 Undesirable condit ions 71 Blank 72 -73 Year in which sub-area enters the Total Apartments to be B u i l t : by Line and Year Each record represents a year and contains the to ta l number of apartments to be b u i l t on each of the 5 l i n e s . If less than 14 years are provided, then $ENDFILE must fo l low the l a s t record. I f exact ly 14 are provided, then no $ENDFILE i s to be inc luded: The format of each record i s as fo l lows : FORMAT(II,1X,5(F6.0)) Column Contents 1 4 2 Blank 3- 8 Number of apartments for l i ne 1 9-•14 Number of apartments for l i ne 2 15-•20 Number of apartments for l i ne 3 21- 26 Number of apartments fo r l i ne 4 27-•32 Number of apartments for l i ne 5 Project S ize Functions The to ta l apartments for each l i ne are a l located on the basis of projects which are executed at each sub-area. The s ize of these projects i s determined randomly from one of 4 cumulative p robab i l i t y funct ions. Up to 60 points may be defined fo r each funct ion (each record defines 1 po in t ) . The records must be ordered by numerical order of the independent va r iab le . I f less 1 than 60 points-are def ined, then a $ENDFILE must appear a f te r the l as t record. I f exact ly 60 points are provided fo r a funct ion then no $ENDFILE i s needed. A l l four funct ions are required. The format for each record i s as fo l l ows : a-2 (See Chapter 7.1) This appendix contains a sample output for the SAMPLE OUTPUT case i f the user chooses PAR=FULL. The following sets of data are printed after each simulation periods i . NORMALIZED CHARACTERISTIC VALUES -These are the normalized values of the fourteen environmental factors, i . e . ; the environmental conditions for each station sub-area (as up-dated at the end of the former time period). i i . FUNCTION VALUES Attractivity scores.corresponding to the above environmental characteristics as determined by the appropriate table functions. ' , i i i . ATTRACTIVITY SCORES Total or composite attractivity score for -each station sub-area. iv . APARTMENT GROWTH Number of apartments allocated to each station sub-area. In addition, a l l i n i t i a l data are listed and at the end, the incremental 'and cumulative apartment growth by station sub-areas for a l l time periods are printed (see sample in Appendix A.d-4). BEGINBI t tG OF T I H B F f B l O D S H O R I U L I Z E D C H » 8 » C T E B I £ T I C V S l U f S 1011 1012 1013 1014 1021 1022 1023 1024 1031 1032 1033 10314 1041 1042 1043 1044 1051 1052 1053 1054 2011 2012 2013 2014 2021 2022 2023 2024 2031 2032 2033 2034 2041 2042 20 4 3 2044 2051 2052 2053 2054 20 61 2062 2063 2064 2071 2072 2073 2014 20 81 2082 20 8 3 2084 2091 2092 2093 2094 2101 2102^ 2103 2104 211 1 2112 2113 2114 2121 2122 2123 2124 2131 2132 2 133 2134 2141 2142 2143 2144 2151 2152 2153 2154 3011 3012 3013 3014 30 21 3022 3023 3024 3031 3032 3033 3034 30 41 3042 30 4 3 3044 30 51 3052 3053 3054 30 61 3062 3063 30 64 3011 30 72 3073 3074 30 61 3082 3083 3084 3091 3092 3093 3094 3101 3102 C2620 1.COOOO 25743 0.82279 C0980 1.00000 0.3CC00 0.0 C2240 0.98460 0.36920 0.6 C1080 1.00C00 1.00000 0 C4550 03930 13151 0216O 0.4CC00 0.0 0. 14560 0.0 0.36CC0 0.0 0.4C000 0.6 0.05341 0.16920 0.21840 0. 0.0 0.0 0.40000 0.0 0.0 0.96670 0.43740 0.0 0, 0.90249 0.125C0 0.0 0 0 . 98460 0.06920 0.0 0, C0160 1.C0C00 0. 1CCC0 0.0 0. 17602 0.83058 0. 16120 0.0 0. 1. 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I II, 0 II. 0. 0. 0. 0. 0. 1. 1. 50CC0 50CCO 50CC0 5CCC0 EOCCC e c c c o e o c c c e c o c o 20CC0 2CCC0 2CCCC 2CCC0 50CCC 50CC0 50CCC 5CCC0 OCCCO o o c c o OCCCO CCCCO 0 a 0 0 0 0 0 0 e o c c o e c c c o e c c c o e c o c o 0 0 0 0 e c c c o ECCCO e o c c o e o c c o 0 0 0 0 0 0 II II v o n c o m e n 2I10CO 2CCC0 2CCC0 20CC0 2CCC0 2CCC0 OOCCO OCCCO COCCO 0.0 0.0 0.0 0.0 0.0 0. 0 0.0 0.0 0.0 0. 0 0.0 0.0 0.0 0. 0 0.0 0.0 0.0 0.0 0.0 6C0C0 60000 6C0C0 60000 0 70000 0 0 0 60000 0 20000 0 0 0 0 6C0C0 60000 6C0C0 60000 0 0 CCOCO 70000 0 20000 0.0 0 0.0 20000 0.0 6C0C0 0.0 0 0.0 COOCO 0.0 60000 0.0 0 0.0 60000 0.0 6C0C0 0.0 60000 0.0 6C0C0 O.C 6C000 6C0CO 60000 6C0C0 60000 6C0C0 60000 e c o c o o . o (,0000 0.0 6 C mill n.o (<llllll() 11,0 MIDCI) 0.0 1,111101) II, I) liOOCO I), ll 60000 0.0 6C0C0 0.0 60000 0.0 6C0C0 0.0 60000 0.0 e c o c o o . o 60000 0.0 6C0C0 0.0 0. 0 0.0 0.0 0.0 0. 0 O.C 0.0 0.20000 1. 0.50000 1. 0.2COOO 1. 0.50000 1. 0.2COOO 1. 0.20000 1. 0.2C00O 0. 0.20000 1. O.JCOOO 1. 0.50000 1. 0.5C000 0. 0.50000 1. 0.2C000 1. 0.2C000 0. 0.2COOO 0. 0.20000 0. 0.ICOOO 1. 0.20000 1. 0.ICOOO 1. 0.2C000 1. C2CO00 1. 0.20000 1. 0.2COOO 1. 0.2C000 1. 0.ICOOO 1. 0.10000 I . O.lCOOO 1. 0.20000 1. O.lCOOO 1. O.lCOOO 1. 0.1C000 1. O.lCOOO 1. 0.2CO00 1. 0.20000 1. 0.2COOO 1. 0.20000 1. 0.2C00O 1. 0.20000 1. O.2CO0O' 1. 0.20000 1. 0.2COO0 1. 0.20000 1. 0.2C00O I . 0.10000 1. 0.2C000 0. 0.20000 1. 0.2C00O 0. 0.20000 1. 0.2C000 1. 0.20000 0. 0.2C000 1. 0.20000 0. 0.2C000 1. 0.20000 1. 0.2C000 1. 0.20000 1. 0.2C000 1. 0.20000 1. 0.2C000 1. 0.2C000 1. 0.2COOO 1. 0.2CO0O 1. 0.2C0O0 1. O.lCOOO 1. 0.10000 1. 0 . ICOOO 1. O.lCOOO 1. 0.2COOO 1. 0.20000 1. 0.2C000 1. 0.20000 1. 0.2C00O 1. 0.20000 1. 0.2CO00 1. 0.20000 1. 0.2C000 0. 0.20000 0. 0.2C000 1. 0.2C000 1. 0.2COOO 1. 0.2C000 1. 0.2C000 1. O.50OOO 1. 0.2C000 1. 0.20000 1. 0.2C00O 1. 0.20000 1. O.lCOOO 1. 0.10000 1. O.lCOOO 0. 0.10000 1. 0.2CO00 1. 0.2C000 1. 0.2CO0O 1. 0.20000 1. O.2C0C0 1. 0.20000 1. 0.2C00O 1. 0.20000 1. 0.2C000 1. 0.20000 OCCOO OCCOO CCCCC OCCCO CCCCC CCCCO c CCCCO CCCCO CCCCO c CCCCO CCCCC 0 C 0 CCCCC OCCCO CCCCC CCCCO CCCCC o c c c c CCCCC OCCCO CCCCC CCCCO CCCCC CCCCO CCCCC CCCCO CCCCC CCCCO CCCCC CCCCC CCCCC CCCCO CCCCC CCCCO CCCCC CCCCC CCCCC CCCCC CCCCC CCCCO c CCCCO c OCCCO CCCCC 0 c c c c c 0 CCCCC OCCCC CCCCC CCCCC CCCCC CCCCO i C i i C 1. C . 2 C 0 0 0 1. 0.2C000 0.2C000 0.20000 1. 0. J l ' l l l i ( I . > (Ml I 0, V ii II c I I . llllllll 0.SD ill! 0.20000 0. 0.2CO0O 1. 0.20000 1. 0.2C000 0. 0.20000 0. O.2CO0O 1. 0.20000 1. 0.2CO0O 1. CCCCC OCCCO CCCCC CCCCO CCCCC CCCCO CCCCC CCCCO CCCCC CCCCC CCCCC CCCCC CCCCC CCCCO CCCCC CCCCO c 0 CCCCC OCCCO CCCCC CCCCC CCCCC CCCCC CCCCC OCCCO CCCCC CCCCO CCCCC CCCCO c CCCCO CCCCC CCCCC CCCCC CCCCC CCCCC CCCCC CCCCC OCCCC CCCCC CCCCO CCCCC CCCCC CCCCC OCCCO c i: <!(<!() (iicc on oo UlCO 0 CCCCC CCCCO c 0 c c c c c CCCCO CCCCC ] C M - I O KJ KJ N O M» CD ^  voooooooo'o-«JO»wooooooooaoooowi/'roooooo.. OOOOOOOOOOOO OOOOOOOOOOOOOOO O O O O O u ui y w ^ ^ w w u UifwM^OiAatJlMliCuKI^  inC uKJd o o o o o o o o o o o o o o o o o o o o o o o o OOOOO-JOOOOOOOOl-l OOOOOOOOOOOOOOO O O O O ~J OOOOO KJ M N N Kl K K j f O f o t o r o r o r o K J t o — _ -o o o o o o o o o o o c i d in «i L)I r ^  KJ w l / I C l o o o o o o o o o o o o o o o o OOOO OOOOOOOOOOOOOOO OOOOO OOOO OOOOOOOOOOOOOOO OOOOO — -•-.OOOOO e e j= a c a J ~ ~ C C £r C -.__.___*Ooooooooo o ra o r» o OOOOOOOO OOOtO OOOOOOOOOOOOOOO o o <—> .J a OOOOOOOO OOOO OOOOOOOOOOOOOOO OOOOO — _»000000000^ — — — — OOOOOOOOOOOOOO 3000000000000000000C o o o o o o o o o c C U ffi o • «• c a !fl u y ui o o ui " - "SOOOOOO O O O O O O C LnKjCucccnu^ diuoysjgLjy-JaiO U3-.-.CT>-C:_._.fO_J^J O^ U"l _i _*UJ»vlO-*taOX)H C f f ffitiflUiCMffitoyi J O - U M O C O H oo«r>io_»oooo o y i i o o o o w w a 3000000000UT100000 0 9>UlOOOOOI> - . — -•OOOOOOOOO-.-* — — -OOOOOOOOOOOOOOOOC J I O W I O M W K j W M r J M - ' — - ^ - ^  •-.OOOOOOOOOOOOOOO o o o o c 00000000000 oOujwi*«KJrowiouj 0>KjO~0CDC>,fil/> OCOO~J^JcO OOOOOOOO OOO>0OOu'lOtS1OOO*nOO<-JOOCOO OOOmOOi^ jOOOOOOOOtCOOQOO . — - * o o o o o o o o o o o o o o o o o o o o o o o o o o o o e o o o o o n o o o o o o o o o n o sjo^^aijirf^uisioc^kiioki OOOOOOOOOt*jOO->OO.tr OOOOOOOOO t/iOOOOOO C CD (J1 - * O kO fO K) on -* CD in O O O o o O <n O O O O O O CD - . O Ul O O CD M tt O O O Ul o J M W M fO h y uuy yy uu wy ui, w-»-.ooooooooc M - o ^ a J o c M ^ c w M j y i c w w - o i O m o a i ^ ' i l o o o o o o o o o o o o o o o c o o o o o o o o o o o o o o o o <wcji>-'wiviyy0iOiier\iOcMOC^iJCuuyoOwu09il9 CUCiOvOOaiUlOui inNC »o sO c ^ ^ Q M -JuMMkJ ymOy D k i a i o ^ K ] ^ y n M O •> in in ru in - • M ^ CD O O KJ OOOOOOOOOw OOO OO OOOOOOO OOOi£>0> o o o o o o o o o o o o o o o o o o o o o o o o o m o (see Chapter 7.1) This sample shows the questions asked during simulation and the range of answers required. SAMPLE PRINTOUT OF INTERVENTION * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * $run p i 0 8 : m o d e l . o 4 = * s i n k * 5 = p l 0 8 : d a t a 6 = - p r f n t 7 = - f i l e ^EXECUTION BEGINS CONSEQUENCES OF RAPID TRANSIT MODEL DATE APRIL 14, 1972 0 4 - 2 7 - 7 2 1 9 : 3 4 : 0 2 R. STUSSI P . BAROSS D.W. PERVIS , PROGRAMMING ENTER A RANDOM NUMBER SEED. ? 3895 TOTAL APARTMENT UNIT NUMBERS HAVE BEEN READ IN FOR 14 TIME PERIODS. I N I T I A L L Y , HOW MANY TIME PERIODS ARE TO 3E SIMULATED? ?2 DO YOU WANT THE PROJECT SIZE FI NOTIONS MODIFIED FOR EACH' LINE? ANSWER 1 FOR YES OR 2 FOR NO. ? 2 SAMPLE PRINTOUT OF INTERVENTION (CONTINUED) BEGINNING OF TIME PERIOD 1 LINE NO. 1 1012 217 .0 1031 212 .0 1034 1.36.0 LINE NO. 2 LINE NO. 3 THIS IS THE END OF TIME PERIOD 1 THE FOLLOWING CHARACTERISTICS OF ONE OR MORE SUBSTATIONS MAY NOW BE CHANGED: 1 SURFACE ACCESS 2 NODAL ITY 3 ZONING 4 CEIL ING CAPACITY (TECHNICAL CONSTRAINTS) 5 COMMERCIAL DEVELOPMENT 6 UNDESIRABLE CONDITIONS DO YOU WISH TO. MAKE ANY CHANGES AT THIS TIME? ANSWER 1 FOR YES OR 2 FOR NO. ?1 ENTER THE STATION NUMBER. .71014 WHICH CHARACTERISTICS OF 1014 DO YOU WANT TO CHANGE? ENTER UP TO 6 CODE NUMBERS AS ABOVE ( I E . 1 , 2 , 3 , E T C . ) WITH AT LEAST 1 BLANK BETWEEN THEM. 11 3 WHAT IS THE NEW VALUE OF INTERVENTION CHARACTERISTIC 2 OF STATION NO. VALID VALUES ARE BETWEEN 0.0 AND 1 2 . 0 PRESENT VALUE IS 9 .0 ?12 S A M P L E P R I N T O U T FOR I N T E R V E N T I O N ( C O N T I N U E D ) I N T E R V E N T I O N A L C H A R A C T E R I S T I C 2 OF S T A T I O N N O . 1 0 1 4 NOW HAS T H E V A L U E OF 1 2 . 0 0 0 0 0 HAT IS THE NEW V A L U E OF I N T E R V E N T I O N C H A R A C T E R I S T I C 3 OF S T A T I O N N O . 10 ] V A L I D V A L U E S ARE BETWEEN 0 . 0 AND 5 . 0 P R E S E N T V A L U E IS 2 . 0 ? 4 I N T E R V E N T I O N A L C H A R A C T E R I S T I C 3 OF S T A T I O N N O . 1 0 1 4 NOW HAS T H E V A L U E OF 4 . 0 0 0 0 0 DO YOU WISH TO CHANGE THE INTERVENTIONAL C H A R A C T E R I S T I C S F ANY MORE STAT I Of ANSWER 1 FOR YES OR 2 FOR NO. ?2 B E G I N N I N G OF T I M E P E R I O D 2 L I N E N O . 1 1 0 3 1 225 . 0 1 0 1 2 2 1 4 . 0 1 0 3 4 2 1 1 . 0 1 0 1 4 1 6 4 . 0 T H I S S T A T I O N OBTA1 NED GROWTH A L R E A D Y IN 1 0 2 3 1 5 6 . 0 T I M E P E R I O D 2 B E C A U S E OF I N T E R V E N T I O N 1 0 2 4 1 5 3 . 0 1 0 3 2 1 3 0 . 0 fNE N O . 2 L I N E N O . 3 DO YOU WISH TO C O N T I N U E S I M U L A T I O N ? ENTER 1 FOR Y E S OR 2 FOR N O . ? 2 DO YOU WANT INCREMENTAL AND C U M U L A T I V E A L L O T M E N T S PRODUCED IN ' F I L E FORMAT 1 AS WELL AS ON P A P E R ? ANSWER 1 FOR YES OR 2 FOR N O . ? 2 THE S I M U L A T I O N HAS B E E N S U C C E S S F U L L Y C O M P L E T E D . S T O P 0 ' • • # E X E C U T I O N T E R M I N A T E D (see Appendix A.a-1) This sample shows a l l error-messagesswhich do not lend to an immediate termination of the simulation. Errors which do lead to an abortion of the simulations are errors i n the data f i l e and i n the command. SAMPLE PRINTOUT FOR E R R O R M E S S A G E S * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * $ run p i 0 3 r m o d e l . o 4 = * s i n k * 5=p108 :da ta 6 = - p r ! n t 7 = - f l l e ^EXECUTION BEGINS CONSEQUENCES OF RAPID TRANSIT MODEL DATE APRIL 14, 1972 0 4 - 2 7 - 7 2 1 9 : 4 7 : 1 6 R. STUSSI P. BAROSS D.W. P E R V I S , PROGRAMMING ENTER A RANDOM NUMBER SEED. ?abcd INVALID REAL NUMBER "ABCD" : INVALID CHARACTER(S) PLEASE RE-ENTER LINE FROM POINT OF ERROR 7 3790 TOTAL APARTMENT UNIT NUMBERS HAVE BEEN READ IN FOR 14 TIME PERIODS. I N I T I A L L Y , HOW MANY TIME PERIODS ARE TO BE SIMULATED? 72 DO YOU WANT THE PROJECT SIZE FI NOTIONS MODIFIED FOR EACH' LINE? ANSWER 1 FOR YES OR 2 FOR NO. ? y e s INVALID INTEGER " Y E S " : INVALID CHARACTER(S) PLEASE RE-ENTER LINE FROM POINT OF ERROR 71 BEGINNING OF TIME PERIOD 1 LINE NO. 1 1012 221 .0 1031 208 .0 i n^4 i 4 n . n SAMPLE PRINTOUT FOR ERROR MESSAGES (CONTINUED) * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * I NE NO. 3 THIS IS THE END OF TIME PERIOD 1 THE FOLLOWING CHARACTERISTICS OF ONE OR MORE SUBSTATIONS MAY NOW 3E CHANGED: 1 SURFACE ACCESS 2 NODAL ITY 3 ZONING 4 CEIL ING CAPACITY (TECHNICAL CONSTRAINTS) 5 COMMERCIAL DEVELOPMENT 6 UNDESIRABLE CONDITIONS DO YOU WISH TO MAKE ANY CHANGES AT THIS TIME? ANSWER 1 FOR YES OR 2 FOR NO. ?no INVALID INTEGER "NO" - I N V A L I D CHARACTER(S) PLEASE RE-ENTER LINE FROM POINT OF ERROR ? 1 ENTER THE STATION NUMBER. ? eg 1i ng ton INVALID INTEGER "EGLINGTON" : INVALID CHARACTER(S) PLEASE RE-ENTER LINE FROM POINT OF ERROR ? 1011* WHICH CHARACTERISTICS OF 1014 DO YOU WANT TO CHANGE? ENTER UP TO 6 CODE NUMBERS AS ABOVE ( I E . 1 , 2 , 3 , E T C . ) WITH)AT LEAST 1 BLANK BETWEEN THEM. ?4 WHAT IS THE NEW VALUE OF INTERVENTION CHARACTERISTIC 4 OF STATION NO. 10! VALID VALUES ARE BETWEEN 0.0 AND 0.0 PRESENT VALUE IS 0.0 ?7 7 .000 IS AN INVALID VALUE FOR INTERVENTIONAL 'CHARACTERISTIC 4 WHAT IS THE NEW VALUE OF INTERVENTION CHARACTERISTIC 4 OF STATION NO. 10: VALID VALUES ARE BETWEEN 0 .0 AND 0.0 PRESENT VALUE IS 0.0 ?0 INTERVENTIONAL CHARACTERISTIC 4 OF STATION NO. 1014 NOW HAS THE VALUE OF 0 .0 DO YOU WISH TO CHANGE THE INTERVENTIONAL CHARACTERISTICS F ANY MORE STAT I Or ANSWER 1 FOR YES OR 2 FOR NO. ?no . . INVALID INTEGER "NO" : INVALID CHARACTER(S) PLEASE RE-ENTER LINE FROM POINT OF ERROR ?2 SAMPLE PRINTOUT FOR ERROR MESSAGES * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * (CONTINUED) BEGINNING OF TIME PERIOD 2 L INE NO. 1 10 31 235.0 1012 2 1 5 . 0 1034 2 1 4 . 0 1023 2 1 1 . 0 1024 1 9 0 . 0 1032 1 5 3 . 0 L INE NO. 2 L INE NO. 3 DO YOU WISH TO CONTINUE S IMULATION? ( ENTER 1 FOR YES OR 2 FOR NO. ? n o INVALID INTEGER "NO" : INVALID C H A R A C T E R ( S ) P L E A S E R E - E N T E R L INE FROM POINT OF ERROR ?2 DO YOU WANT INCREMENTAL AND CUMULATIVE ALLOTMENTS PRODUCED IN ' F I L E FORMAT' A3 WELL AS ON PAPER? ANSWER 1 FOR YES OR 2 FOR NO. ?yws o n f i l e INVALID INTEGER "YWS " : INVALID C H A R A C T E R S ) P L E A S E R E - E N T E R L INE FROM POINT OF ERROR ?2 THE SIMULATION HAS BEEN S U C C E S S F U L L Y C O M P L E T E D . STOP 0 #EXECUTI0N TERMINATED # (see Chapter 7»1) This program executes the complete simulation. I t was programmed by D.W, Pervis, Computing Center, University of B r i t i s h Columbia. PROGRAM FOR SIMULATION MODEL L A N D U S E S I M U L A T I O N C * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * c * * C * THE PURPOSE OF THIS PROGRAM IS TO S IMULATE CHANGES * C * IN PHYSICAL STRUCTURE (LAND USE) IN IT IATED BY RAPID * C * TRANSIT S T A T I O N S . THE MODEL REPRESENTS PART OF £ * C * STUDY CONDUCTED BY * C * - PAUL BAROSS AND * C • * - ROBERT STUSSI * C * U . B . C . SCHOOL OF COMMUNITY AND REGIONAL PLANNING * C * * C * * * * * * * * * : * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * C * * C * DENNIS W. P E R V I S , PROGRAMMING * C * U . B . C . COMPUTING CENTRE * C * * C * MARCH 197 2 * C * * Q ************************* *** ****** ******* 4 * ************ C C * * * CONTROL PROGRAM * * * C L O G I C A L * l I S T < 6 ) / 6 * » ' / , F U L L , L C A L C / . F A L S E . / DIMENSION 0 ( 2 ) , T ( 2 ) COMMON FULL C C * * * WRITE OUT A T I T L E C F U L L = . F A L S E . CALL D A T E I C T ) W R I T E ( 6 , 1 ) D , T W R I T E ( 4 , 1 ) D , T 1 FORMAT(T 1 0 , • CONSEQUENCES OF RAPID T R A N S I T * • / T 5 , 1•MODEL DATE APRIL 1 7 , 1972 • , T 3 5 , 2 ( 1 X , 2 A 4 ) / / , 2 T 1 5 , « R . S T U S S I * , T 3 5 » ' P . B A R O S S * / / T 1 5 , ' D » W . P E R V I S , PROGRAMMING') W R I T E ( 4 , 2 0 ) 20 F O R M A T ( ' ENTER A RANDOM NUMBER S E E D . ' ) C A L L I N F R E E ( 2 7 ,AR) W R I T E ( 6 , 2 1 ) A R 21 FORMAT(* RANDOM NUMBER SEED IS « , F 8 . 2 ) Z=RAND(AR) L A N D U S E S I M U L A T I O N (CONTINUED) C * * * GET PARAMETER STRING AND S E T UP LOGIC SWITCH * * * C CALL PARI 1 S T , N I , 6 , 8 2 , S l O O ) I F ( N I . L T . 6 ) N I = N I + 1 C A L L F I N D C ( I S T , N I , « • , 1 , 1 , N C , N L , £ 1 0 1 ) I F ( N C . E Q . l ) GO TO 102 C A L L F INDST ( I S T . N I . ^ U L L S A . l . N C E l O a ) FULL = . T R U E . C C * * * CALL THE ROUTINE WHICH WILL READ IN A L L I N I T I A L DATA * C 2 CALL OATIN W R I T E ( 4 , 4 ) 4 FORMAT(* I N I T I A L L Y , HOW MANY TIME PERIODS ARE TO 8E S I M U L A T E D ? ' ) C A L L I NFR.EE ( 11 »NT) W R I T E ( 4 , 2 2 ) 22 FORMAT(* DO YOU WANT THE PROJECT S I Z E FUNCTIONS MODIFIED FOR EACH 1' L I N E ? ' / ' ANSWER 1 FOR YES OR 2 FOR N C . « ) 23 C A L L I N F R E E ( 1 1 , I L ) IF( I L . G T . 2 . 0 R . I L . L T . 1 ) G O T O 106 IF( I L . E Q . 1 ) L C A L C = . T R U E . IS=1 8 C A L L G O G O l I S , N T , L C A L C ) I F { N T . E Q . 1 4 ) GO TO 5 W R I T E ( 4 , 6 ) 6 F O R M A T ( ' DO YOU WISH TO CONTINUE S I M U L A T I C N ? ' / * ENTER • , 1» 1 FOR YES OR 2 FOR N O . * ) 9 C A L L I N F R E E ( l l f l l ) IF( I l . L T . l . O R . 1 1 . G T . 2 ) GO TO 104 IF( I 1 . E Q . 2 ) GO TO 5 NTT=14-NT WR I T E ( 4 , 7 ) N T T 7 FORMAT( • YOU MAY SIMULATE UP TO » , 1 3 , / , 1*M0RE TIME PERIODS - HOW MANY DO YOU WANT 1 *) 10 CALL I N F R E E ( 1 1 , 1 1 ) I F ( N T + I 1 . G T . 1 4 ) GO TO 105 IS=NT+1 NT=NT+I1 C A L L I N T E R ( I S - l ) GO TO 8 5 C A L L SIGH S3NI1 S 3 S N o i i v i s a n s o o z = H3V3 3 SNOIiVXSSnS V H i IM S N O I i V i S OE D NOIiDNHd H3d S I N I O d OT 3 (S3I1S I vd31Dvy\/H0) SNOl IDIMflJ - S1IWI1 *** 0 3 ONVIS/£itfa/3S* C 7 l * 2 A 5 ' 0 £ * b J S M 3 N V / 2 1 V O / N0WW03 (S * 0 £ *V) 1DNI * (VI )XVWX*XI 4 ( M 4 S ) S i d V * ( < 7 4 2 4 0 9 ) Z I S V / T i V 0 / NOW WOO 3I 4H*ONM 4>I*OT)NAM*>T.'OT)NX/IlVa/ 110.3 N0WW03 ( S ) D I M V I )N* ( S 4 0 E 4 < 7 4 ^ T ) 3 S 4 ( * / T ) O N NOISN3WIQ 1103 T*1V3I301 ( S ' o e ' V ) D N V i S a3931NI 0 **#****#**********#*****#*******^ 3 * * 3 * W31SAS 3Hi 30 SM0IH0N03 1 V I I I N I 3Hi Sl N 3 S 3 a d 3 « H3IHM * 3 * VIVO 3 H i NI QV3M 01 SI 3NIinO«8nS S I H i 30 SSOdyfld 3HI * 3 * * 3 ** * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 3 3 N i i v a 3 N i i n o b 8 n s ON 3 £Z Oi 03 (•3SN0dS3« a i l V A N I NV SI •*€I*XT)iVWMOJ 9001 II (900T * « 7 ) 3 i I H M 901 01 Oi 00 {»/I*iNVHl y 3 1 V 3 « D SI i * ^ I « X T ) l V W « O d SOOT U N ' I I (SOOT " 7 ) 3 1 IHM SO I 6 Oi 09 ( i'3SN0dS3U a i l V A N I NV SI t 4 £ I 4XI)IvWaOd W)01 II (M)DI*V)31I»M VOT V dOiS (••«313WV«Vd 1D3UU0DNI NV SI .* TV9 4XT )iVWH03 EODI 1SI (£001 4 * 7)3iI«H £01 £ dOi'S ( t ' x N v i e si «3i3wv.avd 30 aaiDvavHO isaid i ) i v w a o 3 zooi ( 2001 4 « 7 ) 31IUM 201 Z dOIS ( t-aNnOd » M t f l 9 ON - U313WVtiVd 133aa03NI t ) i V W a 0 3 TOOT ( T00l'*/)31IUM TOT I dOIS (•*9N01 OOi SI OMiaiS d313WVaVd ,)ivwao3 OODI (OOOT * V ) 3 i i a M OOT 3 ** S39VSS3W a o a a s * ** 3 3 dOIS (•*03131dW03 A.nn3SS333nS N330 SVH NOUVIOWIS 3 H i lUVWUOd £ (C**)31IUM 3 *** dOIS QNtf NOa in3SS333nS V 310N *** 3 (03nNI iN03) N o i i v i n w i s 3 S 0 Q N v l L A N D U S E S I M U L A T I O N (CONTINUED) C *** READ IN THE X VALUES AND WEIGHTS OF EACH FUNCTION *** C DO 90 IEE=1,14 DO 90 IE=1,2 DO 90 IF=1,5 DO 90 IG=1,30 DO 90 IH=1,4 ANEVIS(IH,IG,IF,IE, IEE) = 0. 90 CONTINUE I F ( F U L L ) WRITE(6,60) 60 F0RMAT(T10,» RAW X VALUES OF • • T A B L E " FUNCTIONS'/) DO 1 1=1,14 READ{5,2,END=100)IK*N,NN»W(I),(XN(J ,I) , J =1 , N N ) 2 FORMAT(I1,2(1X,I2),F3.0,10F5.0) N0(I ) = N N C C *** IF DESIRED - WRITE OUT THE DATA ** C I F ( F U L L ) WRITE(6,3) IK,N , N N,W(I),(XN<J,I),J=1 , N N ) 3 FORMAT(IX,I 1,2( I X , I 2 ) , F 3 . 0 , 10F8.2) C C *** CHECK CARD CODING *** C IF( I K . N E . l ) G 0 TO 101 1 CONTINUE C C *** NORMALIZE THE X'S *•** C I F ( F U L L ) WRITE(6,61) 61 FORMAT( '1',T10,'NORMALIZED X'«S'/) DO 6 1=1,14 N=NO( I ) XMAX( I ) = XN ( N , I ) DO 4 J=1 ,N X N ( J , I ) = X N ( J , I ) / X N ( N , I ) 4 CONTINUE C C *** IF DESIRED WRITE OUT NORMALIZED X'S *** C I F(FULL ) WRITE(6,7) (XN(K,I ) ,K = 1 ,N) 7 FORMAT(IX,10F7.5) 6 CONTINUE L A N D U S E S I M U L A T I O N (CONTINUED) C * * * READ IN NORMALIZED Y ' S * * * C I F ( F U L L ) W R I T E ( 6 , 6 2 ) 62 F O R M A T ( ' 1 ' , T 1 0 , ' N O R M A L I Z E D Y ' ' S ' / ) DO 9 1=1,14 R E A D ( 5 , 1 7 , E N D = 1 0 2) I K , N » N N , (YN(J » I ) , J = 1 , N N ) 17 FORMAT(I 1 , 2 ( I X , 1 2 ) , 3 X , 1 0 F 5 . 0 ) C C * * * IF D E S I R E D , WRITE OUT THE DATA * * * C I F ( F U L L ) W R I T E ( 6 , 1 4 ) I K . N , N N , ( Y M J , I ) , J = l , N N ) 14 F O R M A T ( I X , I 1 , 2 ( I X , I 2 ) , 3 X , 1 0 F 8 . 2 ) C C * * * CHECK CARD COOING * * * C IF( I K . N E . 2 ) GO TO 103 C C * * * CHECK NO. OF POINTS TO BE DEFINED C I F ( N N . N E . N O ( I ) ) GO TO 104 9 CONTINUE C C * * * READ IN I N I T I A L STATION C H A R A C T E R I S T I C DATA C I F ( F U L L ) W R I T E ( 6 , 6 3 ) 63 F O R M A T ( » 1 » , T 1 0 , « I N I T I A L STATION C H A R A C T E R I S T I C S ' / ) DO 30 L=1 .5 IS=0 I C ( L ) = 0 DO 10 1=1 ,30 DO 10 J = l , 4 R E A D ( 5 , 1 1 , E N D = 30) I K , S T A N O ( J , 1 , L ) , ( S C ( K , J , I , L ) , K = 1 , 1 4 ) , I N C L ( J , I , L ) 11 FORMAT(I 1 , 1 4 , 1 X , 2 F 6 . 0 , 2 F 5 . 1 , 1 0 X , 4 F 2 . 0 , 8 X , 3 F 2 . 0 , 4 X , 3 F 2 . 0 , I X , I 2) C C * * * IF DESIRED WRITE OUT DATA * * * C I F ( F U L L ) W R I T E ( 6 , 1 2 ) I K , S T A N O ( J , I , L ) , (SC (K , J , I , L ) , K= 1, 14 ) , 1 I N C L ( J , I , L ) 12 F O R M A T ( I X , I l , l X , I 4 , 3 ( l X , F 7 . C ) , l X , F 5 . 0 t 1 0 X , 4 ( l X , F 2 . 0 ) , 8 X , 3 ( l X , F 2 . 0 ) 1 , 1 X , F 6 . 0 , 2 ( 1 X , F 2 . 0 ) , 1 X , I 2 ) I C ( L ) = I C ( L ) + l C C * * * CHECK CARD CODING * * * C IF( I K . N E . 3 ) GO TO 105 C C * * * CHECK THE ORDER OF THE STATION NUMBERS * * * C I F ( S T A N O ( J , I , L ) . L E . I S ) G O T O 106 IS= -STANCH J , I ,L ) 10 CONTINUE L A N D U S E S I M U L A T I O N (CONTINUED) C C *** READ IN THE NO. OF APARTMENT BLOCKS TO BE C DISTRIBUTED ALONG THE VARIOUS LINES C LIMITS - 5 LINES C 14 YEARS *** C I F ( F U L L ) WRITE(6,64) 64 FORMAT! '1',T10,»TOTAL APARTMENT BLOCKS FOR EACH YEAR'/) IX=0 DO 18 1=1,14 READ(5,19,END=21) IK, ( A P T S ( J , I ) , J = l , 5 ) 19 FORMAT(I1,1X,5(F6.0)) IX=IX+1 C C *** IF DESIRED WRITE OUT DATA **# C I F ( F U L L ) WRITE(6,20) I K , ( A P T S ( J , I ) , J = l , 5 ) 20 FORMAT(IX,I1,5(1X,F6 . 0 ) ) C C *** CHECK CARD CODING *** C IF (IK.NE.4) GO TO 107 18 CONTINUE 21 I F ( F U L L ) WRITE(6,22) IX WRITE(4,22) IX 22 FORMAT! • TOTAL APARTMENT UNIT NUMBERS HAVE BEEN READ IN % 1/,' FOR ',I3,« TIME PERIODS.'/) C C *** READ IN THE APARTMENT SIZE FUNCTIONS *** C I F ( F U L L ) WRITE(6,65) 65 FORMAT(«1',T10,'APARTMENT SIZE FUNCTIONS'/) IZ=0 DO 23 1=1,4 IKK=0 DO 29 K=l,60 27 READ!5,24,END=25) IK, ( AS IZ <K, J , I ), J= 1, 2 ) 24 FORMAT(I 1,42X,F5.0,44X,F4.0) IKK=IKK+1 C C *** JF DESIRED WRITE OUT DATA *** C I F ( F U L L ) WRITE(6,26) I K , ( AS I Z ( K , J , I ) , J= 1, 2 ) 26 F0RMAT(1X,I1,2(1X,F5.0)) C C *** CHECK CARD COOING *** C IF ( IK.NE.5) GO TO 108 L A N D U S E S I M U L A T I O N (CONTINUED) C * * * CHECK CARD ORDER * * * C I F ( A S I Z ( K , 2 , I ) . L T . I Z ) GC TO 109 I Z = A S I Z ( K , 2 , I ) 29 CONTINUE 25 IZ = 0 I F ( F U L L ) W R I T E ( 6 , 2 8 ) I K K , I 28 F O R M A T ( I X , 1 4 , • POINTS HAVE BEEN DEFINED FOR PROJ S IZE F U N C . NO. ' , 1 1 3 , ' . * / ) 23 CONTINUE RETURN C C * * * ERROR MESSAGES * * * C 100 W R I T E ( 4 , 1 0 0 0 ) I 1000 FORMAT( ' END OF F I L E ENCOUNTERED AS ' , 1 3 , ' T H CARD OF X V A L U E S . ' ) STOP 5 101 W R I T E ( 4 , 1 C 0 1 ) I 1001 FORMAT! • A 1 DOES NOT APPEAR IN THE FIRST COLUMN OF THE * , 1 3 , ' T H X I VALUE C A R D . ' ) STOP 6 102 W R I T E ( 4 , 1 G 0 2 ) I 1002 F O R M A T ( ' E N D - O F - F I L E ENCOUNTERED AS ' , 1 3 , ' T H Y VALUE C A R D . ' ) STOP 7 103 W R I T E ( 4 , 1 C 0 3 ) I 1003 F O R M A T ( ' A 2 DOES NOT APPEAR IN THE FIRST COLUMN OF THE ' , 1 3 , ' T H Y 1 VALUE C A R D . ' ) STOP 8 104 W R I T E ( 4 , 1 C 0 4 ) I,NO(I),NN 1004 FORMAT( ' FOR FUNCTION NO. • , I 3, ' , ' , I 3 , ' X VALUES HAVE BEEN GIVEN A IND ' , 1 3 , ' Y VALUES - THEY • / ' SHOULD HAVE THE SAME NO. OF V A L U E S . ' 2) STOP 9 105 W R I T E ( 4 , 1005 ) S T A N D ( J , I » L ) , IS 1005 F O R M A T ( ' A 3 DOES NOT APPEAR IN STATION C H A R A C T E R I S T I C CARD NUMBER 1 ' , 1 4 , ' WHICH FOLLOWS NO. ' , 1 4 , ' . ' ) STOP 10 106 WRITE(4 ,1G06 ) I S , S T A N O ( J , I , L ) 1006 FORMAT(* STATION CHARACTERIST IC CARD IS OUT OF ORDER - NO. ' , 1 5 , ' 1HAS BEEN PLACED BEFORE NO. ' , 1 5 , ' . * ) STOP 11 107 W R I T E ( 4 , 1 C 0 7 ) K 1007 FORMAT{ I X , ' A 4 DOES NOT APPEAR IN C O L . 1 OF CARD ' , 1 2 , 1* OF THE TOTAL APARTMENT D A T A . ' ) STOP 12 108 W R I T E ( 4 , 1C08) K , I 1008 FOR MAT( • A 5 DOES NOT APPEAR IN C O L . 1 OF THE ' , 1 3 , ' T H ' , 1« CARD OF A P T . S I Z E F U N C T . NO. » , I 3 , ' . ' ) STOP 13 109 W R I T E ( 4 , 1C09) I , A S IZ. ( K, 2 , I ) , I Z 1009 F O R M A T ( ' CARDS FOR APARTMENT S I Z E FUNCTION N O . * , I 2 , ' A R E ' , 1' OUT OF SEQUENCE ' , / l X , I 3 , ' FOLLOWS ' , 1 3 , * . ' ) STOP 14 END L A N D U S E S I M U L A T I O N (CONTINUED) C C * * * THE PURPOSE OF THIS SUBROUTINE IS TO PERFORM THE C CALCULATIONS INVOLVED IN THE YEAR BY YEAR PROJECTION C OF THE CHANGES IN LAND USE * * * C C - DENNIS W. P E R V I S , PROGRAMMING C INTEGER S T A N 0 ( 4 , 3 0 , 5 ) L 0 G I C A L * 1 F U L L , L C A L C DIMENSION X ( 1 4 , 4 , 3 0 , 5 ) , A T S ( 4 , 3 0 , 5 ) , A C S I Z ( 4 , 3 0 ) , A N E W S ( 4 , 3 0 , 5 , 2 , 1 4 ) 1 , C 0 E F ( 5 ) , I C C ( 5 ) DATA A T S / 6 0 0 * 0 . / COMMON F U L L / D A T 1 / X N M 0 , 1 4 ) , Y N ( 1 0 , 1 4 ) , N O ( 1 4 ) » 1 W ( 1 4 ) , I C ( 5 ) , A S I Z ( 6 0 , 2 , 4 ) , A P T S ( 5 , 1 4 ) , I X , X M A X ( 1 4 ) , 1 I N C L ( 4 , 3 0 , 5 ) / D A T 2 / A N E W S , S C ( 1 4 , 4 , 3 0 , 5 ) / D A T 3 / S T A N 0 C C * * * NOTE - X N ( 1 0 , 1 4 ) - NORMALIZED X ' S FOR EACH POINT OF C THE TABLE FUNCTIONS C Y N ( 1 0 , 4 ) - SAME AS ABOVE FOR Y ' S C NO(14) -NUMBER OF POINTS DEFINED FOR EACH C T A B L E FUNCTION C S C ( 1 4 , 4 , 3 0 , 5 ) - STATION C H A R A C T E R I S T I C S C - 14 C H A R A C T E R I S T I C S C - 4 SUBSTATIONS C - 30 STATIONS C - 5 L INES C W(14) - WEIGHTS OF EACH C H A R A C T E R I S T I C C IC - TCTAL N O . OF SUBSTATIONS ACTUALLY IN C THE MODEL C S T A N O ( 4 , 3 0 , 5 ) - ASSIGNED SUBSTATION NUMBERS C A S I Z ( 6 0 , 2 , 3 )- X AND Y VALUES OF 3 PROJECT S I Z E C FUNCTIONS C MAX. 60 POINTS DEFINED C A P T S ( 5 , 1 4 ) - NO. OF A P T S . TO BE D ISTRIBUTED AMONG C SUBSTATIONS OF A TRANSIT L I N E DURING C A GIVEN TIME P E R I O D . - L IMITS 5 L INES C 14 TIME PERIODS C IX - NO. OF TIME PERIODS ACTUALLY READ IN C X M A X U 4 ) - MAXIMUM RAW X VALUES I F ( N T . G T . I X ) G O TO 110 DO 1 I= IS,NT W R I T E ( 4 , 8 0 ) 1 80 F O R M A K / / ' BEGINNING OF TIME P E R I O D ' , 1 3 / ) L A N D U S E S I M U L A T I O N (CONTINUED) FORMATJ ' l ' , T 1 0 , 'BEGINNING OF TIME P E R I O D ' , 1 3 / ) DO 50 IAC=1,5 DO 51 IAD=1 ,30 00 52 I A E = 1 , 4 ATS( I A E , I A D , I A C ) = 0 . CONTINUE CONTINUE CONTINUE * * * F I R S T C A L C U L A T E ATTRACT IVITY SCORES FOR EACH SUBSTATION GIVEN THE C H A R A C T E R I S T I C QUANTIT IES IN SC * * * 1 F( FULL ) W R I T E ( 6 , 6 0 ) FORMAT( T 1 0 , ' N O R M A L I Z E D CHARACTERISTIC V A L U E S ' / ) DO 24 L L = 1 , 5 I S A T = I C ( L L ) / 4 I F ( I C ( L L ) . E Q . O ) GO TO 24 DO 2 J = 1 , I S A T DO 2 K = l , 4 DO 3 L = l , 1 4 * * * NORMALIZE THE X INPUT VALUES * * * * * * CHECK IF V A L I D X VALUE * * * IF { S C { L , K , J , L L ) . G T . X M A X ( L ) ) GO TO 111 * * * C H E C K TO SEE IF SUBSTATION IS TO BE INCLUDED * * * IF( INCL ( K . J , L L ) . ' G T . I )GG TO 27 * * * NOW DO IT X ( L , K , J , L L ) = S C ( L , K , J , L L ) / X M A X ( L ) GO TO 3 X ( L , K , J , L L ) = 0 . CONTINUE I F ( F U L L ) W R I T E ( 6 , 8 ) STA NC( K , J , L L ) , ( X ( L , K , J , L L ) , L = l , 14) F O R M A T ( I X , I 5 , 1 4 ( I X , F 7 . 5 ) ) CONTINUE * * * IF DESIRED WRITE OUT RESULT *#* CONTINUE * * * NOW FIND THE A T T R A C T I V I T Y SCORES I F ( F U L L ) W R I T E I 6 , 6 2 ) F O R M A T ( ' I ' , T 1 0 , ' F U N C T I O N V A L U E S ' / ) DO 25 L L = 1 , 5 IF( I C ( L L ) . E Q . O ) GO TO 25 ISAT = IC ( L D / 4 DO 10 J = 1 , I S A T DO 10 K = l , 4 DO 11 L = l , 1 4 L A N D U S E S I M U L A T I O N (CONTINUED) C * * * C H E C K FOR ZERO * * * C I F ( X ( L , K , J , L L ) . E Q . O ) G O TO 22 C C * * * F IND T A B L E FUNCTION INTERVAL * * * I J = 0 MM=NO(L) DO 13 M=1,MM I J = I J + l I F ( X ( L » K , J , L L ) . L T . X N ( M , L ) ) G O TO 21 13 CONTINUE C C * * * C A L C U L A T E VALUE * * * 21 X(L , K , J , L L ) = Y N ( I J - 1 , L ) + ( ( X ( L , K , J , L L ) - X N ( I J - 1 , L ) ) * ( ( Y N ( I J , L ) 1 - Y N ( I J - l . L ) ) / ( X N ( I J , L ) - X N ( I J - 1 , L ) ) ) ) GOTO 11 2 2 I F ( I N C L ( K , J , L L ) . L E . I ) X ( L , K , J , L L ) = Y N ( 1 , L ) 11 CONTINUE IF( FULL ) WRITE ( 6 , 8 ) STAND ( K , J , L L ) , (X ( L V K , J v L L ) ,!_= 1* 1 4 ) 10 CONTINUE C C IF DESIRED WRITE OUT RESULTS * * * C 25 CONTINUE C C * * * WEIGHT AND ADD TO GET A T T R A C T I V I T Y SCORES * * * C I F ( F U L L ) W R I T E ( 6 , 6 3 ) 63 FORMAT ( U S T I O , ' A T T R A C T I V I T Y S C O R E S ' / ) DO 26 L L = 1 , 5 I F ( . N O T . L C A L C ) GO TC 94 I C C ( L L ) = 0 COEF( LL )=0. 94 IF( I C ( L L ) . E Q . O ) GO TO 26 I S A T = I C ( L L ) / 4 DO 29 J = 1 , I S A T DO 14 K = l , 4 IF( I N C L ( K , J , L L ) . G T . I ) GO TO 14 I F ( X ( 2 , K , J , L L ) . E G . O . O R . X ( 3 , K , J , L L ) . E Q . O . ) GO TO 28 DO 15 L = l , 1 4 A T S I K , J , L L ) = A T S ( K , J , L L ) + X ( L , K , J , L L ) * W ( L ) / 1 0 0 . 15 CONTINUE GO TO 91 28 A T S ( K , J , L L ) = 0 . L A N D U S E S I M U L A T I O N (CONTINUED) C * * * GET SUM AND MEAN OF ATTRACT IVITY SCORES * * * C 91 IF( .NOT . L C A L O G O TO 14 I C C ( L L ) = I C C ( L L ) + 1 C O E F ( L L ) = C O E F ( L L ) + A T S ( K , J , L L > 14 CONTINUE I F ( F U L L ) W R I T E ( 6 , 6 4 ) ( S T A N O ( K K , J , L L ) , A T S ( K K , J , L L ) , K K = 1 , 4 ) 64 F 0 R M A T ( 4 ( 1 X , I 5 , 1 X , F 7 . 5 , 3 X ) ) 29 CONTINUE I F ( I C C ( L L ) . G T . O ) C O E F ( L L ) = C O E F ( L L ) / I C C ( L L ) 26 CONTINUE C C * * * DETERMINE WHICH FUNCTION TO USE * * * C I F ( I . G T . 8 ) G O TO 53 I F U . L T . 9 . A N D . I . G T . 4 ) GO TO 54 I F ( I . L T . 5 . A N D . I . G T . 2 ) GO TO 57 IF( I . L T . 3 ) NFUM = l GO TO 92 53 NFUN=4 GO TO 92 54 NFUN=3 GO TO 92 57 NFUN=2 C C * * * C A L C U L A T E C O E F F I C I E N T S * * * C 92 IF( . N O T . L C A L C ) G O TO 55 ZO = A M A X l ( C O E F ( 1 ) , C O E F ( 2 ) , C O E F ( 3 ) , C O E F ( 4 ) , C O E F ( 5 ) ) DO 93 J l = l , 5 IF( I C C ( J l ) . E Q . O ) G O TO 93 C O E F ( J l ) = C O E F ( J l ) / Z O 93 CONTINUE C C * DETERMINE THE NUMBER OF UNITS TO BE BUILT C AT EACH SUBSTATION ON EACH L I N E * * * C 55 DO 30 L L = 1 , 5 ACSUM=0 DO 48 I A C = 1 , 3 0 DO 49 IAD=1,4 A C S I Z ( I A D , I A C ) - 0 . 49 CONTINUE 48 CONTINUE C C * * * CHECK TO S E E IF THE L INE E X I S T S * * * C IF( I C ( L L J . E Q . O ) GO TO 3G C C * * * DETERMINE NO. OF STATIONS IN L INE * * * C I S A T = I C ( L L ) / 4 DO 31 J = 1 , I S A T L A N D U S E S I M U L A T I O N (CONTINUED) C IF( I N C L ( K , J , L L ) . G T . I ) G O TO 32 C * * * GET A RANDOM NUMBER * * * C Z = F R A N D ( D U M ) * 1 0 0 . C C * * * NOW FIND FUNCTION VALUES * * * C I J=0 DO 33 11=1 ,60 IJ= IJ+1 I F ( Z . L T . A S I Z ( I I , 2 , N F U N 5 ) GO TO 34 3 3 CONTINUE 34 IF ( I J . E Q . l ) GO TO 35 MI J = I J - 1 ACS I Z ( K , J ) = A S I Z ( M I J , 1 , NFUN) + ( ( Z - A S I Z ( M I J , 2 ,NFUN ) ) * 1( ( A S I Z d J , 1, N F U N ) - A S I Z ( M I J , 1, NFUN ) ) / ( AS IZ (I J , 2 , NFUN ) -2 A S I Z ( M I J , 2 , N F U N ) ) ) ) GO TO 43 35 A C S I Z ( K , J ) = A S I Z ( 1 , 1 , N F U N ) C C * * * M A K E IT AN INTEGER * * * C 43 I F ( L C A L C ) ACS I Z ( K , J ) = A C S I Z ( K , J ) * C O E F ( L L ) AC S I Z ( K , J ) = A I N T ( A C S I Z ( K , J ) + . 5 ) C C * * * ADD UNITS FOR THIS L INE AND CHECK WITH MAX * * * C AC SUM=ACSUM+ACSIZ(K,J ) I F ( A C S U M . G E . A P T S ( L L , I ) ) GO TO 36 32 CONTINUE 31 CONTINUE C C 4 * * ALLOT THE BLOCKS OF UNITS TO SUBSTATIONS * * * C C * * * FIND LARGEST BLOCK OF UNITS * * * C 36 I F ( F U L L )WRITE(6 , 6 6 ) L L W R I T E ( 4 , 6 6 ) LL 66 FORMAT(* L I N E N O . * . 1 3 / ) ACSUM=0. DO 45 L J = 1 , J DO 44 L K = 1 , 4 BLL=0 DO 37 J J = 1 , J DO 38 K K = 1 , 4 I F ( A C S I Z ( K K , J J ) . L E . B L L ) GO TO 38 JN = J J KN = KK B L L = A C S I Z ( K K , J J ) 38 CONTINUE 37 CONTINUE L A N D U S E S I M U L A T I O N (CONTINUED) C * * * FIND SUBSTATION WITH LARGEST ATTRACT IVITY SCORE * * * C B A T S = 0 . DO 39 J J = 1 , I S A T DO 40 KK=1,4 IF( I N C L l K K , J J , L L ) . G T . I ) G 0 TO 40 I F ( A T S ( K K , J J , L L ) . L E . B A T S ) G 0 TO 40 I F ( S C ( 3 , K K , J J , L L ) . L T . . 0 1 * A C S I Z ( K N , J N ) ) GO TO 40 JNN=JJ KNN=KK B A T S = A T S ( K K , J J , L L ) 40 CONTINUE 39 CONTINUE I F ( 8 A T S . E Q . O . ) GO TO 46 C C * * * NOTE NO- OF UNITS AND SUBSTATION * * * C ANEWS(KNN, J N N t L L , 1 , 1 ) = ACS IZ (KN , JN ) WRITE ( 4 , 70) S T A N D ( K N N , J N N t L L ) , ANEWS ( KNN, J i \ N » L L , 1, I ) 70 F 0 R M A T ( 1 X , I 5 , 2 X , F 6 . 1 ) ACSUM=ACSUM+ACSIZ(KN,JN) I F ( A C S U M . G E . A P T S ( L L , I ) ) G 0 TO ACSIZ ( K N , JN) =0. A T S J K N N , J N N , L L ) = 0 . 44 CONTINUE 45 CONTINUE 46 DO 90 I K 1 = 1 , I S A T DO 90 IK2=1 ,4 IQ=I-1 I F ( I N C L ( I K 2 , I K 1 , L L ) . G T . I ) G 0 TO 90 IF( I . E Q . 1 ) I Q = 1 A N E W S ( I K 2 , I K 1 , L L , 2 , I ) = ANEWS(IK 2 , I K 1 , L L , 2 , IQ ) + A N E W S ( I K 2 , I K 1 , L L , 1 , I ) 9 0 CONTINUE I F ( . N O T . F U L L ) GO TO 30 DO 86 10=1, I SAT WRITE( 6, 88) ( S T A N O U K , ID, LL ) , ANEWS ( K K, I D , L L , 1, I) , K K = 1 , 4 ) 88 F 0 R M A T ( 4 ( 1 X , I 5 , 1 X , F 7 . 1 , 3 X ) ) 86 CONTINUE 30 CONTINUE C A L L C E N T ( I ) IF( I . E Q . N T ) GO TO 1 C A L L INTER( I ) 1 CONTINUE RETURN C C * * * ERROR MESSAGES * * * C 110 W R I T E ( 4 , 1 0 1 0 ) IX 1010 FORMAT( • TOTAL TIME PERIODS ARE GREATER THAN • , 1 3 , ' . ' ) STOP 15 111 W R I T E ( 4 , 1 0 1 1 ) S T A N O ( K , J , L L ) , L , S C ( L , K , J , L L J , X M A X ( L ) 1011 F O R M A T ( ' FOR STATION NO. ' , 1 5 , ' C H A R A C T E R I S T I C N O . ' , 1 3 , / ' THE VALU IE IS ' , F 1 0 . 5 , ' BUT SHOULD BE L E S S THAN S F I C S , ' . ' ) STOP 16 L A N D U S E S I M U L A T I O N (CONTINUED) SUBROUTINE CONT( IY ) C C * * * THE PURPOSE Of THIS SUBROUTINE IS TO UPDATE THE STATION C C H A R A C T E R I S T I C S I E . SC ( 1 4 , 4 , 3 0 , 5 ) WITH THE RESULTS OF THE C 1 A B B E R A T I O N S * OF THE CURRENT TIME PERIOD I E . A N E W S ( 4 , 3 0 , 5 ) * * * C L 0 G I C A L * 1 FULL COMMON FULL / D A T 2 / A N E W S ( 4 , 3 0 , 5 , 2 , 1 4 ) , S C ( 1 4 , 4 , 3 0 , 5 ) 1 / D A T I / X N ( 1 0 , 1 4 ) , Y N ( I 0 , 1 4 ) , N 0 ( 1 4 ) , 2 W ( 1 4 ) , I C ( 5 ) , A S I Z ( 6 0 , 2 , 4 ) , A P T S ( 5 , 1 4 ) ,I X ,XMAXJ14) f 3 I N C L ( 4 , 3 0 , 5 ) DO 1 1=1,5 I S A T = I C ( I ) / 4 IF( I S A T . E Q . 0 ) G 0 TO 1 DO 2 J = l , I S A T DO 3 K = l , 4 IF ( I N C L ( K , J , I ) . G T . I Y ) G 0 TO 3 C C * * * ADJUST NO. OF NEW APT UNITS * * * C S C ( 1 ,K, J , I ) = SC( 1 ,K , J , I ) + A N E W S ( K , J , I , l , I Y) C C * * * ADJUST T E C H . CONSTRAINTS * * * C S C ( 2 , K , J , I ) = S C ( 2,K,J , I ) - A N E W S ( K , J , l , 1 , I Y ) C I F ( S C ( 2 , K , J , I ) . L T . 0 » ) S C ( 2 ? K , J , I )=0 • C C * * * ADJUST A V A I L A B L E LAND * * * C S C ( 3 , K , J , I ) = S C ( 3 , K , J , I ) - ( . 0 1 * A N E W S ( K , J , I , i , IY ) ) I F ( S C ( 3 , K , J , I ) . L E . 0 . ) S C ( 3 , K , J , I ) = 0 . C C * * * A D J U S T VACANT LAND * * * C S C ( 4 , K , J , I )=SC(4,K, J , I ) - ( O . G l * A N £ W S ( K , J , I - , l , I Y ) ) I F ( S C ( 4 , K , J , I ) . L E . O . ) S C ( 4 , K , J , I ) = 0 . 3 CONTINUE 2 CONTINUE 1 CONTINUE RETURN END SUBROUTINE I N T E R ( I Y ) C C * * * THE PURPOSE OF THIS SUBROUTINE IS TQ ALLOW THE USER TO C CHANGE THE VALUES OF THE INTERVENTIONAL C H A R A C T E R I S T I C S OF C SUBSTATIONS * * * C L 0 G I C A L * 1 FULL INTEGER S T A N D ( 4 , 3 0 , 5 ) , C O D E S ( 6 ) 7 6 * 0 / REAL R R ( 6 ) / 1 . 0 , 1 2 . 0 , 5 . 0 , < 5 < = 9 9 . 0 , 3 . 0 , 1 . C / COMMON F U L L / D A T 2 / A N E W S ( 4 , 3 0 , 5 , 2 , 1 4 ) , S C ( 1 4 , 4 , 3 0 , 5 ) / D A T 3 / S T A N 0 L A N D U S E S I M U L A T I O N (CONTINUED) C * * * ARE THERE TO BE ANY CHANGES? * * * C W R I T E ( 4 , 1 )IY I FORMAT(* THIS IS THE END OF TIME PERIOD ' , 1 3 , / , 1' THE FOLLOWING C H A R A C T E R I S T I C S CF ONE OR MORE SUBSTATIONS 1 ' / ' MAY NOW BE C H A N G E D : • , 2 / T 5 , • I SURFACE ACCESS ' , 3 / T 5 , ' 2 NODAL I T Y ' , 4 / T 5 , ' 3 Z O N I N G ' , 5 / T 5 , ' 4 C E I L I N G CAPACITY (TECHNICAL C O N S T R A I N T S ) ' , 6 / T 5 , ' 5 COMMERCIAL D E V E L O P M E N T ' , 7 / T 5 , ' 6 UNDESIRABLE C O N D I T I O N S ' , 8 / ' DO YOU WISH TO MAKE ANY CHANGES AT THIS T I M E ? ' / ' ANSWER 1 FOR 9 YES OR 2 FOR N O . « ) 8 C A L L I N F R E E ( 1 1 , I S N ) IF{ I S N . L T . l . O R . I S N . G T . 2 ) G O TO 100 I F ( I S N . E Q . 2 ) R E T U R N C C * * * OK WHAT ARE THEY * * * C 4 W R I T E ( 4 , 3 ) 3 FORMAT ( ' ENTER THE STATION N U M B E R . ' ) C A L L I N F R E E ( 1 1 , I S N ) C C * * * FIND OUT IF THE STATION E X I S T S * * * C DO 5 1=1,5 DO 5 J = 1 , 3 C DO 5 K = l , 4 IF( I S N . E G . S T A N G ( K , J , I ) ) 5 CONTINUE W R I T E ( 4 , 7 ) ISN 7 F O R M A T ( I X , 1 5 , ' IS NOT A INTER ANOTHER NUMBER? * / • GO TO 8 C C * * * WHICH CHARACTERIST ICS ARE TQ BE CHANGED? C 6 W R I T E ( 4 , 9 ) S T A N 0 ( K , J , I ) 9 FORMAT( ' WHICH C H A R A C T E R I S T I C S OF ' , 1 5 , ' DO YOU WANT TO C H A N G E ? ' / 1' ENTER UP TO 6 CODE NUMBERS AS ABOVE ( I E . 1 , 2 , 3 , E T C . ) WITH A T ' / 2« LEAST 1 BLANK BETWEEN T H E M . ' ) CALL I N F R E E ( l l , C O D E S ( l ) , C 0 D E S ( 2 ) , C 0 D E S ( 3 ) , C O D E S ( 4 ) , C 0 D E S ( 5 ) , 1C0DES(6 ) ) C C * * * CHECK TO SEE IF THEY ARE ALL VALID * * * DO 10 L = l , 6 DO 11 M = l , 7 MM=M-1 I F ( C O D E S ( L ) .EQ.MM) GO TO 10 II CONTINUE W R I T E ( 4 , 1 2 ) C 0 D E S ( L ) 12 F O R M A T t I X , 1 9 , ' IS AN INVALID CHARICTERIST IC D E S I G N A T I O N . 1 ) CODES(L ) = 0 GO TO 6 VALID STATION N U M B E R . ' / ' DO YOU WANT TO E ANSWER 1 FOR YES OR 2 FOR N O . ' ) L A N D U S E S I M U L A T I O N (CONTINUED) C C * * * NOW F IND OUT THE NEW VALUES * * * C DO 13 11=1,6 I F ( C O D E S ( I I ) . E Q . O ) G O TO 13 26 W R I T E ( 4 , 1 4 ) C 0 D E S ( I I ) » S T A N C ( K » J » I ) 14 FORMAT ( ' WHAT IS THE NE V. VALUE OF INTERVENTION C H A R A C T E R I S T I C ' , 1 1 3 , ' OF STAT ION N O . • , 15 ) IC I =CODES( I I ) IF( IC I . E Q . 4 ) R R ( I C I ) = 999 9 . G - S C ( 2 , K , J , I) W R I T E 1 4 , 1 9 ) R R ( I C I ) , S C ( I C I + 8 , K , J , I ) 19 FQRMATl ' VALID VALUES ARE BETWEEN 0 . 0 AND ' , F 6 . 1 / , 1' PRESENT VALUE IS ' , F 6 . D 22 C A L L I N F R E E ( 2 7 , R ) C C * * * CHECK IF VALUES VAL ID * * * C I F ( R „ L T . 0 . . O R . R . G T . R R ( I C I ) ) GO TO 23 GO TO 24 23 W R I T E ( 4 , 2 5 ) R , C 0 D E S ( I I ) 25 F O R M A T ( I X , F 7 . 3 , ' IS AN INVALID VALUE FOR INTERVENTIONAL ' l ' C H A R A C T E R I S T I C ' , 1 3 ) GO TO 26 C C * * * OK PUT IT IN * * * C 24 JJ=CODE S( I I) + 8 IF( I C I . N E . 4 ) GO TO 36 S C ( 2 , K , J , I ) = S C ( 2 , K , J , I ) + R S C ( 1 2 , K , J , I ) = + R GO TO 3 5 36 S C ( J J , K , J , I ) = R 35 W R I T E ( 4 , 3 7 ) C O D E S ( I I ) , S T A N O ( K , J , I ) , S C ( J J , K , J , I) 37 FORMAT ( ' INTERVENTIONAL CHARACTERIST IC ' , 1 4 , ' OF STAT ION N O . ' / , 1 1 5 , ' NOW HAS THE VALUE OF « , F 1 0 . 5 ) 13 CONTINUE C * * * ANY MORE? * * * C W R I T E ( 4 , 2 7 ) 27 F O R M A T ( ' DO YOU WISH TO CHANGE THE INTERVENTIONAL C H A R A C T E R I S T I C S IF ANY MORE STATIONS AT THIS T I M E ' / , 2 ' ANSWER 1 FOR YES OR 2 FOR N O . ' ) C C * * * ZERO OUT CODES * * * C DO 28 I J = 1 , 6 CODES ( IJ )=0 28 CONTINUE GO TO 8 L A N D U S E S I M U L A T I O N {CONTINUED) C * * * ERROR MESSAGES * * * C 100 W R I T E 1 4 , 1 0 0 0 ) I S N 1C00 F 0 R M A T ( 1 X , I 4 , ' I S AN INVALID RESPONSE . * ) GO TO 8 END SUBROUTINE SIGH INTEGER S T A N 0 ( 4 , 3 0 , 5 ) L 0 G I C A L * 1 FULL COMMON F U L L / D A T 2 / A N E W S ( 4 , 3 0 , 5 , 2 , 14) , S C ( 1 4 , 4 , 3 0 , 5 ) / D A T 3 / S T A N 0 1 / D A T l / X N ( 1 0 , 1 4 ) , Y N i 1 0 , 1 4 ) , N C ( 1 4 ) , 2 W ( 1 4 ) , I C ( 5 ) C C * * * THE PURPOSE OF THIS SUBROUTINE IS TO PRINT OUT SOME F INAL C R E S U L T S * * * C C C * * * I F DESIRED WRITE OUT FINAL STATION C H A R A C T E R I S T I C S * * * C I F ( . N O T . F U L L ) G 0 TO 35 DO 30 L = l , 5 I S A T = I C ( L ) / 4 I F ( I S A T . E Q . 0 ) G 0 TO 30 DO 31 I = 1 , I S A T DO 31 J = l , 4 W R I T E ( 6 , 1 ) S T A N O ( J , I , L ) , ( S C ( K , J , I , L ) , K = l , 1 4 ) 1 F 0 R M A T ( 1 X , I 5 , 1 4 { 1 X , F 6 . 1 ) ) 31 CONTINUE 30 CONTINUE C C * * * FIND OUT IF ' F I L E FORMAT' OUTPUT IS DESIRED * * * C 35 W R I T E ( 4 , 2 ) 2 FORMAT(* DO YOU WANT INCREMENTAL AND CUMULATIVE A L L O T M E N T S ' / , 1' PRODUCED IN " F I L E F O R M A T " AS WELL AS CN P A P E R ? ' / , 2 ' ANSWER 1 FOR YES OR 2 FOR N O . ' ) 7 C A L L I N F R E E ( 1 1 , I C C ) IF{ I C C . L T . l . O R . I C C . G T . 2 ) GO TO 100 IF( I C C . E Q . 2 ) GO TO 8 DO 20 K = l , 5 I S A T = I C ( K ) / 4 IF( I S A T . E Q . 0 ) GOTO 20 WRITE ( 7 , 3 ) ( ( (STANOl I, J , K ), ( (ANEWS ( I, J , K , L , M ) , L = 1 , 2 ) , 1M=1,14) ,1 = 1,4) , J = l , I SAT ) ) 3 F 0 R M A T ( 1 X , I 5 , 1 X , 2 8 F 7 . 1 ) 20 CONTINUE L A N D U S E S I M U L A T I O N (CONTINUED) C * * * WRITE OUT STANDARD F INAL OUTPUT * * * C 8 DO 4 1=1,4 IA= ( 1*4 ) -3 IB= IA+3 I F ( I . E Q . 4 ) I B = 1 4 W R I T E ( 6 , 5 ) I A , I B 5 F O R M A T ( / 1 PERIODS ' , 1 4 , » TO ' , 1 4 / ) OO 10 L=l ,5 I S A T = I C ( L ) / 4 IF ( I S A T . E Q . O . ) G 0 TO 10 IF( I . E Q . 4 ) GO TO II W R I T E ( 6 , 6 ) ( ( ( S T A N O ( J , K , L ) , ( (ANEWS( J , K , L , M , N ) , M = 1 , 2 ) ,N=I A , I B ) , 1J = 1 , 4 ) , K = 1 , ISAT) ) GO TO 10 11 WRI TE ( 6 , 1 2 ) ( ( ( STANO ( J , K , L ) , ( (ANEWS ( J , K , L , N) ,M=1 ,2 ) ,N= IA , I B ) , 1J = 1 , 4 ) , K = 1 , ISAT) ) 12 F O R M A T ( ( I X , 1 5 , 1 X , 2 ( F 5 . 0 , 1 X , F 5 . 0 ) ) ) 10 CONTINUE 4 CONTINUE 6 F O R M A T ( ( 1 X , I 5 , 1 X , 4 ( F 5 . 0 , 1 X , F 5 . 0 , 3 X ) ) ) RETURN C C * * * ERROR MESSAGES * * * C 100 W R I T E ( 4 , 1 0 0 0 ) I C C 1C0G F 0 R M A T ( 1 X , I 4 , ' IS AN INVALID R E S P O N S E . ' ) GO TO 7 see Chapter 7.2) This program allows to draw histograms and was used to compare actual and simulated apartment growth (see Appendix A.d-1). It was programmed by Dr. H. Koike, University of British, Columbia, and adapted f o r t h i s t h e s is, -C A P A R T M E N T G R O W T H P L 0 T T I N G C THESIS PAUL BAROSS AND ROBERT STUSSI C SCHOOL OF COMMUNITY AND REGIONAL PLANNING C UNIVERSITY OF B R I T I S H COLUMBIA C APRIL 1972 , VANCOUVER, B . C . C PROGRAMMED D A T E : JUNE 1 9 7 1 , P L A C E : U B C , V A N C O U V E R , B . C . , C A N A D A C PROGRAM 'HSTGM1* AND 'HSTGM2* ARE DESIGNED TO PLOT UNIVARIATE AND C B I V A R I A T E PERCENTAGE HISTOGRAMS R E S P E C T I V E L Y . PROGRAMMER: DR. HIR CT OKA KOIKE C ADAPTED VERSION TO PLOT INCREMENTAL AND CUMULATIVE APARTMENT GROWTH C ALONG SUBWAY CORRIDORS (APRIL 1972) C THE PROGRAM IS DESIGNED TO PLOT ACTUAL OR SIMULATED APARTMENT C GROWTH FOR UP TO 4 SUBWAY L I N E S WITH UP TO 12 STATIONS C EACH STATION HAVING 4 STATION S U B - A R E A S C THE INCREMENT OR CUMULATIVE GROWTH A STATION S U B - A R E A R E C E I V E S C IS PLOTTED AS A PERCENTAGE OF THE TOTAL GROWTH OF A C GIVEN SUBWAY L I N E . IN A D D I T I O N , THE PERCENTAGE AND THE C NUMBER OF APARTMENTS ARE WRITTEN AT THE L E F T S IDE OF THE C HISTOGRAM FOR EACH STATION S U B - A R E A C THE PLOTTING CAN BE MADE FOR 4 L I N E S AND L4 TIME PERIODS IN C ONE RUN ( I E . 56 HISTOGRAMS CAN BE PLOTTED) C THE INPUT DATA FOR THE PLOTTING ARE GENERATED 8Y THE C SIMULATION MODEL WHICH FORECASTS APARTMENT GROWTH ALONG C SUBWAY CORRIDORS DIMENSION N A M E ( 1 2 8 , 2 ) , I V A L ( 1 3 0 ) , 1 T I M E M 5 ) , F M T ( 1 4 ) , T I T L E ( 14 , 3 ) ,M ( 5 ) , LI NE (4 , 10) INTEGER TIME C C CONTROL CARDS ( IN T H I S ORDER) C C TIME TIME PERIOD IN WHICH GROWTH TOOK P L A C E C T I T L E T I T L E C A R O , 1 FOR EACH TIME PERIOD 3A4 C L INE NAME OF SUBWAY L I N E S , THERE ARE 4 L I N E » S TO BE C READ I N , ( Y O N G E , B L O O R WEST O L D , BLOGR WEST NEW, C BLOOR E A S T ) 10A4 C NAME NAME OF STATION SUB AREAS AND LOCATION C OF SUBAREA ( N E , N W , S E , S W ) , THE NAME IS ON THE C F I R S T AND SECOND CARD OF EACH 4 CARDS TO BE C WRITTEN EACH I 5 , X , I 2 ( E G . EGL I NIG SW TON SE) C C SET OF CONTROLCARDS AND DATA TO BE READ IN FOR EACH TIME PERIOD C H I S T O G R A M (CONTINUED) C M NUMBER OF STATIONS PER L I N E , THERE ARE 5 N»S TO C BE READ IN WITH N=0 IF THE L I N E IS NOT E X I X T I N G C IN A G IVEN T IME PERIOD C FMT FORMAT, THERE IS ONE FORMAT FOR EACH TIME C PERIOD TO BE READ IN ( I E . 14) C IVAL NUMBER OF APARTMENTS PER STAT ION S U B - A R E A C (CUMULATIVE OR INCREMENT) C READ 1 3 , ( T I M E ( J ) , J = l , 1 5 ) 13 FORMAT(1515) DO 31 K = l , 1 4 31 READ 1 1 , ( T I T L E ( K , J ) , J = 1 , 3 ) 11 FORMAT(19A4) DO 96 1=1 ,4 96 READ 1 1 , ( L I N E ( I , J ) , J = 1 , 10) DO 17 1=1,128 17 READ 1 1 , ( N A M E ( I , J ) , J = l , 2 ) K=l 5 REWIND 4 I F ( T I M E ( K ) . E Q . 9 9 9 9 9 ) GO TO 20 READ 1 4 , (M( I ) ,1 = 1 ,5) 14 FORMAT(515) Ml = l READ 1 1 , ( F M T ( I ) , 1 = 1 , 1 4 ) I F ( M ( 5 ) ) 7 , 7 , 8 8 M(4)=M(4)+M(5) 7 CONTINUE M2=0 MM=1 DO 1 J = l , 4 I F ( M ( J ) . E Q . l ) GO TO 9 N=M(J ) M2=M2+M(J) R E A D ( 4 , F M T ) ( I V A L ( I ) , I=M1,M2 ) C A L L HSTGM1 ( N , N A M E , I V A L , T I T L E , T I M E , L I N E , M l , M 2 , K ,MM) M1=M2+1 MM=MM+1 1 CONTINUE 9 CONTINUE K=K + 1 GO TO 5 20 CONTINUE STOP END H I S T 0 G R A M (CONTINUED) SUBROUTINE HSTGM1 ( N ,NAME , I VAL , TI T L E , TI ME , LI NE , Ml , M2 , K, MM) DIMENSION NAME(12 8 , 2 ) , I V A L ( 1 2 8 ) , P R C N T ( 1 2 8 ) , P E R ( 1 2 8 ) , M S ( 1 2 8 ) , 1 M O O T ( 1 2 8 ) , T I T L E ( 1 4 , 3 ) , I T ( 5 ) , T I M E ( 1 5 ) , L I N E ( 4 , 1 0 ) INTEGER T I M E , S U B C C PRCNT NUMBER OF APARTMENTS PER STATION S U B - A R E A C CALCULATED FROM IVAL INTEGER B L A N K , STAR DATA B L A N K / 1 H / , S T A R / 1 H X / ISUM=0 DO 1 I=M1,M2 1 ISUM=ISUM+IVAL(I ) SMAX=0. I F ( I S U M . E Q . O ) RETURN DO 2 I=M1,M2 P R C N T ( I ) = F L O A T ( I V A L ( I ) ) / F L O A T ( ISUM) * 1 0 0 . SMAX=AMAX1(SMAX,PRCNT( I ) ) 2 CONTINUE I F t S M A X . C - T . l O . ) GO TO 11 F = 5 . GO TO 16 11 I F ( S M A X . G T . 2 0 . ) GO TO 12 F = 2 . 5 GO TO 16 12 I F ( S M A X . G T . 2 5 . ) GO TO 13 F = 2 . GO TO 16 13 I F ( S M A X . G T . 5 0 . ) GO TO 14 F = l . GO TO 16 14 F = . 5 16 CONTINUE DO 20 I=M1,M2 P E R U ) = F * P R C N T ( I ) MS(I ) = I F I X ( P E R ( I ) ) I F ( P E R ( I ) - F L O A T ( M S ( I ) I - . 5 ) 2 0 , 1 8 , 1 8 18 MS( I )=MS( I )+1 20 CONTINUE PRINT 205 205 F O R M A T ( 1 H 1 , 5 X ) PRINT 2 0 0 , ( L I N E l K M , I ) , 1 = 1 , 1 0 ) , ( T I T L E ( K , I ) , 1 = 1 , 3 ) , T I M E ( K ) 200 FORMAT( IK , 9 X , 4 9 H S I M U L A T E D A P A R T M E N T G R O W T H 1 /10X,17HSUBWAY CORRIDOR , 1 0 A 4 / 1 0 X , 6 H Y E A R , 3 A 4 , 5 X , 1 3 H T I ME PERIOD 1 , 1 2 ) DO 25 1=1,5 25 IT t I ) = I * I F I X ( 1 0 . / F ) H I S T O G R A M (CONTINUED) PRINT 2 0 1 , ( IT( I ) , 1=1,5) 201 FORMAT( IH , 5 9 X , 1 6 H N 0 OF A P A R T M E N T S / 1 0 X » 7 H S T A T I 0 N » 4 9 X , 1 0 H A B S O L U T 1 / 1 8 X , 3 H 0 % , I 8 , 1 X , 1 H ? , I 8 , 1 1 X , 1 H S , I 8 , 1 X , 1 H 2 , 1 8 , I X , 1 H ^ , 1 8 / ) DO 40 I=M1,M2 DO 36 L = i , 5 0 36 MOOT(L)=BLANK NMAX=MS(I) IF(NMAX) 3 9 , 3 9 , 3 7 37 DO 38 L=1,NMAX 38 MDOT(L)=STAR 39 CONTINUE IPERCN=PRCNT( I ) 70 PRINT 2 0 2 , ( M A M E ( I , L ) , L = 1 , 2 ) , ( M D O T ( J ) , J = l » 5 0 ) , I V A L ( I ) , I P E R C N 202 F O R M A T ( 1 O X , 2 A 4 , 1 X , 5 0 A 1 , 1 4 , 1 3 ) 40 CONTINUE SUB=N/4 PRINT 2 1 2 , I S U M , T I M E ( K ) , I L I N E ( M M , N X ) , N X = I , 1 0 ) , S U B , N 212 FOR MAT(IH , 6 2 X , 6 H T 0 T A L , I 4 , 3 H 1 0 0 / 1 1 0 X , 1 4 H I N TIME P E R I O D , 2 X , I 2 , 2 X , 1 5 H T H E SUBWAY L I N E . 2 X , 110A4 , / 1 0 X , 5 H H A D , I 2 , 2 X , 115HSTATI0NS WITH , 1 3 , 2 X , 1 8 H S T A T I ON S U B - A R E A S . ) PRINT 3 0 0 , I S U M , T I M E ( K ) 300 F O R M A T ( 1 0 X , 1 4 , 2 X , 5 1 H I S THE TOTAL « OF APARTMENTS BUILT IN TIME H O D ,12 ) RETURN (see Chapter 7.2) This program allows to plot scattergrams: and was used to compare actual and simulated apartment growth (see chapter 7»2). It was programmed by Dr. H. Koike, University of British Columbia, and adapted for this thesis. PROGRAM FOR SCATTERGRAMS c c C S C A T T E R G R A M C C C A P P E R T M E N T G R O W T H SCATTERGRAM Q * * * * * * * * * * * * * * * * * * * * * 4 * 4 * * * * * * * * * * * * * * * * * * * C C THESIS PAUL BAROSS AND ROBERT STUSSI C SCHOOL OF COMMUNITY AND REGIONAL PLANNING C UNIVERSITY OF B R I T I S H COLUMBIA C APRIL 1 9 7 2 , VANCOUVER, B . C . C C C A PROGRAM TO PLOT A S E R I E S OF SCATTERGRAMS C DIMENSION X ( 5 0 O ) , Y ( 5 0 0 ) , I T Y P E ( 5 0 0 ) , F M T ( 2 0 ) , B C D ( 1 5 ) INTEGER BCD C A L L PLOTS 1 REWIND 4 C C IF L0G=1, X AXIS HAS L O G / S C A L E C N T I T L E = NUMBER OF T I T L E CARDS IN CONTROLDECK C IF IFLAG = 1 , SIGN1 IS USED FOR P L O T T I N G , C IBID FOR I F L A G = 2 , 3 C READ 1 0 , L O G , N T I T L E , I F L A G 10 FORMAT(5X« 9 I 5) I F ( L O G . E Q . 9 9 9 9 9 ) GO TO 999 C C N2 IS NUMBER OF CARDS PER STRATA C FMT IS THE FORMAT STATEMENT FOR EACH STRATA C N2 AND FTM FOLLOW EACH OTHER ON SEPERATE C A R D S , C STARTING WITH N 2 , ENDING WITH N2=99 C ENDING WITH N2=99 C N=0 N l = l 2 READ 12 ,N2 12 FORMAT (15) I F ( N 2 . E Q . 9 9 ) GO TQ 3 N=N+N2 READ 1 1 , ( F M T ( I ) , 1 = 1 , 2 0 ) 11 FORMAT(2QA4) C C READ DATA CARDS WITH I T Y P E , X , Y C C THE VALUE OF I TYPE WILL DEFINE THE SYMBOL TO BE PLOTTED C C IF S I G N l IS CHOOSEN, IT Y PE CAN RANGE FROM 1 TO 12 C RESULTING IN PLOTTING SIGNS 1 , 2 , . , 9 , 0 , A , 8 , H . C C IF S I G N 2 , I TYPE CAN TAKE VALUES FROM 1 , 2 , . . 5 RESULT ING C IN PLOTTING SIGNS 4 , 0 , 3 , 3 , 3 (SEE PLOTTING MANUAL) C C IF S I G N 3 , ITYPE CAN TAKE VALUES FR0M1 TO 3 RESULTING C IN PLOTTING SIGNS 3 , 3 , 0 (SEE PLOTTING MANUAL) C 3 , 3 , 0 ( S E E PLOTTING MANUAL) C c c c C S C A T T E R G R A M CONTINUED C R E A C ( 4 , F M T ) ( I T Y P E ( I ) , X ( I ) , Y ( I ) , I = N 1 , N ) N l=K+ l GO TO 2 3 CONTINUE I F ( L O G . N E - 1 ) GO TO 50 DO 45 I=1 ,N 45 X ( I )= ALOG 10( X { I ) ) 50 CONTINUE C A L L P L 0 T ( 3 . , 0 . , - 3 ) C C SET UP OF THE CONTROL CARD DECK C * * * * * * * * * * * * * * * * * * * * * * * * * * * > } , & * * c c C CARD WITH L O G , N T I T L E , I FLA G ( 5 X , 3 I 5 ) C C CARD WITH N 2 , ( 1 5 ) , FOLLOWED BY FMT ON THE NEXED CARD C THIS CARDS ARE REPEATED FOREACH STRATA IN THE D A T A , C AFTER THE LAST PAIR OF N/FTM CARDS, N2 IS = 99 WHICH C INDICATES THE END CF DATA FOR ONE PLOT C C N E X T , N T I T L E T I T L E CARDS FOLLOW WITH X T , Y T , H T , N C H A R C C THEN A NEW SET OF CGNTRCL CARDS BEGINS OR A CARD C CONTAINING LOG=99999 INDICATES THE END OF THE RUN C INDICATES THE END OF THE RUN C DO 100 L = l , N T I T L E READ 1 5 , X T , Y T , H T , N C H A R , ( B C D ( K ) , K = 1 , 1 5 ) 15 F O R M A T ( 3 F 5 . 0 , I 5 , 1 5 A 4 ) IF( NCHAR. -EQ. -1 ) GO TO 60 C A L L S Y M B O L ( X T , Y T , H T , B C D , 0 . ,NCHAR) GO TO IOC 60 IBCD=BCD(1) C A L L S Y M B O L ( X T , Y T , H T , I B C D , 0 . , - 1 ) 100 CONTINUE C A L L S C A T T R ( X , Y , N , I T Y P E , I F L A G ) C A L L P L O T ( 1 0 . , 0 . , - 3 ) GO TO 1 999 C A L L PLOTND STOP END c c c C S C A T T E R G R A M CONTINUED C c SUBROUTINE S C A T T R ( X , Y , N , I T Y P E , I F L A G ) C DIMENSION X ( N ) , Y ( N ) , 1 T Y P E ( N ) ,S IGN 1 ( 1 3 ) , S I G N 2 ( 5 ) , S I G N 3 ( 3 ) INTEGER B C D , S I G N 1 , S I G N 2 , S I G N 3 DATA S I G N 1 / 1 H 1 , 1 H 2 , 1 H 3 , 1 H 4 , 1 H 5 , 1 H 6 , 1 H 7 , 1 H 8 » 1 H 9 , 1 H O , 1 H A , 1 H B , 1 H / , 1 S I G N 2 / 4 , 0 , 3 , 3 , 3 / , S I G N 3 / 3 , 3 , 0 / C A L L S C A L E (X ,N , 4 . , XM IN , DX , 1) C A L L SCALE ( Y , N , 6 . , Y M I N , D Y , 1 ) C A L L A X I S I O . , 1 . , 1 3 H A C T U A L G R O W T H , - 1 3 , 4 . , 0 . , X M I N , D X ) C A L L A X I S ! 0. , 1 . , •S IMULATED GROWTH)' , 1 7 , 6 . , 9 Q . » Y s M I N , D Y ) DO 20 1=1,N I T=I T Y P E ( I ) NCHAR=1 Y( I ) = Y ( I ) + l . GO TO ( 1 1 , 1 2 , 1 3 , 1 4 ) , I F L A G 11 IF( I T . G T . 1 2 ) GO TO 16 BCD=SIGN1 ( IT ) GO TO 15 16 BCD=SIGN1(13) GO TO 15 12 I F ( I T . L T . 1 2 ) GO TO 17 BCD=SIGN1( IT ) GO TO 15 17 BCD=SIGN1(13) GO TO 15 13 BCD= S IGN2{ IT ) NCH*R=-1 GO TO 15 14 BCD = SIGN3( IT) NCHAR=-1 15 CONTINUE C A L L S Y M B O L ( X ( I ) , Y ( I ) , 0 . 1 0 , B C D , 0 . , N C H A R ) 20 CONTINUE RETURN END $COPY PROG data b (See Chapter 6.2) Number of Apartment Buildings b u i l t i n each year Number of Dwelling units per f l o o r t Number of storeys per apartment building!?, Number of dwelling units per apartment building APARTMENT DEVELOPMENTS ALONG SUBWAY CORRIDORS A P A R T M E N T D E V E L O P M E N T A L O N G T H E T O R O N T O S U B W A Y C O R R I D O R S NUMBER OF APARTMENTS BUILT IN EACH YEAR ALONG ALL SUBWAY CORRIDORS THE APARTMENTS BUILT BEFORE 1958 ARE IN A SEPERATE GROUP (SEE MISSING VALUES) YEAR VALUE ABSOLUTE RELATIVE ADJUSTED CUHULATIV: FREQUENCY FREQUENCY FREQUENCY FREQ (PERCENT) (PERCENT) (PERCENT 1959 59 3 1.4 3.2 3.2 1 96 1 61 10 4.8 10.5 13.7 1962 62 3 1.4 3.2 1 6. 8 196.3 63 13 6 . 3 13.7 30. 5 1 964 64 3 1.4 . '3.2 33. 7 1 965 65 12 5.8 '12.6 4 6.3 1 966 66 7 3.4 7.4 53. 7 1967 67 8 3.9 8.4 62. 1 1 968 68 15 7.2 15.8 77. 9 1969 69 1 1 5.3 11.6 89.5 1 970 70 10 4.8 10.5 100. 0 1 958 58 112 54 . 1 MISSING 100. 0 TOTAL 207 100.0 100. 0 100. 0 VALID OBSERVATIONS -HISSING OBSERVATIONS -95 112 A P A R T M E N T D E V E L O P M E N T A L O N G T H E T O R O N T O S U B W A Y C O R R I D O R S AVERAGE NUMBER OF DWELLING UNITS PER FLOOR (FOR ALL APARTMENT DEVELOPMENTS AFTER 1958, UP TO THE YEAR 1970, ALONG ALL SUBWAY LINES) NUMBER OF VALUE ABSOLUTE RELATIVE CUMULATIVE DWELLINGS FREQUENCY FREQU ENCY FREQ PER FLOOR (PERCENT) (PERCENT) LESS THAN 10 6 5 5.3 5.3 6-10 10 17 17.9 23. 2 11-13 1 3 18 18. 9 42. 1 14-16 1 6 25 26.3 6 8.4 17-20 20 23 24.2 92.6 21-3 0 30 7 7.4 100. 0 TOTAL 95 100.0 100. 0 VALID OBSERVATIONS -3ISSING OBSERVATIONS -95 0 A P A R T M E N T D E V E L O P M E N T A L O N G T H E T O R O N T O S U B H A Y C O R R I D O R S NUMBER OF STORIES PER APARTMENT STRUCTURE TABLE 1 APARTMENTS CONSTRUCTED BEFORE 1958 ARE INCLUDED NUMBER OF STORIES VALUE ABSOLUTE FREQUENCY RELATIVE FREQUENCY (PERCENT) CUMULATIVE FR EQ (PERCENT) 2 STORIES 2 54 26 . 1 26. 1 3 STORIES 3 43 2 .8 46.9 4 STORIES 4 10 4.8 51.7 5 STORIES 5 11 5.3 57.0 6-10 STORIES 10 15 7.2 64.3 11-15 STORIES 15 16 7.7 72. 0 16-20 STORIES 20 29 14 .0 86. 0 2 1-30 STORIES 3 27 13.0 99. 0 MORE THAN 3 STORIES 31 2 1. 1 . TO 207 100.0 100.0 MISSING OBSERVATIONS -VALID OBSERVATIONS -0 207 TABLE 2 APARTMENTS BUILT BEFORE 19 58 ARE N C T INCLUDED NUMBER OF STORIES VALUES ABSOLUTE FREQUENCY RELATIVE FREQUENCY (PERCENT) CUMULATIVE FR EQ (PERCENT) 3 STORIES 3 2 2.1 2. 1 5 STORIES 5 6 6.3 8.4 6-10 STORIES 10 13 13.7 22. 1 11-15 STORIES 15 16 16.8 38.9 16-20 STORIES 20 29 30.5 69.5 21-30 STORIES 30 27 28.4 97.9 MORE THAN 30 31 2 2. 1 1 00.0 TO 95 1 00.0 100. 0 VALID OBSERVATIONS MISSING OBSERVATIONS 95 0 A P A R T M E N T D E V E L O P M E N T A L O N G T H E T O R O N T O S U B W A Y C O R R I D O R S NUMBER OF DWELLING UNITS PER APARTMENT STRUCTURE THE APARTMENTS BUILT BEFORE 1958 FORM THREE SEPERATE GROUPS WHICH ARE TREATED AS MISSING VALUES (SEE COLUMN 'FREQUENCY' AND ' ADJ .FREQUENCY • IN TABLE) NUMBER OF VALUE ABSOLUTE RELATIVE ADJUSTED CUMULATIVE DWELLINGS PER FREQUENCY FREQUENCY FREQUENCY FREQ APARTMENT (PERCENT) (PERCENT) (PERCENT) STRUCTURE 4 8- 6 0 60 21 10.1 17 .8 17.8 61- 80 80 15 7.2 12.7 30.5 81-120 120 8 3.9 6.8 37.3 121- 160 160 6 2.9 5. 1 42.4 161-200 200 1 3 6.3 11.0 53.4 201-300 300 29 14.0 24 .6 78. 0 301-400 400 16 7.7 13.6 91.5 + 400 401 10 4.8 8.5 100. 0 L T 20 20 31 15.0 MISSING 100. 0 21-30 30 28 13.5 MISSING 1 00. 0 31-30 47 30 14.5 MISSING 100.0 TO 207 100.0 100.0 100.0 VALID OBSERVATIONS -MISSING OBSERVATIONS -118 89 (See Chapter 6.2) Incremental growth f o r each time period; up to year 1 9 5 9 and 1 9 5 9 to 1 9 7 1 . APARTMENT GROWTH D I S T R I B U T I O N O F A P A R T M E N T U N I T S ANNUAL INCREASE OF APARTMENT UNITS BY TIME PERIOO AND BY STATION SUB-AREA SUBWAY L INE Y O N G E ST AT I ON|NUMBER 1 NUMBER OF A P A R T M e N T S BUILT NUMBER |0F i AND 1 STATION 1UNT IL 1 I N I I M E P E R I 0 0 SUEAREAlSUB - 11958 | 1959 1961 1963 196 5 1967 1969 NUMBER I AREAS 1 1 + 1960 + 1962 +1964 + 1966 + 1968 + 1970 EGLINGTON 1011 1 262 0 0 0 0 168 0 1012 2 802 210 255 31C 343 251 3 00 1013 3 98 0 0 0 0 0 0 1014 4 70 155 159 0 288 369 24 5 DAV I SV I LLE 1021 5 108 0 0 0 0 0 0 1022 6 0 0 0 0 0 0 0 1023 7 101 180 153 0 0 0 0 1024 8 80 0 120 400 580 645 783 ST .CLA IR 1031 9 340 0 215 212 0 0 0 1032 10 62 0 0 0 0 0 311 1033 11 16 0 0 0 0 0 0 1034 12 66 0 222 416 784 748 355 SUMMERHILL 1041 13 0 0 0 0 0 0 0 1042 14 0 0 0 0 0 0 0 1043 15 0 0 0 0 0 0 0 1044 16 0 0 0 0 0 0 0 ROSEDALE 10 51 17 0 0 0 c 0 0 C 1 052 18 0 0 0 0 0 0 0 10 53 19 0 0 0 0 0 0 0 1054 20 0 0 0 c 0 0 C TOTAL # OF APARTMENTS BUILD BY TIME PERIOD, CORRIDOR Y O N G E 2005 545 1124 133 8 1995 218 1 1994 SUBWAY LINE B L 0 0 R W E S T 0 L C ST .GEORGE 2011 1 0 0 0 80 0 0 0 2012 2 217 0 157 0 19 2 150 0 2013 3 0 0 0 0 0 0 0 2014 4 0 0 0 0 0 0 0 SPADINA 2021 5 93 0 207 0 169 0 140 2022 6 0 0 0 0 0 0 0 2023 7 0 0 0 0 0 0 0 2024 8 0 c 0 0 0 0 0 BAT HURST 20 31 9 0 0 0 G 0 0 c 2032 10 0 0 0 0 0 0 0 2033 11 G 0 0 0 0 . 0 0 2034 12 0 0 0 G 0 0 c CHR IST IE 2041 13 0 0 0 0 0 0 0 2042 14 25 0 0 0 0 0 0 2043 15 0 0 0 0 0 0 0 2044 16 0 0 0 c 0 0 C SUBWAY LINE B L O O R W E S T O L C CONTINUED 1958 59/60 61/62 63/64 65/66 67/68 69/70 OSSINGTON 2051 17 0 0 0 C 0 0 0 2052 18 0 0 0 0 0 0 0 20 53 19 0 0 0 0 0 0 0 2054 20 0 0 0 0 0 0 0 DUFFERIN 2061 21 0 0 0 0 0 0 0 2062 22 c 0 0 0 0 0 0 20 63 23 0 0 0 0 0 0 0 2064 24 0 0 0 c 0 0 0 LANDSDCWNE 2071 25 0 0 0 0 0 0 0 2072 26 C 0 0 0 0 0 0 2073 27 0 0 0 0 0 0 0 2074 28 0 0 0 0 0 0 0 DUNDAS WEST 2081 29 0 0 0 0 0 0 0 2082 30 0 0 0 0 0 0 0 2083 31 117 0 c 0 0 0 c 2084 3 2 0 0 0 0 0 0 0 KEELE 2091 3 3 21 0 0 313 520 480 332 2092 34 0 0 0 0 0 0 0 2093 35 0 0 0 0 0 0 0 2094 36 0 0 0 0 0 0 0 TOTAL # OF APARTMENTS BUILD BY TINE PERIOD, CORRIDOR B L 0 C R W E S T 0 L D 43 7 0 364 295 881 630 472 SUBWAY LINE B L 0 0 R WE S T HIGHP/SRK 2101 1 13 5 2102 2 124 2 103 3 74 2104 4 0 RUNNYMEDE 2111 5 0 2112 6 0 2113 7 0 2114 8 0 JANE 2121 9 0 2122 10 0 2123 11 C 2124 12 0 OLD KILL 2131 13 160 2132 14 19 2133 15 234 2134 16 0 ROYAL YPRK 2141 17 0 2142 18 0 2143 19 C 2144 20 0 ISLINGTON 2151 21 0 2152 22 0 2153 23 0 2154 24 0 TOTAL # OF APARTMENTS BUILT BY TIME PERIOO, CORRIDOR B L O O R W E S T NEW+OLO 1219 N E W 0 0 G 0 0 G 0 0 23 1 462 638 525 0 0 0 0 0 0 0 0 0 0 0 0 0 0 c 0 0 C 0 0 0 0 0 0 0 0 0 0 0 0 0 c 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 G 0 0 G 0 0 0 154 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 154 194 0 60 0 0 0 180 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 518 998 1497 1328 997 SUBWAY L INE 8 L 0 O R E A S T 0 L C 1958 5 9 / 6 0 6 1 / 6 2 6 3 / 6 4 6 5 / 6 6 6 7 / 6 8 6 9 / 7 0 SHERBOURNE 3011 1 57 0 0 0 0 0 0 3012 2 0 0 0 c 0 0 c 3013 3 74 0 0 215 0 93 0 301A 4 34 0 2 74 947 951 1697 1347 C A S T L E FRANK 3021 5 0 0 0 0 0 0 0 3022 6 0 0 0 G 0 0 0 3023 7 0 0 0 0 0 0 0 3024 8 G 0 0 0 0 0 0 BROADVIEW 3031 9 165 0 0 180 370 216 C 3032 10 0 0 0 0 0 0 0 3033 11 110 0 0 0 0 0 0 3 034 12 102 0 0 0 0 226 288 CHESTER 3041 13 0 0 0 0 0 0 0 3 042 14 28 0 0 0 0 0 0 3043 15 40 0 0 0 0 0 0 3044 16 0 0 0 c 0 0 0 PAPE 3051 17 80 0 0 0 0 0 0 3052 18 19 0 0 0 0 0 0 3053 19 0 0 0 0 0 0 0 3054 20 0 0 0 0 0 0 0 DONLANDS 3061 21 0 0 0 0 0 0 0 3062 22 24 0 0 0 0 0 0 3063 23 0 0 0 0 0 0 0 3064 24 0 0 0 c 0 0 0 GREENWOOD 3071 25 0 0 0 0 0 0 0 3072 26 0 0 0 0 0 0 0 3 073 27 . 0 0 0 0 0 0 0 3074 28 0 0 0 G 0 0 0 COXWELL 3081 29 0 0 0 0 0 0 0 3082 30 0 0 0 0 0 0 0 3083 31 0 0 0 0 0 0 0 3084 32 0 0 0 G 0 0 0 WOODBINE 3091 33 0 0 0 0 0 0 0 3092 34 c 0 59 0 0 0 0 3093 35 0 G c 0 0 0 0 3094 36 0 0 C G 0 0 C TOTAL NUMBER OF APARTMENTS BUILT BY TIME P E R I O D , CORRIDOR B L O O R E A S T O L D 733 0 3 3 3 13 42 1321 2231 1635 MAIN STREET 3101 1 0 0 0 0 0 0 0 3102 2 0 0 0 0 0 0 0 3103 3 0 0 0 G 0 0 0 3104 4 0 0 0 0 0 0 6 25 V IC TORIA PARK 3111 5 G 0 0 0 0 0 208 3112 6 0 0 0 G 0 0 294 3113 7 0 0 0 0 0 0 0 3114 8 0 0 0 0 0 0 0 WARDEN 3121 9 0 0 0 0 0 0 330 3122 10 0 0 0 G 0 0 C 3123 11 0 0 0 0 0 0 0 3124 12 C 0 0 0 0 0 0 TOTAL # CF APARTMENTS BUILT BY TIME P E R I O D , CORRIDOR B L O O R E A S T OLO+NEW 733 0 333 134 2 1321 2231 3 09 2 (See Chapter 6.2) Cumulative growth 1 9 5 9 - 1 9 7 0 , including apartments h u i l t before 1 9 5 9 * f o r each time period. D I S T R I B U T I O N O F A P A R T M E N T U N I T S TOTAL CUMULATIVE NUMBER OF APARTMENT UNITS BY TIME PERIOD AND BY STATION SUB-AREA, INCLUDING APARTMENTS BUILT UP TO 1958 ( INCLUSIVE) SUBWAY L INE Y O N G E STATICNlNUMBER j N U M B E R O F A P A R T M £ N T S NUMBER |OF 1 8 U I L T U P T O Y E A R .. • * AND (STATION! SUBAREA|SUB 1 NUMBER 1 AREAS I .1958 1960 19 62 1964 1966 1968 1970 EGLINGTON 1011 1 26 7 0 0 0 0 168 0 1012 2 802 1C12 1267 157 7 1920 2171 2471 1013 3 9 8 98 98 98 98 98 98 1014 4 70 225 384 384 67 2 1041 1286 D A V I S V I L L E 1021 5 108 108 108 108 108 108 108 1022 6 0 0 0 C 0 0 0 1023 7 101 281 434 434 4 34 434 434 1024 8 80 80 200 600 1180 182 5 26 08 ST .CLAIR 1031 9 340 340 555 767 767 767 767 1032 10 62 62 62 62 62 62 3 73 1033 11 16 16 16 16 16 16 16 1034 12 66 66 288 7 04 1488 2236 2591 SUMMERHILL 1041 13 0 0 0 0 0 0 0 1042 14 0 C C C 0 0 0 1043 15 0 0 0 0 0 0 0 1044 16 0 0 0 0 0 0 0 ROSEDAL E 1051 17 0 0 0 0 0 0 0 1052 18 0 0 0 G 0 0 0 1053 19 0 0 0 0 0 0 0 1054 20 0 0 0 0 0 0 0 SUBWAY LINE B L O O R W E S T 0 L 0 ST .GEORGE 2011 1 0 0 0 8C 80 80 80 2012 2 217 217 374 374 566 716 716 2013 3 C 0 0 0 0 0 0 2014 4 0 0 0 0 0 0 0 SPADINA 2021 5 93 93 300 30C 469 469 609 2022 6 0 0 0 0 0 0 0 2023 7 C 0 0 0 0 0 0 2024 8 0 0 0 0 0 0 0 BATHURST 2031 9 0 0 0 0 0 0 G 2032 10 0 0 0 0 0 0 0 2033 11 0 0 0 0 0 0 0 2034 12 0 0 0 0 0 0 C SUBWAY LINE B L O O R W E S T O L C CONTINUE*) 1958 1960 1962 1964 1966 1968 1970 CHRISTIE 2 041 13 0 0 0 0 0 0 0 2042 14 25 25 25 25 25 2 5 25 2043 15 C 0 0 0 0 0 0 2044 16 0 0 0 0 0 0 0 OSSINGTON 2051 17 0 0 0 0 0 0 0 2052 18 0 0 0 0 0 0 0 2053 19 0 0 0 0 0 0 0 2054 20 0 0 0 0 0 0 0 DUFFERIN 2061 21 0 0 0 0 0 0 0 2062 22 0 0 0 G 0 0 G 2063 23 0 0 0 0 0 0 0 2064 24 0 0 0 0 0 0 0 LANDSDOWNE 2071 25 0 0 0 0 0 0 0 2072 26 0 0 0 0 0 0 0 2 073 27 0 0 0 0 0 0 0 2074 28 0 0 0 0 0 0 0 DUNDAS WEST 2081 29 0 0 0 c 0 0 0 2082 30 0 0 0 0 0 0 0 2083 31 117 117 117 117 117 117 117 2084 32 0 0 0 0 0 0 0 KEELE 2091 33 21 21 21 334 854 1334 1666 2092 34 0 0 0 0 0 0 0 2093 35 0 0 0 0 0 0 0 2094 36 0 0 0 0 0 0 0 SUBWAY LINE B L 0 0 R WE S T N E W HIGH PARK 2101 1 135 135 135 135 135 135 135 2102 2 124 124 124 355 817 145 5 1980 2103 3 74 74 74 74 74 74 74 2104 4 0 0 0 0 0 0 0 RUNNYMEDE 2111 5 0 0 0 0 0 0 0 2112 6 0 0 0 c 0 0 0 2113 7 0 0 0 0 0 0 0 2114 8 c 0 0 G 0 0 0 JANE 2121 9 0 0 0 0 0 0 0 2122 10 0 0 0 0 0 0 G 2123 11 0 0 0 0 0 0 0 2124 12 C 0 0 0 0 0 0 OLD MILL 2131 13 160 160 160 160 160 160 160 2132 14 19 19 19 19 17 3 173 173 2133 15 234 234 234 234 234 234 234 2134 16 C 0 0 0 0 0 0 ROYAL YORK 2141 17 0 0 0 0 0 0 0 2142 18 0 G 0 c 0 0 0 2143 19 0 0 0 0 0 0 0 2144 20 0 0 0 0 0 0 0 ISLINGTON 2151 21 0 0 154 348 348 408 408 2152 22 0 0 0 18C 180 180 180 2153 23 0 0 0 0 0 0 0 2154 24 c 0 0 0 0 0 0 SUBWAY L INE B L O O R E A S T O L D 1958 1960 1962 1964 1966 1968 1970 S H E R B C U R N E 3011 1 57 57 57 57 5 7 57 57 3012 2 0 0 0 0 0 0 0 3013 3 74 74 74 289 289 382 382 3014 4 34 34 308 1255 2206 3903 5250 C A S T E L FRANK 3021 5 0 0 0 G 0 0 C 3022 6 0 0 0 0 0 0 0 3023 7 0 0 0 0 0 0 0 3024 8 0 0 C 0 0 0 c BROADVIEW 3031 9 165 165 165 345 715 931 931 3032 10 0 0 0 0 0 0 0 3033 11 110 110 110 110 110 110 110 3034 12 102 102 102 102 10 2 328 616 CHESTER 3 041 13 0 0 0 0 0 0 0 3042 14 28 28 28 28 2 8 28 28 3043 15 40 40 40 40 40 40 40 3044 16 0 0 0 0 0 0 0 PAPE 3051 17 e c 80 80 80 80 80 80 3052 18 19 19 19 19 19 19 19 3053 19 0 0 0 G 0 0 G 3054 20 0 0 0 0 0 0 0 DONLANDS 3061 21 c 0 0 0 0 0 0 3062 22 24 24 24 24 24 24 24 3063 23 0 0 0 C 0 0 0 3064 24 0 0 0 0 0 0 0 GREENWOOD 3071 25 0 0 0 0 0 0 0 3072 26 0 0 0 0 0 0 0 3 073 27 0 0 0 0 0 0 0 3074 28 0 0 0 0 0 0 0 COXWELL 3081 29 0 0 0 0 0 0 0 3082 30 0 0 0 0 0 0 c 3083 31 0 0 0 0 0 0 0 3084 32 0 0 0 0 0 0 0 WOODBINE 3091 33 0 0 0 0 0 0 0 3092 34 0 0 59 5 9 59 59 59 3093 35 0 0 0 0 0 0 0 3094 36 0 0 0 0 0 0 0 SUBWAY L INE B L 0 0 R E A S T N E W MAIN STREET 3101 1 0 0 0 0 0 0 0 3102 2 0 0 0 0 0 0 0 3103 3 0 0 0 c 0 0 0 3104 4 0 0 0 0 0 0 625 VOCTORIA PARK 3111 5 0 0 0 0 0 0 208 3112 6 0 0 0 0 0 0 2 94 3113 7 0 0 0 0 0 0 0 3114 8 0 0 0 0 0 0 0 WARDEN 3121 9 0 0 0 0 0 0 330 3122 10 0 0 0 c 0 0 C 3123 11 0 0 0 0 0 0 0 3124 12 0 0 0 0 0 0 0 (See Chapter 6.2) Cumulative growth 1959-1970, excluding apartments b u i l t before 1959* f o r each time period. D I S T R I B U T I O N O F A F A . R T M E N T U N I T S TOTAL C U M U L A T I V E NUMBER OF APARTMENTS BUILT BY TIME PERIOD AND BY STATION SUB-AREAS, EXCLUDING APARTMENTS BUILT UP TO 1958 (INCLUSIVE) SUBWAY LINE Y O N G E STATION|NUMBER | N U M B E R 0 F A P A R T M E N T : NUMBER |OF | AFTER 19 58 , UP TO YEAR : • • * • • « * AND |STATION I SUBAREA|SUB I NUMBER |A REAS | 1960 1962 1964 1966 1968 1970 EGLINGTON 1011 1 0 0 0 0 168 168 1012 2 2 10 465 775 1118 1369 1669 1013 3 0 0 0 0 0 0 1014 4 155 314 314 602 971 1216 DAVISVILLE 1021 c 0 0 0 0 0 0 1022 6 0 0 0 0 0 0 1023 7 180 333 333 333 333 33 3 1 024 8 0 120 52 0 1100 1 745 2528 ST.CLAIR 103 1 9 0 215 427 4 27 42 7 427 1032 10 0 0 0 0 0 3 1 1 1033 11 0 0 0 0 0 0 1 034 12 0 222 63 8 1422 21 70 2525 SUMMERHILL 104 1 1 3 0 0 0 0 0 0 1042 1 4 0 0 0 0 0 0 1 043 1 5 0 0 0 0 0 0 1 044 16 0 0 0 0 0 0 R OS EDALE 1051 17 0 0 0 0 0 0 1052 18 0 0 0 0 0 0 1053 19 0 0 0 0 0 0 1054 20 0 0 0 0 0 0 SUBWAY LINE B L O O R W E S T C L D ST.GEORGE 2011 1 0 0 80 80 80 80 2012 2 0 157 157 349 499 499 2013 3 0 0 0 0 0 0 2014 4 0 0 0 0 0 0 SPADINA 202 1 5 0 207 207 376 3 76 516 2022 6 0 0 0 0 0. 0 2023 7 0 0 0 0 0 0 2024 8 0 0 0 0 0 0 BATHURST 2 03 1 9 0 0 0 0 0 0 2032 10 0 0 0 0 0 0 2033 11 0 0 0 0 0 0 2034 12 0 0 0 0 0 0 SUBWAY LINE B L O O R W E S T O L D CONTINUED 196 0 1962 1964 1966 1968 1970 CHRISTIE 204 1 13 0 0 0 0 0 0 2042 14 0 0 0 0 0 0 2043 1 5 0 0 0 0 0 0 2044 16 0 0 0 0 0 0 OSSINGTON 205 1 17 0 0 0 0 0 0 2052 18 0 0 0 0 0 0 2053 19 0 0 0 0 0 0 2054 20 0 0 0 0 0 0 DUEFERIN 2061 21 0 0 0 0 0 0 2062 22 0 0 0 0 0 0 2063 23 0 0 0 0 0 0 2064 24 0 0 0 0 0 0 LANDS DOWNE 2071 25 0 0 0 0 0 0 2072 26 0 0 0 0 0 0 2073 27 0 0 0 0 0 0 2074 28 0 0 0 0 0 0 DUN DAS WEST 2081 29 0 0 0 0 0 0 2 082 30 0 0 0 0 0 0 2 083 31 0 0 0 0 0 0 2084 32 0 0 0 0 0 0 KEELE 2 091 33 0 0 313 833 1313 1645 2092 34 0 0 0 0 0 0 2093 3 5 0 0 0 0 0 0 2094 36 0 0 0 0 0 0 SUBWAY LINE B L 0 0 R WE S T N E W HIGHPARK 2101 1 0 0 0 0 0 0 2102 2 0 0 231 693 1331 1856 2103 3 0 0 0 0 0 0 2104 4 0 0 0 0 0 0 RUNNYMEDE 2 111 5 0 0 0 0 0 0 2112 6 0 0 0 0 0 0 2113 7 0 0 0 0 0 0 21 14 8 0 0 0 0 0 0 JANE 2 121 9 0 0 0 0 0 0 2122 10 0 0 0 0 0 0 2123 1 1 0 0 0 0 0 0 2124 12 0 0 0 0 0 0 OLD HILL 213 1 13 0 0 0 0 0 0 2132 14 0 0 0 154 1 54 154 2133 1 5 0 0 0 0 0 0 2134 16 0 0 0 0 0 0 ROYAL YORK 214 1 17 0 0 0 0 0 0 2142 18 0 0 0 0 0 0 2143 19 0 0 0 0 0 0 2144 20 0 0 0 0 0 0 ISLINGTON 215 1 21 0 154 348 348 408 4 08 2152 22 0 0 1 80 180 180 180 2153 23 0 0 0 0 0 0 2154 24 0 0 0 0 0 0 SUBWAY LINE 8 L 0 0 R E A S T 0 L D 1960 1962 1964 1966 1968 1970 SHERBOURNE 3011 1 0 0 0 0 0 0 3012 2 0 0 0 0 0 0 3013 3 0 0 215 215 308 3 08 3014 4 0 27 4 1221 2172 3869 5216 CASTLE FRANK 302 1 5 0 0 0 0 0 0 3022 6 0 0 0 0 0 0 3023 7 0 0 0 0 0 0 3024 8 0 0 0 0 0 0 BROADVIEW 3 03 1 9 0 0 180 550 766 766 3032 10 0 0 0 0 0 0 3033 11 0 0 0 0 0 0 3034 12 0 0 0 0 226 514 CHESTER 304 1 13 0 0 0 0 0 0 3 04 2 14 0 0 0 0 0 0 3 04 3 15 0 0 0 0 0 0 3044 1 6 0 0 0 0 0 0 PAPE 3 051 17 0 0 0 0 0 0 3 05 2 18 0 0 0 0 0 0 3053 19 0 0 0 0 0 0 3054 20 0 0 0 0 0 0 DONLANDS 3061 21 0 0 0 0 0 0 3 06 2 22 0 0 0 0 0 0 3 063 23 0 0 0 0 0 0 3064 24 0 0 0 0 0 0 GREENWOOD 3071 25 0 0 0 0 0 0 3072 26 0 0 0 0 0 0 3073 27 0 0 0 0 0 0 3074 28 0 0 0 0 0 0 COXWELL 3081 29 0 0 0 0 0 0 3 082 30 0 0 0 0 0 0 3083 31 0 0 0 0 0 0 3084 32 0 0 0 0 0 0 WOODBINE 3091 33 0 0 0 0 0 0 3092 34 0 59 59 59 59 59 3093 35 0 0 0 0 0 0 3094 36 0 0 0 0 0 0 SUBWAY LINE E L 0 0 R E A S T N E W MAIN STREET 3101 1 0 0 0 0 0 0 3 102 2 0 0 0 0 0 0 3103 3 0 0 0 0 0 0 3 104 4 0 0 0 0 0 625 VICTORIA PARK 3111 5 0 0 0 0 0 208 3 112 6 0 0 0 0 0 294 3113 7 0 0 0 0 0 0 3 114 8 0 0 0 0 0 0 WARDEN 3121 9 0 0 0 0 0 33 0 3122 10 0 0 0 0 0 0 3 123 1 1 0 0 0 0 0 0 3124 12 0 0 0 0 0 0 statistical analysis (See Chapter 6.3) In order to e s t a b l i s h the table functions, the relationships between apartment growth and the environmental factors had to be established. As already mentioned, c o r r e l a t i o n and regression analysis i s not suitable to determine the shape of the table functions, because the relationships between apartment growth and the environmental factors are hypothesized as non-linear. The use of logarithmic scales did not improve the r e s u l t s , because most of the ordinal variables have only a few values which they can assume.1 Crosstabulation served as a substitute f o r c o r r e l a t i o n analysis. The tables contain , as shown i n Table A*;c-1-I and Table A . c - l - I I , the frequency, the row, column and t o t a l percentage and the category t o t a l s (absolute and i n percent, at the l e f t side and bottom of the 1. Ordinal scale i t s e l f i s not hindering to execute regression, since non-parametric regression packages are a v a i l a b l e . However, ' there were no non-linear regression packages e a s i l y accessible. STATISTICAL ANALYSIS CROSS-TABULATION AND CORRE-LATION ANALYSIS table) of the j o i n t appearances of att r i b u t e s -environmental conditions i n t h i s case. SURACC COUNT ROW PCT IAV ERAGE ABOVE ROW COL PCT I AVERAGE TOTAL TOT PCT I 0.0 T 1.00 'T 1. PARKLD : I - I I 0.0 I 62 I 27 I 89 NO PAR KLAND I 69.7 I 30.3 I 69. 5 I 77. 5 I 56. 3 I I 48.4 I T 21.1 I T 1.00 J _ I 18 1" I 21 J . I 39 PARKLAND I 46.2 I 53. 8 I 30. 5 I 22.5 I 43.8 I I 14. 1 I _ T 16.4 I T COLUMN 80 i . 48 X 128 TOTAL 62.5 37.5 100.0 TABLE A.c-l-I SAMPLE OF CROSS-TABULATION COUNT = Absolute Frequency ROW PCT = Relative Row Frequency (Percentage) COL PCT = Relative Column Frequency (Percentage) TOT TCT * Relative Total Frequency (Percentage) By inspection (without rigorous s t a t i s t i c a l t e s t of s i g n i f i c a n c e ) , the i n t e r r e l a t i o n between apart-ment growth and environmental factors and among the factors could be obtained. I t was also tested how apartment development over time ( i . e . , the cumulative and incremental number of apartments i n each year) was related to the i n i t i a l environmental C R O S S T A B . U L A T I O N A N A L Y S I S TABLE A. c - l - I I SAMPLE MATRIX FOR THE VARIABLES... BUI LAG BUILDING AGE MIXTURE NEIGHG NEIGBCRHOCDCLALITY CROSS-TABULATION FOR BUILDING AGE AND NEIGHBORHOOD QUALITY NEIGHQ C C U M I ROW PCT [ PRED N'CN LCW AVERAGE HICK ROW COL PCT [RESID. QLALI TY QUALITY OLALITY TOTAL T CT PCT I O.C I 1 . C C I 2.CO] 3. CCI BUI LAG C .C 3 [ 13 I 1 ] [ G I 0 I 14 NCNE BEF 1920 [ 92.9 I 7.1 [ O.C 3 [ C.C I 1 1. 0 I 100.0 I 1.5 [ 0 .0 [ 0.0 I IG. 2 I 0.8 [ O.C [ 0.0 I 1 .00 I 0 I 1 I 0 [ 2 I 3 O-IC % i JEF 192C [ 0.0 I 33.3 1 [ 0.0 [ 66.7 I 2 .4 [ C. c I 1.5 ] [ O.C I 9. 1 I I 0 .0 I 0.8 [ O.C 1 1.6 I 2.CC [ c [ 5 1 [ 14 1 14 I ? 1 11-20 8 EEF 1920 I 0 .0 I 15.2 1 42.4 1 42. 4 I 26. C [ 0.0 I 7 .6 t 53.8 I 63.6 I [ C C I 3.9 ] [ 11.0 [ 11 • C I 3 .00 [ 0 I 7 1 [ 1 [ 1 I c J 21-20 % BEF 192C [ C.G [ 77.8 [ 11.1 1 [ 11.1 I 7.1 [ c.c [ 1C.6 1 [ 3.8 I 4.5 I I 0 .0 I 5.5 [ 0.8 1 [ C. 8 I 4.CC I c [ 8 1 I 3 I 4 I 15 31-40 % EEF 1920 I C .0 I 53.3 J t 20.C 3 [ 2 6.7 I i i . e [ 0.0 I 12.1 [ 11.5 I 18.2 I [ C O 1 [ 6.3 1 [ 2.4 I 3.1 I 6 .00 I 0 I 2 [ 8 I C I IC 51-6C * BEF 1920 [ C C I 20.0 [ 80 .0 3 t G.C I 7 .9 t C.C [ 3. C 1 30.8 1 [ C.C I [ C.G I 1.6 [ 6.3 [ C.C I 7 . C C 1 [ C I 42 ] [ 0 I 1 I 43 61-7C % BEF 192C [ 0.0 I 97.7 ] [ 0. C I 2.3 I 33.9 I 0.0 I 63 .6 I 0.0 3 4. 5 I t 0.0 I 33.1 [ 0.0 [ 0.8 I COLUMN 13 66 26 22 127 TOTAL 10 .2 52.0 20.5 17 .3 1C 0 . 0 conditions. However* a d i s t i n c t trend over time could not be determined which would show exactly how the influence of the i n i t i a l environmental conditions changes. However, i t was not expected to obtain t h i s r e s u l t , f o r a simulation would not be necessary i f patterns could be is o l a t e d that e a s i l y . The only s i g n i f i c a n t r e l a t i o n could be detected among apartment growth i t s e l f , i . e . , the pooling e f f e c t of apartment construction. Table A . c - l - I I I gives the c o r r e l a t i o n matrices which were calculated f o r the variable 'cumulative apartment growth*. 1. i . e . , the number of apartments, the only variable f o r which time-series data are available, was treated as a dependent variable and related to the environmental conditions at time of the introduction of the t r a n s i t l i n e s . TABLE A.c-l-III C O R R E L A T I O N M A T R I X PEARSON CORRELATION MATRIX FOR THE VARIABLES: CUMULATIVE NUMBER OF APARTMENTS BUILT ON TIME PERIOD POOLING EFFECT OF APARTMENT DEVELOPMENTS S U B W A Y L I N E Y O N G E 1959 /1960 1 9 6 1 / 1 9 6 2 1963 /1964 1965 /1966 1 9 6 7 / 1 9 6 8 1969 /1 970 5 9 / 6 0 6 1 / 6 2 63 /64 6 5 / 6 6 6 7 / 6 8 6 9 / 7 0 1 .0000000 0 . 9 7 7 4 7 1 0 0 . 8 9 4 6 4 4 9 0.7.357091 0 .6135920 0 .5550011 0 . 9 7 7 4 7 1 0 1 .0000000 0 . 9 5 6 5 1 2 8 0 . 8 3 5 7 3 1 4 0 . 7 2 9 7 8 6 9 0 .6712341 0 . 8 9 4 6 4 4 9 0 . 9 5 6 5 1 2 8 1 .0000000 0 . 9 5 0 3 5 0 9 0 . 8 7 7 7 8 4 1 0 . 8 3 4 9 3 3 0 0 .7357091 0 .8357314 0 . 9 5 0 3 5 0 9 1 .0000000 0 .98 19852 0 . 9 5 8 3 7 4 7 0 . 6 1 3 5 9 2 0 0 . 7 2 9 7 8 6 9 0 . 8 7 7 7 8 4 1 0 . 9 8 1 9 8 5 2 1 . 0 0 0 0 0 0 0 0 . 9 8 9 7 4 0 0 0 . 5 5 5 0 0 1 1 0 . 6 7 1 2 3 4 1 0 . 8 3 4 9 3 3 0 0 . 9 5 8 3 7 4 7 0 . 9 8 9 7 4 0 0 1 . 0 0 0 0 0 0 0 A L L S U B W A Y L I N E S 1959 /1960 1 9 6 1 / 1 9 6 2 1963/1 964 1965 /1966 1 9 6 7 / 1968 1969/1 970 5 9 / 6 0 6 1 / 6 2 6 3 / 6 4 65 /66 6 7 / 6 8 6 9 / 7 0 1 .0000000 0 . 9 5 4 5 6 8 3 0 . 7 7 6 1 0 3 9 0 . 6 2 3 3 9 8 6 0 . 4 8 9 1 0 4 6 0 . 4 2 7 7 5 1 2 0 . 9 5 4 5 6 8 3 1 .0000000 0 . 8 9 1 7 4 2 9 0 . 7 6 6 9 0 9 8 0 .6469781 0 . 5 8 8 0 9 1 7 0 . 7 7 6 1 0 3 9 0 . 8 9 1 7 4 2 9 1 .0000000 0 . 9 5 9 5 2 8 0 0 . 9 0 1 1 6 5 5 0 . 8 6 1 7 8 2 9 0 . 6 2 3 3 9 8 6 0 . 7 6 6 9 0 9 8 0 .9595280 1 .0000000 0 .9805204 0 .9530 225 0 . 4 8 9 1 0 4 6 0 . 6 4 6 9 7 8 1 0 . 901 1655 0 . 9 8 0 5 2 0 4 1 . 0 0 0 0 0 0 0 0 . 9 8 7 9 8 8 2 0 . 4 2 7 7 5 1 2 0 . 5 8 8 0 9 1 7 0 . 8 6 1 7 8 2 9 0 . 9 5 3 0 2 2 5 0 . 9 8 7 9 8 8 2 1 . 0 0 0 0 0 0 0 (See Chapter 6 . 3 ) For the second set of s t a t i s t i c a l analysis, a number of 'logical trees* were constructed. 1 They were expected to reveal the pattern of environ-mental conditions and to contribute to the formu-lation of both weight and shape of the table functions. The logical trees i n Figures A.c - 2 - 1 andcA»:e-2^2-give for a l l stations which received growth over the period under consideration ( 1 9 5 9 -1 9 7 0 ) the i n i t i a l environmental conditions (at the time of the introduction of rapid transit). Sim-i l a r l y , Figures A.c - 2 - 3 and A.c -2 -4 - show, the logical trees constructed for stations which received no growth i n order to test i f the envir-onmental factors also work on the 'negative* side, i.e., i f they value the missing attractivity. STATISTICAL ANALYSIS; LOGICAL TREE ANALYSIS 1. They were obtained through an alteration df the SPSS program "Breakdown". The information from the tree can he obtained by •reading* i t h o r i z o n t a l l y and v e r t i c a l l y . Horizon-t a l l y , common appearance f o r several environmental c h a r a c t e r i s t i c s f o r a c e r t a i n number of stations i s displayed. This i s an i n d i c a t o r of the weight of an environmental f a c t o r . I f a l l branches of the tree have either many or no encircled frequencies, the variables i n the column headings have high weights because they discriminate well stations which receive growth from those which do not. V e r t i c a l l y , the occurrence and frequencies of the values of each environmental variable can be inspected.* For a given breakpoint i n the table function ( i . e . , a t t r a c t i v i t y score = 0.8), the number of stations which achieve ("pos") or do not achieve ("neg") the corresponding environmental value can be summed up. This procedure y i e l d s information on the shape of the table function. 1. In the beginning, trees were computed including the whole value range of each environmental variable. This resulted i n huge, complicated trees which then were narrowed down. The logical trees allow to determine the shape of the table functions and the weight of the environ-mental factors. They reveal the pattern of envir-onmental conditions along the subway lines and how well specific environmental factors discrim-inate between stations which received apartment growth and stations which did not. LOGICAL TREES Abbre vi ati on s TOTAL Number of station sub-areas included in logical tree analysis NEG Number of station sub-areas with attractivity scores less than .8 for the environmental factor in the respective column heading POS Number of station sub-areas with attractivity scores between .8 and 1 for the environmental factor i n the respective column heading. Environmental Factors: ASTART Pooling effect of apartment construction BUILAG Building age mixture NEIGHQ Neighborhood quality LOTSIZ Average lot size PARKLD Proximity to major open space SURACC Surface accessibility NODAL Measurement of nodality ZON Zoning COMDEV Commercial development UNDOON Undesirable conditions No. OP ENVIRONMENTAL FACTORSi STATION SUB-AREAS NODAL NEIGHQ SURACC COMDEV PARKLD 103 NEG 93 95 75 85 77 POS 10 8 28 18 26 TOTAL 103 103 103 103 103 FIGURE A.c-2-1 LOGICAL TREE FOR STATION SUB-AREAS WITHOUT GROWTH Station sub-areas which s a t i s f y the c r i t e r i o n ' ttHEG!? • No, of Station Sub-areas ENVIRONMENTAL VARIABLES COMDEV | ASTART| LOTSIZ | ZON | BUILAG 1 1 © NEG 85 102 7k 80 POS 18 1 29 23 TOTAL 103 103 103 103 61 42 103 O Station sub-areas which s a t i s f y the c r i t e r i o n of "NEG". No. of ENVIRONMENTAL VARIABLES Station W Vain \^ A A Sub-areas ASTART | LOTSIZ | BUILAG| ZON j PARKLD POS 17 21 19 21 13 NEG 8 6 12 TOTAL 25 25 25 25 25 FIGURE A.c-2-3 LOGICAL TREE, STATION SUB-AREAS WITH GROWTH O Station sub-areas which s a t i s f y the c r i t e r i o n of "POSM. ill No. of S t a t i on Sub-areas ENVIRONMENTAL VARIABLES SURACCl UNDCON I NODALI NEIGHQ I PARKLD POS 2 0 2 1 18 18 1 3 NEG 5 k 7 7 1 2 TOTAL 2 5 2 5 2 5 2 5 2 5 o Station sub-areas which s a t i s f y the c r i t e r i o n of "POS". FIGURE A . c - 2 - 4 LOGICAL TREE, STATION SUB-AREAS WITH GROWTH Tables A.c-2-I and A.c-2-II summarize the r e s u l t s RESULTS OP LOGICAL TREE of the l o g i c a l tree analysis and gives the weight ANALYSIS c o e f f i c i e n t s which, are explained twice. The weight c o e f f i c i e n t s are s l i g h t l y higher f o r the analysis of stations without apartment growth, which indicates that the environmental factors discriminate better f o r those stations than f o r the stations which received growth. In p a r t i c -ular , the variables 'building age mixture* and •proximity to major open space* perform r e l a t i v e l y weak i n the analysis; t h e i r weights are reduced accordingly. The weight coefficients for individual variables indicate how good the chosen weight of a given environmental factor applies to a l l station sub-areas and i f the factor discriminates for the stations which received growth as well as for the stations which did not receive apartment growth. The coefficients are computed as follows: i . For stations WITH apartment growth: Weight Coefficient = 1 - NEG TOTAL ii» For stations WITHOUT apartment growth: Weight Coefficient = 1 - POS TOTAL NEG - Number of station sub-areas with attractivity scores less than .8 for the environmental factor i n the respective column heading POS 4 Number of station sub-areas with attractivity scores between .8 and 1 for the environmental factor i n the respective column heading. TOTAL-Number of stations which received growth (i) or which received no growth (kk). The values of the weight coefficients can assume re;-:*-®*. values between 0. and 1, one being the "best" value. WEIGHT CO-EFFICIENTS FOR INDIVIDUAL VARIABLES The overall coefficient indicates how good a given OVEMLL WEIGHT set of weights for a number of environmental COEFFICIENTS factors w i l l perform in the analysis. Any set of weights does apply with varying accuracy to the different station sub-areas. That makes i t d i f f i c u l t to arrive at an optimal set of weights. The overall weight coefficient i s computed as follows and assumes again values between 0. and 1. i . For stations WITH apartment growth» Sum of NEG for a l l Overall Weight_ ^ _ Environmental Factors Coefficient ~ " TOTAL times Number of Environmental Factors ii« For stations WITHOUT apartment growtht Sum of POS for a l l Overall Weight Environmental Factors Coefficient = 1 " TOTAL times Number of Environmental Factors; Number of Station sub-areas = 1 0 3 ENVIRONMENTAL FACTOR Measurement of nodality Neighborhood q u a l i t y Surface a c c e s s i b i l i t y Commercial development Proximity to major open space Pooling e f f e c t of apartment growth Average l o t size Zoning Building age mixture FREQUENCIES NEG 9 3 9 5 7 5 8 5 7 7 102 7k 80 61 POS 10 8 28 18 2 6 2 9 2 3 kZ WEIGHT COEFFICIENT . 9 0 . 9 2 . 7 3 . 8 3 . 7 5 • 9 9 . 7 2 . 7 7 • 5 9 TABLE A . C - 2 - I SUMMARY OF LOGICAL TREE ANALYSIS FOR STATIONS WITHOUT APARTMENT GROWTH OVERALL WEIGHT COEFFICIENT 8 0 Number of Station sub-areas = 25 TABLE A.c-2-II ENVIRONMENTAL FACTOR FREQUENCIES POS NEG WEIGHT COEFFICIENT Pooling e f f e c t of apartment 1? growth Average l o t size 21 Building age 19 mixture Zoning 21 Proximity to 13 major open space Surface 20 a c c e s s i b i l i t y Undesirable 21 conditions Measurement 18 of nodality Neighborhood 18 qua l i t y 8 6 12 5 7 7 .68 .84 .76 .84 .52 .80 .84 .72 .72 SUMMARY OF LOGICAL TREE ANALYSIS FOR STATIONS WITH APARTMENT GROWTH OVERALL WEIGHT COEFFICIENT .76 (See Chapter 6.3) The Suttman table allows a ranking of variables wHiSH aM a l l unldiffiigHsioMal i n tK§ saml aitfelfetldn (e.g., increasing i f they express favourable con- ', d i t i o n s ) and cumulative - that i s each variable contributes to the reduction of unexplained l -variance i n an increasing order. Both are s a t i s -f i e d i n the present case. The r e s u l t s of the analysis give an i n d i c a t i o n of the weight of the variables and of the pattern of appearance ( i . e . , the table demonstrates f o r stations with (1) a high score of an environmental score with a high weight, and (2) which do have apartment growth,, how many 2 other a t t r a c t i v i t y scores are high too). 1. 2. That means that some variables are better indicators f o r a phenomenon than others and that they can be ranked. For d e t a i l s , see SPSS Subprogram Guttman Scale. The cut-off point of the scale f o r each v a r i -able can be determined by the researcher. This allows again to t e s t values i n the table func-ti o n s . The success of the manipulation of the cut-off points i s measured by the c o e f f i c i e n t of r e p r o d u c i b i l i t y - Table A.c-3-VII. c-3 STATISTICAL ANALYSIS; GUTTMAN TABLES AND GUTTMAN SCALES Figure A.c-3-1 explains how to i n t e r p r e t the Guttman table. Tables A.c-3-I to A.c-3-VI show the r e s u l t s of the analysis f o r the Yonge subway l i n e and f o r the whole subway system. Scale type Responses below here are errors Passed—should have  220 CASES WERE PROCESSED 12 COR 5.5 PTC) WERE MISS STATISTICS.. COEFFICIENT OF REPRODUCIBILITY = 0.8109 MINIMUM MARGINAL REPRODUCIBILITY = 0.5373 PERCENT IMPROVEMENT = 0.1731 COEFFICIENT OF SCALABILITY = 0 . W 9 Respondents with a score of 2  Who failed item ' N H E L P ' Respondents with a score of 2 Who passed i tem- 'NHELP' 57y^Total respondents with a score of 2 Total nonmissing cases Total errors Respondents passing item  % passing item  Respondents failing item  % failing item FIGURE A. c-3-1 INTERPRET-ATION OF GUTTMAN TABLE Source» SPSS Manual, p. 200 S U B F I L E t O N G P . W D L O O H H Y E I L O O R E B L O O R VF.L" B L O O R I E { T 0 T A I. ) U S I N G S U H A C C S U H F - A C S S C C F . S S I I I T I . T T T . D I V I S I O N r c i N T * 1 . 0 0 U N D C O H IIMriK:;ift:.ni.F. C O N D I T I O N S D I V I S 1 0 1 P O I N T a 1 . C O N O D A L H F . A S I I i . ' ^ r t F N r O K M O D A L I T Y l l T V T Ii T U H P C ! B T H . 0 0 R U G I I O NT. I N I I 1 U 1 I I M C U U Q U A L I T Y ti l v i s H i d p o H I T « 2 . C O c i i / o C t l f l l l L A r I V E A P A N T . i n N T C R O W T l l I IP 1 0 1 9 7 0 D I V I S I O N P C I N T 1 . 0 0 A S T A R T S O O r A P T A T T I H S O F S I B S T A F T D I V i r . i o s P O I N T 1 . C 0 L O T S I Z A V A m t . F . L O T S I Z E D I V I S I O N P C I N T = 3 . 0 0 l i u l u r . D 1 I I L D 1 N G A G P . M X U I R F . D I V I S I O N P O I N T 1 . CO Z O N 2 0 N I H O D i v i s i o n P 0 I N T - 2 . 0 0 1 T O R V A L U E S E O I I A L T O C I Y 1 S 1 0 H r O I N r A N D A B O V E « « • « • « » » • » » I T K B . B B S P . r ' 0 i T A 1 e o 1 E S R n in K i l l ) N E I G M Q A S T A 0 1 1 0 - E l l R I - ' . R B -I o ior n I I 0 b l 2 £ R K I I 2 6 1 1 I 6 1 1 1 I 1 11 2 1 9 I I 2 1 2 1 2 0 I I no oi no I i is oi 1 5 i i 1 oi i i i 0 0 1 0 1 1 0 1 - - - T - E B R I T I 7 1 - E E R I I-I H I I I I 0 T 1 I 0 1 I I 0 1 I I 0 1 1 1 0 - - - i - r . B R -I 1 0 1 0 I I ] 6 1 I 6 1 T I I I - I F R I I 1 1 1 I-1 I I 1 I 0 I -P .RP . I S I I 1 I 0 1 T 1 0 1 1 I 0 I ur i i 6 1 I I 6 1 1 I 1 0 1 - E S R T I 0 1 I-I I I 0 1 I I 0 1 suns P C T S E H ( J O B S 1 0 3 2 5 0 0 2 0 0 1 5 1 C 2 2 6 0 0 2 0 0 1 1 0 T . R R -1 3 2 6 3 1 3 9 1 0 5 B a a o •ERR-1 - E R R 1 0 5 7 1 3 1 7 2 1 1 7 S O 6 6 3U 2 0 P O 0 8 6 3 3 f l 7 1 2 7 8 6 1 .11 5 0 3") 9 3 6 2 0 2 9 9 2 7 2 9 1 0 1 1 0 1U 0 6 17 0 0 1 2 H 0 1 0 0 0 0 0 1 2 8 1 B 6 * • » * * * * * * G U T T f l A W S C A L E 1 Y E S ) 0 S I X G S U R A C C S U R F A C E A C C E S S I B I L I T Y , D I V I S I O N P O I N T = 1 . CO u . s c c c s U H D S S I R A H L E C O N D I T I O N S D I V I S I O N P C I HT 1 . 0 0 N O D A L flEAUUREr'.SNr O P K O D S t T T Y D I V I S I O N P O I N T - 3 . C O H S I H U Q NR IC .HE-ORnc .O I ) Q U A L I T Y D I V I S I O N P C I NT 2 . 0 0 C 1 9 7 C C U M U L A T I V E A P A R T f l B K T G R C W T H U P T O 1 9 7 0 D I V I S I O N P O I N T - 1 . 0 0 A S T A RT S O O F A P T A T T i n R O F S I M S T A R T m v i s r o N P C I N T = 1 . 0 0 L O T S I Z A v A : i A n r L O T S I Z E D I V I S I O N P O I N T 2 . 0 0 B U I L A G a n I L D I t I G A G E n I X T U R E D I V I S I O N P C I NT = 1 . 0 0 Z O M 2 0 N I K G D I V I S I O N P O I N T = 2 . C O 1 F O R V A L U E S U A L T O D I V I S I O N P O I N T A N D A B O V E * * * * * * * * * * 1 T E K . . R O D A L B E S P . . 0 1 I 0 1 I 1 - E R R - 1 - PR R- 1 I I I I B 9 1 0 ior 0 1 0 J S I- - E R R I I I I I 6 I 1 6 1 1 6 1 I I- - E R R I I I I 7 1 3 0 I 1 2 1 1 I 1 r I I 6 I 2 1 1 3 0 1 I 1 1 i I I 5 I 1 0 1 1 0 1 I I 1 I I I « I 0 1 1 1 0 1 I I 1 I I I 3 I 0 O I 0 0 1 I I I I I I 2 I 0 0 1 0 0 1 I I 1 I I I 1 I 0 0 1 0 O I I l I I i 1 0 I 0 0 1 0 0 1 S U B S 7 I S 7 I B P C T S 2B 7 2 2 B 7 2 S i m o n s 0 8 1 2 5 2 0 2 1 I 0 1 I 0 1 I 0 1 T 0 1 I 0 1 I 0 1 T T O T A L — I- F F F - 1 - F a n - 1_ E R R - I- EI> R- 1- E R R — — I- E R R — 1 I I I I I I 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 I I I I I I I I I I I I 7 1 3 0 I 2 5 1 0 7 1 0 7 1 0 7 1 0 7 1 7 I I T I I I I I 1 T I I 11 0 3 1 0 ir 0 I t 0 3 1 0 3 1 0 31 3 E R R I I I I I I t I i I r I 2 1 0 3 I 1 2 3 1 2 1 1 2 1 0 3 1 0 3 1 3 1 - - E R R I I I I I I I 1 1 I I 0 1 0 1 1 0 1 I 1 0 1 0 i i 0 1 1 0 11 1 I I- -I RF 1 I I I I I I I I I 0 1 1 0 1 1 0 1 0 11 1 0 1 0 1 I 0 11 1 I I I- - E R R I I I I I I I I I 0 1 0 0 1 0 0 1 0 . 0 1 0 0 1 0 0 1 0 0 1 0 I I 1 1 - - E R R I I I I I I I I 0 1 0 0 1 0 0 1 0 0 1 0 O I 0 0 1 0 0 1 0 I I 1 I I- E R R I I I I I I I O I 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 i I I I I I- E R R I i I I I I I 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 2 0 u 2 1 « 2 1 2 2 3 2 2 1 0 2 5 0 2 5 2 5 BO 1 6 8 1 1 6 B 9 2 B 9 2 0 1 0 0 0 1 0 0 2 3 1 3 0 2 0 2 0 0 . 0 0 0 2 6 TABLE A.c«-3-I GUTTMAN TABLE A L L SUBWAY L I N E S , A L L STATION SUB-AREAS (128) TABLE A . c r 3 - H GUTTMAN TABLE A L L SUBWAY L I N E S , STATION SUB-AREAS WHICH RECEIVED GROWTH (25) BETWEEN 1959-1970 • « • « 4 » * « • G U T ! B A N S C A L E ( 8 f G A 1 I V E ) 0 S C1970 D I V I S T O K P O I N T = 1 .00 A S T A R T fc'n O F A P T A T T I K E O F S I B S T A F T C I V I S I O K P C I HT = 1 .00 Z O N Z O ? : i ! l G D I V I S T O N P O I N T = 2.00 R E I C H O N E I C . R E O R M C O C Q U A L I T Y E I V I S I O N P O I N T • 2.00 H O D A L DFASBaSlMr CF H C D A L I T Y D I V I S I O N P O T N T - 3. 00 S U R A C C S U R F A C E A C C E S S I i l l l . l i t , E I V I S I O K P O I N T = 1 .00 C O B E E V C O B B C R C I A L L E V E L O P B E S T D I V I S I O N P O I N T - 1 .00 L O T S I Z A V A R A G E L O T S I Z E C I V I S I O K P C I N T 2.00 RESP = 1 FOR VALUES EQUAL TO DIVISION POINT AND ABOVE * * « • * ITER.. LOTSIZ SURACC E 8 G A T 7 I , V E 6 0 1 1 0 1 1 0 f R R I- E R R I - F . R R -I I 0 21)1 0 281 0 BSBI I I I 1.11 1 21.1 1 I IRR1 I I 101 8 20) 9 I I 1(. 1R I 21 I 1 11 I 1 1 I I I 01 I I 0 I I I 01 I 21 1 I 21 I I 21 I I 01 I 1 01 I 1 I 0 I-F.RF-I 281 0 t I 261 1 t I 111 11 • EH R I SI I-I HI I I 01 I I 01 I I 01 1 I 0 I-ERR-I 281 0 I I 28 I i I I 151 6 I I 71 » -ERRI I UI 2 I I 51 0 I 1 11 1 I I 51 5 I I 01 0 1 T 0 I-F P R-I I 261 I I 221 I I 71 I t 5 I • ER RI I 1 I 10TAI 81 I-I I 01 I SUBS PCTS ERRORS 71 57 113 27 118 38 80 63 26 37 29 12 91 71 18 32 25 18 9 6 75 18 28 22 ICO 26 78 20 12 n — 1 _ IBB- ]- ERR — 1 t I I 281 0 28 1 0 281 I I T 291 0 291 3 261 1 I I I I I 271 0 28 I 1 271 I I I I 1 I 91 1 101 2 91 I I I I I I 51 3 HI « 31 I I I I I I m 10 21 3 9T EIIR I I I I I T 01 6 21 2 61 I- -ERR I I I I I 01 5 01 5 01 I 1 - ER RI I I I 01 b 0 I 0 01 102 25 103 20 108 80 20 80 16 8« 0 20 0 20 0 128 C A S E S H E R E P R O C E S S E D 0 ( O R 0.0 P C T ) W E R E B I E E I N G SUBFILE YONGE G U T 1 B A N C A I 6 ( r o S U R A C C S U R F A C E A C C E S S 18 11. I T Y , U N D C O N U N L J E S I R A S L F . C O N D I T I O N S K U D A L BF.ASmsEli^ NT O F N O U A L T T Y NKIGIIO N M M I U U I I I I C O D ' . X I A W T Y C 1 9 7 0 C U B U L A T I V E A P A ItTM E NTGR O U T 1 I U P T O 1 9 7 0 A S T A R T N O O F A P T A T T I B ? C F S I B STAtT L O T S IZ A V A R A G E L O T S I Z E B U 1 L A G U U I L ' J I I U ; A G E B I X T I R K Z O N Z O N I N G 1 F C R V A L U E S E O U A l TO EIV1SION P O I N T A N D A B O V E T A L ) 11 S I B G D I Y I S I O K P O I N T = 1. 00 D I V I S I O N P O I N T = 1. 00 D I V I S I O N P O I N T U . 00 D 1 V I S I O N P O I N T 2. 00 D I V I S I O N P O T N T 1. 00 D I V I S I O N P O I N T 1. CO D I V I S I O N P O I N T 3. 00 D I V I S I O N P O I N T -- 1. 00 D I V I S I O N P O I N T * 2. 00 • • • * * « * * * « » RES p = ITEB.. HEIGHQ C1970 AST R E S P . . 0 1 1 0 1 I 0 ERR- j _ KKR — 1 -ERR T I I 0 9 1 0 61 0 6 T 0 T I- -ERR I & 1 I I. 8 1 0 1 1 0 1 I 0 t I- E I I R I ' I I 7 1 1 1 I 1 l : 0 I I I I 6 I 2 01 2 o; 0 I I I I 5 I 1 01 1 01 1 I I I U I 2 01 2 o: 2 I I I 1 3 I 3 01 3 0. 3 I I I I 2 I i or 0 I 3 I i I i 1 I 0 01 0 o: 0 I i I i 0 I 0 01 0 o: 0 SUBS 12 8 12 8 9 PCTS 60 H O 60 uo U5 ERRORS 0 2 0 1 0 0 ERR-0 ER fi-l l 0 1 T-ERR-I 1 1 I I 21 I I 21 I I II -EPRI I 0 I I-I 01 1 I 0 I I 2 - ERR 0 E R i t -3 - E R R 8 12 UO 60 li 0 0 I 0 I 0 12 8 12 6 0 00 6 0 0 3 1 0 1 1 0 I I TOTAL -ERR I-ERR 1 I 61 I I 1 I I I 21 I I 01 I I 11 I I 21 I I 31 T I 61 I I II I I 21 I I 21 I I 11 I I 21 I I 31 I 21 - E R R I I 0. 16 20 80 1 0 3 15 3 31 I I 0 1 0 OT I Eft RI I I 01 0 01 — I 1 17 0 20 85 0 100 0 0 0 TABLE A.C^3-IH GUTTMAN TABLE ALL SUBWAY LINES, STATION SUB-AREAS WHICH DID NOT RECEIVE GROWTH (103) BETWEEN 1959-1970 TABLE A.C3-IV GUTTMAN TABLE SUBWAY LINE YONGE, ALL STATION SUB-AREAS (20) • • • • ***** G U T T r A N SCALR ( TES ) U S I N G SOIIACC SURFACE ACCESSIBILITY, DIVISION POI NT 1. 00 DNDCON UNDESIRABLE CONDITIONS DIV IS ION POINT = 1 . 00 NODAL BEASUHSflENr OF NODAI.ITI DIVISION POINT 1 . 00 HLIOHO KEIGI1BOHI1CUD OUALITT DIV IS ION POINT = 2 . CO CI970 CUMULATIVE A PARTBEHTGROWTR UP TO 1070 DTVISION POI NT = 1. 00 ASTA RT NO OP APT AT TIBE OF SIN STAFT DIVIS ION POINT = • 1 . CO LOTS IZ AV AH AG P, LOT S17.E DIVISION POT NT = 2. 00 BDILA C BUILDING AGE MATURE DIVISION POINT 1 . CO ZON 7.0HING DIVISION POI NT 2. 00 RESP - 1 FCR VALUES EQU A I T E R . . UNDCON NEIGflQ s u h a c c HODAL R E S P . . 0 1 I 0 1 I 0 1 I 0 1 I 1-ERR — I -EBR- 1_ ERR- ! - ERR— 1-I 1 I I ' I E 9 1 0 61 0 bl 0 61 0 61 S I- ERRI I I I I I I I 6 I i i A 1 11 U 21 0 21 I -ERRI I I I 1 I I 7 I 0 01 0 01 0 OI 0 01 I 1- - ERR I I I I I I 6 I 0 0] 0 OI 0 01 0 01 I I I- ERRI I I I I 5 I 0 o: 0 OI 0 01 0 01 I I I I-I I I I a I 0 o: 0 01 0 01 0 01 I I I I I I I I 3 I 0 01 0 01 0 01 0 01 I I I I I I I I 2 I 0 OI 0 01 0 01 0 01 I I I I I I I I 1 I 0 01 0 01 0 01 0 0 I I I I I I I I I 0 I 0 0 0 01 0 0 1 0 01 S U B S 1 7 1 7 0 B 0 8 P C T S u 88 1.1 Ob 0 1 C 0 0 1 0 0 ERRORS 0 1 1 0 0 0 0 0 CIVISIOH POINT AND ABOVE 1 1 0 1 1 0 1 I-ERR 1 -ER R 1 I 61 0 61 0 I I I 21 I I 01 I I 0 I I I 01 -EFR1 I 0 1 I-I 01 ] I 0 1 I I 01 I I 0 I I t 21 I I 01 01 I I 01 I I 01 - ERRI I 01 I-I 01 I I 01 I I 01 0 -EH R 0 E R R I 0 - I - E R R -1 61 0 I I 21 0 I I 01 0 I I 01 0 I I 01 0 I I 01 0 I I 01 0 I I 01 0 - E F R I I 01 0 I 1 1 TOTAL 1 I 6 1 6 I I 01 I I 01 I I 01 I I 01 I I 01 I I 01 -ERRI I 01 1 0 0 0 6 1 0 0 0 8 0 1 0 0 0 0 0 1 0 0 0 1 » G A T 1 V ) 0 S N G CI'770 DIV ISION POI NT = 1. 00 ASTART NO OF APT AT Til* OF SIB STAFT CIVISIOH PCINT = 1. 00 ZON ZONING DIVISION POI NT 2. 00 NEIGHO NEIGIIPORMCOD CUAL11Y CIVISJON PC I NT = 2. oo HOD A I BEASORE PENT OF NO C A LITY DIVISION POI NT = 3. 00 SUR ACC SURFACE ACCESSIBILITY, DIVISION POINT 1 . 00 COBDEV COBBEHCIAL CEV FLOPBENT DIVISION POI NT - 1 . 00 LOTSIZ AVAHAGE LOT SI7E CIV IS10N PCINT - 2. 00 RESP = 1 FOR VALUES EQUAL TO DIVISION POINT AND ABOVE »••••»»»«•• ITEB.. LOTSIZ SURACC HRIGHC. 0 1 1 0 1 •ERR I-ERR 0 2 E F R 1 3 E RH • ER R-0 0 IFF-1 I 0 I-ERR-I 21 0 I I 1 1 II I I 1 I -EE BI T 1 I I-1 01 1 I 1 I I I OI I 2 -ERR 0 • 1 I. 0 •EF.R I-ERR-21 I 01 I I 31 I I 11 I I 01 I I 11 - ERH I I II I-01 I 0 1 I T O T A L ERR 1 I 0 2 1 2 I i ' 1 31 1 I I 3 01 3 I I 0 1 1 1 I I 0 21 2 I I 0 II 1 I I 1 II 2 I I 0 St 5 ERRI 0 I 0 01 0 01 0 01 0 01 0 01 0 01 0 0 1 0 01 0 S O B S 13 7 12 0 11 9 ft 1 2 8 1 2 8 1 2 8 1 2 5 1 5 20 P C T S 65 35 60 oo 55 05 10 60 oo 60 00 60 10 60 25 75 E R R O R S 0 5 1 3 0 0 0 2 0 • 0 2 1 3 0 5 0 22 TABLE A.c-3-V GUTTMAN TABLE SUBWAY LINE YONGE, STATION SUB-AREAS WHICH RECEIVED GROWTH (8) BETWEEN 1959-1970 TABLE A.C-3-VI GUTTMAN TABLE SUBWAY LINE YONGE, STATION SUB-AREAS WHICH DID NOT RECEIVE GROWTH (20) BETWEEN 1959-1970 20 CASES W2Pr. PROCESSED 0 (OR 0.0 PCT) WERE n 15 5 ING Table A.c-3-VII summarizes the c o e f f i c i e n t s which measure the qu a l i t y of the Guttman analysis. The c o e f f i c i e n t of r e p r o d u c i b i l i t y i s calculated from the number of errors and should l i e i n the order of . 9 f o r a good r e s u l t . The c o e f f i c i e n t of  s c a l a b i l i t y indicates i f the hierarchy of the variables i s cl e a r (cumulative e n t i t y discussed above). Values around . 6 are considered good. Low values of t h i s c o e f f i c i e n t combined with low values of percent improvement c o e f f i c i e n t indicate that there i s high c o r r e l a t i o n among variables (which reduces, of course, the s c a l a b i l i t y ) . For a l l cases i n the table, t h i s c o r r e l a t i o n i s r e l a -t i v e l y high except f o r the Yonge l i n e . This corridor shows f o r a l l stations and f o r the sta-tions which received no growth a h i e r a r c h i c a l pattern of environmental f a c t o r s . Based on the Guttman analysis, f o r each Guttman table a new variable was constructed which s u b s t i -tutes or summarizes a l l the environmental f a c t o r s . The frequency d i s t r i b u t i o n of these scale values are shown i n Table A.c - 3 -VIII ( 1 - 3 ) . In Table 1, 23 s t a t i o n sub-areas have a scale value higher than 7 (there are 25 s t a t i o n sub-areas which ac t u a l l y COEFFICIENTS OF GUTTMAN ANALYSIS GUTTMAN SCALE received growth). 1 This indicates that the environ-mental factors are l i k e l y to explain apartment growth well. A l l Subway Lines Subway Line Yonge A B C A B C A l l Stations .84 .1 . 3 5 . 9 2 . 2 3 . 7 5 Stations with Growth .88 .02 .16 . 9 7 . 0 0 . 0 0 Stations* without Growth .81 .1 .26 .86 . 2 5 .64 * For t h i s case, the environmental values were reversed i n order to obtain a cumulative, unidimensional scale. A C o e f f i c i e n t of Reproducibility B Percent Improvement C Co e f f i c i e n t of S c a l a b i l i t y TABLE A.c - 3 -VII COEFFICIENTS OF GUTTMAN ANALYSIS 1 . Table 3 , which i s the complement to Table 1 with the reversed environmental values, shows 2 5 s t a t i o n sub-areas with scores of l e s s than 4, as was to be expected. G U T T M A N S C A L E A N A L Y S I S TABLE A . C - 3 - V I I I COMPARISON OF THE E N V I RON f E NT A L C A RACIER 1ST I CS OF THE STATIONS WHICH HAD APARTNENT D E V E L C P * E N T 5 IN THE T INE PERIOD 1 9 5 9 - 1 9 7 0 WITH THOSE OF THE STATIONS WHICH HAD INC GPOWTH T A B L E 1 GUTTMAN SCALE VALUES FOR ALL STATIONS ******* SCALE ABSOLUTE ADJUSTED C L N L L A T I VE VALUE FREQUENCY FREQUENCY ADJ FREO ( P E R C E N T ) ( P E R C E N T ) 1 1 0 .8 0 .8 2 15 11 .7 1 2 . 5 3 40 31 .3 4 3 . 8 4 29 2 2 . 7 66 .4 5 13 10 .2 7 6 .6 6 7 5 . 5 8 2 . 0 7 8 6 .2 88 .3 8 5 3 . 9 92 .2 9 10 7 .8 I C O . C TOTAL 128 1 0 0 . 0 1 0 0 . 0 V AL ID 0 BS ER VA T ION S 128 TABEL 2 GUTTMAN SCALE VALUES FCR THE STATIONS 4*44*4# WHICH HAD APARTMENT DEVE LOPNENTS SCALE ABSOLUTE ADJUSTEC CUMULATIVE-VALUE FREQUENCY FFEQUENCY ACJ FPEQ (PERCENT) (PERCENT) 4 1 4 . 0 4 . C 1 4 . 0 8 . 0 6 3 12 .0 2 0 . 0 7 5 20 .0 40 .0 8 5 2 0 . G 60 .C 9 10 4 C . C 100 .0 TOTAL 25 1 0 0 . 0 100 .0 VAL ID OBSERVATIONS 25 G U T T M A N S C A L E A N A L Y S I S CONTINUED 1 NEGATIVE' ENVIRONMENTAL SCORES 'NEGATIVE' ENVIRONMENTAL SCORES ARE THE COMPLIMENTS TO THE •NORMAL• ONES. THEY INDICATE STATIONS WHICH ARE NOT FAVORABLE TO APARTMENT DEVELOPMENT. THEIR SCALE VALUES SHOULD THEREFORE BE H I G H IF A STATION IS N O T SUITABLE FOR APARTMENT DEVELOPMENT. TABLE 3 GUTTMAN SCALE VALUES FOR ******** NEGATIVE ENVIRONMENTAL SCORES SCALE ABSOLUTE ADJUSTED CUMULATIVE VALUE FREQUENCY FREQUENCY ADJ FREQ (PERCENT) (PERCENT) 1 5 3.9 3.9 2 8 6.2 10. 2 3 12 9.4 19.5 4 7 5.5 25. 0 5 1 1 8.6 3 3.6 6 28 21.9 55.5 7 29 22.7 78. 1 8 28 21.9 100. 0 TOTAL 128 100. 0 100. 0 VALID OBSERVATIONS - 128 simulation fl.d (See Chapter 7 .2) The moving averages 2, 3 and 5 should be i n t e r - CONTROL MEASUREMENT correlated among themselves. However, there could FOR MOVING AVERAGES be a p o s s i b i l i t y that the moving average 5 (or even 3) could be biased, i f i n many cases f o r example the f i r s t , second and t h i r d time period of the moving average have apartment growth, but the fourth and f i f t h not. Some of the corresponding moving averages of the lower-order could then be zero (and not greater than zero as the higher-order moving average). To check the v a l i d i t y of the moving averages, they were correlated among them-selves. The r e s u l t i n g c o r r e l a t i o n c o e f f i c i e n t s are quite high, as shown i n Table A.d-1-1. TIME PERIOD CORRELATION BETWEEN S22 S22 S32 S23 S23 S33 S24 S2k S25 S25 S35 S 3 2 S52 S52 S33 S53 S53 S34 S5k S5k S35 S55 S55 CORR. COEFF. .01 .89 .95 1. .92 .95 NA* NA* .91 .85 .77 .99 SIG. LEVEL .002 1004 .001 .001 .013 .001 .016 .001 .021 .001 No. of CASES 7 7 7 5 5 7 3 3 5 10 7 7 NA - Not applicable because number of cases i s too small. TABLE A.d-1-1 INTER-CORRELATION AMONG MOVING AVERAGES (See Chapter 7.2) The Histograms compare actual and simulated MODEL CALIBRATION; growth f o r each time period and f o r each l i n e * HISTOGRAMS The apartment growth i s given i n dwelling units and as a percentage of the t o t a l apartment growth of a l i n e f o r each st a t i o n sub-area. The number of stations and st a t i o n sub-areas and the t o t a l apartment growth f o r the given time period are shown at the bottom of the histograms. S I • g l I I ! I SOBBAT COBRIOOB I IEAR 1959/1960 0 t A P A R D I C C I I B E PEBIOD T H E I T G I C B T 20 1 JO 1 10 Or AP1BTSEITS ABSOLOT I • 0 X 50 m i n i i i i i i i I I I i m i n i m i I I I I I I I I I I I i i i i m i i m i i n m m i i i i x i i i i i ECIIB BB CTOI BE SB SB DATIS BB SI L L S BE SB SS ST. BB C L U B BE SB SE SOBBE BB ( B I L L BE SB SE j BOSE BB ! DALE I E SB SE T O T l l I B TIBE PERIOD 1 TBE SOBBAT L I I E I 0 I G E I (AD 5 STATIOIS BITS 20 STATIOI SOB-AREAS. 678 IS THE I0TAL I OP APABTB EBTS SOILI I I TIRE PEBIOD 1 I I I I I I I I I I I I I I I I I I I X I I I I I I I I I 0 0 27) AO 0 0 0 0 0 0 0 208 0 0 V»7 29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30 0 0 678100 E P P E C T I T SOBBAT COBBIDOB TEAR 1959/1960 I A P A R T T 0 B G S TIBE PEBIOD B E I T GtOBTH 20 I 10 OP APABIREBTS ABSOLOT X AO X SO • 1 0 0 BE i l l i n i u m I I I I I I t i z i i i z i x i i i i i x i i i i i t 210 38 SB 0 0 SE I I X I I I I I I I I I I I I I X I I I I I I I Z I I I 1S5 28 BB 0 0 I S 0 0 SB I I m i n i m i i m m i m m m m 180 33 SE 0 0 I B 0 0 IE 0 0 SB 0 0 SE 0 0 BB 0 0 BE 0 0 SB 0 0 SE 0 0 I B 0 0 IE 0 0 SS 0 0 SB 0 0 ROSE DALE TOTAL t l TIRE PERIOD 1 THE SOBBAT LIRE T O I G B BAD S STAtlOIS BITH 20 STATIOI SOB-AREAS. 5«S tS TBE TOTAL I Or APARTR SITS BUILT 11 T I K I PEBIOD 1 595100 S I B O L A t E O A P SOBBAI CORRIDOR T O B TEAR H 6 1 / 1 9 6 2 A I T G E TIBE PEBIOD B 1 I T 6 I O I T B 0 X S X 10 I ts X BO Or APABTRESTS ABSOLOT X 20 X 25 BGLIP l » 0 0 CTOB BE i i i m i m m i i i m i i n m 159 13 SB 0 0 SE i m m m m n i i m i m i i i s n 12 DATIS RB ' 0 0 BILLE BE 0 0 sv i i i i m i i i i i i x i m m m i i 156 12 ss i i i m i i m i i i i i i m i m n 155 12 ST. IB i m m i m m i n m i m i m i i i i m i i i i i i i i i i 268 22 CLAIB i s i m m i i m i i i m m m i i i s « 12 SB 0 0 ss i n i m m i i i n i i m i m n 157 13 SORRE RB 0 0 BRILL BE 0 0 SB 0 0 SE - .„:... 0 0 BOSE I B 0 0 DALE I ! ' 0 0 SB 0 0 I SB 0 0 TOTAL 1203190 II TIRE PEBIOD 2 THE SOBBAI LIRE I O B G E BAD 5 STATIOIS I I I R 20 STATIOI SOB-AREAS. 1203 I S THE TOTAL I Or APABTB EBTS BOUT I B TIRE PEBIOD 2 E P E E C T I V E A P A R T SOBBAI COHBIDOR I O B G E IEAB 1961/1962 TIRE PEBIOD E S T G I O B T B 0 X S X 10 X 15 X BO or A PAST]EBTS ABSOLOT X I i m m m i i m i n i m u m i n n m i n i m i i i m i l i u m i n n t i n n m i i EGLII BB GTOI IE SB SS DAVIS I B TILLS BE SB i i i i i i i i i i m i i m i i m i i i i ss m m i n m i m i i m ST. BB i i i i m i m i i i i i i i i i i i i i i i i i i i i i i i i i n CLAIB BE SB SE 11 m m m n m i n i m u m i n n m m i SOBBE BB RBILL BE SB SE BOSS BB DALE BE SB SE TOTAL I I TIRE PERIOD 2 THE SOBBAI L i l t T O B G E BAD S STAHOSS BITH 20 STATIOI S0S-AIEAS. 1121 IS TBE TOTAL I Or APABIREBTS B0ILT IB TIRE PEBIOD . 0 0 255 22 0 0 1S9 IB 0 0 0 0 153 13 120 10 21S 19 0 0 -0 0 222 19 0 0 S I B D L A T E SOBBAI CORRIDOR TEAR 1963/196B 0 X 0 A P A B T B I O B G E TIRE PEBIOD B I T C B 0 B T R 10 X 20 X 30 X BO OP APIBTSEITS ABSOLOT X «0 X 50 E C L I I I B 0 0 CTOB BE m m i m i i i m i m m m t 339 26 SB 0 0 SE 0 0 DATIS BB 0 0 BILLS BE - 0 0 SB m m i n m i i i m i 281 19 ss i m m i m n i 198 13 ST. I B 0 0 CLAIB IE m i n i m i 158 10 s i 0 0 sr. i m m i i m i i m i i i i i m m i t2S 29 SORRE IB 0 0 B H I L l BE 0 0 SB 0 0 SE 0 0 BOSE SB 0 0 DALE, BE 0 0 . S B 0 0 SE 0 0 TOTAL 1A51130 IB TIRE PEBIOD 3 THE SOBBAI L I U T 0 B C t BAD 5 STAt lOIS B U R 20 STATIOI S0B-ABEAS. 1951 IS THE TOTAL • 0T APABTRESTS BUILT I I TIBS PEBIOD 3 E T P E C T I T E A SOBBAT c o s a i o o a I o TEAS 196J/196B P A R T B G E TIBE PEBIOD E S T C B O B T B STATIOB 0 X BO OP A PART B BITS ABSOLOT X «0 X SO EGLIS BB 0 0 GTOI BS i m i I I m m I I i I I I m i 310 23 SB 0 0 SS 0 0 DATIS SB 0 0 T ILLS i : 0 0 SB 0 0 s : i m i i m m t i i m i n i m u m 100 29 ST. IB i m m i i m i m • 212 15 CLJ IB SE 0 0 SB 0 0 SE i m m i i m n i i i m m m i m i (16 31 SORRE SB 0 0 I B I L l IE 0 0 SB 0 0 SE 0 0 BOSS IB ' 0 0 DAIS BS 0 0 SB 0 0 SS 0 0 TOTAL 1)18100 IB TIRE P H I 0 D 3 THE SOBBAT U S E T O B G E BAD 5 STIt lOBS SITR 20 STATIOI SOB*AREAS. 13)8 IS TBI TOIIL I Or APia t f l f lTS BUILT IB I IBE FERIO0 3 i i • gii i i D A P A B T R B I S O B B A T c o s a i D O B i o s s E I U 1 1 9 6 5 / 1 9 6 6 T i a c P E R I O D T C B 0. I T S T A T I O I «o or A F A R T R S S T S A B S O L O T X 0 * 10 I 20 1 30 « « 0 * 5 0 i i x i x x i x i x x i x x x x i x i x i x x i x x x i i x x x x i x x x x x z x x i x x x t e n s I B c t o i I E S B SB B i n s «u B I L L S I S sa S E I X X I I I X X X S T . I B C L U B I E S B SB I X X H I I I I X I I I X I I I I I I X I X I I I I I I I I I X I I X I I I I I I I I I X Sonne i n • a m « E S B SB • O S E an 0 1 L B I E S B S E T O T A L 2 0 8 0 1 3 0 X I T I B S P E B I O D « T H E S O B B A I L I B ' T O R E S B A D 5 STATIOIS BITff 2 0 S T A T I O I S O B - A R E A S . 2 0 8 « I S - T H E T O T A L I O f A P A B I R E B T S B O U T I I I I R E P E R I O D < 0 0 950 15 0 0 0 0 0 0 0 0 0 0 IB 3 s 0 0 0 0 0 0 9S1 •s 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 c r r t C T I B f A P A R T H E R T S B C B T I S O B B A I C O R R I D O R I 0 I G E 1 E » B 1 9 6 5 / 1 9 6 6 T I D E P E B I O D » • i o o r A P A B H E S T S S T I T I O I I B S O L O T X 0 * 10 X 2 0 I 3 0 t « 0 * S O Z G L I I I B C T O I I E X I Z I I I I I X I X I I I I I I S B S E I I I X I I I I I I X X I X O T I S I B B I L L S I E 51 S E I I I I I I I X X I I I X I I I I I I I X X U I I X I I S T . 11 ' C L U B I E S B S E I t l l l l l l l l l X I X I I X I X I I I I I Z I X I I X t X I I X X I X I S O R R E I B I B I L l I E S B S E R O S E I B DALE B S ' SB S E 0 03 9 3 1 1 0 0 28 8 1 9 0 0 0 0 0 0 58 0 2 4 0 0 0 0 0 0 78 9 3 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 T O T A L 1 9 9 5 1 0 0 i I B T H E PERIOD • • TBI SOBBAI .IIBE T O B C E B A D 5 S T A T I O I S R I T H 2 0 S T A T I O I S O B - l l f A S . 1 9 9 5 I S T H E T O T A L I O r A P 1 R T R F I T S B D I L T I I T I N E P E R I O D I S I 8 0 L A E E D A P I R t B B I T G I 0 B T » SOBBAI C O R R I D O R T 0 B G E I E A R 1 9 6 7 / 1 9 6 8 T I R E P E B I O D 5 10 o r I P A B T R E S T S S T A T I O I I B S O L O T X 0 t 1 0 % 2 0 1 3 0 1 » 0 1' 5 0 0 0 S J 7 2 5 0 0 0 0 0 0 0 0 0 0 10 7 7 0 3 0 0 0 0 0 0 16 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 » o b T O T A L 2 0 7 7 1 0 D XI TIRE PERIOD 5 T H E SOBBAT LIRE T 0 I S E B A D S S T A t l O I S I I T H 2 0 S T A T I O I S O B - A R E A S . 2 « 7 7 I S T B E T O T A L I o r A P A R T R F I T S B O U T I I T I R E P E R I O D 5 t G L t l I B C T O I I S I X I I I X I I I X I I I I I I I I I I I X I I X I S B S E O A B I S I B B I L L S I S S I S S I X X X X I X I I X I X I I X I I X X X X X I X I I X I I I I I I I I X I I I X I X X S T . I B C L U B I E SB . ., S E I I I X I I I X I I I I I I X 1 I I I I I I I X I I I I I I X ' S O R R E I B . ' IB I L L I E " , S B S E B O S E I B O A L B I E S I S B E F F E C T I V E A P A B T B S B T C 1 0 B T H SOBBAT C O B R I O O R I O I G E T E A R 1 9 6 7 / 1 9 6 8 T I R E P E B I O D 5 (0 OF A PARTS E H T S S T A T I O I A B S O L U T X O X 1 0 X 2 0 X 3 0 X 4 0 X S O E G L I I I B I X I I I t l X G T O . B E XI11AIAIIIIX SW s s i x x x i x x i i i x i x x x i i D A V I S I B T I L L B I E S B S E I X X X X X I I X X X I X X I I I X I I X I X X I X I X X X S T . I B C L A I R B E S B S B I I X X X X X I I X X X X X I X X I I I X X I X X I I X X I X X X I S O R B S i v B B I L L I E 168 1 251 1 1 0 0 369 1 6 0 0 0 0 0 0 6 0 S 2 9 0 0 0 0 0 0 70 S 3 0 0 0 6 0 SB 0 0 S 0 0 BOSE S B (j 0DALE I S 0 0 SB 0 0 SB 0 0 T O T A L 2 1 8 1 1 0 0 I I TIRE PEBIOD S T B S SOBBAT LIKE I 0 I G S B A D S S T A T I O H S I I T H 2 0 S T A T I O I S O B - A B E A S . 2 1 8 1 I S T H E T O I A L • O r A P A B T S E K T S S O I I I I I T I R E P E B I O D S S X R D L A F E D A P A B T R E I T C I O B T 8 SOBBAT C0RBIDO6 I 0 B G E T E A S 1 9 6 9 / 1 9 7 0 T I R E P E B I O D 6 10 OP A F A STB EITS S T A T I O B A B S O L O T ( O X 10 X 2 0 I 30 X RO X S O B G L I B B B I X X X I t l l l l l 2 0 0 1 1 CTOI I S 0 0 SB 0 0 S S I I I I I I I I I I I I 2 6 6 1 2 D A T I S BB 1 I I I I I I U I I 2 0 6 1 1 T I L L S I ! 0 0 SB 0 0 S E I X I X X I I I X I I I I 1 X I 1 I I I I I X X I I X I I I I I I I I I 7 8 0 3 6 ST. I B 0 0 C L A X B I E X l l l Z I I I X X I X I t l t l l l X I I I I I I I I I 6 2 6 2 8 SB 0 0 S S . 0 0 S O B B E B B , 0 0 BHXLL I E 0 0 SB 0 0 SE 0 0 BOSE I B 0 0 DALE I E 0 0 SB 0 0 SE 0 0 T O T A L 2 1 6 2 1 3 0 I I T I R E P E B I O D 6 T R E S O B B A I L I B S I O I G E BA D S STAriOIS B U R 2 0 S T A T I O I S D 9 - A R I A S . 2 1 6 2 I S T H E T O T A L I O F A P A R T R S I T S B U I L T I I T I B S P E B I O D 6 i r r R C T I I E A P A B T R B I T G I C B T B SOBBAI COBBI003 1 0 I 2 E I E A B 1 9 6 9 / 1 9 7 0 T I 9 S P E B I O D 6 1 0 Or A P 1 8 T 1 E I T S S T A T I O I A B S O L O T X 0 X 10 X 2 0 I 10 1 ( 0 1 S O E G L I I B B 0 0 C T O I I E I I I I I I I I I X I X I I I 3 0 0 1 5 S B . 0 0 S E I I I I I I I I I I I I 2 0 $ 1 2 D A T I S t J 0 0 1 I L L S I E 0 0 S B 0 0 S E i x i i i i i i i r i i u i i i i x i i i i i I X I I I I I I I n i x i x 78 3 3 9 S T . B B 0 0 C L A I i I E I l I I I I M I I t l X X I I 3 1 1 1 5 S B 0 0 S B I X I I I I I I I I Z I I I I I I X 3 S S 1 7 soR.o: BB 0 0 H i l l I E 0 0 S I 0 0 S E 0 0 B O S S I B 0 0 D A L I S E 0 0 S B 0 0 S B 0 0 T O T A L 1 9 9 0 1 3 0 IB TIRE PEBIOD ( T R S SUBBAI LITE f D B C I B I D 5 S T A t l O I S I I I H 2 0 S T A T I O B S O S - A R l l S . 1 9 * 0 I S T H E T O T A L I O F APABT.1EHTS B O I L ! t l F I R E P E B I O D 6 s I b o t i r i d i p a i t b e i t c i SOBSAI C08RID08 8 L 0 0 B B E S T OL 1EAB 1965/1166 H U E PIBIOD « -STATIOI 30 « • O OF I P I I T B E I T S ABSOLOT 1 «0 t SO ST.ce «• 0 0 OICE >E I I I I I I I I I I I I I I I I I I I I I 19 3 20 S> 0 0 SE 0 0 S M • a I I I I I I I X X I X X I I I I I X I I I I I I I X I X X I 28* 39 SUA IE 0 0 ss 0 .0 SE 0 0 BATS • V 0 0 • 1ST BE 0 0 SS 0 0 SE 0 0 C B B I S IV 0 0 T I E IE 0 0 sa 0 0 sr. 0 0 ossti is 0 0 CTOI IE 0 0 sa 0 0 SE 0 0 norrt • a 0 0 i n IE 0 0 sa 0 0 SE 0 0 Li I D S ia 0 0 M I I E IE 0 0 SB 0 0 SE 0 0 DOBD aa 0 0 *S 1. IE 0 0 sa 0 0 SE 0 0 KEELE IS xiixiixixiixxxixixixxixxixxxxxxxxxxixxixxxxxxxxxx »57 as IE 0 0 S» 0 0 SE 0 0 TOTAL 934130 X I TIBE PERIOD • TRE S08BAT LIIE B L O O R B E S T O L D BAD 9 STiltOHS SITH 36 STATIOI SOB-AREAS. 130 IS TRE TOTAL I OT APABTBEHTS BOUT I I TIBE PERIOD « E T r E C T I f Z A P A R I B E I T C I O SOSIAT CORRIDOR B L O O I W E S T O L D I BAB 1965/1966 T I B S PIBIOD * 0 I 20 f • 0 I CO « 10 Or APABTBEITS ABSOLOT % 80 * 100 ST.CE I i o i c e as m i n i m i sa s e s p a ia m i n i m D I I A I E SS SE BATH IS 0«ST HE SS SE C B S I S I S T I E IE sa SE < . • ossn is CTOI SE SB SB o o r r E i s * I I I VE sa SE L A I D S SB DOBIE IE sa SE DOID I B AS B. IE SB SE k e e l e ia xxximixiixxiiixxxxiixrxiixix IE SB SE TOTAL 681100 I I T I B E PERIOD « THE SOBWAI L I K E B L O O R B E S T O L O •AD 9 S T A H O I S WITH 36 STAT 103 SOB-ABEAS. 881 I S T R I TOTAL I Or APABTBEITS B O U T I I T I B E PEBIOD • 0 0 192 21 0 0 0 0 169 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S20 59 0 0 0 0 0 0 SI 1 U l l ! D A P A I T B E I T G I O B T SOBBAI CORRIDOR B L O O I B I S T O L D 1EAR 1967/1968 TIBE PEBIOD S •0 Or I P I I T B E I T S E r r E c t i i E SOBSAI CORRIDOR B IEAI 1967/1968 A P A R T f l E I T G I C L O O I B E S T O L D TIBE PEBIOD 5 •0 Or APABTBEITS STAT10I ABSOLOT I STAIIOB ABSOLOT % t l 20 « 80 * «0 I 80 1 100 0 % 20 I 10 1 60 t 80 X 100 ST.dE I B 0 8 ST.CE I B •» 0 0 IE u n t i l 177 1« OESE I E IlllXXXXXXXt ISO 23 SB 0 0 sa 0 0 SE 0 D : SE 0 0 SPA I V IIIIIXXIX 21S 11 SPA I S 0 0 DIIA I E 0 0 DIIA I E 0 0 SS 0 0 SB 0 0 SE 0 0 SE 0 0 b a t b BB 0 0 BATB I I 0 0 ORST I E 0 o ; OBST I E 0 0 SB 0 0 S I 0 0 SE 0 0 ' SE 0 0 cans ia 0 0 - CBIIS I S 0 0 Mi l .IE 0 0 TIB I E 0 0 SB 0 o ; SS 0 0 SE 0 0 SE 0 0 OSSIl I S 0 0 OSSII I I 0 0 CTOI I E 0 o . CTOI • X 0 0 SB 0 0 S I 0 0 SE 0 0 1 SE 0 0 oorrE • a 0 o ; d o f t e I S 0 0 i n BE 0 0 : i n I I 0 0 SB 0 o • ss . 0 0 SE 0 0 ; SE 0 0 LAUDS I B 0 o ; I A I D S I S e 0 DOWSE I ! 0 0 . DOBIE I E 0 0 SB 0 8 i SS 0 0 SE 0 0 SE 0 0 DOBD I B 0 0 DOID I S 0 0 IS a. I E 0 o • AS 1. I E 0 0 SB 0 0 SS 0 0 St 0 0 • SE 0 0 I E I L E I S i i i i i i m m m m i i i m m i m i i i 803 47 . KEELE sa iimmiiiixiixmiiiiiii ixxxiixxixxt • 80 76 I E 0 0 ; I E 0 0 SS 0 0 sa 0 0 SE 0 0 SB 0 0 TOTAL 1195130 TOTAL 630100 I I TIBE PEBIOD 5 THE SOBWAJ L i l t 8 L 0 0 1 1 B S T 0 L D I I TIBE PERIOD 5 TRE SOBSAT LIIE B L O O I S E S T O L D •AD 9 STAtlOIS WITH 36 STATIOI SOB * AB EAS • HI 0 9 STUrlOIS WITH 36 S T A T I O I SOB-ABEAS. 119S IS THE TOTAL i o r APiBrSEKTS BUILT I I TIBE PEBIOD S t 33 IS THE TOTAL 1 Dr APASIIlEirs BO 1 LT IB TIBE PEBIOD S no ft « o •« VI O 0. oo ! H ft* O o » M O CO fr* • a w o frJ ad 3 3* O* H t» M CD W Q cn M M M M U O w U 0 II Is I ii ii i ! n | > CD CT* 91. °" I -J M M ' fr* O fr* 3 , k* c V) — zt, U u 3k t» ft* • K « V! — •« « -O *J « " O -« — •J ca fr-B) (fl M C w n M »« •-«»-»-•J i* ae »- •* *~ ic. K «*l fr-» JC to y *t D r< e> •« -i fci . tw E S! (S * an »•« C 3 a ~4 o ft. *-» il ft* a:*-: «o tr, »-u <* »— «c M H . 33-O CP-M •« A •C C f- . •s c o. a •« • •w O o o o M M M M ' o • O W o M t-i B tu • Cl. « t* B 4 0 H B at « O ft. o c* -HS •J O *> D *-IM B 4 • M H ca 4 D M M VI M K M H W M »1 M M M » 22 * S • D *- " * i o B th M B •> o a M I X H & > o o * "< CO CM M %n •J m «* *• O N a . »» o - v> M « to to flu t* •O ; • C ! H 10 W f. fr* O Id fi «* O * M mi tC, f. t^  hi at r, M o ti tt tw o M »• V) M l« H »J K fr- ft» ** IV to "t M t» a, *.«».< o r, p. in -» u ft. re a: o t- M *» Dl *o hi bl •< "> O Q •>< M O t-i M .1 M «*•"«: N V I H Vt M *> M B Ci ts m rt) M B M l * O O t- o n •-tu H ' O ul •= D IN t» ^t m « o — tn o cw to o *i •< *• •w O o m «i m o O r* » N. I O M3 C »- o •4 M at « H a < •* O M I-VI t" Vl s! i s .25 ss •x •< M ta w U m M ^ a= a. M O D W Bl B a mi O K ^ w: » a> o o OS IH IH 4/> >J O O N w f- m M in M U M B Ml (A © Or tt O - ! - » - • tu o o O CO O f* O o ot • K V. m* •* fr. AC M o -t in fr* o v a O n to t- H t-V) CO W -1 M ' IC H s i ft. x o cm M O Ml M m CM H a MI «j o »0 H W t- 10 at)B|]!iHVUIillll1Btiail»H»»|g](i|iH • MtnVlnBMMmBnVtBBVltfIMM „ N U B tatd M M M ml M J M M >* U M H O • O -t | M « J O M - B O W « | •! M O O V) tw « * M B *) O B M M M U M fr. o o . Q H f r l O tw it «i ta (E fr* B f H H Cw M k ) < A H B M t>» ca - 2 S I/I M t>-™ fr* K D ' fr* J « O —J « o CL. O O ft-o o m at o O f-a v M *~ a* V o *o u t> *- at o I Eft ee H ca •*• «*J D ftJ fr-1/1 ft* l/l V> ft H to ta fr* —• « o <<V)0 0> tO tA 1 -I ft. o o • « o O r - • 0 OA 01 S. as O O >o = " 8 • « fr* co *» ** O fcJ fr* IA *M M m • £ « ! if! Hi si IS. Is li is II is I U O IN < OO c CD is n; i . " pa ii si i : 1= ii 1= II H I y Vt M M w» tu H fr* *4 aa o •c tyi o cw co o ft* O s co c 9 O C > o S S ce "ft ISH i l l hi h: s p Ess 1=1 J l II \l 8s i si ii ii O ve a M • » to H M f «• O f* a ca O ** irt O ct. a o •«•«•-P. o o •*. t* o > o o > £ o o M a* fti * c CO t-4 55 hi hi ;ii II - S I i i i 11 II I? IE I ii II 5= i! In I I I D L I ! ! g iriBT R E I T C I O I T B SOBBAI COR RIOOB B L O D B E A S T 1EAI 1967/1968 TI3E PEBIOD 5 10 Of APABTRESTS STB HOB IBSOtOT t OS 10 t 20 1 30 * 10 I 50 SHCBB BB 0 0 ooiax BS 0 0 SB 0 0 SE BIIIIIIIIIIIIIlIIIIIIIIIIIIIIIIlIIIIIXIII.il 10 37 • 3 C1STL IB 0 0 PSAIA IE 0 0 SB 0 0 SE 0 0 BBOID BB SIIIIIIIIIIXXIXXIIIIIIXIIII 639 26 TUB SE 0 0 SB 0 0 SE 0 0 CBBS BB e 0 TEB as 0 0 SB 0 0 SB 0 0 DAPS IB 0 0 • E 0 0 SB 0 o; SS 0 0 : OOB BB 0 0 tIBDS BE 0 0 SB 0 0 SE 0 0 GBEEB BB 0 0 BOOB SB 0 0 SI 0 0 . SE 0 o -• col SB 0 0 • Ell a s 0 0 SB 0 0 SE 0 0 • 000 BB 0 0 '• BIBB BE 0 0 SB 0 0 SE 0 0 ' • BIB IB 0 0 ST. BE 0 0 SB * . o 0 SB 0 0 B I : T . IB IIIIIIIIIIXI 2S0 It PABK BE 0 0 SB 0 0 SE 0 0 IBB SB IIIIIIIXIIIIIIIIII • 29 17 DEB BE 0 0 SB 0 0 SE 0 0 TOTAL 2385100 II TIRE PESIOO 5 THE SOBBAI LIIE B L O O I E A S T BID 12 STAtlOSS BITH «8 STATIOI SUB-ABEAS. 2385 IS TRE TOTAL I or APARTR SITS BOIL! II TIRE PEBIOD 5 E P T B C T I T E A P A R T B E B T C B O I T I SOBBAT C0RRID08 B L O O R E A S T TEAB 1967/1968 TIRE PERIOD 5 • 0 OP A PAITREITS STATIOI ABSOLOT 1 0 « 20 S 40 % 60 S , 80 E 100 SHSBB BB 0 0 00 RIB as 0 0 SB II 91 4 SE m i n i m i I n i i i n i i i i i i i i i i i i i i i i n i 1697 76 CA STL IB 0 0 FRAIIC IE 0 0 SB 0 0 SB 0 0 BROAD BB n m 216 » • ISB IE 0 0 SB 0 0 SE i n n 226 10 CRES SB 0 0 TER BE 0 0 SB 0 0 SE . 0 0 PAPS II 0 0 as 0 0 SB 0 0 SE 0 0 DOB BB 0 0 LAIOS IE 0 0 SB 0 0 SE 0 0 CBEEB SB 0 0 BOOO IE 0 0 ss 0 0 SE 0 0 CM IB 0 0 BELL BR 0 0 SB 0 0 SE 0 0 BOOD II 0 0 BIRE RE 0 0 SB 0 0 SE 0 0 (iilS KW 0 0 ST. IE 0 0 SI > 0 0 SE 0 0 IICT. BB - 0 0 PARK IE 0 0 SB 0 0 SE 0 0 BAB SB 0 0 DES BE 0 0 SB 0 0 SE 0 0 TOTAL 2232100 IS TIRE PERIOD 5 TBS SOBBAI LISB B I O O B E A S T BAD 12 STAtlOBS BITH »8 STATIOI SOB-AREAS. 22J2 IS TBE TOTAL I Or APABTBESTS BOILT IS TIRE PEBIOD 5 9 ooiaaa aaii I I IIIOB simuiatdt jo i m o i XBI SI zeoc -SIBBt-BOS I C I H I S Bt HUB SBOIJflS 21 OVH I S I 2 B O O I B BBI1 IIAB0S 2B1 9 OOiaad BBI1 BI 1 T I 0 I OS I J IQIOSBT SIIIBlBIdT 20 01 I OC • 1 B 0 I 9 ooiaaa j u n oiei/6961 a«ai i s T a a o o i a Booiaaoo IIHOOS l a a i i B i d t 2 i i i 3 i 2 2 a s - aoiaad ayii si •snai-l s a a a o o 0C1S81C 11X01 11I0B SIBBBlBBdl 20 B 1 T I 0 I I HI SI SBIt SOS ICI1I1S Bt H i l l S I O U I I S l\ OIH i a aan l t a a n s BHI % aoiaad ayii si 0 0 as 0 0 0 0 as i 0 0 0 0 ai aid • 0 0 ot occ m i n i m i HI 8 ia : tt 191 0 0 as 0 0 0 0 IS 0 .0 6 «« i i m i m i as ssia ' 6 t 802 m i n i BI "ton : 0 0 OJ « ! m i n i i m m m m as e 682 0 0 as i 0 0 0 0 31 •is ; 0 0 0 0 HI ai »B : 0 0 0 0 as 0 0 0 0 OS 0 0 0 0 • B aaia 0 0 0 0 - ns 0008 0 0 0 0 as 0 0 0 0 as 0 0 0 0 as i m 0 0 0 0 BI 103 0 0 0 0 as 0 0 0 0 as 0 0 0 0 . 31 oooa 0 0 0 0 as saaao 0 0 0 0 as 0 0 0 0 as 0 0 0 0 as sasn 0 0 0 0 HI BOO 0 0 0 0 as 0 0 0 0 as 0 0 0 0 ai 0 0 0 0 as a*ij 0 0 0 0 as 0 0 0 0 as 0 0 0 0 an 831 0 0 0 0 aa S3H3 0 0 6 882 I I I I I I I I I as o: SC* 0 0 as 0 0 0 0 aa aan 0 0 0 0 at 01089 6 tic 0 0 as 0 0 0 0 . BS 0 0 0 0 ai B H 8 2 0 0 0 0 HI I I S 13 0 0 It l t d i m i i i i i m m i m m m m i m i n i m u m as It est 0 0 as 0 0 0 0 ai a aa oo 0 0 0 0 • aa aaaus 0 0 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I i i m i i i i m m i m i I I I I I I I I I I i m i m m 11 m i n i m u m as as aa BB as as aa a a as as aa BB as as aa as as as aa aa as as aa aa as as aa aa as as aa aa as as aa aa as as aa aa as as aa aa as •a a • ia 1114 "131* •IS BIBB B U S oooa naa 103 0001 12289 SOU! ( 0 0 K l S8B3 aaia oioaa i B i a i I I S 13 aa aa a 88 oo BBSBS Of « I 1010SBI SIiaulMd! 20 01 I OC s. e i a o a o aoiaaa asu l s i a i b o i l I I • l 1 M I OKI/1961 asai aooiaBOD ifiaos. a i i i o u i s (See Chapter 7«2) The difference between actual and simulated MODEL CALIBRATION; apartment growth i s expressed as a percentage PERCENTAGE DIFFERENCE of the simulated growth. The comparison i s made f o r each time period and each moving average; f o r each station sub-area which received either actual or simulated growth or both. C O M P A R I S O N O F E F F E C T I V E A N D S I 1 0 1 4 I E D A P A R T M E N T G R O W T H DIFFERSHCE EXPRESSED AS PERCENTAGE OF SIMULATED GROWTH 999 SIMULATED GROWTH WAS LESS THAN 1/3 OF THE ACTUAL GROWTH 444 GROWTH W&S SIMULATED WHEN MO GROWTH ACTUALLY TOOK PLACE E ' EFFECTIVE GROWTH (IN DWELLING UNITS) S • SIMULATED GROWTH (IN DWELLING UNITS) P PERCENTAGE DIFFERENCE ( (E-S)/S*100) APPLICABLE TO LINE YONGE YONG E YONGE STATION AREA TIME PER 1 TIME PER 2 TIME PER 3 E1 1 S 1 1 P11 E12 S 12 P12 E13 S13 P13 EGLINGTON 1012 210 273 -23 255 159 60 310 389 -20 EGLINGTON 101-4 155 0 999 159 154 3 0 0 0 DAVISVILLE 1023 1 80 0 999 153 156 -2 0 281 444 DAVISVILLE 1024 0 0 0 120 155 -23 400 1 98 102 ST.CLAIR 1031 0 208 444 215 268 - 20 212 0 999 ST.CLAIR 1032 0 0 0 0 154 444 0 158 444 ST.CLAIR 1034 0 197 444 222 157 41 416 4 25 -2 APPLICABLE TO LINE YONGE,BWO,BEO ALL LINES ALL LINES STATION AREA TIME PER 4 TIME PER 5 TIME PER 6 E14 S 14 P14 E15 S 15 P 15 E1 6 S16 P16 EGLINGTON 1011 0 0 0 168 0 999 0 244 444 EGLINGTON 1012 343 95 0 -64 251 637 -61 300 0 999 EGLINGTON 1014 288 0 999 369 0 999 245 266 -8 DAVISVILLE 1021 0 0 0 0 0 0 0 246 444 DAVISVILLE 1024 580 183 999 6451077 -40 7 83 780 0 ST.CLAIR 1032 0 0 0 0 0 0 311 626 -50 ST.CLAIR 1034 7 84 95 1 -18 74 8 763 -2 355 0 999 ST.GEORGE 2012 0 193 444 150 177 - 15 0 1 80 444 SPADINA 2021 0 284 444 0 215 444 140 281 -50 KEELE 2091 0 457 444 480 803 -40 332 33 1 0 HIGHPARK 2102 0 0 0 638 283 125 52 5 298 76 ISLINGTON 2151 0 0 0 60 0 999 0 0 0 SHERBOURNE 3014 0 916 444 16971037 64 1347 888 5 2 BROADVIEW 3031 0 756 444 216 639 -66 0 311 444 BROADVIEW 3034 0 0 0 226 0 999 288 638 -55 MAIN STREET 3104 0 0 0 0 0 0 625 289 116 VICTORIA PARK 3111 0 0 0 0 280 444 208 0 999 VICTORIA PARK 3 112 0 0 0 0 0 0 294 298 - 1 WARDEN 3121 0 0 0 0 429 444 330 76 1 -57 P E R C E N T A G E C O M P A R I S O N (CONTINUED) APPLICABLE TO LINE YONGE STATION AREA TIME E32 EGLINGTON 1012 775 EGLINGTON 1014 314 DAVISVILLE 1023 33 3 DAVISVILLE 1024 52 0 ST.CLAIR 1031 427 ST.CLAIR 1032 0 ST.CLAIR 1034 638 YONGE PERIOD 2 TIME PERIOD 3 S32 P32 E33 S3 3 P33 821 -6 908 1498 -39 154 104 447 1 54 190 437 -24 153 437 -65 353 47 1 100 536 105 476 -10 427 268 59 312 444 0 312 444 779 -1 8 1422 1 533 -7 APPLICABLE TO LINE YONGE YONGE, BWO, BEO STATION AREA TIME PERIOD 4 TIME PERIOD 5 E3 4 S34 P3 4 E35 S3 5 P35 EGLINGTON 1011 168 0 999 168 244 -3 1 EGLINGTON 1012 904 1976 -54 894 1587 -44 EGLINGTON 1014 657 0 999 902 266 999 DAVISVILLE 1021 0 0 0 0 246 444 DAVISVILLE 1023 0 28 1 444 0 0 0 DAVISVILLE 1024 1625 1458 11 2008 2040 -2 ST.CLAIR 1031 212 0 999 0 0 0 ST.CLAIR 1032 0 158 444 311 626 -50 ST.CLAIR 1034 1 94 8 2139 -9 1887 1714 10 ST.GEORGE 2012 0 0 0 342 550 -38 SPADINA 2021 0 0 0 309 780 -60 KEEL E 2091 0 0 0 1 332 1591 - 16 HIGHPARK 2102 0 0 0 1625 581 180 OLD MILL 2132 0 0 0 154 0 999 ISLINGTON 2151 0 0 0 60 0 999 P E R C E N T & G E C O M P A R I S O N (CONTINUED) APPLICABLE TO LINE STATION AREA EGLINGTON 1012 EGLINGTON 1014 DAVISVILLE 1023 DAVISVILLE 1024 ST.CLAIR 1031 ST.CLAIR 1032 ST.CLAIR 1034 APPLICABLE TO LINE STATION AREA EGLINGTON EGLINGTON EGLINGTON DAVISVILLE DAVISVILLE ST.CLAIR ST.CLAIR ST.GEORGE SPA DIN A KEE'LE SHERBOURNE SHERBOOHME BROADVIEW BROADVIEW MAIN STREET VICTORIA PARK VICTORIA PARK WARDEN YONGE TIME PERIOD 2 E22 S22 P22 565 548 3 159 154 3 153 437 -65 520 353 47 427 268 59 0 3 12 444 638 582 10 YONGE, BWO,BWNN, BEN TIME PERIOD 4 E24 S24 P24 YONGE TIME PERIOD 3 E23 S23 P23 653 1339 -5 1 288 0 999 0 281 444 980 381 157 212 0 999 0 1 58 444 1200 13 76 - 13 YONGE,BWO, BWN,BEN TIME PERIOD 5 E25 S25 P25 1011 168 0 999 168 244 -31 1012 5 94 1587 -63 551 63 7 - 14 1014 657 0 999 614 266 13 1 1021 0 0 0 0 246 444 1024 1225 1260 -3 1428 1857 -23 10 32 0 0 0 311 626 -50 1034 1532 17 14 -11 1 103 7 63 45 2012 342 370 -8 150 3 57 -58 2021 169 499 -66 140 496 -72 2091 1000 1260 -21 812 1134 -28 301 3 0 0 0 93 0 999 3014 0 0 0 3044 1925 58 30 31 0 0 0 216 9 50 -77 3034 0 0 0 514 63 8 - 19 3104 0 0 0 625 289 1 16 3111 0 0 0 208 280 -26 3112 0 0 0 294 298 - 1 3121 0 0 0 3 30 1190 -72 (See Chapter 7.2) In Chapter 7.2, the r e s u l t s of c o r r e l a t i o n analysis MODEL CALIBRATION; between actual and simulated growth were given. CORRELATION ' -'. ANALYSIS Here, additional correlationecoeffieiBntssare l i s t e d which measure the-pooling e f f e c t of'apart-ment growth. As found i n Chapter 6.3, the actual apartment growth shows very d i s t i n c t l y a pooling e f f e c t . The following Tables A.d-^-1 to A.d-4-4 indicate that the corresponding trend f o r the simulated apartment growth i s les s marked, i n p a r t i c u l a r i f the growth i n each time period i s compared. However, the comparison for' the moving averages exhibits more e x p l i c i t l y the pooling e f f e c t of simulated growth. 1 S02 SO 3 S04 so 5 S22 S23 S24 S 2 5 . 9 7 ( . 0 0 7 ) NA S02 1 S22 1 S03 .88 (.024-) S23 . 9 3 (.011) 1 S04 • .86 (.167) . 9 9 (.047) S24 . 9 9 ( . 0 4 3 ) . 9 7 ( . 0 7 7 ) Correlation C o e f f i c i e n t Level of Significance Not Applicable because the number of cases i s too small. SO 5 NA NA . 3 6 (.102) S 2 5 .16 ( . 3 9 9 ) NA .46 (.214) POOLING EFFECT OF SIMULATED APARTMENT GROWTH TABLE A.d-4-1 COMPARISON FOR EACH TIME PERIOD S 3 2 S33 S 3 4 S.35 S 3 2 1 S33 . 9 3 (.001) S 3 4 .81 ( . 0 5 0 ) . 8 9 (.021) S 3 5 .64 ( . 1 2 3 ) . 6 5 (.117) .81 ( . 0 9 5 ) S 5 2 1 S 5 3 S 5 4 S 5 5 . 9 2 ( . 0 0 1 ) .72 ( . 0 3 4 ) .91 (.002) S 5 2 S 5 3 S 5 4 S 5 5 1 . 9 7 C o r r e l a t i o n C o e f f i c i e n t C . 0 0 7 ) L e v e l o f S i g n i f i c a n c e NA N o t A p p l i c a b l e b e c a u s e t h e number o f c a s e s i s t o o s m a l l . . 7 2 ( . 0 5 2 ) • 9 2 ( . 0 0 5 ) 1 . 0 ( . 0 0 1 ) TABLE A . d - 4 - 3 COMPARISON FOR MOVING AVERAGE 3 TABLE A . d - 4 - 4 COMPARISON FOR MOVING AVERAGE 5 (See Chapter 7.3) For each model run which tests, an alternative p o l i c y (and s i m i l a r l y f o r each s e n s i t i v i t y anal-y s i s ) , the following outprint i s produced by the simulation model. I t gives the incremental and cumulative apartment growth by sta t i o n sub-areas and time period f o r each subway l i n e . TESTING OF ALTERNATIVE POLICIES SAMPLE OUTPRINT F U T U R E A P A R T M E N T D E V E L O P M E N T FUTURE INCREMENTAL AND CUMULATIVE GROWTH FOR TIME PERIODS 5 - 1 4 (1967 - 1986) SIMULATED ACCORDING TO O F F I C I A L P O L I C I E S AND THE TORONTO PLAN ( S E E CHAPTER 7 . 3 ) INCR = INCREMENTAL APARTMENT GROWTH PER TIME PERIOD CUM = CUMULATIVE APARTMENT GROWTH UNTIL TIME PERIOD S U B W A Y Y 0 N G E STAT 1 9 6 7 - 1968 1 9 6 9 - 1970 1 9 7 1 - 1972 1 9 7 3 - 1974 ION INCR CUM INCR CUM I NCR CUM INCR CUM 1011 0 . 0 . 2 4 4 . 2 4 4 . 0 . 2 4 4 . 0 . 2 4 4 . 1012 6 3 7 . 2 4 0 8 . 0 . 2 4 0 8 . 0 . 2 4 0 8 . C . 2 4 0 8 . 1013 0 . 0 . 0 . 0 . 0. 0 . 0 . 0 . 1014 0 . 1 5 4 . 2 6 6 . 4 2 0 . 8 7 0 . 1 2 9 0 . 6 4 9 . 1 9 3 9 . 1021 0 . 0 . 2 4 6 . 2 4 6 . 0 . 2 4 6 . 6 4 4 . 8 9 0 . 1022 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 1023 0 . 4 3 7 . 0 . 4 3 7 . 1 5 8 . 5 9 5 . c . 5 9 5 . 1024 1 0 7 7 . 1 6 1 3 . 7 8 0 . 2 3 9 3 . 0 . 2 3 9 3 . 0 . 2 3 9 3 . 1031 0 . 4 7 6 . 0 . 4 7 6 . C . 4 7 6 . 0 . 4 7 6 . 1032 0 . 3 1 2 . 6 2 6 . 9 3 8 . 3 2 2 . 1 2 6 0 . G. 1 2 6 0 . 1033 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 1034 7 6 3 . 2 4 9 3 . 0 . 2 4 9 3 . 0 . 2 4 9 3 . C . 2 4 9 3 . 1041 0 . 0 . 0 . 0 . 0. 0 . c. 0 . 1042 0 . 0 . 0 . 0 . 0 . 0 . c . 0 . 1043 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 1044 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 1051 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 1052 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . 1053 0 . 0 . 0 . 0 . G. 0 . 0 . 0 . 1054 0 . 0 . 0 . 0 . 0 . 0 . 0 . 0 . F U T U R E A P A R T M E N T G R O W T H S U B W A Y B L 0 O R W E S T STAT 1967- 1968 1969- 1970 1971- 1972 1973- 1974 ION INCR CUM INCR CUM INCR CUM INCR CUM 2011 0. 0. 0. 0. 0. 0. c. 0. 2012 177. 370. 180. 550. 0. 550. 0. 550. 2013 0. 0. C. 0. 0. 0. 0. 0. 2014 0. 0. 0. G. 0. 0. 0. 0. 2021 215. 499. 281. 780. 0. 780. 0. 780. 2022 0. 0. 0. 0. 0. 0. c. 0. 2023 0. 0. 0. 0. 0. 0. c. 0. 2024 0. 0. 0. 0. 0. 0. G. 0. 2031 0. 0. 0. 0. 0. 0. 0. 0. 2032 0. 0. 0. 0. 0. 0. 0. 0. 2033 0. 0. 0. 0. 0. 0. c. 0. 2034 0. 0. 0. 0. 0. 0. 0. 0. 2041 0. 0. 0. 0. 0. 0. 0. 0. 2042 0. 0. 0. 0. 0. 0. G. 0. 2043 0. 0. 0. 0. 0. 0. 0. 0. 2044 0. 0. 0. 0. c. 0. 0. 0. 2051 0. 0. 0. 0. 0. 0. 0. 0. 2052 0. 0. 0. 0. 0. 0. 0. 0. 2053 0. 0. 0. 0. c. 0. 0. 0. 2054 0. 0. 0. 0. 0. 0. 0. 0. 2061 0. 0. 0. 0. 0. 0. 0. 0. 2062 0. 0. 0. 0. 0. 0. 0. 0. 2063 0. 0. 0. 0. 0. 0. 0. 0. 2064 0. 0. 0. 0. 0. 0. c. 0. 2091 803. 1260. 331. 1591. 0. 1591. 0. 1591. 2092 0. 0. 0. 0. 0. 0. 0. 0. 2093 0. 0. 0. 0. 0. 0. 0. 0. 2094 0. 0. 0. 0. 0. 0. 0. 0. 2101 0. 0. 0. 0. G. 0. 0. 0. 2102 283. 2 83. 298. 581. 289. 870. 636. 1506. 2103 0. 0. 0. 0. 0. 0. 0. 0. 2104 0. 0. 0. 0. 0. 0. 0. 0. 2111 0. 0. 0. 0. G. 0. 0. 0. 2112 0. 0. 0. 0. 0. 0. 0. 0. 2113 0. 0. 0. 0. 0. 0. 0. 0. 2114 0. 0. 0. 0. G. 0. 0. 0. 2121 0. 0. 0. 0. 0. 0. C. 0. 2122 0. 0. 0. 0. 1452. 1452. 225. 1677. 2123 0. 0. 0. 0. 0. 0. 0. 0. 2124 0. 0. 0. 0. 0. 0. c. 0. 2131 0. 0. 0. 0. 0. 0. c. 0. 2132 0. 0. 0. 0. 0. 0. 214. 214. 2133 0. 0. 0. 0. c. 0. C. 0. 2134 0. 0. 0. 0. 0. 0. 0. 0. 2141 0. 0. 0. 0. 0. 0. 0. 0. 2142 0. 0. 0. 0. 0. 0. 0. 0. 2143 0. 0. 0. c. 0. 0. 0. 0. 2144 0. 0. 0. 0. 0. 0. 0. 0. 2151 0. 0. 0. 0. c. 0. 0. 0. 2152 0. 0. 0. 0. 0. 0. 15G. 150. 2153 0. 0. 0. 0. 0. 0. C. 0. 2154 0. 0. 0. 0. 0. 0. 0. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y B L 0 O R E A S T STAT 1967- 1968 1969- 1970 1971- 1972 1973- 1974 ION INCR CUM INCR CUM INCR CUM INCR CUM 3011 0. 0. 0. 0. 0. 0. 0. 0. 3012 0. 0. 0. 0. c. 0. 0. 0. 3013 0. 0. 0. 0. 0. 0. C. 0. 3014 1037. 1953. 888. 2841. 628. 3469. 1519. 4988. 3021 0. 0. 0. C. 0. 0. 0. 0. 3022 0. 0. 0. 0. 0. 0. G. 0. 3023 0. 0. 0. 0. 0. 0. 0. 0. 3024 0. 0. 0. 0. C. 0. 0. 0. 3031 639. 1395. 311. 1706. 215. 1921. c. 1921. 3032 0. 0. 0. 0. 0. 0. 0. 0. 3033 0. 0. 0. 0. 0. 0. 0. 0. 3034 0. 0. 638. 638. 181. 819. 0. 819. 3041 0. 0. 0. 0. 0. 0. 0. 0. 3042 0. 0. 0. 0. 0. 0. 0. 0. 3043 0. 0. 0. 0. 0. 0. 0. 0. 3044 0. 0. 0. 0. 0. 0. c. 0. 3051 0. 0. 0. 0. 0. 0. 0. 0. 3052 0. 0. 0. 0. 0. 0. 0. 0. 3053 0. 0. 0. 0. 0. 0. 0. 0. 3054 0. 0. 0. 0. 0. 0. c. 0. 3061 0. 0. 0. 0. 0. 0. 0. 0. 3062 0. 0. 0. 0. 0. 0. 0. 0. 3063 0. 0. 0. 0. 0. 0. 0. 0. 3064 0. 0. 0. 0. 0. 0. 0. 0. 3071 0. 0. 0. 0. c. 0. 0. 0. 3072 0. 0. 0. 0. 0. 0. G. 0. 3073 0. 0. 0. 0. 0. 0. 0. 0. 3074 0. 0. 0. 0. 0. 0. 0. 0. 3081 0. 0. 0. 0. 0. 0. 0. 0. 3082 0. 0. 0. 0. 0. 0. c. 0. 3083 0. 0. 0. 0. 0. 0. 0. 0. 3084 0. 0. 0. 0. 0. 0. 0. 0. 3091 0. 0 . 0. 0. 0. 0. c. 0. 3092 0. 0. 0. 0. 0. 0. 0. 0. 3093 0. 0. 0. 0. 0. 0. 0. 0. 3094 0. 0. 0. 0. 0. 0. c. 0. 3101 0. 0. 0. 0. 0. 0. 0. 0. 3102 0. 0. 0. 0. 0. 0. 0. 0. 3103 0. 0. 0. 0. c. 0. 0. 0. 3104 0. 0. 289. 289. 0. 289. c. 289. 3111 280. 280. 0. 280. 144. 424. 0. 424. 3112 0. 0. 298. 298. 115. 413. 0. 413. 3113 0. 0. 0. 0. 0. 0. G. 0. 3114 0. 0. 0. 0. 0. 0. 0. 0. 3121 429. 429. 761. 1190. 363. 1 553. 0. 1553. 3122 0. 0. 0. 0. 0. 0. 0. 0. 3123 0. 0. 0. 0. 0. 0. 0. 0. 3124 0. 0. 0. 0. 0. 0. 0. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I N E Y O N G E STAT 1975- 1976 1977- 1978 1979- 1980 1981- 1982 ION INCR CUM INCR CUM INCR CUM INCR CUM 1011 155. 399. 789. 1188. 633. 1821. 0. 18 21. 1012 0. 2408. 0. 2408. 0. 2408. 0. 2408. 1013 0. 0. 0. 0. 0. 0. C. 0. 1014 0. 1939. 0. 1939. 0. 1939. 0. 1939. 1021 0. 890. 0. 890. 0. 890. 0. 890. 1022 0. 0. 0. 0. 0. 0. c. 0. 1023 532. 1127. 0. 1127. 0. 1127. 0. 1127. 1024 0. 2393. 0. 2393. c. 2393. 0. 2393. 1031 0. 476. 0. 476. 0. 476. 0. 476. 1032 277. 1537. 0. 1537. 0. 1537. 0. 1537. 1033 0. 0. 0. 0. 140. 140. 0. 140. 1034 0. 2493. 0. 2493. 0. 2493. G. 2493 . 1041 0. 0. 0. 0. 0. 0. C. 0. 1042 0. 0. 0. 0. 0. 0. 0. 0. 1043 0. 0. 0. 0. 0. 0. 0. 0. 1044 147. 147. 617. 764. 140. 904. c. 904. 1051 0. 0. 0. 0. 0. 0. 0. 0. 1052 0. 0. 0. 0. C. 0. 613. 613. 1053 0. 0. 0. c. 0. 0. 0. 0. 1054 0. 0. 0. 0. 0. 0. 465. 465. F U T U R E A P A R T M E N T G R 0 W T H S U B W A Y L I N E B L 0 0 R W E S T STAT 1975- 1976 1977- 1978 1979- 1980 1981- 1982 ION INCR CUM INCR CUM INCR CUM INCR CUM 2011 0. 0. 0. 0. 0. 0. 0. 0. 2012 0. 550. 0. 550. 0. 550. 0. 550. 2013 0. 0. 0. 0. 0. 0. 0. 0. 2014 0. 0. 0. 0. 0. 0. c. 0. 2021 0. 780. 0. 780. 0. 780. 0. 780. 2022 0. C. 0. 0. 0. 0. 0. 0. 2023 0. 0. 0. 0. 0. 0. 0. 0. 2024 0. 0. 0. 0. 0. 0. 0. 0. 2031 0. 0. 0. 0. 0. 0. 0. 0. 2032 0. 0. 0. 0. 0. 0. 0. 0. 2033 0. 0. 0. 0. 0. 0. c. 0. 2034 0. 0. 0. 0. 0. 0. 0. 0. 2041 0. 0. 0. 0. 0. 0. 0. 0. 2042 0. 0. 0. 0. 0. 0. c. 0. 2043 0. 0. 0. 0. 0. 0. 0. 0. 2044 0. 0. 0. 0. 0. 0. 0. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I N E B L 0 0 R W E S T STAT 1975- 1976 1977- 1978 1979- 1980 1981- 1982 I ON INCR CUM INCR CUM INCR CUM INCR CUM 2051 0. 0. 0. 0. 0. 0. 0. 0. 2052 0. 0. 0. 0. 0. 0. C . 0. 2053 0. 0. 0. 0. 0. 0. 0. 0. 2054 0. 0. 0. 0. 0. 0. 0. 0. 2061 0. 0. 0. 0. 0. 0. c. 0. 2062 0. 0. 0. 0. 0. 0. 0. 0. 2063 0. 0. 0. 0. 0. 0. 0. 0. 2064 0. 0. 0. 0. 519. 519. 54 7. 1066. 2071 0. 0. 0. 0. 0. 0. 0. 0. 2072 0. 0. 0. 0. 0. 0. 0. 0. 2073 0. 0. 0. 0. 0. 0. 0. 0. 2074 0. 0. 0. 0. 0. 0. c. 0. 2081 0. 0. 0. 0. 0. 0. 0. 0. 2082 0. 0. 0. 0. 0. 0. 0. 0. 2083 0. 0. 0. 0. 0. 0. 0. 0. 2084 0. 0. 0. 0. 0. 0. 0. 0. 2091 0. 1591. 0. 1591. c. 1591 . 0. 1591. 2092 0. 0. 0. 0. 0. 0. c. 0. 2093 0. 0. 0. 0. 0. 0. c. 0. 2094 0. 0. 0. 0. 0. 0. 0. 0. 2101 0. 0. 0. 0. 0. 0. 0. 0. 2102 444. 1950. 0. 1950. 0. 1950. c. 1950. 2103 0. 0. 0. 0. 0. 0. 0. 0. 2104 0. 0. 0. 0. 0. 0. 0. 0. 2111 0. 0. 0. 0. 0. 0. c. 0. 2112 0. 0. 0. 0. 0. 0. 0. 0. 2113 0. 0. 0. 0. 0. 0. 0. 0. 2114 0. 0. 0. 0. c. 0. 0. 0. 2121 0. 0. 0. 0. 0. 0. 0. 0. 2122 0. 1677. 294. 1971. 0. 1971. 0. 1971 . 2123 0. 0. 0. 0. 0. 0. 0. 0. 2124 0. 0. 0. 0. 0. 0. 0. 0. 2131 0. 0. 0. 0. 0. 0. 0. 0. 2132 0. 214. 0. 214. 0. 214. 0. 214. 2133 0. 0. 0. 0. 0. 0. 0. 0. 2134 0. 0. 0. 0. 0. 0. 0. 0. 2141 0. 0. 0. 0. c. 0. 0. 0. 2142 0. 0. 0. 0. 0. 0. 0. 0. 2143 0. 0. 0. 0. 0. 0. 0. 0. 2144 0. 0. 0. 0. 0. 0. 0. 0. 2151 759. 759. 0. 759. 0. 759. 0. 759. 2152 0. 150. 600. 750. 0. 7 50. c. 750. 2153 0. 0. 222. 222. 662. 884. 611. 1495. 2154 0. 0. 0. 0. 0. 0. 0. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I N E B L 0 0 R E A S T STAT 1975- 1976 1977- 1978 1979- 1980 1981- 1982 I ON INCR CUM INCR CUM INCR CUM INCR CUM 3011 0. 0. 0. 0. C. 0. C. 0. 3012 0. 0. 0. C. 0. 0. C. 0. 3013 0. 0. 0. 0. 0. 0. 0. 0. 3014 0. 4988. 0. 4988. 0. 4988. 0. 4988. 3021 0. 0. 0. 0. 0. 0. 0. 0. 3022 0. 0. 0. 0. 0. 0. c. 0. 3023 0. 0. 0. 0. 0. 0. 0. 0. 3024 0. 0. 0. 0. 0. 0. 0. 0. 3031 0. 1921. 0. 1921. 0. 1921. c. 1921. 3032 0. 0. 0. 0. 0. 0. 0. 0. 3033 0. 0. 0. 0. 0. 0. 0. 0. 3034 784. 1603. 362. 1965. 0. 1965. 0. 1965. 3041 0. 0. 0. 0. 0. 0. 0. 0. 3042 0. 0. 0. 0. 0. 0. 0. 0. 3043 0. 0. 0. 0. 0. 0. 0. 0. 3044 0. 0. 0. 0. 0. 0. 0. 0. 3051 0. 0. 621. 621. 0. 621. 643. 1264. 3052 0. 0. 0. 0. 0. 0. G. 0. 3053 0. 0. 0. 0. 1512. 1512. 19C. 1702. 3054 0. 0. 0. 0. 0. 0. 0. 0. 3061 0. 0. 0. 0. c. 0. 0. 0. 3062 0. 0. 0. 0. 0. 0. 0. 0. 3063 0. 0. 0. 0. 0. 0. c. 0. 3064 0. 0. 0. 0. 0. 0. 0. 0. 3071 0. 0. 0. 0. 0. 0. 0. 0. 3072 0. 0. 0. 0. 0. 0. c. 0. 3073 0. 0. 0. 0. 0. 0. 0. 0. 3074 0. 0. 0. 0. c. 0. 0. 0. 3081 0. 0. 0. c. 0. 0. c. 0. 3082 0. 0. 0. 0. 0. 0. c. 0. 3083 0. 0. 0. 0. G. 0. 0. 0. 3084 0. 0. 0. 0. 0. 0. 0. 0. 3091 0. 0. 0. 0. 0. 0. 0. 0. 3092 0. 0. 0. 0. 0. 0. 0. 0. 3093 0. 0. 0. c. 0. 0. 0. 0. 3094 0. 0. 0. 0. 0. 0. c. 0. 3101 0. 0. 0. 0. - 0. 0. 0. 0. 3102 0. 0. 0. 0. 0. 0. c. 0. 3103 0. 0. 0. 0. 0. 0. c. 0. 3104 738. 1027. 0. 1027. 0. 1027. 0. 1027. 3111 0. 424. 0. 424. 0. 424. 0. 424. 3112 0. 413. 0. 413. 0. 413. 25G. 663. 3113 0. 0. 0. 0. 0. 0. 0. 0. 3114 0. 0. 0. 0. 0. 0. C. 0. 3121 0. 1553. 239. 1792. 0. 1792. c. 1792. 3122 0. 0. 0. 0. 0. 0. G. 0. 3123 0. 0. 0. 0. 0. 0. 0. 0. 3124 0. 0. 0. 0. 0. 0. C. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I STAT 1983- 1984 ION INCR CUM 1011 0. 1821. 1012 0. 2408. 1013 229. 229. 1014 0. 1939. 1021 0. 890. 1022 0. 0. 1023 0. 1127. 1024 0. 2393. 1031 0. 476. 1032 0. 1537. 1033 251. 391. 1034 0. 2493. 1041 0. 0. 1042 0. 0. 1043 0. 0. 1044 0. 904. 1051 0. 0. 1052 526. 1139. 1053 0. 0. 1054 0. 465. N E Y O N G E 1985- 1986 INCR . CUM 0. 1821 . 0. 2408. 325. 554. 0. 1939. • 0. 890. 0. 0. 0. 1127. 0. 2393. 0. 476. 0. 1537. 0. 391. 0. 2493. 0. 0. 0. 0. 0. 0. 0. 904. 0. 0. 535. 1674. 0. 0. 0. 465. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I N E B L O O R W E S T STAT 1983- 1984 1985- 1986 ION INCR CUM INCR CUM 2011 548. 548. 6 0 5 . 1153. 2012 0. 550. 0. 550. 2013 0. 0. 0. 0. 2014 0. 0. 0. 0. 2021 0. 780. 0. 780. 2022 0. 0. 0. 0. 2023 0. 0. 0. 0. 2024 0. 0. 0. 0. 2031 0. 0. 0. 0. 2032 0. 0. 0. 0. 2033 0. 0. 0. 0. 2034 0. 0. 0. 0. 2041 0. 0. 0. 0. 2042 0. 0. 0. 0. 2043 0. 0. 0. 0. 2044 0. 0. 0. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I N E B L O O R W E S T STAT 1983- 1984 1985- 1986 ION INCR CUM INCR CUM 2051 0. 0. 0. 0. 2052 0. 0. 0. 0. 2053 G. 0. 0. 0. 2054 0. 0. 0. 0. 2061 0. 0. 0. 0. 2062 0. 0. 0. 0. 2063 0. 0. 0. 0. 2064 638. 1704. 0. 1704. 2071 0. 0. 0. 0. 2072 0. 0. 0. 0. 2073 0. 0. 0. 0. 2074 0. 0. 0. 0. 2081 0. 0. 0. 0. 2082 0. 0. 0. 0. 2083 0. 0. 0. 0. 2084 0. 0. 0. 0. 2091 0. 1591. 0. 1591. 2092 0. 0. 0. 0. 2093 0. 0. 0. 0. 2094 0. 0. 0. 0. 2101 0. 0. 0. 0. 2102 0. 1950. 0. 1950. 2103 0. 0. 0. 0. 2104 0. 0. 0. 0. 2111 0. 0. 0. 0. 2112 0. 0. 0. 0. 2113 0. 0. 0. 0. 2114 0. 0. 0. 0. 2121 0. 0. 0. 0. 2122 0. 1971. 0. 1971. 2123 0. 0. 0. 0. 2124 0. 0. 0. 0. 2131 0. 0. 0. 0. 2132 0. 214. 0. 214. 2133 0. 0. 0. 0. 2134 0. 0. 0. 0. 2141 0. 0. 0. 0. 2142 0. 0. 0. 0. 2143 0. 0. 0. 0. 2144 0. 0. 0. 0. 2151 0. 759. 0. 759. 2152 0. 750. 0. 750. 2153 0. 1495. 244. 1739. 2154 0. 0. 0. 0. F U T U R E A P A R T M E N T G R O W T H S U B W A Y L I N E B L O O R E A S T STAT 1983- 1984 1985- 1986 ION INCR CUM INCR CUM 3011 0, 0. 0. 0. 3012 0. 0. 0. 0. 3013 273. 273. 0. 273. 3014 0. 4988. 0. 4988. 3021 0. 0. 0. 0. 3022 0. 0. 0. 0. 3023 0. 0. 0. 0. 3024 0. 0. 0. 0. 3032 0. 0. 0. 0. 3033 187. 187. 0. 187. 3034 0. 1965. 0. 1965. 3041 0. 0. 0. 0. 3042 0. 0. 0. 0. 3043 0. 0. 0. 0. 3044 0. 0. 0. 0. 3051 326. 1590. 0. 1590. 3052 0. 0. 0. 0. 3053 284. 1986. 0. 1986. 3054 0. 0. 0. 0. 3061 0. 0. 0. 0. 3062 0. 0. 0. 0. 3063 0. 0. 0. 0. 3064 0. 0. 0. 0. 3071 0. 0. 0. 0 . 3072 0. 0. 0. 0. 3073 0. 0. 0. 0. 3074 0. 0. 0. 0. 3081 0. 0. 0. 0. 3082 0. 0. 0. 0. 3083 0. 0. 0. , °-3084 0. 0. 0. * 0 . 3091 0. 0. 0. 0. 3092 0. 0. 0. 0. 3093 0. 0. 0. 0. 3094 0. 0. 0. 0. 3101 0. 0. 975. 975. 3102 0. 0. 0. 0. 3103 0. 0. 0. 0. 3104 0. 1027. 0. 1027. 3111 0. 424. 0. 424. 3112 0. 663. 0. 663. 3113 0. 0. 0. 0. 3114 0. 0. 594. 594. 3121 0. 1792. 0. 1792. 3122 0. 0. 0. 0. 3123 0. 0. 0. 0. 3124 0. 0. 0. 0. 

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