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A method of designing resource inventories (soil-vegetation-landform maps) with user involvemen Pottinger, Edmund Ladner 1981

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A METHOD OF DESIGNING RESOURCE INVENTORIES (SOIL-VEGETATION-LANDFORM MAPS) WITH USER INVOLVEMENT by EDMUND LADNER POTTINGER B.Sc., The University of B r i t i s h Columbia, 1973 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE i n THE FACULTY OF GRADUATE STUDIES (Department of S o i l Science) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA October 1981 © Edmund Ladner Pottinger, 1981 I n p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t o f t h e r e q u i r e m e n t s f o r an advanced degree a t t h e U n i v e r s i t y o f B r i t i s h C o l u m b i a , I a g r e e t h a t t h e L i b r a r y s h a l l make agree t h a t p e r m i s s i o n f o r e x t e n s i v e c o p y i n g o f t h i s t h e s i s f o r s c h o l a r l y p u r p o s e s may be g r a n t e d by t h e head o f my department o r by h i s o r h e r r e p r e s e n t a t i v e s . I t i s u n d e r s t o o d t h a t c o p y i n g 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 g a i n s h a l l n o t be a l l o w e d w i t h o u t my w r i t t e n p e r m i s s i o n . Department o f J>Ol I ~>C~ l ^ n C t f The U n i v e r s i t y o f B r i t i s h C o l u m b i a 2075 Wesbrook P l a c e Vancouver, Canada V6T 1W5 i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and s t u d y . I f u r t h e r ABSTRACT This study tested a method of incorporating preferences of potential users i n the design and presentation of a soil-vegetation-landform map and information r e t r i e v a l system. The method u t i l i z e d a questionnaire-inter-view program designed to e l i c i t responses from potential user's. This test of the method tackled a small part of the whole problem by testing a small group of variables to enhance product u t i l i t y . A l l examples were based on a real resource inventory of a small watershed i n the mountainous coastal rainforest region of B.C. The study looked at seven dependent variables: map scale, mapping unit symbol, mapping unit variables ( d i f f e r e n t i a t i n g c r i t e r i a ) , interpretive (deriva-tive) map legends, s o i l c l a s s i f i c a t i o n , interpretive map presentation and general map presentation. For each dependent variable a number of questions (based on real examples) were asked to see i f there was a consensus of opinion. The dependent variables were compared with a variety of independent variables, such as job group, decision making l e v e l , education l e v e l , etc. After a p i l o t questionnaire-interview program, a l l the i d e n t i f i e d potential users of the maps were sent questionnaires or interviewed. Tape recorded interviews using the questionnaire acted as a check on the effectiveness of the questionnaire. Combining questionnaires and i n t e r -views, there were 238 responses, which was roughly 80% of the established population. The results showed that the method could work. A consensus opinion was obtained on map scale, mapping unit symbol, interpretive legends, s o i l classification, interpretive map presentation and part of general map presentation. Interestingly, map producers tended to have significantly different views from the rest of the population. There were some vari-ables for which no consensus was reached. There was either no consensus, or no real conclusions could be drawn due to poor question wording and poor examples. A summary of the results, was sent to the interviewees to clarify some of the unanswered questions and have them ratify the results of the questionnaire-interview program. The summary was a substitute for a prototype map which would have been used in a real inventory situation. Results from the summary program generally indicated corroboration of the conclusions, although this summary technique was definitely considered inferior to a direct testing of the prototype map. In conclusion, the method worked and could be incorporated in future inventories (future recommendations are included). It is an inexpensive and relatively simple procedure with which to test possible inventory, mapping and presentation techniques. The fact that this study indicated a significant difference between the desires and/or requirements of the map producers and the map users suggests a technique of this sort is certainly a necessity. It should also act as a very good user-producer relations and education tool. Introducing the maps to the potential users and having them involved in their design should improve information flow. i v TABLE OF CONTENTS Page ABSTRACT i i LIST OF TABLES v LIST OF FIGURES v i i ACKNOWLEDGEMENTS v i i i CHAPTER 1 - INTRODUCTION 1 CHAPTER 2 - OBJECTIVES AND CHOICE OF STUDY AREA 5 CHAPTER 3 - EXPERIMENTAL DESIGN 14 CHAPTER 4 - DESIGN OF THE INTERVIEW AND QUESTIONNAIRE PROGRAM 28 CHAPTER 5 - RESULTS OF THE QUESTIONNAIRE-INTERVIEW PROGRAM AND ANALYSIS OF PATTERNS 32 CHAPTER 6 - DISCUSSION OF QUESTIONNAIRE AND INTERVIEW RESULTS 77 CHAPTER 7 - TESTING THE PROTOTYPE 85 CHAPTER 8 - SUMMARY AND CONCLUSIONS 93 CHAPTER 9 - RECOMMENDATIONS FOR FUTURE STUDY ..97 REFERENCES 102 APPENDIX I QUESTIONNAIRE APPENDIX I I INTERVIEW DATA APPENDIX I I I QUESTIONNAIRE AND INTERVIEW DATA APPENDIX IV SUMMARY OF RESOLUTIONS V LIST OF TABLES Table Page 1 L i s t of Job Groups and Abbreviations 24 2 Potential Independent Variables 24 3 Response Rate vs. Job Group 33 4 Decision-Making Level vs. Job Group 35 5 Map Scale Most Often Used: A S t a t i s t i c a l Comparison of Job Groups 37 6 Comparison of Scale Used with Decision-Making Level 38 7 Comparison of Pedologists and Ecologists Desired Map Scale with other users Desired Map Scale 39 8 Comparison of Scale chosen with Decision-Making Level 40 9 Desired Map Scale as Determined by Transect Division 42 10 Comparison of Inset, Variable Intensity Choice with Job Group 45 11 Comparison of Interview and Questionnaire Results for Variable Intensity, Inset Choice 46 12 Map Unit Symbol Preference vs. Job Group 48 13 Map Symbol Choice vs. Relevant Experience 47 14 Map Symbol Choice vs. Whether or Not the Answer i s Based on Experience 50,, 15 Comparison of the Map Symbol Choice in the Interview and Questionnaire Program 5.1 16 The Top Three Differentiating C r i t e r i a by Job Group 54 17 % of Responses rating D i f f e r e n t i a t i n g C r i t e r i a , 8 or Higher 55 18 Number of Times the Diff e r e n t i a t i n g C r i t e r i a were one of the Top Three Choices for each Job Group 19 % of Respondents i n Each Job Group Who Rated Soils Information the Information Most Often Lacking When a Decision i s to be Made 20 U t i l i t y of S o i l C l a s s i f i c a t i o n vs. Job Group 60 v i LIST OF TABLES (Continued) Table Page 21 Example of Ve rba l i z ed Nomenclature 61 22 Percentage of High Rat ings of D e r i v a t i v e Map Legend Examples by Each Job Group 64 23 Rat ings fo r I n t e r p r e t i v e Legends vs. Map Un i t Symbol Choice 65 24 Comparison of I n te rv iew vs. Quest ionna i re High Rat ings For D e r i v a t i v e Legends 66 25 D e r i v a t i v e Map P r e sen t a t i on vs. Job Group , 69 26 Legend S ize vs . Job Group 71 27 Legend S ize Responses f o r the In terv iew and Ques t i on -n a i r e Programs 72 28 Map Base Rat ing of Greater Than 7 vs. Job Group 74 29 Low Rat ings vs. Map Base Type 74 30 Des i re f o r Contours on Maps vs. Job Group 76 31 Dependent V a r i a b l e Reso lut ions 78 v i i LIST OF FIGURES Figure Page 1 Incorporating a User Survey as an Integral Part of a Resource Inventory, Mapping Program 9 2 Degree of Substantiation Compared with Ease of Inter-pretation 21 3 Examples of the size range of the Smallest Mapping Units Separated along the Transect and Corresponding Scales 41 4 Map Symbol Ratings for the Whole Population 49 5 Interpretive Map Choices 63 6 Example of Possible Management Unit Boundaries 70 7 Flow Chart for 2 Questionnaire-Interview Program 99 ACKNOWLEDGEMENTS Well Bett, a l l finished. The author wishes to thank the many members of the U.B.C. Department of Soil Science who have acted as a sounding board for ideas. A study of this sort requires such a large amount of support to be successful that i t makes i t d i f f i c u l t to thank individuals, but specifically thanks are given to Mark Sondheim and Hans-Peter Schreier for their help with the s t a t i s t i c a l analysis and Professor Henry Hightower of the UBC School of Planning for his comments throughout the Study. Thanks and appreciation are expressed for the direction and support from Les Lavkulich as thesis supervisor. Similar contributions from the author's committee members are gratefully acknowledged. Special thanks go to the people at Agriculture Canada who supported and aided the author over two summers and the project through a l l of its phases. Keith Valentine and Dave Moon deserve extra appreciation for their comments, support,indulgence and direction, while s t i l l allowing a very large degree of freedom. Thanks go to a l l of the job corp program employees who helped gather much of the information - 560 stamps requires a lot of licking! This research was made possible by the Summer Job Corp Program of Agriculture Canada which supported the author and the Study for two summers. Last and most importantly the author would like to thank his grand-father for the financial, moral and inspirational support of many years. 1 CHAPTER 1 INTRODUCTION Nature, to be properly managed must be understood. In our society we tend to separate the r e s p o n s i b i l i t y for managing natural ecosystems (resources) from the r e s p o n s i b i l i t y for understanding them. Scientists or researchers perform the l a t t e r function and inform those who are responsi-ble for managing resources of the possible management options and the resulting repercussions. I t follows that good communication between sc i e n t i s t s and resource managers i s necessary to enable proper resource management. Information r e t r i e v a l systems maps currently play a major role in communicating between the s c i e n t i s t and the manager. A tremendous amount of resources are devoted to map information systems. Unfortunately mapped information i s often assembled by map-makers (hereafter called "producers"), who are not d i r e c t l y trained i n map-making (eg. s o i l s c i e n t i s t s , ecologists, etc.) and used by manager-map users 2 (hereafter c a l l e d "users") who are often untrained i n the use of those s p e c i f i c maps. As well as being untrained in map use, users generally have l i t t l e , i f any, organized involvement in the map-making process. The producers attempt to create a product which the users can use, but often u n i l a t e r a l decisions are made by producers concerning which information i s necessary and how the information should be presented (Valentine 1978). Recently a number of studies have been conducted to determine the e f f e c t i v e n e s s / u t i l i t y of s o i l maps or mapping systems (Beckett and Bie 1978, Hansen and Richards 1979, McKnight 1979, Valentine 1981). The main purpose of these studies has been to evaluate the performance of e x i s t i n g maps. In t h e i r conclusion, a l l of these studies indicated the need for change and the need to improve information flow between users and producers. Hansen and Richards (1979) suggested incorporating user feed-back i n the design of a land information system. Hansen and Richards also noted that p o t e n t i a l users of map systems are often unaware that the maps e x i s t , or cannot understand them and consequently w i l l not try to use them. This problem stems from the fact that most resource studies and maps have h i s t o r i c a l l y been produced for a s p e c i f i c user or user group who d i r e c t l y or i n d i r e c t l y requested the maps (eg. the CLI System*). The s p e c i f i c users are then presented with and *The Canada Land Inventory was i n i t i a t e d i n 1961 by the A g r i c u l t u r a l R e h a b i l i t a t i o n and Development Act (Rees 1977) as a survey of resources i n Canada. The user group was p o l i t i c i a n s and planners engaged in resource management at broad scales. Its scale and focus r e f l e c t the s p e c i f i c users ( i . e . p o l i t i c i a n s and planners) needs, but tend to ignore people responsible for more s i t e s p e c i f i c management. 3 sometimes trained in the use of the maps while other potential users of the maps are often ignored. The purpose of this project was to test a method which could be used to incorporate potential and specific users' preferences and needs in the design of maps and information retrieval systems. Communication and collaboration with both types of users (potential and specific) during a resource inventory should improve the ultimate flow of information from the producers to the users. Maps and information retrieval systems could be designed to include information perceived as necessary to make decisions from both the users and producers point-of-view. Communication and collaboration w i l l also allow the producers to effectively enlighten users reducing the possibility for misinterpretation. An organized communication system has an added advantage of acting as a user-producer relations and advertising tool. Obviously the topic in it s entirety is rather large and unweildy. Attempting to cover a l l aspects of the topic in a test of the method could submerge the results in a multitude of details. A better option would be to concentrate on a small area of the whole topic such as testing a group of variables which might enhance the product u t i l i t y . Different physical systems may require different management strategies and thus different resource data w i l l be necessary. Con-sequently, the study needed a specific focus. Increasingly intensive forest management in B.C. and elsewhere, suggested that a forested eco-system with the potential for intensive management would be a good choice. To ensure validity and allow for a reasonable test of the method, actual resource inventory data should be used as the basis for a study. A data set and relationships between the s o i l , vegetation and landform 4 with potential for intensive management were already assembled (Pottinger 1978). Consequently the type of information to be presented and the method of presentation to the users were the variables to be decided for this study. It was f e l t an attempt should also be made to assess the producers' perception of what was necessary and compare that with the users' perception. For this purpose, a producer set composed of resource inven-tory s p e c i a l i s t s (pedologists and ecologists) was included i n the study. The rest of the study group consisted of a wide variety of potential users responsible for managing various aspects of the forested ecosystem's resources. Management of resources by a wide variety of managers requires a resource information system f l e x i b l e enough to provide information for speci f i c decision-making by various managers with diverse backgrounds. Although i t would be optimistic to believe that one system could be a panacea, i t i s reasonable to assume that a cooperative effort w i l l improve information flow from producers to users. This study i s a test of a proposed step i n that direction. 5 CHAPTER 2 OBJECTIVES AND CHOICE OF STUDY AREA INTRODUCTION As indicated i n Chapter 1 the overall purpose of this study i s to test a method of user involvement i n a resource inventory. This o v e r a l l purpose can be separated into two separate objectives. This chapter out-lines the objectives of the study and the principles underlying these objectives. I t then discusses the rationale for choosing a forested ecosystem as the study area, why a study i n this area would be b e n e f i c i a l , and the study area's location. OBJECTIVES There are two objectives of this study based on the following i n t e r -related p r i n c i p l e s : 1 . the need to assess data requirements for decision making before the inventory begins, and 2. the need to make methods of data presentation useful and understand-6 able to the potential "users" and present data which reflects their information needs. Principle 1 The f i r s t of these principles, the need to assess data requirements, implies that before gathering data for decision-making we must acquire an understanding of the users' evaluative framework. An evaluative framework can be defined as "the rules which individuals or groups follow in making choices from among alternatives" (Bross 1953). Environmental information is only one part of the users' information needs for evaluation; user's must also have p o l i t i c a l and economic input. However, i f the environ-mental part of their information requirements is neglected the other parts w i l l dominate their decisions. Only after determining the environmental portion of the users' col-lective information requirements can data needs for resource inventories be assessed. If this step is missed or hastily dispatched, then data gathered is lik e l y to be insufficient, inappropriate and/or useless. Decisions made by using studies with insufficient and/or useless data are like l y to be faulty decisions. Consequently, we must carefully assess the various managers' information needs before beginning a resource inventory. The f i r s t objective of this study was to see i f i t is possible to acquire an appreciation of some of the information needs of forest managers. If this proved possible, the information could then be used to help design a better resource inventory which would collect needed in-formation. The s k i l l s of the researcher would s t i l l be necessary to assess the important resource information, but a knowledge of managers' needs would improve communication. 7 Principle 2 The second principle concerns data presentation. The biological and physical ecosystem, although incredibly complex, is fin i t e at any one site. That i s , i t can be given bounds and ranges and these bounds and ranges are limited. In contrast, the possibilities for presenting and interpreting this information are many. On a map one can use a variety of scales, myriad differentiating c r i t e r i a , innumerable map symbol types, etc. in a wide variety of combinations limited only by inventiveness. Map making is and always has been a creative art which blends these ingredients. There are many written and unwritten rules in the art of map making, but each map system has its own character; no two are identical. It is important that a map system's method of data presentation accurately and efficiently conveys the necessary information to prospective users. The wide variety of presentation and interpretation possibilities implies that a user survey technique could help to establish those items and methods which are considered best and most important by the user. The second objective of this study was to u t i l i z e the desires of prospective users of the maps to improve the design of the maps. Possible Results If the method is successful, i t could accomplish three things in future resource inventories: 1 . Users being involved from the beginning w i l l know more about the existing maps when they are produced. 2. The users should be able to extract necessary information more easily because they w i l l have had some input into the maps' design. 3. The producers w i l l know more about users' needs and desires as well 8 as t h e i r a b i l i t y to understand and consequently w i l l be be t te r able to communicate t h e i r knowledge to the user s . Of course, the o v e r a l l purpose of t h i s study was to t e s t the method to see i f data gathered from t h i s type of study could be used i n the des ign of d e t a i l e d s o i l - v e g e t a t i o n - l a n d f o r m maps of f o re s ted environments i n c o a s t a l B r i t i s h Columbia. F igure 1 shows how the method could be employed as an i n t e g r a l part of f u tu re resource i n ven to r i e s i f t h i s t e s t proves s u c c e s s f u l . RATIONALE FOR CHOOSING A FORESTED ENVIRONMENT One reason f o r choosing a f o re s t ed environment was that u n t i l now there has been very l i t t l e d e t a i l e d s o i l - v e g e t a t i o n - l a n d f o r m inventory of f o r e s t ed areas i n B. C. The i n fo rmat i on that e x i s t s i s u s u a l l y sma l l s ca le (1:50,000 or s m a l l e r ) . Recent ly the B. C. Forest Serv ice has i m -plemented the "Resource F o l i o P lann ing System"; a p lan to produce l a r g e r s ca le maps than prev ious surveys. So i t i s l i k e l y that t h i s study w i l l p rov ide some u s e f u l i n f o rmat i on as w e l l as t e s t i n g the method. Prev ious surveys of f o re s ted areas i n B.C. have tended toward eco -l o g i c a l or b i o p h y s i c a l land c l a s s i f i c a t i o n s . Examples of t h i s approach are the Canada Land Inventory (CLI) and the b i o g e o c l i m a t i c system ( K r a j i n a 1965). The b i o g e o c l i m a t i c system i s an ecosystem c l a s s i f i c a t i o n . Most maps which have been produced us ing t h i s system are smal l s ca le and are not gene ra l l y usable as a s i t e s p e c i f i c p lanning t o o l . I n t e r p r e t a t i o n s or p r e s c r i p t i o n s are based on the user f i r s t i d e n t i f y i n g the ecosystem a s s o c i a t i o n as de f ined i n a handbook and then app ly ing the i n t e r p r e t a t i o n from the handbook ( K l i n k a 1977). Although t h i s system may prove e f f e c t i v e , i t has the inherent disadvantage PRIMARY USER SURVEY RESOURCE INVENTORY * PROTOTYPE OF INVENTORY PRESENTATION SECONDARY USER SURVEY ^  FINAL INVENTORY PRESENTATION 10 of relying on the user to do his own f i e l d work instead of merely identi-fying his site location and reading the prescription for that site. In areas which are not particularly complex, this method could be useful. However, where soil-vegetation-landform changes are subtle or complex, mapping at a relatively detailed scale is s t i l l necessary because the user may not be able to differentiate subtle or hidden changes. In contrast to the latter system, the CLI program relied on maps. It mapped most of the Province by using aerial photographs to interpret and differentiate mapping units at a reconnaissance scale (1:50,000). The CLI is a heirarchical biophysical classification system which uses landform, s o i l , and vegetation to differentiate mapping units. Mapping units are "eco-systems" of "homogeneous" vegetation, soils, and landforms. These units are used as a basis for identifying a wide range of capability classes for forestry, agricultural and other resource uses (Rees 1977). The CLI Forest Capability maps show capability (based on predicted yields) only, which is inadequate for intensive management. We could consider using another biophysical type of inventory technique for forested areas. However, an analysis of the various biophysical methodologies used in different regions, (Wiken 1978) concluded that a l l agencies employing this technique utilized similar c r i t e r i a for defining or differentiating mapping units. In contrast, the management objective and biophysical base, and thus information requirements of each agency is different. It is therefore unlikely that a standardized methodology w i l l suit every user or every environment. Inventories designed for different predictive purposes in different areas should u t i l i z e different c r i t e r i a to define mapping units. In some areas such as the coastal mountain zone, a criterion such as slope may be extremely important. In more gently sloping areas, slope may be of l i t t l e consequence to resource managers. In the same respect, management in areas of intensive agricultural utilization requires a very different set of data and differentiating c r i t e r i a than management in areas of intensive forestry. The successional trend or serai stages of an agricultural f i e l d are of minor importance to the manager, but in forested areas succession patterns may be of primary importance. Data and differentiating c r i t e r i a must be designed for applicability in each specific type of survey and should in part be based on the users' needs. Associated with this concept is the necessity of highlighting different types of information for different management areas. Background data may remain the same in two instances, but different information should be presented on the maps. The data presented should be dependent on the purpose of the maps. This is not meant to imply that single purpose resource surveys should be conducted. On the contrary, a coordinated holistic approach to resource management necessitates the integration of the maximum amount of information useful to various types of managers now and in the future. Unit differentiation should be conducted using c r i t e r i a the scientist feels best characterize the ecosystem, which are useful to the majority of potential users and which wi l l be identifiable for a long period of time barring some cataclysmic event (e.g. landslide). If only one type of data such as forest capability or vegetation community, is used to define mapping units, the map produced may be useless for certain user groups or for future decision making. Using more than one differentiating c r i t e r i a may also pose problems. Classification systems by their nature delimit boundaries defining classes in systems which are a continuum (Cline 1949). Class boundaries in a con-tinuum are often arbitrarily defined. The arbitrary nature of class 12 boundaries means that i f two different systems of classifications such as soils and vegetation are super-imposed on a large scale map, i t is very unlikely that their class boundaries w i l l coincide except in cases with sharp boundaries, i.e. s o i l - water. In a resource survey, the individual responsible for map making integrates the information at hand and assesses the most important differentiating criteria(on). This may be vegetation, slope, or s o i l types. The units which are differentiated and the dif-ferentiating c r i t e r i a employed are biased by the producer's background. If a user survey has been conducted then the users' desires can also be incorporated in his decision. In summary, a forested area was considered a good area to test a user input type of survey, because large scale soil-vegetation-landform maps are not common in forested areas (CLI maps are relatively small scale) and managers w i l l be less biased by existing maps. Also, inventories of this type are presently being perceived as a v i t a l necessity for integrated resource management and have been experimented with recently (Moon 1979, B. C. Forest Service 1975). STUDY AREA The area on which this study focusses is the Woodfibre Creek watershed; a small watershed 48 km. north of Vancouver. It drains into Howe Sound and is 40 km.^  in area. The forest is a virgin stand of timber which is within the Coastal Western Hemlock submontane biogeoclimatic subzone (Krajina 1969, Klinka 1977). A large scale (1:15840) mapping project, recently completed, was carried out under the auspices of Agriculture Canada and the B. C. Forest Service (Moon 1980). The latter project was an attempt to define the optimal scale and information level for management decisions of a forestry 13 related nature with an engineering and ter r a i n bias. During the summer of 1978 a project i n the same watershed gathered s o i l s and vegetation data (Pottinger 1978). This data was to be used i n establishing the correct location of mapping unit boundaries and to check the accuracy of the mapping technique. The information collected for this project was used as a basis for a l l examples i n the study. It consisted of very detailed soil-vegetation-landform data along a continuous transect perpendicular to the contours of the slope. There were vegetation releves, detailed s o i l descriptions and analyses of physical and chemical properties from 26 plots at 30 meter intervals (with a random 20 m horizontal variation) up the slope. The transect crosses four mapping units which were delineated on the s o i l map. A l l of the examples which were used i n the study and the f i n a l mapping system i t s e l f are based on the existing data for the Woodfibre Creek watershed. In this way the study kept to a real system rather than inventing relationships. The data was very detailed, but only a small amount of the t o t a l available data was used. I t was only necessary to use enough data to create a transect on which to base p o s s i b i l i t i e s for presentation. 14 CHAPTER 3 E XPE RIME NTAL DESIGN INTRODUCTION This chapter d e t a i l s the des ign of the t e s t of the method; the hypo-theses, the way i n which the hypotheses were te s ted and the c r i t e r i a by which the hypotheses were accepted or r e j e c t e d . UNDERLYING HYPOTHESES For the purpose of exper imenta l des ign there are two hypotheses unde r l y i ng t h i s study based on the p r i n c i p l e s and o b j e c t i v e s d i scussed i n Chapter 2. They a re : 1. Managers of f o r e s t ed lands can assess t h e i r needs from invento ry s p e c i a l i s t s to improve resource d e c i s i o n s . 2. Forest resource managers l i k e i n fo rmat i on portrayed i n a s p e c i f i c way common to the m a j o r i t y . 15 A th i r d hypothesis which i s inferred i s : 3. I t i s possible to use information from a questionnaire-interview program as an aid for determining a mapping system method i n a given area for a set of resource managers. The hypotheses were tested using c r i t e r i a outlined i n this chapter under the heading Analysis Design and Hypothesis tests. CHOICE OF RESPONSE TO TEST THE HYPOTHESES: Interviews and Questionnaires Responses were from two sources; a questionnaire program and an interview program. The majority of responses were from the questionnaire program; primarily because of the wider scope offered by this approach. The interview program was designed as a secondary or backup response to evaluate the effectiveness of the questionnaire. A number of researchers (Oppenheim 1966, Berdie and Anderson 1974) recommend the interview backup approach. Both questionnaire and interview programs have their shortcomings. Some of the more common reasons for these shortcomings are: 1. Poor question wording. 2. Leading question or question sequence e.g. "When did you stop beating your wife?". 3. Respondents fear of f a i l u r e . 4. Respondents boasting. 5. Relying on a respondent's memory. 6. Poor visua l impact of the questionnaire. 7. Respondents not understanding the question. However, careful design of the questionnaire and interview program can reduce most of the effect of these shortcomings. The section on questionnaire design w i l l delve further into this subject.. 16 It is important to note that questionnaires differ from interview programs (Oppenheim 1966). In the latter the interviewer has a tremendous effect on the respondent. The respondent may tailor his answer to his perceived concept of the interviewers desires, whereas in a questionnaire a repondent can be completely candid. Although with a questionnaire there is no one there to influence has response, questionnaires have the inherent problem that the respondent may not understand the question. In the interview situation the interviewer can clarify the question (though he may bias the response by doing so). Consequently, the interviews are used as a check to ensure questions were understood. By tape recording the interviews a future check for biasing can be conducted where necessary. The interviews in this program also gave the interviewer, some subjective insight into the needs of the various managers. Another advantage of a questionnaire (coupled with interviews) program is the large sample size which is possible for a limited budget. In this study i t was possible to send questionnaires to, or interview, the whole established population of users and prospective users. Although this is definitely not the whole population i t is a large proportion of potential users. Another appropriate approach would be a workshop technique (such as Holling et al 1977). The main advantage of the workshop technique would be the increased level of interaction between various individuals of different disciplines involved in the map system design. The main dis-advantage is that the workshop technique deals with a relatively small sample of the population. It is also very expensive to conduct, requiring highly paid participants to devote approximately one f u l l week of their time. The questionnaire-interview program probably requires more time in 17 t o t a l , but the cost i s not charged to the p r o j e c t . The q u e s t i o n n a i r e - i n t e r v i e w technique was chosen over a workshop because the sample s i z e f o r the former could be the whole e s t ab l i s hed popu la t i on - a l l of the p o t e n t i a l users which could be i d e n t i f i e d . FACTORS TO BE VARIED: Before the ques t i onna i re ( i nc luded i n Appendix I) was des igned, the v a r i a b l e s f o r which responses were des i red were e s t a b l i s h e d . The two types of v a r i a b l e s were dependent v a r i a b l e s and independent v a r i a b l e s . Dependent v a r i a b l e s were the v a r i a b l e s f o r which a consensus of op in ion might be e s t a b l i s h e d . They were decided upon a f t e r a review of the l i t e r a t u r e (eg. Benson 1978, Canadian S o i l Survey Committee 1978, Fores t Ab s t r ac t s from 1974-1979 (on S i t e C lass Assessment, S o i l F a c t o r s , S o i l , C l a s s , Assessment and S o i l F a c t o r s ) , Keser 1970, Kore lus and Lewis 1976, Moon 1980, Sprout 1976, USDA S o i l Survey S t a f f 1951 and 1975, Va l en t i ne et a l 1980) and e x i s t i n g map systems (eg. S o i l Surveys of B.C. up to 1979, the CLI system and s e l e c ted S o i l Surveys from A l b e r t a , Manitoba, New York, The Nether lands , B r i t a i n and S co t l and ) . Independent v a r i a b l e s were the " u se r " groups and subpopulat ions f o r which a concensus of op in ion on a dependent v a r i a b l e could be obta ined. The i n d i v i d u a l v a r i a b l e s of each type are exp la ined below. DEPENDENT VARIABLES: 1. Map Sca le , Pa r t I The s ca le of mapped i n f o rmat i on and mapping un i t s i z e are two of the more important quest ions to be cons idered before producing a map. E s -p e c i a l l y s ince the cost of a 1:20,000 s ca le map i s roughly 10 times more than the cost of a 1:50,000 sca le map (Sprout 1976, Benson 1978), though 18 t h i s r e l a t i o n s h i p i s f l e x i b l e . Sprout 1976 indicated that cost can be reduced by t a i l o r i n g information to the objective and the underlying p r i n c i p l e of the study, so a user survey w i l l help reduce costs of more detailed maps by focussing the objectives of the survey. Admittedly there are l i m i t s to every budget, but i n any decision, costs should always be associated with benefits. The cost of a more det a i l e d map may be 10 times greater, but the benefits could be 20 times greater. The scope of this study only allows for a q u a l i t a t i v e evaluation of benefits, but future studies could attempt a quantitative evaluation. Desired mapping unit size and scale were evaluated i n two ways: a. People were asked to choose the best scale of map for the i r work (see appendix 1, question 28). b. Respondents were asked to separate perceived map units on a transect which depicted actual information gathered at Woodfibre (see appendix 1, question 59A). A t h i r d approach to determine desired scale was used i n the interview program only. Respondents were given a set of a e r i a l photographs of 1:7,000, 1:20,000, 1:80,000 and 1:1 m i l l i o n scale. Without having been t o l d the scales of the photographs, respondents chose which scale of photograph would be most appropriate for the i r management purposes. Map Scale, Part I I , Constant vs. Variable Intensity vs. Inset Maps In some instances i t may be conceivable to consider variable i n -tensity or inset maps to give greater d e t a i l on maps. Respondents were asked whether or not variable i n t e n s i t y surveys and/or inset maps were acceptable. They were asked which they would choose, to see i f there was a desire for more d e t a i l i n areas with greater resource c o n f l i c t s . 19 2. Map Unit Symbol The type of map unit symbol and the type of connotative information contained in it are very important. If users do not understand the system or do not find it useful, the maps will not be used*. The only way to determine whether or not a legend and map symbol combination is adequate or appropriate is to ask potential users. Any other approach is an arbi-trary, usually biased, guess. There are essentially three types of map unit symbol - legend com-binations (Valentine et al 1979). These are: a. A closed or non-connotative type. b. A semi-closed or semi-connotative type. c. An open or totally connotative type. Real examples of each type were designed for the study (see appendix I, page 3). The respondents were asked to rate each of the three legend types on a scale of 10 as well as indicating which of the three legend types they preferred. This question group was not designed to assess the respondents' desire for greater or lesser substantiation. Each of the three examples contained approximately the same amount of information. The only differences were in the size and complexity of the map unit symbol and the method used to extract information from the legend. *during the interview program, roughly 10% of respondents complained they had difficulty using the legends of some of their existing maps and consequently did not use them very often. In the interview program 66% did not, presently use soil maps. Therefore the 10% above constitutes roughly 1/3 of the total users in the interview program. 20 3. Map Unit Variables, Part I The following 8 variables are some of the more important d i f f e r e n t i a t i n g c r i t e r i a in forested environments. This question was designed to determine the u t i l i t y of each of these variables as a di f f e r e n t i a t i n g c r i t e r i a . a. Site moisture regime. b. Slope. c. Depth and type of forest f l o o r . d. Standard s o i l variables. e. Vegetation. f. S u r f i c i a l deposits. g. Elevation. h. Aspect. Each of the respondents was asked to rate these variables with respect to their own management and decision-making purposes assessing their usefulness for separating map units. They were given a diagram showing how the variables related to the Woodfibre transect i n order to help them focus the question (see appendix I page 5). Map Unit Variables, Part I I : Information Lacking In order to assess what type of information should be stressed i n an inventory of this sort, respondents were asked what type of information they found most often lacking when making a resource management decision. There were s i x general categories: Soils Vegetation Terrain Hydrology 21 Climate Topography The respondents were asked to number their choices i n order of preference. 4. Interpretive Map Legends Interpretive information can be presented i n a range of levels of substantiation. The degree of substantiation accompanying an interpretive map was varied from the least amount of substantiation to the greatest amount of substantiation. Holling et a l 1977 after Gross et a l 1973 related ease of interpretation and degree of substantiation graphically (Figure 2). A greater degree of substantiation results i n a decrease i n the ease of interpretation. Figure 2 Degree of Substantiation Compared with Ease of Interpretation points low high points low high degree of sub- ease of i n -stantiation terpretation This question grouping was designed to find out where, on the above scales, respondents wished to be (page 8, Appendix I ) . 22 5. Soil Classification Soil classification is important from a scientific viewpoint in any mapping program*; so are other classification systems (eg. vegetation). This question grouping (Appendix I, question 117, etc.) was designed to discover the extent to which s o i l classification is used. It was assumed that i f s o i l classificaton was not important to the majority of users then i t would be unnecessary in the legend and on the map. It could be in-cluded in the report only. 6. Interpretive Map Presentation There are two extremes of derivative information presentation: one soil-vegetation-landform map with an interpretive legend or many maps, or one soil-vegetation-landform map and many interpretive maps each with their own legend. The two extremes were given in an illustration in the questionnaire, (Appendix I, page 9) and respondents were asked some questions on their preferences. As well as the obvious differences, there are futher subtle distinc-tions between these two mapping approaches. For instance, one interpre-tive map may have less mapping units than another, because some units have been combined under the same interpretation. *Soil Classification is a scientific classification system which groups similar soils into classes. In this way experience gained from scientif-ic studies in one area can be extrapolated to other areas with similarly classified soils. This is useful for producers, but is not always useful for users. Another s i g n i f i c a n t d i f f e r e n c e e x i s t s i n the map types. The one-map system por t rays the h ighest l e v e l of s u b s t a n t i a t i o n by a u t o m a t i c a l l y s imul taneous ly p rov i d i ng a l l of the base data w i t h the i n t e r p r e t a t i o n s . Whereas, w i t h many maps there are a range of p o s s i b i l i t i e s . Consequently, t h i s dependent v a r i a b l e i s r e l a t e d to the dependent v a r i a b l e s of i n t e r -p r e t i v e map legends and map un i t symbols. These quest ions were not designed to look at a l l of these i s sues -only to determine whether people would ra ther have one map or many maps. Respondents were asked to ra te each approach on a s ca le of 10, from ex-c e l l e n t to poor. They were asked which of the methods they l i k e f o r d e t a i l e d maps, eg. 1:20,000 to 1:2,000 sca le and which of the two methods they l i k e d f o r reconnaissance maps, g reater than 1:50,000 sca le (Appendix I, page 10). 7. General Map P r e s e n t a t i o n Respondents were asked t h i s l a s t group of quest ions to d i s cove r u s e r s ' de s i r e s p e r t a i n i n g to legend s i z e , s p e c i a l p re sen ta t i on techn iques, map base type, and the presence or absence of contours (Appendix I, page 10). These quest ions were designed to help i n determin ing more e a s i l y understood and be t te r l i k e d map p re sen ta t i on types. INDEPENDENT VARIABLES: Subpopulat ions The main independent v a r i a b l e used i n the study was the u s e r ' s job group. Expecta t ions were that t h i s would prov ide the b iggest s p l i t i n t o subpopulat ions w i t h d i f f e r e n t responses to the dependent v a r i a b l e s . The job groups and t h e i r abb rev i a t i on s are l i s t e d i n Table 1 below. TABLE 1 L i s t of Job Groups and Abbreviations Job Group Abbreviations 1. Zone Foresters Zfores 2. S i l v i c u l t u r a l i s t s S i l v i c s 3. Fish and W i l d l i f e B i o l o g i s t s , Fish&W 4. Company Foresters CFores 5. Forest Rangers Ranger 6. Forest Engineers Eng 7. Recreation Planners Recrea 8. Pedologists and Ecologists Ped+Ec 9. Resource Planners Resplan 10. Planning Inventory and Valuation of the Forest Service PIVFS A number of other questions were asked which were thought to be po t e n t i a l independent v a r i a b l e s . These are l i s t e d i n Table 2. TABLE 2 P o t e n t i a l Independent Variables Number of times a respondent uses s o i l , vegetation or e c o l o g i c a l maps. Percentage of work done i n the f i e l d . Where a user would consult a map, i . e . , f i e l d or o f f i c e . How often a respondent was d i r e c t l y or i n d i r e c t l y responsible for resource management decision. What type of information a respondent feels i s most often lac k i n g . The l e v e l of education of the respondent. The number of years of experience both in present job and t o t a l relevant experience. Number of short courses. Interview respondents. Questionnaire respondents. 25 Some of the dependent variables also functioned as independent variables. For example, desired legend type was compared with map pre-sentation type to see i f there were any relationships. CHOICE OF LEVELS A l l of the data collected was of nominal or, at best, ordinal scale.* Consequently, the s t a t i s t i c a l tests used had to be non-parametric techniques. One or two questions in each question sequence were designed to obtain the key response for each dependent variable. These questions were usually closed questions, i.e. questions with a limited number of re-sponses, to avoid vague answers. They were also usually accompanied by examples which allowed for real comparisons. The frequency d i s t r i b u t i o n of responses to these key questions was the important data for analysis. ANALYSIS DESIGN The data was collected on the questionnaire and interview forms i n a format which allowed for easy transference to MVOMR cards and computer processing. The data was designed to be compiled using a multi-variate tabulation program (UBC MVTAB) written by Bjerring 1977. This program was formulated for use with questionnaires. The data was such that the Kolmogorov-Smirnov test for one sample and for two unrelated samples, the Chi square test, and the binomial test could be used to analyse trends and test for s t a t i s t i c a l significance. These tests are explained i n Seigel 1955, Lindgren and McElrath 1970, and Blalock 1960. Their main function i s to determine i n the two sample *See Siegel 1955 for a good explanation of ordinal, nominal and other scales. 26 TESTING THE PROTOTYPE After the questionnaire results were analyzed and conclusions reached a further test was conducted. This test i n a real mapping situation should be the presentation of the preliminary maps to the users. In this study no map was being produced. Therefore a substitute prototype was designed which summarized the resolutions established by the questionnaire-interview program. This summary was sent to the interviewees only, because they generally spent more time understanding the questions than the questionnaire respondents and should be more aware of the issues. INTERPRETATION: Hypotheses Tests Interpretation measured the significance of the whole population's responses and any sig n i f i c a n t differences between subpopulations defined by independent variables, i . e . job group, etc. Interpretation also tested for any si g n i f i c a n t difference between the interview and the questionnaire subpopulations. The size of the interviewee subpopulations meant that no s t a t i s t i c a l analysis of these subpopulations could be conducted. However, trends were looked at and the whole interviewee subpopulation was compared with the whole questionnaire subpopulation and analysed s t a t i s t i c a l l y to look for differences between the programs. If results were found to be sign i f i c a n t the probability l e v e l at which they were tested i s indicated as P < x. Acceptance or rejection of the hypotheses outlined at the beginning of this chapter was based on the following: Hypothesis #1 i s d i f f i c u l t to "prove" or "disprove" using a study of this sort. There i s a problem defining what "their needs ..." are as 27 written in the hypothesis. Are their needs their perceived needs or their actual needs? As i t i s impossible to determine their actual needs this study focusses instead on their perceived needs. The hypothesis would be accepted i f the majority of dependent variables were resolved by a s t a t i s t i c a l l y s i g n i f i c a n t (p < .05) majority of respondents. This would imply there was a general consensus among users of what their needs are for decision making. Hypothesis #2 could be accepted i f the population exhibited a s t a t i s t i c a l l y s i g n i f i c a n t (p < .05) trend for at least some of the dependent variables. Such a trend for a dependent variable would show that a si g n i f i c a n t number of forest resource managers l i k e information portrayed i n a spec i f i c way as opposed to the n u l l hypothesis of no sp e c i f i c choice. Hypothesis #3 would be accepted i f the majority of the dependent variables were resolved by a s t a t i s t i c a l l y s i g n i f i c a n t (p < .05) majority and i f the resolutions as outlined in the summary were also accepted. However, acceptance of the resolutions i s not necessarily positive proof that hypothesis #3 i s true. Although without a test of a real map prototype, i t ' s the best possible conclusion i f the resolutions are accepted. 28 CHAPTER 4 DESIGN OF INTERVIEW AND QUESTIONNAIRE PROGRAM INTRODUCTION Design of a questionnaire and the mailing and follow-up details are extremely important in any questionnaire program. This chapter explains some of the procedures which were used to encourage maximum response and ensure that questions were not ambiguous or misunderstood. GENERAL I n i t i a l l y , there was no way to assess the size of the potential user population. It was also unclear how may job category subpopulations there were, eg. foresters, engineers, fish and wildlife biologists, etc. Could the whole population be sampled or would a sub-sample be necessary? After phoning the various agencies and companies involved to deter-mine the number of users or potential users, the discovered population was found to be small enough to send questionnaires to or interview a l l identified possible users. 29 The population was separated into 10 groups based on profession and job. Five people were chosen to be interviewed from each group. These people were not sent questionnaries. A few of the interviewees did see the questionnaires before they were interviewed, but this was the excep-tion rather than the rule. The questionnaire form was used as a basis for the questions asked i n the interview. A l l answers i n the interviews were recorded on the forms, allowing for direct comparison of the interviews with the questionnaire responses. The interviews were also tape recorded for future reference; s p e c i f i c a l l y to check that interviewer bias had not shifted the results. One job group, Planning, Inventory and Valuation of the Forest Service (PIVFS), was not interviewed. This group was a heterogeneous mixture of potential users taken en masse from the B.C. Forest Service directory. Many of this group of respondents indicated maps and resource studies were completely foreign to them. Therefore, they were not i n -cluded i n the interview program. QUESTIONNAIRE DESIGN As mentioned above, the questionnaire was designed to be used as a format for the interview program as well as a mailed questionnaire. There were seven sections to the questionnaire. The f i r s t section established responses to the majority of the independent variables. Each of the re-maining sections with the exception of the section asking information about job and experience was designed to obtain responses to the dependent variables. Questions about job and experience were put i n the middle of the form to reduce their impact. The f i r s t question or paragraph i n each section was designed as a f i l t e r to introduce the respondent and heighten his awareness to the 30 subject. The next question or questions asked for the key answer(s) necessary for analysis. To guard against possible misunderstanding or lack of appropriate answers and allow for further c l a r i f i c a t i o n , a non-dir e c t i v e open question (eg. comments) followed most of the question groups. This type of question sequence was adapted from Oppenheim (1966) and i s termed a funnel approach. The main question i n each group was usually a closed question to en-sure comparable answers. This meant using specific examples based on the Woodfibre data. A l l of the words used in the questionnaire were scrutinized for pos-sib l e double meanings. Berdie and Anderson (1974) l i s t e d a number of words which had this problem. They were avoided. Other important factors considered i n the design of the questionnaire were those shown on page 15 as well as: 1. Maintaining rapport. 2. A good explanation i n the cover l e t t e r . 3. Physical and verbal appeal. 4. Rating scales as well as actually choosing a response. 5. Actual rating scale rather than words, eg. 1 to 10. Numerous other considerations were made based on readings from Parten 1950, Payne 1951, Oppenheim 1966, Berdie and Anderson 1974 and Robinson et a l 1975. The questionnaire and cover l e t t e r are included i n their entirety i n Appendix I. A p i l o t program was used in this study, but rather than just sending the questionnaire out, a portion of the interviewees were used in the p i l o t program. It was f e l t that this would give more direct results and lead to better questionnaire design. So, 10 interviewees, and 20 mailed questionnaires were included in the p i l o t study. These respondents covered a cross-section of the job groups. Mailing D e t a i l and Followup Letter Although not r e a l l y part of the study methods, mailing and follow-up de t a i l s are extremely important i n encouraging higher response rates and consequently are included in this report. In the questionnaire program everything possible was done to en-courage responses. The questionnaire was printed on blue paper, so i t would stand out on a respondent's desk. I t was sent out with a stamped self-addressed envelope. The stamps on the envelope were specially chosen for their bright colours to make the respondent f e e l obligated to return the completed form and the return envelope was wrapped around the questionnaire with the stamps showing on the f i r s t page of the questionnaire, so they were sure to be seen. After a short i n t e r v a l a follow-up postcard l e t t e r was sent to everyone who had not responded. The postcard proved quite effective and we experienced a noticeable increase i n responses. There were other techniques used, but the foregoing were the most ef f e c t i v e . Phoning a l l non-respondents i s a f i n a l way to boost response. Due to the tremendous response rat i o we considered this unnecessary. Also, as w i l l be mentioned i n the next chapter, there was only one job group with a high proportion of non-respondents. 32 CHAPTER 5 RESULTS OF THE QUESTIONNAIRE-INTERVIEW PROGRAM AND ANALYSIS OF PATTERNS RESPONSE A t o t a l of 270 questionnaires were mailed. Thirty-two questionnaires were cancelled (due to redundancy, resignations, e t c . ) , leaving a t o t a l of 238 p o t e n t i a l respondents. Of t h i s 238, 182 people responded to the questionnaire. F i f t y - s i x people were i d e n t i f i e d as d e f i n i t e non-respondents. The interview program consisted of 48 people. Thus, the t o t a l of both programs was 230 respondents out of a possible 286 or rough-l y 80% of the t o t a l established population. The response data for the various job groups indicates that the major group of non-respondents was the PIVFS group (Table 3). This can be explained by the fact that the names for this group were taken from the Forest Service d i r e c t o r y . Many of the respondents in this group indicated on th e i r questionnaires that they would not use s i t e maps i n th e i r work. PIVFS i s composed of a heterogeneous mixture of people who range from TABLE 3 Response Rate VS Job Group ZFores S i l v i c Fish&W CFores Range Eng Recrea Ped&Ec Resplan PIVFS Respondents 14 24 16 24 30 31 6 26 15 38 Non-Respondents 3 3 5 6 1 6 0 4 1 26 % Non-Respondents 20% 11% 26% 23% 3% 19% 0 13% 6% 41% Job Group abbreviations are shown in Table 1, page 24. 34 economists to photo-interpreters. There was no simple way to distinquish which of these respondents would be relevant. Consequently, the group i s quite different from the other groups. A second group which i s mixed, although they responded well, i s Pedologists and Ecologists. This group consists of vegetation ecologists, s o i l surveyors and people who would generally be interested i n mapping. A l l of this group are well educated professionals and are generally producers as opposed to users. As w i l l be indicated their views tend to be different from the rest of the groups'. RESULTS The data were analyzed using a large variety of the possible com-parisons of dependent and independent variables. Some of these proved relevant and some irrelevant. Appendices I I and I I I contain a l l of the tables showing s i g n i f i c a n t comparisons. Only those comparisons which were considered relevant to the proposed resolutions for the dependent variables are discussed. In each of the comparisons made, the interview subpopulation was compared with the questionnaire population. If there was no difference the results were combined. In many cases the data were very si m i l a r . In some the opposite was true. General Characteristics: Decision-Making, Map Use Location, etc. A l l groups had a high percentage of daily or weekly decision-makers with the exception of Pedologists-Ecologists (Ped-EC) and Planning Inven-tory and Valuation (PIVFS) which had a s i g n i f i c a n t l y lower decision-making l e v e l (Table 4). Zone Foresters and Engineers make decisions s i g n i f i c a n t -l y more often than the rest of the population. TABLE 4 Decision-Making Level vs. Job Group (Interview and Questionnaire) Decision-Making Level ZFores Silv i c Fish&W C Forest Ranger Eng Recrea Ped&Ec Resplan PIVFS TOTAL almost every day % 93B 57 44 75 70 81B 33 18x 45 lOx 51x about once/week % 7 24 28 21 23 6 7 21 36 -7 17 about once/month % 0 14 6 4 7 10 17 18 9 13 10 less than once/month % 0 5 17 0 0 3 17 25 9 38 13 never % 0 0 0 0 0 0 16 18 0 30 8 no response % 0 0 5 0 0 0 0 0 0 2 1 Total # of respondents 15 21 18 28 30 31 6 28 11 40 230 B - significantly greater number than the whole population (P<.01). X - Significantly smaller number than the whole population (P<.05). 36 The former relationship has an interesting corollary. The people who make maps, pedologists and ecologists, are generally not responsible for using the maps as often to make decisions; a situation which tends to support the use of a technique like this to determine whether the users' desires coincide with those of the producers. A slight majority, though certainly not a significant majority, of a l l groups would use maps most often in the office. A l l groups would use detailed site maps, i f available. PIVFS were significantly different than the other groups in that 27.5% would not use detailed soil-vegetation-landform maps. This has important consequences in other relationships of this group with the whole user population. MAP SCALE: PART I Present Use The most commonly used scale range was 1:11,000 to 1:20,000. Of the specific professional groups, engineers used larger scale maps (>1:10,000 scale) whereas fish and wildlife biologists, pedologists-ecologists and recreation planners used smaller scale (1:20,000 to 1:50,000) maps. Rangers and Zone foresters used 1:10000 to 1:20000 almost exclusively. See Table 5 for a s t a t i s t i c a l evaluation. Comparing scale used with level of decision-making, the majority of. those who make decisions every day used larger scale maps than those who make decisions less than once a month or never (see Table 6). TABLE 5 Map Scale Most often Used: S t a t i s t i c a l Comparison of Job Groups ro d d Job Group ZFores S i l v i c Fish&W CFores Ranger Eng Recrea Ped&Ec Resplan PIVFS Total Job Scale most  Group often used ZFores 1:11000 - 1:20000*S d d d d d S i l v i c 1:11000 - 1:20000S d d Fish&W 1:21000 - 1:50000NS d CFores 1:11000 - 1:20000NS d Ranger 1:11000 - 1:20000*S d d d d d d Eng < 1:10000NS d d d Recrea 1:21000 - 1:50000NS Ped&Ec 1:21000 - 1:50000S d d Resplan 1:1100Q - 1:20000NS PIVFS 1:11000 - 1:20000S Total Pop 1:11000 - 1:20000S * Although these two groups use the same scale as the to t a l Population, almost a l l of them use this scale rather than the majority. d Significant difference between these two groups at p<.05 using Kolmogorov Smirnov two sample test. S Significant majority at p<.05 using Kolmogorov Smirnov one sample test. NS Not s i g n i f i c a n t majority at p<.05 using Kolmogorov Smirnov one sample test. 38 TABLE 6 Comparison of Sca le Used w i t h Dec i s ion-Mak ing L e v e l D e c i s i o n Making Leve l 1 2 3 4 5 6 Sca le Used Almost D a i l y Weekly Monthly < Monthly Never Pop. < 1:10,000 20% d 15% 5% 10% d 0% d 14% 1:11000 -1:20000 55 52 45 47 42 51 1:21000 -1:50000 13 20 32 40 26 21 1:51000 -1:100000 2 3 9 9 0 2 > 1:100000 2 3 0 0 5 2 Not answered 8 7 9 3 27 10 100% 100% 100% 100% 100% 100% T o t a l ResponsesJ 117 40 22 30 19 230 d S t a t i s t i c a l l y S i g n i f i c a n t D i f f e r ence s (p<.05): The almost d a i l y group uses l a r g e r s ca le maps than the l e s s than once/month and never groups. Des i red Sca le Assessment: Method I A s t a t i s t i c a l l y s i g n i f i c a n t m a j o r i t y (76%) of the whole popu l a t i on chose 1:20,000 sca le or l a r ge r (Table 7 ) ; of t h i s group the ma jo r i t y (60%) chose 1:20,000 sca le maps (see quest ion 31, Appendix I ) . Ped-Ec were the only s t a t i s t i c a l l y s i g n i f i c a n t l y d i f f e r e n t group. They gene ra l l y de s i red smal le r sca le maps (Table 7 ) . 39 TABLE 7 Comparison of Pedologists and Ecologists Desired Map Scale  with Other "users" Desired Map Scale JOB GROUP Desired Map Scale Ped-Ec Population % response 1:10000 14 30 1:20000 39 46 1:50000 36 d 12 d 1:100000 7 2 Other 4 10 Total Number of Respondents 28 230 d - these values indicate a sig n i f i c a n t difference (p < .05) between these sub-populations. Comparing decision making l e v e l with scale of map chosen, a sig n i f i c a n t s h i f t towards larger scale maps i s evident for individuals who make decisions almost daily and weekly (Table 8). Desired Scale Assessment: Method 2 A second method used to test for an individual's desired map scale was to ask them to separate a transect into perceived mapping units (see question 260 i n Appendix I ) . By measuring the length of the smallest perceived management unit i d e n t i f i e d and extrapolating to determine an appropriate scale which would present this unit, i t was possible to 40 TABLE 8 Comparison of Scale Chosen with Decision-Making Level Decision Making Level 1 2 3 4 5 6 Map Desired Scale Almost Daily Weekly Monthly < Monthly Never Pop. 1:10 000 38 42 14 13 5 30 1: 2O.0:OO 48 38 45 57 37 46 1:50000 3 10 23 30 32 12 1:100.000 2 2 5 0 0 2 Other 5 9 0 11 7 Not answered 3 4 0 15 3 Total 117 40 22 30 19 230 d S t a t i s t i c a l l y Significant Differences at p<0.5 - Almost daily users desire larger scale than monthly, less than monthly, and never user groups. - Weekly users desire larger scale than the less than monthly and never user groups. estimate actual scale perceived necessary for management. Figure 3 gives three examples of ways i n which the transect was divided and corresponding map scales assuming 0.25 cm^  as the smallest unit. The transect was p a r t i c u l a r l y suited to this type of question, because i t had a short (70 meter) h i l l slope at i t s lower end and a small rocky outcrop towards the upper end. The respondent could choose whether or not the l a t t e r items, which would be represented at large scale only, were necessary for decision-making. Fig. 3 Examples Of The Size Range Of The Smallest Mapping Units Separated Along The Transect And Corresponding Scales SUB XER.IC D O U G L A S F I R S H L A L P I P S I S S E W A M O S S XERIC R . E D C E D A R . I > O U C , L A S F I R S A L A L M O S S MESIC D O U G . L A S F I R W E S T E R N H E M L O C K R E . D H U C K J - E B E R R Y P E E R . F E R N S T E P M O S S BCOJ-OCK ou tc rops . Hyqwc W E S T E R N H E M L O CK R E D C E D A R A M . A B I L I S F I R F O A M F L O W E R D E V I L ' S C L U B E L D E R . D E E R F E R . N 1-iBiOO Mixed c o L t u v i a l and. M o r a i n a i , d e p o s i t s ( t o b b l y , U x u n i ^ t e i r t u r e ) SUB HYDRIC KSS> C E D A R W W T E R . N H E M L O C K . D E V I L ' S C J - U B S K . U N K . C A B B A G E A L A S K A B L U E B E R R Y S P H A G N U M M O S S Compox led Morairxal <Leposits CbouUi€ry f Loamij texture) • F L u v i a l deposits (bouldery , o,rtLveUy tcnturt ) Of the people who responded to t h i s que s t i on , the m a j o r i t y wished 1:10,000 to 1:20,000 sca le maps. The second most popular choice was l e s s than 1:10,000 sca le maps. The r e s u l t s are shown i n Table 9 below. TABLE 9 Des i red Map Sca le as Determined by Transect D i v i s i o n Repre senta t i ve Sca le Percent Response > 1:10000 36 1:10000 - 1:20000 59 1:21000 - 1:50000 5 > 1:50000 T o t a l Responses 168 The group w i th the l a r ge s t percentage of respondents who wished 1:10,000 or l a r g e r s ca le was Ped-Ec (50%). Th is seems i n d i r e c t c o n f l i c t w i th t h e i r r e l a t i v e l y strong de s i r e f o r 1:50,000 maps under Method 1. Th is c o n f l i c t probably r e s u l t s from past p o l i c y and a pe rcept i on of g reater costs f o r l a r g e r sca le maps imp ly ing l e s s area covered by e x i s t i n g manpower and resources ; a good example of de s i r e vs. t r a d i t i o n . Des i red Sca le Assessment: Method 3 The t h i r d method used to est imate des i red sca le was part of the i n t e r v i e w program on l y . Respondents were asked to s e l e c t a working sca le by choosing one of a v a r i e t y of a e r i a l photographs at d i f f e r e n t s c a l e s . Photo sca les were not d i vu lged p r i o r to t h e i r cho i ce . Roughly 75% of the respondents chose the 1:20,000 sca le photographs as t h e i r de s i r ed working s c a l e . Nobody chose 1:1,000,000 s ca le and no one chose 1:80,000 s c a l e . So the remaining 25% chose the 1:7,500 sca le photographs. This r e i n f o r c e s the prev ious r e s u l t s . 43 Map Scale Summary There were three methods used to assess desired map scale. A l l three indicated that 1:20,000 or larger scale maps were the most desirable. The only dissenting job group was Ped-Ec. They presently use smaller scale maps and wanted smaller scale maps. People who make decisions less often also used and wanted smaller scales. These two groups both had lower levels of decision-making, which should decrease the overall importance of their desires. The job groups which had a majority desiring larger than 1:20000 scale maps were Zone Foresters, s i l v i c u l t u r a l i s t s and engineers. None of these majorities were s t a t i s t i c a l l y s i g n i f i c a n t and as larger scale i s less economical, more weight should be given to a smaller scale. 1:20,000 was the scale chosen by the majority. This should lead to the l o g i c a l conclusion that 1:20,000 i s the best scale and would s a t i s f y the most needs. O r i g i n a l l y , 1:20,000 was selected as one of the possible choices, because i t i s the standard "metric" scale being adopted by the Forest Service. There i s the p o s s i b i l i t y that a s l i g h t l y larger or smaller scale would be more appropriate. For instance, maybe 1:15,000 i s a better scale because of easier photo interpretation; but 1:15,000 scale maps would cover only 56% of the area of a similar sized 1:20,000 scale map. Consequently, a s l i g h t l y larger scale i s economically u n j u s t i f i -able. Some respondents said 1:20,000 scale photos were too small scale for accurate a e r i a l photo interpretation. If larger scale photos are needed for photo interpretation then that should be a separate considera-t i o n . Our concern here i s with f i n a l map scale and map unit size not working scale. Perhaps, because of the economy of smaller scale, we should think of 1:25,000 or 1:35,000 scale maps. This i s a p o s s i b i l i t y , but the response 44 from the survey i s d i r e c t l y opposed to this conclusion. If anything, the respondents wanted greater d e t a i l and larger scale. From the data gathered, i t seems reasonable to conclude that 1:20,000 i s the best scale for future soil-vegetation-landform maps in our study area. MAP SCALE, PART I I : CONSTANT VS. VARIABLE INTENSITY VS. INSET MAPS As can be seen i n Table 10, a large majority of the t o t a l population and each of the subpopulations would accept variable intensity of mapping (see question 34 Appendix I ) . A similar number would also accept inset maps. When asked to choose between an inset map, variable intensity or both, an inset map was preferred by the t o t a l population. Although some populations, noteably and s t a t i s t i c a l l y s i g n i f i c a n t l y pedologists and ecologists, would rather have both an inset map and variable intensity than one or the other (Table 10). If one looks at the number of re-spondents who wanted either both or an inset map, there i s an overall majority as well as a majority i n a l l groups who would want to have inset maps; less than 1/3 of the respondents in each group wanted variable intensity alone. When the questionnaire results were compared with the interview r e s u l t s , there was a difference. The interview respondents, as shown i n Table 11 were more strongly i n favour of inset maps. The s h i f t away from a "Both" response was l i k e l y due to the interviewer indicating that variable intensity maps are inevitable due to access. Consequently, most interviewees perceived the both option as hypothetical only. This probably accounts for the s i g n i f i c a n t decrease in the Both choice. TABLE 10 Comparison of Inset Variable Intensity Choice with Job Group ZFores S i l v i c Fish&W CFores Range Eng Recrea Ped&Ec Resplan PIVFS Total Inset % 33 43 56s 43 27 36 50 18 66 35 37 Variable Intensity % 7 19 17 14 23 32 0 14 9 18 18 Both % 53 29 22 36 47 26 0 61ds 9 35 36 Not Answered % 7 10 6 7 3 6 50 7 18 12 10 # of Respondents 15 21 18 28 30 31 6 28 11 40 230 d This value implies th i s group has a s i gn i f i can t l y different choice than the rest of the population (p<.05 using K-S two sample). S S ign i f icant choice for th i s Job Group (p<.05). 46 TABLE 11 Comparison of Interview and Questionnaire Results for  Variable Intensity, Inset Choice Inset % Variable Intensity % Both % Not Answered % Total # of Responses Questionnaire 34 17 42d 8 182 Interview 48 23 13d 17 48 d - Indicates a significant difference in the responses of the two populations (p<.01 using K-S two sample test). Constant vs. Variable Intensity vs. Inset Maps Summary From the data i t appears that inset maps are the majority choice. A-large majority of the population and of each subpopulation would like inset maps or inset maps and variable intensity maps. This desire is reinforced by the previously discussed data on map scales, where 36% of the population wanted 1:10,000 scale maps (see Table 7). To accommodate this desire, inset maps should be an option for the producer in areas with greater resource conflict potential. MAP UNIT SYMBOL TYPE As indicated in Table 12 a semi-connotative map unit-legend type was favoured overall (question 38, Appendix I), although five of the ten professional groups had a preference for a connotative symbol type. When the ratings (see Questions 239, 240, 241, Appendix I) were reviewed the 47 semi -connotat ive symbol was ra ted s i g n i f i c a n t l y h igher than e i t h e r of the other two symbols (F igure 4 ) . Comparing p r e f e r r ed map symbol w i t h r e l evan t years exper ience i n -d i ca ted that there was a s i g n i f i c a n t d i f f e r e n c e between responses by people w i th a low number of re levant years exper ience ( l e s s than 10 years) and those w i th a h igh number of re levant years of exper ience (greater than 10 y ea r s ) . Those w i t h l e s s job exper ience p r e f e r r ed a semi-connotat ive map symbol-legend type whereas those w i t h more job exper ience chose a connota t i ve map symbol-legend type (Table 13). TABLE 13 Map Symbol Choice v s . Relevent Exper ience Low Relevant High Relevant Map Symbol Type Years Exper ience Years Exper ience Non Connotat ive (%) 18 16 Semi Connotat ive (%) 48 34 Connotat ive (%) 26<j 42^ Not Answered (%) 8 8 T o t a l Responses 114 111 d - These values i n d i c a t e a s t a t i s t i c a l l y s i g n i f i c a n t d i f f e r e n c e (p < .05) between these two popu la t i on s . Another i n t e r e s t i n g comparison was d i scovered when the r a t i n g of a connota t i ve legend was compared w i t h whether or not the respondent had based h i s answer on exper ience w i t h the use of t h i s type of map (ques t ion 42, Appendix I ) . Those who had not based t h e i r answer on exper ience w i th t h i s type of map chose the connotat ive symbol more o f t e n , whereas those TABLE 12 Map Unit Symbol Preference vs. Job Group Job Group Map Unit Symbol Type ZFores Silv i c Fish&W CFores Range Eng Recrea Ped&Ec Resplan PIVFS Total Non Connotative % 20 5 17 11 20 29 50 14 27 10 17 Semi Connotative % 33 33 33 53 47 29 0 57sa 73sb 35 41 Connotative % 40 57 39 36 27 39 33 14d Od 43 34 Not Answered % 7 5 11 0 6 3 17 15 0 12 8 Total // of responses 15 21 18 28 30 31 6 28 11 40 230 STATISTICAL SIGNIFICANCE d - Silv i c u l t u r a l i s t s are significantly different from Resource Planners and Pedologists--Ecologists (p<.05) • sa - Pedologists-Ecologists have a significant choice (p<.01). sb - Resource Planners have a significant choice (p<.05). FIGURE 4 Map Symbol Ratings fo r the Whole Population Response Frequency % Very Low Low Moderate High Rating 50 who had based their answer on experience with this type of map chose a connotative symbol less often (Table 14). TABLE 14 Map Symbol Choice vs. Whether or not the Answer  is Based on Experience Map Symbol Choice Answer Based on Experience Answer Not Based on Experience Non Connotative (%) Semi Connotative (%) Connotative (%) Not Answered (%) Total Response 31 5 159 16 43 10 61 21 d - s i g n i f i c a n t l y different responses (p<.02) A third comparison showed that the population with higher relevant years of experience had s i g n i f i c a n t l y greater numbers of people who did not base their choice on map experience. These three relationships tend to indicate that the connotative symbol is better l i k e d u n t i l a person has had experience using i t . This was also corroborated by comparisons of the ratings for each symbol type with map experience and relevant experience (Appendix I I I ) . The interview program's responses on map unit symbols were s i g n i f i -cantly different than those of the questionnaire (Table 15). TABLE 15 Comparison of the Map Symbol Choice in the Interview and Questionnaire Programs  Response From Questionnaire Response From Map Symbol Choice Program Interview Non Connotative (%) Semi Connotative (%) 42 35 Connotative (%) 36 27 Not Answered (%) 10 3 Total Response 182 48 d - a significant difference between these populations (p<.02 using K-S two sample test). There was a significant increase in the preference of a non connota-tive symbol and a decrease in the desire for a connotative symbol. Three possible explanations can be given. 1. The interviewer influenced the choice by his own bias, or, 2. the interviewer c l a r i f i e d the nature of the choice, or, 3. the interview group was different by chance. Undoubtedly, the interviewer had some influence on the choice of map symbol type. What type of influence could only be ascertained by re-viewing the tapes A review of the interview tapes indicated no undue bias by the interviewer. The main influence of the interviewer was cl a r i f i c a t i o n that a l l of the examples contained an equal amount of in-formation. The interviewees' most common misconception was believing the ..52 connotative example was more detailed, because to most of them i t seemed more d i f f i c u l t to interpret. Dispelling this belief probably caused the s h i f t i n desire toward the simpler approach of a non-connotative symbol. Of course, there i s the third p o s s i b i l i t y that the interviewee population was different from the main population. This cannot be proved or disproved, but i n most other questions they responded s i m i l a r l y to the questionnaire population. In fact, i n many situations they were sur-p r i s i n g l y close. The questions on map symbols and legends were the only questions where s i g n i f i c a n t l y different responses occurred. This would lead one to conclude c l a r i f i c a t i o n caused the differences. Map Unit Symbol Type Summary Although the semi-connotative symbol was preferred o v e r a l l , there was no s i g n i f i c a n t choice of symbol type. When the ratings from one to ten were compared, the semi-connotative symbol type was rated s i g n i f i c a n t l y higher. Comparing symbol choice with some of the independent variables reinforced the semi-connotative symbol as the optimal.choice and comparing map experience with symbol choice highlighted a s t a t i s t i c a l l y s i g n i f i c a n t s p l i t . Respondents who based their answer on map experience had a si g n i f i c a n t choice of a semi-connotative symbol. Interviewees were s i g n i f i c a n t l y more fond of a non-connotative symbol. Although they made no choice between the symbols, their rating of the symbols also indicated a s i g n i f i c a n t preference for a semi-connotative symbol type. In summary, i t seems the semi-connotative symbol i s preferred by more of the respondents. It i s more strongly preferred when people base their answer on experience and interviewed people were less fond of the connota-tive symbol possibly when they learned i t contained no more information. Consequently, the semi-connotative symbol seems to be the optimal symbol choice. The connotative symbol i s the second choice by the whole population. To s a t i s f y the groups with a high desire for a connotative symbol more connotations can be added. The two groups who had the highest u t i l i t y for the l a t t e r were s i l v i c u l t u r a l i s t s and engineers. As w i l l be noted i n more d e t a i l l a t e r , i n the section on d i f f e r e n t i a t i n g c r i t e r i a , s i l v i c u l t u r a -l i s t s have the strongest preference for s i t e moisture information, and Engineers for slope and s u r f i c i a l deposits. Slope i s already included i n the representative semi-connotative symbol and s i t e moisture can be coded i n the symbol by sequencing the mapping units from d r i e s t to wettest (eg. A, B d r i e s t ... W, X, Y wettest). This leaves s u r f i c i a l deposits which can be added as a l e t t e r or number next to slope. In summary, the symbol would have connotations of slope, s i t e moisture, and s u r f i c i a l deposit. An example i s shown i n Appendix IV. MAP UNIT VARIABLES, PART 1: DIFFERENTIATING CRITERIA Respondents were asked to rate the eight d i f f e r e n t i a t i n g c r i t e r i a as to t h e i r usefulness for separating map units. Table 16 shows the per-centage of high ratings (ratings of 8, 9, or 10) for each of the d i f f e r e n -t i a t i n g c r i t e r i a by each job group. Most groups had no s i g n i f i c a n t preference for t h e i r three highest rated c r i t e r i a compared to t h e i r fourth highest. Two groups and the t o t a l population did have s i g n i f i c a n t l y higher ratings for t h e i r f a v o r i t e c r i t e r i a . The % of high ratings ( i . e . greater than 7) for each c r i t e r i a i s shown in Table 17. There i s no s t a t i s t i c a l difference between the top three. However, slope i s rated s i g n i f i c a n t l y higher than s o i l factors (the fourth). TABLE 16 The Top Three Differentiating Criteria by Job Group Job Group highest ZFores Silvi c 80 95 S moisture moist Fish&W 89 slope CFores Range Eng 86 veg 80 slope 96 S slope Recrea 67 soils Ped&Ec 93 moist Resplan PIVFS 100 S slope Total Pop. 70 75 S slope slope 73 91 S 87 82 77 71 67 89 2nd highest slope veg veg moist veg elev veg s o i l 64 65 69 sur f i c i a l deposits moist moist 3rd highest 73 57 81 78 67 68 50 surfi c i a l veg slope elev slope moist deposits slope 82 55 surfi c i a l deposits veg 60 so i l 68 veg NS NS NS NS NS NS NS NS S No significant preference for any of the f i r s t three and the fourth. A significant preference' for one or two of the f i r s t three choices over the fourth choice. TABLE 17 % of Responses Rating Differentiating C r i t e r i a 8 or Higher Di f f e r e n t i a t i n g S o i l S u r f i c i a l Forest C r i t e r i a Slope Moisture Vegetation Factors Elevation Aspect Deposits Floor % of Ratings higher than 7 75% 69% 68% 60% 57% 51% 47% 37% IT) TABLE 18 Number of Times the Di f f e r e n t i a t i n g C r i t e r i a were i n the Top Three Choices for Each Job Group Dif f e r e n t i a t i n g S u r f i c i a l Forest C r i t e r i a Slope Vegetation Moisture S o i l Deposits Elevation Aspect Floor Number of occurences in top 3 9/10 7/10 6/10 3/10 3/10 2/10 0/10 0/10 56 Table 18 shows the number of times c r i t e r i a were rated i n the top three by each of the job groups. The same three c r i t e r i a are desired stronger again, though the order of second and th i r d has changed s l i g h t l y . D i f f e r e n t i a t i n g C r i t e r i a Summary From the tables, i t can be seen that slope, s i t e moisture regime, and vegetation were given the highest ratings. This should mean that we ought to use those c r i t e r i a to di f f e r e n t i a t e our mapping units, because they are the most important to the users for their management purposes. Offsetting this i s a c o n f l i c t i n g viewpoint. It i s more l i k e l y that the users do not always want their units divided using the same c r i t e r i a . For one area and one spe c i f i c resource manager, slope may be of primary importance, but i n other locations or for other resource managers, vegeta-tio n , s o i l type, moisture regime or s u r f i c i a l deposit may be more im-portant. Limiting the inventory s p e c i a l i s t (producer) before the inventory begins would be unsatisfactory. The producer should be free to choose appropriate d i f f e r e n t i a t i n g c r i t e r i a . The optimum situation would consist of a mix of the two positions. Bearing i n mind the users' preference for certain d i f f e r e n t i a t i n g c r i t e r i a the producer could develop appropriate c r i t e r i a for the area to be mapped as i t i s being mapped. In the lat e r stages of user involvement, users would have the op-portunity to condone or object to d i f f e r e n t i a t i n g c r i t e r i a employed. This would be after the f i e l d work and preliminary mapping are finished. The producer would not be forced to use only a s p e c i f i c set of d i f f e r e n t i a t i n g c r i t e r i a and the user would be able to indicate his preference. 57" MAP UNIT VARIABLES, PART I I : I n fo rmat ion Lack ing Respondents were asked what type of i n fo rmat i on was most o f t en l a c k i n g when making a resource management d e c i s i o n (quest ions 26 and 27 i n Appendix I ) . S o i l was the overwhelming choice by a l l groups except r e c r e a t i o n p lanner s , pedo log i s t s and e c o l o g i s t s , and resource planners (Table 19). These groups were s i g n i f i c a n t l y d i f f e r e n t than the re s t of the p o p u l a t i o n , a lthough s o i l was s t i l l t h e i r l a r g e s t f i r s t cho i ce . The second cho ice of i n fo rmat i on type that was cons idered l a c k i n g was hydrology by a s l i m margin w i th s o i l s and vege ta t i on c lo se behind. There was a tremendous v a r i e t y between the d i f f e r e n t groups; some groups choosing s o i l s , some groups choosing hydrology and some groups choosing v e g e t a t i o n . None of the second choice r e s u l t s were s t a t i s t i c a l l y s i g n i f i c a n t . I n fo rmat ion Lack ing Summary A l l of the groups f e l t s o i l i n fo rmat ion was the number one item most o f t en l a c k i n g . This r e i n f o r c e s the use of a semi connotat ive legend. With the l a t t e r , the surveyor i s fo rced to separate s p e c i f i c s o i l -vege ta t i on - l and fo rm un i t s as the bas i s f o r h i s map d e l i n e a t i o n s . These u n i t s can then be used as a foundat ion i n the r e p o r t . Although s o i l may not have been used to separate map d e l i n e a t i o n s , i n f o rmat i on can be presented g i v i n g the c h a r a c t e r i s t i c s of the s o i l s i n each d e l i n e a t i o n . S o i l s are u n l i k e l y to be homogeneous because they were not used to d i f f e r e n t i a t e u n i t s , but t h e i r range of p r ope r t i e s can be g iven f o r the s p e c i f i c map u n i t . On maps us ing a connotat ive map symbol, i n t e g r a t i o n of s o i l and vege t a t i on i s not always necessary. S p e c i f i c un i t s can not be dea l t w i th or can only be dea l t w i th gene ra l l y because of the l a rge number of TABLE 19 % of Respondents in Each Job Group Who Rated Soils Information the  Information Most Often Lacking When a Decision is to be Made Total Job Group ZFores Si l v i c Fish&W CFores Range Eng Recrea Ped&Ec Resplan PIVFS Pop. % of people who rated s o i l #1 87s_ 52s 44 s 68s 60s 52£ 33ns 21ns 27ns 40s 49s s^  Significant choice with respect to the second highest choice (p<.05) s This job group is significantly skewed toward this choice, but rating of soil is not significantly higher than #2 choice (p<.05). ns this is the largest response for this job group, but i t is not significantly higher than the smallest response (p<.05). Also these groups are significantly different from the rest. p o t e n t i a l map u n i t s . Th is mu l t i t ude of un i t s r e s u l t s from not having to group s o i l - v e g e t a t i o n - l a n d f o r m i n f o rma t i on . The report would u s u a l l y dea l w i t h s o i l , v e ge t a t i o n , and landform as separate t op i c s r a ther than dea l i ng w i th them as i n t e g r a t ed u n i t s . Thus, w i t h a semi-connotat ive legend, a lthough s o i l s i n fo rmat i on may not have been used d i r e c t l y as a primary d i f f e r e n t i a t i n g c r i t e r i a ( i . e . a map u n i t may have a range of s o i l s ) i t can be presented i n an i n teg ra ted f a s h i on , map u n i t by map u n i t . This i s not as e a s i l y accomplished w i th a connotat i ve legend where grouping i n f o rmat i on to form map u n i t s i s not necessary and a much l a r ge r number of map un i t s r e s u l t . CLASSIFICATION SYSTEM As can be expected pedo log i s t s are the only group w i t h a h igh u t i l i t y f o r s o i l c l a s s i f i c a t i o n data . The remaining groups have low to moderate u t i l i t y and i n c e r t a i n groups, such as Zone f o r e s t e r s , f i s h and w i l d l i f e b i o l o g i s t s , company f o r e s t e r s , rangers, and eng ineers , g reater than 35% of the group f e l t s o i l c l a s s i f i c a t i o n was never important to them (Table 20). In a l l groups, except pedo log i s t s and e c o l o g i s t s , g reater than 50% found s o i l c l a s s i f i c a t i o n i n fo rmat i on important l e s s than 20% of the t ime. A l l of the groups w i th the except ion of pedo log i s t s and e c o l o g i s t s f e l t they had adequate knowledge of s o i l c l a s s i f i c a t i o n and understood i t i n a genera l way. They d id not seem to have a de s i r e to have someone to e x p l a i n i t . Pedo log i s t s and e c o l o g i s t s , as can be expected, a l ready understood the system w e l l . C l a s s i f i c a t i o n System Summary The low u t i l i t y of s o i l c l a s s i f i c a t i o n data imp l i e s i t should occupy a l e s s prominent p o s i t i o n i n the mapping system. I t seems unnecessary to TABLE 20 U t i l i t y of S o i l C l a s s i f i c a t i o n VS Job Group Total Job Group ZFores S i l v i c Fish&W CFores Range Eng Recrea Ped&Ec Resplan PIVFS Pop. never imp. 40% 29% 44% 36% 40% 42% 33% 3 d% 27% 30% 32% imp. 0 - 20% of the time 33 29 17 14 23 10 33 7d 45 25 20 Total <20% 73 58 61 50 63 52 66 72 55 52 d S i g n i f i c a n t l y different from a l l other groups (P<.01). 6 1 include c l a s s i f i c a t i o n information on the map. In fact i t may result i n negative u t i l i t y . Many interviewees were adamant in their desire for s i m p l i f i c a t i o n and reduction of obfuscating nomenclature. If during the survey i t i s deemed necessary to include c l a s s i f i c a t i o n - type information on the map, verbal descriptions can be used (Table 21). TABLE 21 Example of Verbalized Nomenclature Orstein Ferro - iron and humus r i c h s o i l with an extremely Humic Podzol hard, often impermeable cemented s o i l layer close to the surface. Terric Humic - shallow organic deposit of partly decom-F i b r i s o l posed fibrous organic material overlying a well decomposed organic deposit with mineral material at greater than 60 cm depth. Accurate s c i e n t i f i c communication is the principle underpinning com-plex systems of nomenclature, but i n an inventory system of this sort we are communicating with both s c i e n t i s t s and laymen. Losing the lay audience through the arrogance of expecting them to learn and therefore understand a complex c l a s s i f i c a t i o n system i s unacceptable. Specialists who understand the system should be capable of synthesizing a simplified version for the layman. S c i e n t i f i c a l l y accurate nomenclature must not be omitted, but should be background for those who want i t . Another important reason for this approach i s that nomenclature i s constantly changing; especially i n younger sciences such as s o i l science. Users are not only the present group of managers and s c i e n t i s t s , but also 62 those of the future. Future users may not have easy access to present day nomenclature. Many older s o i l maps have more limited usefulness today because the c l a s s i f i c a t i o n system has been remodeled. If general de-scriptions are used as well as s c i e n t i f i c names this problem can i n part be circumvented. INTERPRETIVE (DERIVITIVE) MAP LEGENDS There were four legend choices for derivative map legends (see Figure 5). Of the four, the choice with the most amount of substantiation was rated highly by a l l groups except f i s h and w i l d l i f e b i o l o g i s t s , pedologists and ecologists, and resource planners (Table 22). The l a t t e r two groups were s i g n i f i c a n t l y different from the t o t a l population. A l l three showed a moderate preference for the two legends with the most information and a strong preference for the legend with the l i m i t a t i o n and nothing more (example 2). The other groups rated example 4 as their number 1 choice (Table 22). The % high rating (rating of greater than 7) for derivative map legend #4 by the population as a whole i s s i g n i f i c a n t l y higher than for the other examples (Table 23). This i s due to a s i g n i f i c a n t l y larger number of high ratings by those who wished a connotative map symbol. However, there was no real decrease i n the ratings by those who wished a semi-connotative and non-connotative map symbol. They were s t i l l high and consequently we can assume the l a t t e r groups have l i t t l e negative u t i l i t y for derivative map legend #4. The non-connotative group was s i g n i f i c a n t l y different from the whole population i n their strong desire for derivative legend example #2. How-ever, their desire for the l a t t e r example was not s i g n i f i c a n t l y stronger than for examples #3 or #4. FIGURE 5 Interpretive Map Choices M»„« * r e nroduced which give c a p a b i l i t y or s u i t a b i l i t y ratings. These ratings are interpreted from information on the s o i l , landform and vegetation of ^ ^ r f T h - L ^hev a«called i n t e r p r e t i v e maps. They can range from maps with a map symbol consisting of a number for each capab i l i t y and no explanation, a s i t e . Thus, they aw' C"U«* e x B i a n a t i o n s . Below i s a group of methods for describing the s u i t a b i l i t y of the same map unit. These examples S u i t a b i l i t y for Road Bu i l d i n g Example 1 Example 2 Example 3 Map Symbol s u i t a b i l i t y ^ Map Symbol s u i t a b i l i t y ^ " ^ l i m i t a t i o n ( s ) Map Symbol s u i t a b i l i t y ^ c ^ t - _ s u r f i c i a l deposits 2w l i m i t a t i o n U ) Legend* 2 - moderately suitable Legend* 2 - moderately suitable w - wetness l i m i t a t i o n , s o i l saturates for short periods during the year Legend* 2 - moderately suitable w - wetness l i m i t a t i o n , s o i l saturates for short periods during the year c/t - colluvlum over compacted morainal deposits Example 4 Map Symbol s u i t a b i l i t y slope c / t : l , 2 — t e x t u r e s u r f i c i a l deposits l l m l t a t l o n ( s ) Legend* 2 - moderately suitable w - wetness l i m i t a t i o n , s o i l saturates for short periods during the year c/t - colluvlum over compacted morainal deposits 1,2 - cobbly loamy to bouldery loamy texture d - slope, 50-703: *Note: the legend on the map would not appear exactly ns above l e t t e r s and numbers which make up the map symbol - s i m i l a r to example 2, page 4 It would consist of tables with explanations for each of the TABLE 22 % of High Ratings fo r De r i v a t i v e Map Legend Examples by Each Job Group  T o t a l Job Group ZFores S i l v i c Fish&W CFores Range Eng Recrea Ped&Ec Resplan PIVFS Pop. Example 1 13 0 6 4 3 6 0 14 27 0 6 Example 2 20 29 50 11 27 23 33 43 82 d 8 27 Example 3 60 38 39 50 43 45 17 21 55 38 40 Example 4 60 71 39 57 70 71 33 27 63 # of Respondents 15 21 18 28 30 31 6 28 11 40 230 s d S i g n i f i c a n t cho ice (p<.02) This va lue imp l i e s t h i s job group i s s i g n i f i c a n t l y d i f f e r e n t from the t o t a l popu la t ion (p<.05). 65 TABLE 23 Ratings for Interpretive Legends vs. Map Unit Symbol Choice Interpretive Legend //l Map Unit Symbol Choice No Non- Semi-Rating i Choice connotative connotative Connotative Total very low (0-1)% 32 15 33 28 28 low (2-4)% 37 41 28 55 45 moderate (5-7)% 26 26 26 13 21 high (8-10)% 5 18 3 4 6 Total # of responses 19 39 94 78 230 Interpretive Legend #2 very low % 26 3 6 4 6 low % 21 10 34 36 30 moderate % 32 31 35 43 37 high % 21 56d 25 17 27 Total # of responses 19 39 94 78 230 Interpretive Legend #3 very low (0-1)% 26 5 4 3 6 low (2-4)% 10 5 6 6 7 moderate (5-7)% 32 46 44 57 47 high (8-10)% 36 44 46 34 40 Total # of responses 19 39 94 78 230 Interpretive Legend #4 very low (0-1)% 26 5 11 1 8 low (2-4)% 16 20 21 8 16 moderate (5-7)% 21 31 26 12 21 high (8-10)% 37 44 43 79d 55 Total # of responses 19 39 94 78 d - s i g n i f i c a n t l y different than the whole population and a l l of the other sub-populations (p<.05). 66 In the i n t e r v i e w program the r e s u l t s were skewed away from legend example #4. Table 24 below shows the d i f f e r e n c e . TABLE 24 Comparison of I n te rv iew vs Quest ionna i re h igh r a t i n g s  f o r D e r i v a t i v e Legends Example #1 Example #2 Example #3 Example H T o t a l Number of Responses I n te rv i ew Re su l t s (% r a t i n g s >7) 19 50 d 50 35 48 Ques t ionna i re Re su l t s (% r a t i n g s >7) 6 27 40 182 d - s i g n i f i c a n t l y h igher va lue i n d i c a t i n g ques t i onna i re and i n t e r v i e w popu lat ions have d i f f e r e n t d i s t r i b u t i o n s (P<.05 us ing K-S two sample t e s t ) . This i s c on s i s t en t w i th the data f o r the map symbol type which sug-gested a s i m i l a r s h i f t away from a more complex symbol type. The s h i f t could have been as a r e s u l t of the same three reasons - i n t e r v i ewe r b i a s i n g ; i n t e r v i e w e r c l a r i f i c a t i o n ; or i n te rv iewees being a s l i g h t l y d i f f e r e n t p o p u l a t i o n . The second p o s s i b i l i t y i s the most l i k e l y . The respondents were a f r a i d of i n f o rmat i on l o s s . Consequently, they wanted the example w i t h the most i n f o r m a t i o n . A review of the tapes i n the i n t e r v i e w program, i n d i c a t e d the i n t e r v i ewe r c l a r i f i e d the i s sue i nvo l ved and a l l a y e d fea r s of i n f o rmat i on lo s s by e x p l a i n i n g that these d e r i v a t i v e maps would always be accompanied by the maps from which they were de r i v ed . This c l a r i f i c a -t i o n was probably the reason f o r the s h i f t . This s h i f t should be taken into account when designing derivative legends. I t appears that users want f u l l substantation i f the information w i l l not be presented i n another map which i s readily available. The nature of derivative maps i s such that they are often used remote from the soil-vegetation-landform maps on which they are based, so substantiation should be contained on the derivative map to be eff e c t i v e . Derivative Map Legend Summary Derivative legend example #4 i s the choice which w i l l s a t i s f y most of the groups. It i s a s t a t i s t i c a l l y s i g n i f i c a n t choice by the whole popula-t i o n . However, the interview group shifted away from this choice and their view should be considered. Example 3 therefore thought to be the best choice. Reasons for this decision w i l l be explained i n the next chapter i n the discussion of the resolution. The three job groups who were opposed to this choice can be accom-modated. Pedologists and ecologists make the maps, so they don't r e a l l y need to be s a t i s f i e d , they need to be convinced that the other groups want this legend type and that i t i s probably therefore the best choice. Maps used by f i s h and w i l d l i f e biologists should be designed using derivative legends with less complexity showing the l i m i t a t i o n only. As for the th i r d group, resource planners, they are a small minority and w i l l have to be content with the maps as they are produced. They use too wide a variety of derivative maps which they would use to attempt catering to their needs. DERIVATIVE MAP PRESENTATION Respondents were f i r s t asked to rate one map and many maps (see questions 321 and 322, Appendix I ) . The overall preference was for one 68 map by a l l groups except fish and wildlife biologists, resource planners, and pedologists and ecologists. Pedologists and ecologists as a subpopu-lation were again significantly different than the whole population. Their preference was for many maps. A large majority (greater than 70%) of a l l groups rated both legends at least moderately high (rating greater than 4 out of 10). When asked to choose between one map and many maps at a detailed scale, a significant majority of the whole population chose one map. How-ever, half of fish and wildlife biologists and recreation planners and a majority of pedologists-ecologists and resource planners chose many maps. Table 25 below shows how the majority in each group responded and in-dicates which groups had a s t a t i s t i c a l l y significant choice. Derivative Map Presentation Summary From the results, one map with an interpretive legend would seem to be the optimal alternative. The majority of the population wanted one map and the ratings showed there were not too many people opposed to that position (Appendix III). Of the groups which liked many maps better, the pedologists and ecologists group was the only one with a significantly different choice. From previous data we have seen they use maps for decision-making less often so their views are less important as users. The one counterpoint which suggests many maps may be better is the increasingly common practise of separating groups of mapping units to form management units. These units are not soil-vegetation-landform units. They consist of any number of the latter units grouped according to similar management treatments. Some respondents suggested this practise would not be possible with only one map. This is not true. Grouping of mapping units into management units can be done in the design of the TABLE 25 Derivitive Map Presentation Vs. Job Group ZFores Si l v i c Fish&W C Forest Ranger Eng Recrea Ped&Ec Resplan PIVFS TOTAL Many Maps (%) 20 33 50 46 43 26 50 57d 55 35 40 One Map (%) 67s 67s 33 54 57 65s 17 36 36 58s 52s No Answer (%) 13 - 17 - - 10 33 7 9 8 8 Number of Responses 15 21 18 28 30 31 28 11 40 230 S - Significant choice for this job group (P<.05) using K-S one sample test. d - Significantly different than the total population using K-S two sample test. 70 legend and by using a different set of lines around those mapping units which separate two management units as shown in Figure 6. This would not add any extra lines to the maps and would allow for separation of manage-ment units by the map makers or by users. FIG. 6 Example of Possible Management Unit Boundaries Mapping unit boundary Mapping unit + Management unit boundary There w i l l undoubtedly be some agencies and individuals who w i l l s t i l l want to have their own single purpose derivative maps. Providing l i n e maps, on request, with map unit boundaries only and ensuring an adequate derivative legend in the report w i l l f a c i l i t a t e in-house produc-tion of derivative maps. Individuals or agencies who want a spe c i f i c map can then make i t themselves. This point i s also important because of rapid advances i n computer technology i n map r e t r i e v a l systems. Any mapping system should be de-71 signed to be adaptable. The principle underlying computerized map retrieval systems is that of many maps. If we leave the option of many maps open to the user we also leave that option open for future computer technology. GENERAL MAP AND LEGEND PRESENTATION  Legend Size When asked to choose between a large legend and a small legend, there was l i t t l e consensus in any of the groups. The population as a whole was split evenly over this question, while some of the groups had a preference for large legends. TABLE 26 Legend Size vs Job Group Legend Prefer-ence Zfores S i l v i c Fish + w Cfores Ranger Eng Recrea Ped +Ec Respla PIVFS Total large (%) 60 48 56 54 63s 36 33 39 36 40 47 small (%) 40 48 33 46 37 58 50 54 64 50 47 No res-ponse 0 4 11 0 0 6 17 7 0 10 7 No. of res-ponses 15 21 18 28 30 31 6 28 11 40 230 S - significant choice by this job group (P<.05) using K-S one sample test 72 TABLE 27 Legend Size Reponses for the Interview and Questionnaire Programs Interviewees % response Questionnaire Respondents % response Total % response large legend 38 49 47 small legend 44 47 no answer 6 7 7 # of respondents 48 182 230 S - s i g n i f i c a n t choice for this group (P<.05 using Binomial test) Rangers were the only group whose choice was s t a t i s t i c a l l y s i g n i f i c a n t . The remainder of the groups and the population as a whole did not have a strong preference (Table 26). When the interview data was compared with the questionnaire data there was a difference. The interviewees as i s shown i n Table 27 had a s i g n i f i -cant preference for a small legend, but there was not a s i g n i f i c a n t difference between the interview and questionnaire populations. They could s t i l l have similar response di s t r i b u t i o n s . This i s d i f f i c u l t to interpret and any of three possible conclusions could be drawn: 1) the questionnaire responses were from the same popula-tion as the interviewers, but they deviated (naturally) more toward the large legend choice, 2) the interviewees were influenced by the interviewer and modified their choice because of a c l a r i f i c a t i o n of the issues, or 3) the interviewer was biased and his bias affected the results. 73 Legend Size Summary There i s no method of resolving which of the three aforementioned p o s s i b i l i t i e s i s true. A review of the tapes might indicate the presence or absence of a bias by the interviewer, but that i s unlikely due to the nature of the question. This question should have had an example with i t so that respondents had something to trade-off against. One person's small legend may be another person's large legend and vice versa. As a consequence no r e a l resolution can be recommended. Map Base Respondents were given a choice of three map bases: an orthophoto, a p l a i n l i n e map i n black and white, and a colour map. A l l groups except for recreation planners, resource planners, Zone foresters and pedologists-ecologists had a preference for a colour map base. The l a t t e r group rated an orthophoto map base higher and the former three groups had no stronger preference. Otherwise, orthophotos were rated less strongly than colour maps by a l l groups and no group had a preference for plain l i n e maps. The population as a whole had a s i g n i f i c a n t l y stronger preference for colour maps. Again, pedologists and ecologists were the only group which was s i g n i f i c a n t l y different: they rated orthophotos higher. Colour maps are rated highest, orthophotos second highest and l i n e maps lowest (Table 28). However, the low ratings (less than 5) for orthophoto base maps or l i n e maps are not s i g n i f i c a n t l y more than those for colour maps, indicating a s l i g h t l y lower preference ( i e . less ratings >7), but not a s i g n i f i c a n t l y higher d i s l i k e (Table 29). 74 TABLE 28 Map Base Rating of Greater than 7 vs. Job Group Map Base Zfores S i l v i c Fish + W Cfores Ranger Eng Recrea Ped +Ec Respla PIVFS Total Colour (% ratings >7) 47 62 61 71 60 65 33 54 64 40 56 s Ortho-photo (% ratings >7) 47 52 50 29 30 39 33 68 d 64 45 44 Black & White (% ratings >7) 13 19 17 39 27 32 17 29 18 20 25 Total # of res-pondent 15 21 18 28 30 31 6 28 11 40 230 d - s i g n i f i c a n t l y different from the rest of the groups (p<.05 using K-S two sample test) s - s i g n i f i c a n t l y stronger map base choice (p<.05 using K-S two sample test) TABLE 29 Low Ratings vs. Map Base Type Colour A e r i a l Line Map low ratings 19 25 27 75 Map Base Summary The higher preference for colour maps must be kept i n view when making maps. Colour maps seemingly have few interpretive advantages over a l i n e map. The major benefit i s as a result of the a b i l i t y of the human eye to rapidly separate colour and distinguish patterns. The evident popularity of colour maps may also be a si g n i f i c a n t factor i n increasing map usefulness. People may perceive more benefits than actually exist and thus may be more w i l l i n g to use the maps. The marketing advantages should not be overlooked. People are impressed by packaging. Cosmetic appeal i s one consideration, but an aerial" photo base (orthophoto) has actual interpretive advantages. It presents more spa t i a l data than either of the other choices. A s k i l l e d manager can interpret what most map delineations have been based upon. One can also see the patterns of variation within a complex unit. A e r i a l photos have a tem-poral disadvantage because a forest may be harvested or grow and the a e r i a l photo base becomes dated. By providing l i n e maps as well, capable of being coloured this eventuality can be planned for. There i s also the p o s s i b i l i t y of updating the photo base. The author believes that an a e r i a l photo base i s a better alternative than a colour map base. The choices given i n the questionnaire did not give a real trade-off situation. Further studies of this type should use rea l examples so that a real comparison can be made. In this example the managers had no way of comparing real situations. Pedologists and eco-l o g i s t s who use a e r i a l photographs and appreciate their values rated the a e r i a l photo base higher. In this situation their choice i s important. Their experience allows them to properly evaluate the alternatives. Therefore in this s i t u a t i o n an orthophoto map base may be better than the other choices, although the users may not perceive i t as better. User education should not be overlooked. Contours A l l groups had a strong preference for contour lines on s o i l -vegetation-ecological maps. There were no dissenting groups (Table 30). TABLE 30 Desire for Contours on Maps vs. Job Group Contour Desire Zfores S i l v i c Fish + W Cfores Ranger Eng Recrea Ped +Ec Respla PIVFS Total Yes % 71s 100 s 86 s 73 s 74 s 67 s 86 s 82 s 80 s 79 s No % 27 19 0 14 23 13 0 7 18 17 15 No % answer 0 10 0 0 4 13 33 7 0 3 6 Total # of res-pondent 15 21 18 28 30 31 6 28 11 40 230 S - Significant choice (p<.05) This response was the same i n the interview program, but there was some concern for l e g i b i l i t y . Respondents were worried that too many contour lines may detract from the maps. Some suggested putting contour lines on an overlay which accompanies the map or having fewer intervals. Contour Summary Contours are strongly preferred by a l l groups and should be included with the maps. I t may be best to include them as an overlay rather than on the map to guard against i l l e g i b i l i t y . This question should be asked i n the summary of responses. 77 CHAPTER 6 DISCUSSION OF QUESTIONNAIRE AND INTERVIEW RESULTS INTRODUCTION The l a s t chapter discussed the results of the questionnaire-interview program and analysed the patterns. This chapter looks at the resulting resolutions for each variable and summarizes the salient points. In some cases the resolution of the population's choice i s less concrete than others. In the program, seven dependent variables (or variable groups) were tested to see i f trends could be established which would indicate a choice for each variable. It was shown that i n most cases there was enough evidence to show the majority choice. The variables and their resolutions are shown in Table 31. In the sections which follow the resolution for each variable w i l l be examined. Map scale resolution was reasonably concrete. A very large majority of respondents i n each of the three methods used wanted 1:20,000 scale or larger. The cost and opportunities foregone by larger scale and the fact TABLE 31 Dependent Variable Resolutions Dependent Variable Choices (1) A Map Scale B Intensity <1:10,000 1:20,000 1:50,000 >1:100,000 Inset maps Variable accuracy both Both (2) Map Unit Symbol Non-connotative Semi-connotative Connotative (3) Map Unit Variables Part I Di f f e r e n t i a t i n g C r i t e r i a Slope Vegetation Moisture S o i l S u r f i c i a l Deposit Elevation Aspect Forest Floor Part II Information Lacking Resolution 1:20,000 Semi-connotative Differentiating c r i t e r i a ordered as to preference but resolution l e f t up to the producer. Dependent Variables Choices (4) S o i l C l a s s i f i c a t i o n No real Choice U t i l i t y Level (5) Interpretive Map Legend (6) Interpretive Map presentation (7) General Map Presentation Legend Size S o i l s , Vegetation, #1 Soils Terrain, Hydrology, #2 Hydrology, Climate, Topography or vegetation Map Base Type Contours Contours Measure ex.1 no substantiation ex.2 least substantiation ex.3 moderate substantiation ex.4 most substantiation one map many maps large legend small legend colour orthophoto l i n e present absent Resolution Resolution by Induction example 3 Moderate Substan-t i a t i o n one map no resolu-tion be-cause of a lack of examples orthophoto (producer choice) present 79 that the majority of respondents wanted 1:20,000 made this the best choice. The interview program corroborated this choice. Constant vs. Variable Intensity vs. Inset Map resolution was straightforward. The majority wanted an inset map or both. Any map which i s produced has some degree of variable intensity so any map with an inset map f a l l s into the both category. A large majority of users would there-fore choose both. This conclusion leaves no element of doubt as to the preferred choice. Map Symbol resolution was also well defined, but not as simple. The questionnaire group chose a semi-connotative map symbol, but did not have a strong enough preference to make i t a s i g n i f i c a n t choice over a non-connotative symbol. An examination of the rating for each symbol type indicated a s i g n i f i c a n t l y stronger rating for a semi-connotative symbol than for a connotative or non-connotative symbol. The interview program also indicated this conclusion, but the second choice i n the interview program was a non-connotative symbol. In the questionnaire program the second choice was a connotative symbol. This s h i f t i n desire did not affect the f i n a l resolution, but i t was an important occurrence as i t reinforced the f i n a l resolution. As a r e s u l t , the f i n a l resolution for a semi-connotative symbol was a clearer choice. D i f f e r e n t i a t i n g C r i t e r i a resolution was not easily decided. The premise at the outset of the study was that d i f f e r e n t i a t i n g c r i t e r i a could be established by discovering the users' perceptions of the most useful variables for separating mapping units. These variables would become the d i f f e r e n t i a t i n g c r i t e r i a . There i s a major flaw i n this approach. Each variable may be im-portant as a d i f f e r e n t i a t i n g c r i t e r i a i n different areas. The s p e c i a l i s t s responsible for inventorying must decide which variables are appropriate 80 for which areas with the users' desires as only one input. Users' desires cannot be used as the sole governing factor. Also the use of one standard set of c r i t e r i a to d i f f e r e n t i a t e map units may not be appropriate. Nature being diverse w i l l not f i t a s i m p l i s t i c format. The d i f f e r e n t i a t i n g c r i t e r i a established as most important should be used as a guide for the surveyor not as his f i n a l choice. Therefore, no resolution was reached. Interpretive Map Legend resolution suffered from a questionnaire interview difference i n preference. In the questionnaire program, legend example four was the si g n i f i c a n t favourite, while the interview group chose example three with example two as their second choice and four as their t h i r d choice. This type of s p l i t i s d i f f i c u l t to resolve. Which group's preference i s more important? The interview group i s smaller so their responses are weaker from a s t a t i s t i c a l stance, but they have had the advantage of c l a r i f i c a t i o n . The same choice s p l i t occurred with the map symbol choice. Fortunately, i n that example there were only three choices. The swing away from the connotative symbol to the two other symbols did not result i n an ambivalent choice. There was s t i l l a s i g n i f i c a n t l y stronger preference for the semi-connotative map symbol type. The s h i f t i n interpretive map symbol choice was similar. The questionnaire responses showed a s i g n i f i c a n t l y stronger preference for example four. This preference was s i g n i f i c a n t l y weaker i n the interview program which had no s i g n i f i c a n t choice. The main difference, from the map symbol solution, was the presence of the fourth category. S i g n i f i -cantly weaker preference for example 4 and 1 resulted in increased preference for both example 2 and 3. This change i n attitude probably resulted from a clearer under-8 1 standing of the question. A review of the tapes indicated many respond-ents in the interview program at f i r s t chose example #4. When they d i s -covered that these derivative maps (legends) would not stand alone, i . e . they would be accompanied by a soil-vegetation-landform map, they altered their opinion and opted for example 2 or 3. They had a weaker preference for #1 because i t did not give the l i m i t a t i o n , so i f instead of both 2 and 3, there had been only one other choice they would have chosen i t . The immediate implication of the l a t t e r statement i s that 3 choices are better than four for e l i c i t i n g a consensus. With this variable that i s incorrect. In the survey results i t was established that example 1 and example 4 are less popular. The presence of two middle examples helps to fine tune our choice. Example 2 contains the le v e l of l i m i t a t i o n and the reason. Example 3 contains the le v e l of the l i m i t a t i o n , the reason and one of the more important variables ( i n this case the s u r f i c i a l deposit). It i s valuable knowing that respondents would l i k e to have that extra piece of information. A three choice system would not establish this desire. In summary the resolution of interpretive map legend type desire was successful. The f i n a l choice was example 3. There was a l i t t l e more d i f f i c u l t y a r r i v i n g at this decision than there would have been i n a three choice s i t u a t i o n , but the decision i s better as more user information has been obtained. For the summary of resolutions, example 4 was used as the f i n a l resolution to see how well respondents would accept i t . As w i l l be seen the results corroborate the foregoing discussion. S o i l C l a s s i f i c a t i o n System resolution was derived through induction. I t was assumed that because the majority of users had a low u t i l i t y for s o i l c l a s s i f i c a t i o n they did not want that information on the map. It i s 82 probable that this i s the right conclusion, but i t cannot be confirmed from the data. The reason for this so that this question sequence was poorly designed. I t should have asked a direct question such as: "where would you l i k e information on S o i l c l a s s i f i c a t i o n : report or legend?" This i s an extremely important point. A l l questions must be asked di r e c t -l y and c l e a r l y . Any conclusions drawn can only be based on the question, asked. The conclusions which can accurately be stated on s o i l c l a s s i f i c a t i o n are: " S o i l C l a s s i f i c a t i o n i s important less than 20% of the time to the majority of users" and "the majority wish no increased understanding of the subject". These statements can only lead to educated guesses on s o i l c l a s s i f i c a t i o n presentation. Interpretive Map Presentation resolution was based on a clear con-sensus. There was a sign i f i c a n t preference for a single map with a s o i l -vegetation- landform and derivative legend. The same preference occurred i n the interview program. General Map Presentation resolutions were varied and they w i l l be examined separately. 1. Legends size - there was no clear choice by the questionnaire group, but the interview group had a sign i f i c a n t preference for a small legend. This tends to indicate that a small legend i s preferred once the concept i s c l a r i f i e d . One d i f f i c u l t y i n this question i s the lack of a comparison. In the questionnaire program, no one r e a l l y new what was implied by a small legend. One person's small legend could be another person's large legend. In the interview program this problem was overcome to some extent, but not i n a regulated, consistent fashion. The i n t e r -viewer would naturally explain i t more to some people than other 83 people depending on the l e v e l of inquisitiveness. In this question there should have been a spe c i f i c range of examples, then the results would have been more conclusive. No real resolution could be established, because neither a large nor a small legend was properly defined with examples. 2. Map Base Type resolution from the data alone was concrete, but some interpretation was deemed necessary. A colour map base was rated s i g n i f i c a n t l y higher than either an orthophoto or black l i n e base by the whole population. Pedologists-ecologists were the only s i g n i -f i c a n t l y different group. They rated an orthophoto higher. Ortho-photos were s t i l l rated f a i r l y highly by the rest of the population. Although colour and l i n e maps present the same amount of informa-t i o n , colour maps have a sl i g h t real advantage over a l i n e map because they exhibit patterns more eas i l y . An a e r i a l photo not only shows patterns but also presents some added information. Consequent-l y , as the respondents rated orthophotos r e l a t i v e l y highly, the author f e l t that they should be the optimum choice. They present more information and the population as a whole rated them r e l a t i v e l y highly. 3. Contours resolution was straightforward. Contours were wanted on the map by such an overwhelming majority of respondents that there was no question. The population d e f i n i t e l y wanted them on the maps. This resolution was the easiest and surest of a l l , though the method of presenting them was l e f t open. Summary and Conclusions of Questionnaire and Interview Results The resolutions for map scale, map symbol, constant vs variable intensity vs inset maps, interpretive map legends, interpretive map pre-84 sentation and part of general map presentation were based on s t a t i s t i c a l l y s i g n i f i c a n t choices made by the user population. These choices were not always the best choices from a producer's viewpoint and required some further input (eg. orthophotos show more information than colour maps), but they were useful in reaching a user-based decision. The three resolutions which were not based d i r e c t l y on choices or which were impossible to make were those for d i f f e r e n t i a t i n g c r i t e r i a , s o i l c l a s s i f i c a t i o n and legend size. In the case of d i f f e r e n t i a t i n g c r i t e r i a , they cannot be established by users, they must be established by mappers. The s o i l c l a s s i f i c a t i o n questions were poorly worded and the answers obtained did not lead to any useful conclusions. The third item which f a i l e d to y i e l d a resolution, legend size, f a i l e d because there were no examples to choose amongst. Without an example, a large or small legend i s undefinable. For future surveys of this nature these are three p i t f a l l s to avoid. F i r s t l y , • t h e r e must be a p o s s i b i l i t y of users being able to determine a resolution. Secondly, question wording must be such that a l o g i c a l con-clusion can be reached. Third, wherever possible resolutions should be determined by trade offs between examples based on a real system. This study attempted that for a l l seven variables but f a i l e d i n two and p a r t i a l l y for one. 85 CHAPTER 7 TESTING THE PROTOTYPE INTRODUCTION This stage of map system design i s meant to test the prototype of the f i n a l product. A substitute was used, instead of a r e a l prototype map because t h i s study did not produce a map. In theory, at this stage of map design a preliminary map should be presented to po t e n t i a l users. This procedure would be used to help f i n a l i z e any choices s t i l l open to the producers and e l i c i t general comments on the map system design. In this study, the substitute consisted of a summary of proposed mapping techniques based on a summary of the respondents desires. It was termed a r e s o l u t i o n of hypotheses, because to begin with each dependent variable was given the n u l l hypothesis that users as a group had no s p e c i f i c preference. The questionnaire and interview program undertook to disprove the n u l l hypotheses. Thus, the expression "resolution of Hypo-theses" i s appropriate. The f u l l summary i s presented i n Appendix IV. 86 Questions Asked Along with the summary of desires and resolutions of hypotheses, three questions were asked. The purpose of these questions was to test the p o s s i b i l i t y of resolving issues which were not c l a r i f i e d in the o r i g i n a l survey or which could have arisen during the inventory stage. These questions were: 1 . With the map symbol and legend type summary: "Would you rather have different connotative items than slope and landform/surficial de-posits ?" Yes No If yes, what? 2. With the mapping homogeneity (inset vs variable intensity) summary: "Do you f e e l an inset map i s a luxury or a necessity for proper management ?" Luxury | j Necessity j j 3. With the map presentation (contours) summary: "Do you l i k e the concept of contours as an overlay?" Yes J^J No | | The questions were r e a l i s t i c in that they had not been asked before and were brought up r e l a t i v e l y frequently in the interview program. During a real mapping project i t i s probable that many more questions would arise. 87 Respondents The summary was sent with a copy of the o r i g i n a l questionnaire to a sub-sample of the population. This subsample consisted mainly of the interviewees. It was f e l t they had a better understanding of the purpose of the study and the issues involved having been obliged to spend a longer time on the o r i g i n a l forms than the questionnaire respondents due to the nature of the interview procedure. A few more pedologists and ecologists were included than were part of the interview program. This was to increase their representation to the same level as the other groups. They were more poorly represented i n the interview program than the other groups, because their comments were expected to be biased by their own mapping techniques and i t was unl i k e l y they would need the c l a r i f i c a t i o n provided by the interviewee. A t o t a l of 48 summaries were sent out. Of these, 30 (62.5%) re-spondents returned their completed forms. The remaining 18 were phoned to determine why they had not responded and to ask their general opinion as well as answers to the three s p e c i f i c questions. A further 5 more re-sponded over the telephone. The remainder had moved, were not interested, had lost their form and were sent another, or were away on holiday. Results Map Scale - There were only two dissenters who wanted a different scale. One respondent said that 1:20,000 limited the possible yearly area coverage with the present inventory s t a f f . Another respondent did not agree that 1:20,000 should always be used. The remaining respondents were i n accord with the choice of 1:20,000 scale. Therefore, 1:20,000 seems an appropriate choice. 88 Map symbol and legend type - Two people s t i l l preferred a connotative symbol (a Zone forester and a Company Forester) but the remainder were s a t i s f i e d . The question on other d i f f e r e n t i a t i n g c r i t i e r a e l i c i t e d 2 yes responses, neither of which said what other connotative items they would l i k e . The rest of the respondents answered no or didn't reply (3 no re-p l i e s ) . It seems that the resolution as i t stands i s acceptable. D i f f e r e n t i a t i n g C r i t e r i a - The resolution for this variable was proposed largely to see i f the respondents were seriously considering the ramifications of the resolution. Using slope, s i t e moisture and vegetation as mapping unit d i f f e r e n t i a was a direct translation of respondents desires without any input from people responsible for studying the area and making the maps. As mentioned previously, the d i f f e r e n t i a t i n g c r i t e r i a to be used should not be preset. It i s up to the individual responsible for the inventory to decide what c r i t e r i a are most appropriate in which area. Otherwise, an extremely important variable which i s localized could be overlooked. Twelve of the t h i r t y respondents objected to this resolution, i n -cluding a l l of the pedologist and ecologist group. The majority suggested s u r f i c i a l deposits were much more appropriate as d i f f e r e n t i a t i n g c r i t e r i a and the remainder l e f t i t open. The group that did not disagree with the resolution included four people who said they agreed and the remainder had no comment. It i s evident from the responses that a large proportion (almost half) of the respondents were not s a t i s f i e d with the resolution. Apart from the non-responses or no comments which are impossible to analyse, there were 4 respondents who d e f i n i t e l y agreed and 12 who d e f i n i t e l y d i s -agreed. This would seem to be a rejection of the resolution and shows that a negative response i s possible. 89 C l a s s i f i c a t i o n System - There were no dissenters; a l l of the re-spondents either agreed or made no comment (17 agreed the remainder made no comment). This resolution was not objected to by any respondent and therefore would seem acceptable. Mapping Homogeneity - Of those who answered the question there were 22 individuals who f e l t that Inset maps were a necessity and 8 who thought they were a luxury. Of the l a t t e r group, two people said a ground survey i s necessary anyway, so why bother with a 1:5,000 inset map. Two more said i t would be too expensive and would severely reduce the manpower available for inventorying. There were two respondents who thought inset maps were a necessity, but they disagreed with stream size as a c r i t e r i a . It was expected that more people would disagree with this policy. Certainly some streams of that size have enough resource c o n f l i c t s to warrant the extra d e t a i l . Those that do should be i d e n t i f i e d , but a blanket policy for a l l major streams would be a waste of resources. In summary most of the respondents agreed with the principle of including inset maps and few cared i f i t was done on a general policy basis, i.e. a l l major streams. Derivative Map Legends - Five of the respondents f e l t that example 4 was redundant. Three of the respondents took exception to prescriptions being made, because knowledge increased and prescriptions changed with time. Of the remaining people, eight agreed with the resolution and the rest made no comment. The issue of prescriptions and changing knowledge i s a separate issue which could be looked at in another study. It i s important, but i s not relevant to the issue at hand which i s derivative map legends. The other comments should be addressed. 90 Example 4 was put forward as the resolution, beause that i s what the whole population wanted. However, after looking carefully at the data, example 3 appeared to be more appropriate. This i s borne out i n this part of the study. Enough people made the comment that example 4 i s redundant to consider reducing the amount of substantiation. The resolution as was indicated by the questionnaire group should be modified to agree with these comments and the results obtained from the interview program. The l a t t e r results showed a s h i f t away from excessive d e t a i l provided the d e t a i l existed somewhere else ( i e . the report or the s o i l s map). Therefore legend example 3, as was determined in Chapter. 5 and 6, appears to be the best choice. Derivative Map Presentation - There were only two dissenters to this resolution. They preferred the many map system. Eleven respondents agreed with the proposal and the remaining respondents made no comment. The resolution seems to be reasonable as i t stands. Map Presentation - Legend size: three respondents said they pre-ferred a large legend. None of the other respondents made any comments. There i s s t i l l no resolution to this issue. - Map Base: two respondents s t i l l preferred a li n e map. Of the remainder two said orthophotos were too costly, eight agreed with the resolution and the rest made no comment. The resolution seems reasonable as i t i s . - Contours: there were 15 negative answers to the concept of contours as an overlay only and 15 positive answers. From th i s i t would seem that contours should be put on the map. They should not be so detailed that they obscure the remaining information, but they should be present. Why should they be on the map i f half of the users are for them and half against? 1) A separate mylar overlay i s expensive. If only half of the people want one and the other half want them on the map then i t doesn't seem worth the extra cost for the overlay; 2) the a v a i l a b i l i t y of the overlay may become a d i f f i c u l t y and those who need contours w i l l be without; and 3) people have already said they want one map. This f i t s better with that desire. Taking a l l three points into consideration, contours should be included on the map rather than as an overlay only. Discussion of Results The resolution of map scale, map symbol, interpretive legend type, c l a s s i f i c a t i o n system, mapping homogeneity and derivative map presentation were accepted with l i t t l e disagreement. The remaining three resolutions, d i f f e r e n t i a t i n g c r i t e r i a , derivative map legends, and general map presen-tation ( s p e c i f i c a l l y the contour resolution), were accepted by most, but there were a large number of dissenters. The results for the l a t t e r variables were not conclusive, but the response would suggest that these resolutions were not acceptable and recommendations were made based on the comments. Summary and Conclusions of the Prototype Test The method used here i n attempting feedback on the mapping system has serious shortcomings. It does e l i c i t a response, but i t does not give conclusive a n a l y t i c a l results. There i s no method with which the subjective responses and the large number of no comments can be accurately interpreted. They can only be used as a rough guide. In a real mapping situation a suggestion would be to show the pre-liminary mapping system or a mock-up of the map i f possible and then ask respondents for answers to spe c i f i c questions. This w i l l avoid the problem 92 associated with no comments (ie. does no comment "mean agreement or disagreement ?). In any situation where analysis of the situation is desirable specific closed questions must be asked. Asking for comments does not yield concrete data. 93 CHAPTER 8 SUMMARY AND CONCLUSIONS ANALYSIS SUMMARY At the beginning of Chapter 3, three hypotheses were postulated: 1) Managers of forested lands can assess their needs from inventory s p e c i a l i s t s to improve resource decisions; 2) Forest resource managers l i k e information portrayed in a sp e c i f i c way common to the majority; and 3) It i s possible to use information from a questionnaire-interview program as an aid for determining a mapping system method i n a given area for a set of resource managers. At the end of Chapter 3 i n the section on Interpretation, the c r i t e r i a with which these hypotheses would be accepted or rejected was established. From the analysis of the data, hypothesis #1 and #2 can be accepted. The population did show an analysable s t a t i s t i c a l l y s i g n i f i c a n t trend for f i v e of the seven dependent variables. The variables for which a resolution was not possible were either poorly worded or impossible to 94 resolve. Hypothesis #3 can also be accepted, though more tentatively than the other hypotheses. It appeared from the response to the prototype that most of the resolutions were acceptable. Those that were not accepted were discussed i n the previous Chapter. For the hypothesis to be t o t a l l y accepted this trend would have to be analysed. Unfortunately the responses from the summary were not such that they could have been analysed to test this hypothesis properly. So, the result i s that this hypothesis appears from the data to be true. Questionnaire-Interview Study Summary Although the test was generally successful, there are some areas which can be improved i n future studies. In the questionnaire-interview part of the study two and two-thirds of the dependent variables were not successfully resolved. The f i r s t of these, Map unit variables ( d i f f e r e n t i a t i n g c r i t e r i a ) was not resolved, because i t i s an impossible variable for users to resolve. This question could be expanded to include more sp e c i f i c items which may not be d i f f e r e n t i a t i n g , but which are useful information to users. For example s o i l factors could be divided into standard s o i l factors and each item could be rated. This would be much more satisfactory than a general question on s o i l . Other variables could be treated s i m i l a r l y . Then instead of attempting to find a concrete resolution of d i f f e r e n t i a t i n g c r i t e r i a , the decision can be l e f t to the resource inventory s p e c i a l i s t with the users' desires i n mind. The second dependent variable which was not resolved was s o i l c l a s s i -f i c a t i o n . The questions asked i n this example would not provide anything but a general statement on the u t i l i t y of s o i l c l a s s i f i c a t i o n informa-ti o n . No resolution was reached with respect to what l e v e l of c l a s s i f i c a -95 tion should be presented and where i t should or could be presented. To prevent t h i s , question sequences should always be designed with a sp e c i f i c n u l l hypothesis or hypotheses i n mind (for example: "Users do not want s o i l c l a s s i f i c a t i o n information on the maps"). Doing so w i l l ensure that resolutions, i f possible, result i n s o l i d recommendations. The t h i r d variable which was only p a r t i a l l y resolved was map presentation - s p e c i f i c a l l y the legend size part of map presentation. In this question the lack of an example was a major problem. Even i f the question had been resolved with a si g n i f i c a n t majority the results would be inconclusive. One person's large legend could be another person's small legend. The results from the interview program were s t a t i s t i c a l l y s i g n i f i c a n t possibly because the interviewer c l a r i f i e d what was meant, but the lack of an example s t i l l prohibited a concrete resolution. A l l questions which can be accompanied by examples to choose from should be so accompanied. This w i l l ensure that the results are concrete. Otherwise conclusions w i l l be fabrications. Prototype Study Summary The test of the prototype was not a complete success. I t did manage to e l i c i t comments and resolutions were changed based on those comments, but i t did have two major problem areas. F i r s t , the prototype summary was a substitute for a real prototype map. Second, asking for comments and leaving an open question resulted i n no real assessment of how people f e l t about the system. It would have been better to have a question sequence following each resolution. Then spec i f i c questions with a closed answer could be asked. This would give a much more satisfactory feeling for acceptance or rejection than a blank space i n the comments space and i t would allow some real analysis of responses. 96 Conclusion From the study results the method appears to work. There are a few areas where improvements are necessary due to the experimental nature of the method. However, this should not be considered a deterrent to i t s future use. Certainly the discrepancy between the desires and perceived desires of the producer group and the user population indicates a method similar to this i s a necessity. Pedologists and ecologists (map producers) were s i g n i f i c a n t l y different in a large number of instances. If for no other reason than that, this method should be employed on at least an experimental basis for future resource surveys. The method w i l l provide a necessary source of user information and feedback for producers. Although this test of the method only attempted to resolve a narrow range of items, many more topics could be covered. The method w i l l not solve a l l map making problems, but i t w i l l aid i n designing maps which communicate better with the user. Users w i l l be brought i n contact with producers and w i l l f e e l less remote. Users w i l l know more about the maps and the problems involved i n making them. The method w i l l not only aid i n map design, i t w i l l also act as an advertising t o o l . The users w i l l know the maps are coming when they are done and w i l l be educated in their usage. This technique i s a r e l a t i v e l y inexpensive and effective way of communicating with resource managers and would easily f i t into present resource inventory programs. It would be a shame to u t i l i z e our inventory and research resources producing maps which did not continually attempt to better communicate the knowledge necessary for managers to properly manage our resources. 97 CHAPTER 9 RECOMMENDATIONS FOR FURTHER STUDY This study was only a test of the method. Incorporating this type of study in the design and production of a real inventory is the next logical step. Obviously the amount of time and detail spent on this study are not available for every resource inventory. However, considering the costs of resource inventories i t is reasonable to assume that at least 3 f u l l man months with c l e r i c a l staff support could be made available for the primary stage and one f u l l man month for the secondary stage (as well as part-time input throughout the resource inventory). The author feels this would be the minimum amount of time necessary to incorporate a similar technique in a major resource inventory program. Based on those time constraints here is a summary of the necessary steps involved. 98 Primary Stage Step 1 Reconnaissance of the area to be surveyed and development of a number of representative transects. Step 2 Develop a potential user l i s t ( s p l i t this into an interview and questionnaire sample). Step 3 Design the p i l o t questionnaire (so that computerized data manipu-l a t i o n i s possible). Step 4 Send the p i l o t questionnaire to a sample of the users and i n t e r -view a second sample of the users. Step 5 Redesign the questionnaire based on experiences with the p i l o t questionnaire. Step 6 Send out the f i n a l questionnaire to a l l potential users except the proposed interviewees. Step 7 While the questionnaire i s being returned conduct the interview program. Step 8 Two weeks after the questionnaire i s sent out, send a reminder. Step 8a Phone or otherwise contact non-respondents, i f necessary. Step 9 Tabulate data and analyse trends (this step f i t s in with Step 3). Step 10 Use trends as an aid while conducting the survey. Interim Data Gathering for the Resource Inventory Secondary Stage (after data c o l l e c t i o n for the resource inventory) Step 1 Assemble information for presentation of prototype. Step 2 Design a set of questions on the prototype. Step 3 Send prototype to potential users. Step 4 Analyse results. 9 9 FIGURE 7 F l ow Chart for Questionnaire-Interview Program Stage One Week Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 Step 8, 8al Step 9 Step 10 XXX 6 7 8 9 10 11 12 XXXXXXXXXXXXXXXXXXX XXX( 'XXXXXXX( 'xxx xxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxx xx: 2 xxx Stage Two RI 1 2 3 4 xxxJ Step 1 Step 2 Step 3 Step 4 XXX XXX XXX X|X 100 F igure 7 i s a f low chart showing the time a l l o tment s f o r each s tep. The Resource Inventory data gather ing stage (RI i n the f i g u r e ) i s obv ious -l y much longer than i s shown. Steps 1 and 2 of the secondary stage i n the user survey can be conducted s imul taneous ly w i t h resource i nven to ry . B i o p h y s i c a l Data Requirements (Time) In the t e s t of the method the data used as a bas i s f o r ques t ions , the Woodfibre da ta , was c o l l e c t e d over a 16 week per iod by a crew of 6 peop le. This amount of data could be cons idered u n r e a l i s t i c a l l y de-t a i l e d . I t i s u n l i k e l y that a resource i nvento ry program would have the l uxu ry of such a d e t a i l e d study and i t c e r t a i n l y would not f i t i n t o the a l l o t t e d time span. However, i n the user survey only a sma l l amount of the t o t a l a v a i l a b l e data on Woodfibre was used. I t was only necessary to use the data to c reate a t r an sec t on which to base p o s s i b i l i t i e s f o r p r e s e n t a t i o n . This data could be assembled r a the r q u i c k l y by us ing a e r i a l photos to des ignated r ep re sen ta t i ve t r a n s e c t s , coupled w i th a few days f i e l d work to gather the data . Other Time The remainder of the time schedul ing on the f low chart i s reasonable i f t h i s t e s t of the method i s used as a backup to a r e a l resource i n ven -t o r y . The same computer program MVTAB and MVOMR cards could be used f o r data process ing to reduce the preparatory computer work. The computer p r i n t o u t and an analyses w r i t t e n i n the margins would be p e r f e c t l y adequate as a f i n a l product to guide the Study. There i s no r e a l need f o r a d e t a i l e d repor t w r i t e - u p . 10 1 Caution on Questionnaire Design Questionnaire design i s a tricky business and care should be taken to consult the l i t e r a t u r e cited i n this report as well as the most recent available at the time of the inventory. A l l possible precautions should be taken during the questionnaire design phase to ensure question se-quences and wording are well planned. This i s by far the most important phase of the study i f extra time i s available i t should be spent here. 102 REFERENCES Beckett, P.H.T. and S.W. Bie. 1978. Use of s o i l and land system maps to provide s o i l information i n Au s t r a l i a . Tech. Paper 33, Div. of So i l s , CSIRD Aust r a l i a . 76 pp. Benson, W. Arthur. 1978. The role of biophysical inventory and analysis i n the integrated management of resources i n B r i t i s h Columbia from Integrated Management Resources Conference. Centre for Continuing Education Univ. of B.C. 67-90 pp. Berdie, D.R., J.F. Anderson. 1974. Questionnaires: Design and Use. The Scarecrow Press, Inc. Metuchen, New Jersey. 225 p. Bjerring, J . 1977. Multivariate Contingency Tabulations. The University of B r i t i s h Columbia, Computing Centre, Vancouver, B.C. 114 pp. Blalock. 1972. Social s t a t i s t i c s . 2nd ed. New York, McGraw-Hill. 583 p. B r i t i s h Columbia Forest Service. 1975. Forest resource planning i n B r i t i s h Columbia. A brief submitted to The Royal Commission on Forest Resources. Bross, Irwin, D.J. 1953. The nature of decision. In Design for de-ci s i o n . New York, Macmillan. 18-32 pp., 276 p. Cline, M.G. 1949. Basic principles of s o i l c l a s s i f i c a t i o n . S o i l S ci. 67: 81-89. Canada S o i l Survey Committee, Subcommittee of S o i l C l a s s i f i c a t i o n . 1978. The Canadian system of s o i l c l a s s i f i c a t i o n . Can. Dep. of Agric. Publ. 1646. Supply and Services Canada, Ottawa, Ont. 164 pp. Gross, J.E., J.E. Roelle and G.L. Williams. 1973. Progress report: program onepop and information processor: a systems modelling and communications project. Colorado Cooperative Fish. Wildl. Res. Unit, Colorado State Univ., Fort C o l l i n s , Colorado, 327 pp. Hansen, John A.G. and N.R. Richards. 1979. Professional reaction to published land information in southern Ontario. J. S o i l Water Cons. 34: 144-148. Holling editor 1977. Adaptive environmental assessment and management. Institute of Resource Ecology, Univ. of B r i t i s h Columbia, Vancouver, B.C., Canada. 597 pp. Keser, N. 1970. A mapping and interpretation system for forested lands of B.C. Report No. 54. B.C. Forest Service, V i c t o r i a , B r i t i s h Columbia, Canada. 29 pp. Klinka, K. 1977. Guide for the Tree Species and Prescribed Burning i n the Vancouver Forest D i s t r i c t Second Approximation, Ministry of Forests, Vancouver, B.C. 42pp. 103 Korelus, V.J. and T. Lewis. 1976. Biophysical mapping of Cowichan division south. Vol. 1, Pacific Logging Co., Victoria, B.C., Canada. Krajina, V.J. 1969. Ecology of forest trees in British Columbia. Ecology of Western North America Vol. 2:1, Department of Botany, Univ. of British Columbia, Vancouver, B.C., Canada. 146 pp. 1965. Biogeoclimatic zones in British Columbia. Ecology of Western North America. Vol. 1:1 Dept. of Botany, University of British Columbia, Vancouver, B.C., Canada 1-17 pp. Lindgren, B.W. and G.W. McElrath. 1970. Introduction to probability and statistics (third edition). The Macmillan Company, New York, New York. 305 pp. McKnight, M.D. 1979. Use of Ontario Soil Survey reports by selected user groups. Unpubl. M.Sc. thesis, Univ. of Guelph, Guelph, Ontario. 105 pp. Moon, D.E. 1979. A comparison of four levels of s o i l and ecological mapping in forested watersheds. Draft paper, Agriculture Canada, Vancouver, B.C. Moon, D.E. et al 1980. Soil and Vegetation Mi l l and Woodfibre Creeks Watersheds British Columbia, Agriculture Canada, Vancouver, B.C. Oppenheim, A.N. 1966. Questionnaire design and attitude measurement. Basic Books Inc., New York. 298 pp. Parten, M. 1950. Surveys, polls and samples: practical procedures. Harper and Bros., New York. 624 pp. Payne, S.L. 1951. The art of asking questions. Princeton University Press, Princeton, N.J. Pottinger, 1978. Detailed s o i l , vegetation landform data along a transect at Woodfibre, B.C., unpublished data. Agriculture Canada, Vancouver, B.C. Rees, W.E. 1977. The Canada Land Inventory in Prospective. Report #12, Fisheries and Environment Canada. Lands Directorate, Ottawa, Ontario, Canada. 40 pp. Robinson, I.M., W.C. Baer, T.K. Banerjie and P.G. Flachsbart. 1975. Trade-off games. In Michelson, W. ed. Behavioural research methods in environmental design. Halstead Press. Siegal, Sydney. 1956. Non-parametric statistics for the behavioural sciences. New York, McGraw-Hill, 312 pp. Sprout, P.N. 1976. Inventory scale and priorities from Proceedings: Natural resource inventory: methodology, availability, interpretation. Sponsored by Centre for Continuing Education and the Association of British Columbia Foresters. Univ. of British Columbia, Vancouver, B.C., Canada. 104 USDA S o i l Survey Staff. 1951. S o i l survey manual, Ag r i c u l t u r a l Handbook 18. USDA S o i l Conservation Service, U.S. Government Printing Office, Washington, D.C. USDA S o i l Survey Staff. 1975. S o i l survey manual, Ag r i c u l t u r a l Handbook 18, revised unedited manuscript USDA S o i l Conservation Service, U.S. Government Printing Office, Washington, D.C. Valentine, K.W.G., W.C. Naughton, and M. Navai. 1981. A questionnaire to users of s o i l maps i n B r i t i s h Columbia: results and implications for design and content. Can. J. of S o i l Science 61: 123-135 pp. Valentine, K.W.G., J. Nowland, J. Day, and J. Shields. 1978. The method of mapping s o i l s i n Canada: a f i r s t attempt at an analysis. Agri-culture Canada, Vancouver, B.C., Canada. 39 pp. Wiken, E. 1978. Methods of ecological land c l a s s i f i c a t i o n . Unpubl. M.Sc. thesis, Univ. of B r i t i s h Columbia, Vancouver, B.C. Canada. For. Abs. 1974-1979. Computer Research of a l l items on Site-Class Assess-ment, S o i l Factors, S o i l (W) Class (W) Assessment and S o i l (W) Factors. 105 A P P E N D I X I Q U E S T I O N N A I R E 1 0 6 Land Resource B.C. Pedology Unit Research Ins t i tu te Land Resource Research In s t i tu te Dear This survey i s part of a study which i s attempting to i d e n t i f y YOUR needs fo r de ta i l ed so i l -vegetat lon- landform maps of forested environments. The Pedology Unit of the Land Resource Research I n s t i tu te i s sponsoring the study and i t i s being sanctioned by the Research D iv i s ion of the B.C. Forest Serv ice. We have designed the survey to f i nd your preferences of map sca le , map presentat ion technique, information l e v e l and Information type. A l l of the examples shown are based on ac tua l data gathered at Woodfibre Creek, a small watershed (about twenty ta>2) dra in ing Into Howe Sound. The watershed i s a t y p i c a l U-shaped hanging g l a c i a l v a l l ey with a small high energy stream. The area f a l l s wi th in the Coastal Western Hemlock wet b iogeocl imat ic subzone and the vegetat ion cons i s ts of a v i r g i n stand of timber i n a l a t e success ional stage. The survey form looks, at f i r s t glance, to be very long. Please don't be discouraged! This i s p a r t i a l l y due to the lengthy examples needed to portray a r e a l - l i f e s i t ua t i on and i t i s a l so necessary to do j u s t i c e to the subject. We would appreciate i t i f you could f i l l out the form and return i t in the enclosed sel f -addressed stamped envelope as quick ly as pos s ib le . We need the answers f o r the second part of our study. I t i s imperative that the form 16 f i l l e d out by you alone. Any co l l abo r - at ion w i l l destroy the re su l t s . When we use the word " y o u , " we mean you persona l ly , not your o f f i c e or the people who work with you. We are Interested i n your personal opinions. The survey forms have been coded to a id i n the ana lys i s of the re su l t s . However, we would l i k e to s tress that your reply w i l l be held in the s t r i c t e s t confidence. At no time w i l l questionnaires be i d e n t i f i e d by respondent. I f you have any questions about the purvey or would l i k e a second copy, please contact Ned Pott inger at I. Thank you for your cooperat ion. S incere ly , Ned Pott inger Project Leader 107 computer D S E R S U R V E Y cgde I GENERAL o  I 12. Do you U B e maps i n your work? Yes What sort? No 13. Do you usua l ly use 1. s o i l , 2. vegetat ion, or 3. eco log i ca l maps? ( c i r c l e appropriate numbers) i n the f i e l d : 1 2 3 i n the o f f i c e : 1 2 3 Number of times (roughly) you would consult a 1. s o i l , 2. vegetat ion, or 3. eco log i ca l map. i n the f i e l d i n the o f f i c e 215 1. never 1. never t - L D 2. never 2. never 217 3. never 3. never 218 1. times/year 1. times/year 219 2. times/year 2. times/year 220 3. t imes/year 3. times/year 22. What percentage of your work i s done i n the f i e l d % the o f f i c e % 2 3 . Would you, i f they ex i s ted , use la rge sca le (eg. 1:20,000 to 1:2,000 sca le ; s o i l maps? Yes NO 201 Where would you use them most o f ten: i n the f i e l d i n the o f f i c e 24 How often are you responsible ( d i r e c t l y or i n d i r e c t l y ) for resource management decis ions eg. harvest ing and s i t e preparat ion p rac t i ce s , road l o ca t i on , s i l v i c u l t u r e , planning, e tc . ? Never Almost every day About once/week About once/month Less than once/month 25 When making a resource management dec i s ion do you f ind you have too l i t t l e information too much information wrong information enough information other What type of information do you f e e l i s most often lacking? I f you choose more than one, please order your choices i . e . 1 as f i r s t choice, 2 as second choice, e tc . 26 #1 cho i ce S o i l s 27 #2 choice „ f r . Vegetation Ter ra in Hydrology Climate Topography What other informat ion, i f any, would you ask for? page 2 108 SCALE Do you f e e l a need for more deta i led ( larger scale) maps? Yea What sort? No What map scale(s) do you use? <1:10,000 1:11,000 - 1:20,000 1:21,000 - 1:50,000 1:51,000 - 1:100,000 > 1:100,000 Which one do you use most often? i f you were to se lec t only one sca le of s o i l s map tor your work, what sca le would you choose? <1:10,000 1:20,000 1:50,000 > 1:100,000 other Why would you choose th i s scale? The cost of producing a map of ha l f the sca le i s approximately 4002, eg. a 1:10,000 map Is four times the cost of a 1:20,000 map to cover the same area. Would your previous choice of scale be Influenced by cost of production? Yes No Why? Would you l i k e more than one sca le of s o i l map? Yes (please l i s t them i n order of preference): No Do you think i t would be worthwhile having d i f f e ren t l e ve l s of accuracy of information on a s o i l s map? For Instance, more p lo t s per un i t area i n problem areas and less p lo t s per un i t area in l e s s sens i t i ve areas. Yes No Would the fact that th i s implies va r i ab le accuracy and thus va r i ab le r e l i a b i l i t y bother you? No Would you l i k e to have an inset map with more deta i l ed information on s p e c i f i c s i tes? eg. 1:20,000 map with a more de ta i l ed 1:5,000 inset map of more complex areas. Yes No Which would you rather have? An inset map Var iab le accuracy Both page 3 III MAP tlNIT PRESENTATION 109 Below Is a diagram of a map uni t Ident i f ied i n the Woodfibre Creek area. There are a number ot d i f f e ren t ways of presenting th i s information on a map. On the opposite page there are three examples. Each example has the same amount of information, the  only d i f ference i s in presentat ion. Please rate each one i n the space below the diagram. Douglas fir, Wtsttm hemlock, Rtd huckleberry, Twinflovrar Deer fern, Stepmoei. 239. 240. / 241. 42. 43. 4 4 . 4 5 . 4 8 . 249. 50. 2m >30m organic layar (fibrimor) cobbly loamy tejtjre frouldary loamy feature Which ot the three examples would be best for your purpose1! Could you rate each example on the fo l lowing sca le : Example 1 very use fu l Example 1 Example 2 Example 3 Example 2 very usefu l Example 3 very use fu l 10 9 8 7 6 5 4 3 2 1 0 10 9 8 7 6 5 4 3 2 1 0 10 9 8 7 6 4 3 2 1 0 not usefu l not usefu l not use fu l Is your answer to the above questions based on experience with s o i l maps as we l l as th is s p e c i f i c example? Yes No What i s your Job? Could you please f i l l i n the appropriate blanks. Completion [date(s)] Type(s) 4 6 . 4 7 . Technica l Diploma(s) Un iver s i t y Degree(s) Number of years experience in present Job To ta l number of years of relevant Job experience Number of short course(s) Type(s) Other (please spec i fy ) MA? SKI? P^ SENTATION XA? LUI SYMBOL EXAMPLE 1 - S ax?Iair.£d ur.it symbol wr.ich his r.o csr.r.otctlor.s. The inioraaclor. or. the -,ap ur.it is showr. and the lager.d. Soils are g- v i ! n r.a=es ar.i the report gives more detailed ir.£ornatior.. page A Map Symbol Soil Drainage Slope Parent Material Vegetation Dominant Soil Classification Dominant Subordinate WOODFIBRE MILL BEAR TAX well to imper-fectly drained 71-1001 deep blanket of cobbly loamy to bouldery lossy texture; inactive colluvial, mor&inal, ar.c debris torrent deposits; steep to very steep mountain slopes with some gullies Coastal Western Hemlock wet subzone; Douglas Fir, Wester?. Henlock, Alaska Blueberry. Deerfern. Twin-flower , and Stepmoss Orthic and Cleyed Ferro-Humic Podzol MAP UNIT SYMBOL / W - B 9 EXAMPLE 2 - a compound map unit symbol with some connotations. The soil-landform-vegetation components are separated and slope ' is shown ir. the cap unit symbol. Information on the map units is explained under the heading of each different ,__ . c_ ^ i „ A - H M D m i l rho ronnrt oiven more detailed information-map unit components (dominant-subordinate) lope Map Unit Soil Drainage Parent Material Vexetatlon Dominant Soil Classification Componen t Dominant Subordinate w UOODFIBRE MILL well to moderately well drained cobbly loamy to bouldery loamy textured blankets, mixed colluvial and moralnal deposits; mountain slopes of high relief, 71-100X Coastal Western Hemlock wet subzone; Douglas Fir Twlnflower habitat type Orthic Ferro-Humlc Podzol B BEAE TAN well to Imperfectly drained bouldery loamy to cobbly loamy textured blankets and veneer; inactive colluvial, moralnal, and debris torrent deposit*; gullies and steep to very steep mountain slopes, 71-100X Coastal Western Hemlock wet subzone; We6tem Hemlock - Foamflower habitat type Cleyed Ferro -Humic Podzol MAP UNIT SYMBOL EXAMPLE 3 - a compound map map unit. The component. unit symbol which is totally connotative. All of the Information in the legend is coded into the report explains the legend and map area in more detail, but It doe. not explain each map unit or vegetation associated with dominant soil, dominant s o i l texture dominant solli type dominant s o i l drainage slope. subordinate t o l l drainage subordinate s o i l type subordinate s o i l texture associated vegetation depth of s u r f i c i a l deposits parent material surface expression LEGEND Soil DralnaRe Soil Classification Parent Material Associated VeRetatlon 1 rapid-well 2 mod-well 3 imperfect 4 poorly 5 very poorly A Orthic Ferro-Humic Podzol B Orthic Humo-Ferric Podzol C Cleyed Ferro-Humic Podzol D Cleyed Humo-Ferric Podzol E Regoaol a colluvial over moralnal b moralnal c colluvial (talus) d fluvial 0 non forested 2 Plpslssewa 3 Salal A Twlnflower 5 Foamflower 6 Lady Fern 7 Skunk Cabbage 9 avalanche Texture Slope Surface Expression Depth of Surficial Deposits a bouldery loamy b cobbly loamy c gravelly loamy d gravelly sandy e sandy f loamy 10 excessive >100Z 9 very steep 71-10GX 8 steep A6-70X 7 moderate 31-452: 6 low < 30Z S ateep I inclined V gullied H hummocky v veneer (less than 1 meter) p blanket (greater than 1 meter) page 5 IV MAP UNIT VARIABLES 111 I f you look at the cross sect ion on the fo l lowing page you can see that a number of var iab les (vegetat ion, s o l i , s lope, e t c . ; change as you move u p h i l l . We use these var iab les to separate d i f f e r e n t map un i t s . The type of var iab les used depends to some extent on the map's purpose. For your own management and dec i s ion making purposes, could you rate the fo l lowing var iab les on the i r usefulness for separating map uni ts along the transect. Could you please do th i s by ass igning a value trom 10 to 0, (10 means i t i s always usefu l and 0 means never). 2 5 1 , 1. S i te Moisture Regime Always Useful 10 Never Useful 252. 2. Slope Always Useful 10 2 1 Never Useful 253. 3. Depth and Type of Forest Floor (Duff Layer) Always Useful 10 Never Useful 2 5 4 . 4. Standard S o i l Var iables (drainage, texture, c o l o r , s o i l type, s t ruc ture , chemical composition, parent mater i a l , topography) 255. 5. Vegetation Always Useful 10 Always Useful Never Useful Never Useful 10 256. 6. S u r f i c i a l Deposits Always Usefu l 10 8 / Never Useful 257. E levat ion Always Usefu l 10 Never Useful 258. 8. Aspect Always Useful 10 8 Never Useful 259. Are there other var iab les that you would l i k e used to separate map units? Yes, they are No Do you have any comments on map uni t var iab les? 260 Could you please separate the h i l l s l o p e on the opposite page into one or more management units - i . e . areas which you would t reat with a s ing le management p rac t i ce . VARIATIONS ALONQ A HILL5LOPE AT WOODFIBRE SUB X E R I C »ou«i-«)» FI« • A L A L P I P S I S t C W A MD C1DAB. HOM tOU(LM PM WMTCKN MIMLOCH •JtO HuCKLCMKaV PCKR.FEKI4 ( T t P M O t t Hyqwc waSTIHN MCMLOCft ULP caOAN APlAftlLI* PM FOAM n e w t * P C V I L ' * C L U * a. L O C K DEear ia iM MMI ttywuc W U T I U W W 1 « C * O A V 1 L . ' S U . U * •K.UNK. C A M * * } * JPMAqHUM MOJI page 7 INTERPRETIVE MAPS Haps are produced which give capability or suitability ratings. These ratings are Interpreted from information on the soil, landform and vegetation of a site. Thus, they are called Interpretive maps. They can range from maps with a map symbol consisting of a number for each capability and no explanation, to maps with a large map symbol and full explanations. Below is a group of methods for describing the suitability of the same map unit. These examples are based on suitability for road building. However, this range of examples is possible for all situations, eg. species selection, burning recommendations, slope stability, wildlife capability, etc. Note: Both Interpretive and so11-landform-vegetation maps would be available - not Just Interpretive maps. Suitability for Road Building Example 1 Example 2 Example 3 Example 4 Map Symbol suitability^ Map Symbol suitability^ "^limitation (s) Map Symbol suitability^ c / t _sur f i c l a l deposits . limitation (a) Map Symbol suitability slope c/t: 1,2—texture surficial deposits limitatlon(s) Legend* 2 - moderately suitable Legend* 2 - moderately suitable . . . .^ w - wetness limitation, soli saturates for short periods during the year Legend* I ^ " " " l i m i t a t i o i ? soil saturates for short periods during the year c/t - colluvium over compacted moralnal deposits Legend* I S S - ' l i - S ' t S - ! -II Curates for short periods during the year c/t - colluvium over compacted moralnal deposits 1,2 - cobbly loamy to bouldery loamy texture d - slope, 50-70* • i» o o .k„v. It would consist of tables with explanations for each of the *Note: the legend on the map would not appear exactly ns above. It would consist letters and numbers which make up the map symbol - similar to example 2, page 4. 1 1 4 P.ge8 INTERPRETIVE MAPS Could you rate each example on the following scales: Example 1 Excellent Poor 10 9 B 7 6 5 4 3 2 1 0 10 9 8 7 6 5 4 3 2 1 0 10 9 B 7 6 5 4 3 2 1 0 10 9 8 7 6 5 4 3 2 1 0 - Poor Example 2 Excellent 10 9 B 7 b 3 « j « i u Example 3 Excellent Poor Example 4 Excellent Poor 9 8 7 6 5 4 3 2 1 0 Are these answers based on experience with these types of maps as well S B this example Yes No Do you have any consents on Interpretive maps? SOIL CLASSIFICATION Do you know of the Canadian System of Soi l Classification? Tes No Do you find the Canadian System of Soi l Classification useful? Tes No never heard of i t On a nap, is the so i l classif ication (eg. Orthlc Huao-Ferric Podzol or Ortstein Ferrc—Humic Podzol) important to you? Every time About X of the time Never I would l ike to understand sore about Soi l Classification but: a. I have not had time to learn b. I have not had someone to explain i t c. neither a nor b; I understand i t in a general way d. _____ I understand i t very well e. none of the above; I don't want to understand i t Do you have any comments on so i l classification? HAP PRESENTATION Below are two examples of how Interpretive information can be presented; either as i n Example A - on separate i n t e r p r e t i v e maps, or as i n Example B - in the legend of a s o i l map. EXAMPLE A: A soil-vegetation-landform map with a legend s i m i l a r to the following S o i l s Slope Parent Material Map Symbol U Vegetation Woodfibre-Bear 71-100* rubbly, c o l l u v i a l veneer over- Coastal Western Hemlock wet subzone; W l y i n g compacted moralnal blanket; Douglas Fir-Twinflower habitat type bouldery loamy texture S o i l C l a s s i f i c a t i o n Orthic Humo-Ferric Podzol, Gleyed Ferro-Humic Podzol AND separate maps showing Road S u i t a b i l i t y . S u s c e p t i b i l i t y of Slope F a i l u r e . Burning Recommendation. Species Selection. W i l d l i f e Capability. Recreation Capability, etc. S o i l Road Suit. Eg. etc. EXAMPLE B: One map with the soil-vegetation-landform information as well as the s u i t a b i l i t y and c a p a b i l i t y Interpretations i n the legend. S u s c e p t l b i l -S o l l Road i t y to Slope Burning Species W i l d l i f e Recreation S£bol 3 o U . Slop. Parent Material Vegetation C l a s s i f i c a t i o n S u i t a b i l i t y F a i l u r e Recommendation Selection Capability Capability W Woodflbre-Bear 71-100Z rubbly, c o l l u - Coastal v i a l veneer Western overlying com- Hemlock pacted moralnal wet Bub-blanket; boul- zone; dery loamy Douglas texture Flr-Twln-flower habitat type Orthic Humo- moderate: Fe r r i c Podzol, wetness Gleyed Ferro- l i m i t a t i o n Humic Podzol moderate do not burn Douglas F i r ; Western Hemlock low low 116 page 10 V I I MAP PRESENTATION C o u l d y o u r a t e each app roach o f t h e p r e c e d i n g p a g e : 321. A E x c e l l e n t 123. 124. 126. 127. Poor 10 9 8 7 6 5 4 3 2 1 0 322. B E x c e l l e n t Poor 10 9 8 7 6 5 4 3 2 1 0 Which o f t h e two methods do y o u l i k e b e s t f o r d e t a i l e d « P s . g . 1 : 2 0 . 0 0 0 t o 1 : 2 . 0 0 0 s c a l e A Which o f t h e two methods do you l i k e b e s t f o r r e c o n n a i s s a n c e naps? e g . 1 : 5 0 . 0 0 0 t o 1 : 1 0 0 , 0 0 0 o r g r e a t e r A B Do y o u have any comments on t h e p r e s e n t a t i o n o f i n t e r p r e t i v e i n f o r m a t i o n ? Would y o u r a t h e r have a l a r g e l e g e n d ( e g . one w h i c h e x p l a i n s e v e r y t h i n g i n d e t a i l ) o r a s m a l l l e g e n d ( e g . a summary w i t h t h e d e t a i l s e x p l a i n e d i n t h e accompany ing r e p o r t ) . L a r g e l e g e n d 125. S m a l l l e g e n d When y o u l o o k a t a s o i l - v e g e t a t i o n map. a r e y o u a b l e t o p i c t u r e i n y o u r m ind wha t t h e s o i l s and v e g e t a t i o n a r e l i k e ? Yes No Can y o u t h i n k o f a way o f p r e s e n t i n g s o i l - v e g e t a t i o n - l a n d f o r m i n f o r m a t i o n w h i c h w o u l d make i t e a s i e r f o r you t o u n d e r s t a n d ? No Yes How? What s o r t o f map w o u l d y o u r a t h e r u s e : c o u l d y o u r a t e each t y p e on a s c a l e o f 0 t o 10 (10 i s e x c e l l e n t , 0 i s p o o r ) . 22g ' ' -• -•• - • — - — v \ «-K m 0 329. D l u e p r i n t o r o n i ; . a u u w * . * w w „ r i i i . . | • i i r c . a c o l o r map w i t h c o l o r s s e p a r a t i n g u n i t s a s 10 0 uel1 as l i n e s and symbols I I I I I I I I I I I a . o r t h o p h o t o mosa ic ( a e r i a l p h o t o g r a p h ) w i t h l i n e s and symbols on i t 10 I— b . p l a i n map w i t h l i n e s and symbols on i t , e g . b l u e p r i n t o r b l a c k and w h i t e map 10 *-c . a  w e l l  l  10 1-d . o t h e r ( e x p l a i n ) : , , 10 I I I I I I I I I I 0 I I I I I 0 331. i i i i i I i l I l I Would y o u l i k e t o have c o n t o u r s Bhowing t o p o g r a p h y on a s o i l map? Y e 6  132. *> — I f y o u have any comments on s o i l - v e g e t a t i o n - l a n d f o r m s u r v e y s , e i t h e r g e n e r a l o r s p e c i f i c , w o u l d y o u p l e a s e w r i t e them down. 117 A P P E N D I X I I I N T E R V I E W D A T A { < I 118 'J J .) o X IP II UJ CO > o *- UJ ro r- a a. «r o IU *- - J et o to u. z n X z CO X D ITS I' U CO * 7 •> 3 z «- UJ n iu z tn L . II § <NJ LL cr «. C- f> V) _J II CM t— *w o * O C m r - x IV - u. II Q I L in LL J Z ITA •> fS, < »« CM CO D U N •* o M V> cc INI M m o z co o * -> •«•• •> ci or 1  CO O » U. *• a U C I LU < UJf l r- #» O l l 1 n i ' c LU UJ > II C CO L_> > li O tL II z O «> IM l'.> Vfs LL) to ,» CO Z Ci N* II •* • II » a. t5 CD > UJ CM L_ U N O CL > - < X * O O N u- UJ » U~"U, O, u- KJ C uj t r z o < t- •> »-i II o w x «a v to c-CJ ^ < II ex. * - O * •> u. * U II CM < z * r-U- - J « «* a —J u: <T O O — _j co U- — (J £ Ui Z O ii • UJ tO * - a, o o. •- to r- ur > H Z> ~t -t to UJ Q M a o> U II Q. < u-i li *> • t- (/) LTt H - i m —< y *T iv) co O - CO II iH O • 1 »- » O ~* (1 iu un J - M 7 V O II •• Y> tA CO 7 fft I » a a. *a X T U, UJ J3 < •-• x * z J)- «t ex r U J it in >- -o* r < m i cn jx *> £ • u. • ; rr\ r-\ ct —* ' - * ru o co •* Z _ i -* *JU n •D f\J i'l 3 * > > U» Ii II l-H j IA •I CO f*\ LL II z I 1  lO - «0 CJ H ' •-» » C (NJ 7 m l » c- * _»»-»-' — LJ • c -« */> o o z , i -•ii n <f rr i 1 LL LL' U _i II — > U. rr" •' 'u —* ir * LO » > t 7^  ec »-• Z to Z < < UJ O i oor H it a. ITI * CD || ^ ( LL UL' LL O. UOj H O l/> O. II IT* H M H Ol • co • IM . i/ ; Z I Z UJ I . < or o i U & LL* I H N »- n * 0*> rc, •* I eg CM l *^  t- o? m I* -« CO oo *0 II »Ti •4- m IM 1— M »_ CJ y X ^ J-, II at* I i^. > ^ in > O co co •> CO CD I <* » IT IM I — I • NQ> H m • CO .. X II IT* co > m — u, m > o > | CO _. . rr« —« ^ in IA !** CM o c-—« fM r^ i ^ fM T a o P UJ Z a »- II L J 43 H m fM I IM or «x . L'J CM . X -o ^ -4- ^ > • > ! N ^ II I ~H «fr O OD 4- IA| CM (Ml II 4* II ll |i II m co »-i »a IM ^ m > > > M II N H H H C -LO • jun o zr W >- u — .1 - O k- O-_ - a i c fc »-x e -M z i -• o « a. I (U X rr-|/> fy> » CO r\) ^- I * I » • I 00 O *N* iTl . CJ n H II it * v" f> m < - > > > • > : ID O >• to < << z z «NJ in O CM| ° - | O *| * -* * a. CM n — •c « a rc, rO r j CL * H —i • or mji or » - - 5 X ^ o II - a. -4 - » rr" t- II •ft v • • - ii II • Z -- II rO • m • > X o i r-• z x 3" -it . UJ — <3 -J l_J O li !/• C C i-t « H X i » a co I x mi-X rr, I— -o • CC X II (1 ro x - II -• CO i » LU CO «• I "J X • z <s c *-1  X '»-* *3 to •» - I — < i X r- r. C w -J - [ - z — &\ X Z Jt -II o + — . (_> x : x m w u| k- . II . O - LL * I zc n f*^  n o [ II o - -» 3*" • en o | r*"i II ^ • • - n OJ J o x| '_) u *«• > LL /Y 1 iU _J < _j n ir n > « - I LU "V. z z n C O * ar co —. co * • _j - co i i «f ^ M n O O- K II » v 1 < o . . II M H J> - • > > > co| jf UN O O ul CJ fM > > > m 111 UJ in til I x- J" X X < < < < <d z z z z z UL' CO o * o: <r - I z n H • co + CJ > --r4COO>| U. - r j : <r I tr o * n O CM to 1^  u . J Z Z J ' LL o <r <r o i y ti II n a r to c-• " J " O •-: • > o a ? o j y r z : U X rJ<HH H ' H • - ^ cn o L UL CJ r < • : x T : < -a : z ar x x < «a z z ' T ft »i I • > fM > . : *- x T : [ < < < • r z z : i < I CO X f • w >[( I OJ ^ : x x i < < : z z i x 4 < z z • -4" —• — . O an rr* i co m • LL, LU ' X I : « <i -a z z z O o nrrsTTT=TS"T~ N A M E V I I ( V ! 2- : * O S . L * O S t : 5 0 S , L i O i , e 6 0 S . L 6 0 S . E 7 0 S . L 7 0 S I S E T 2 0 1 T O ' 0 ' G R O U P 2 1 > l ' - " i ' , • ( ) • 2 0 1 ' 1 ' G R O U P 2 1 • 6 , - , 4 » 2 0 1 ' 2 ' G R O U P 2 1 ' B ' - ' I ' 2 0 _ 1 _ ! . 3 ! C O U P 2 1 ' ' 2 0 1 • S E T V K 3 T O ' 0 ' G R O U P V K 1 ' 1 ' - ' 3 ' V K 3 G R O U P V K ] • * " - , 7 ' V K 3 G R O U P V K 1 * B ' ? ' • VK 3 S E T V F 3 T P ' 0 ' G R O U P V F 1 G R O U P V F 1 G R O U P V F 1 V F 3 ' 1 ' V F 3 ' 2 ' - • 7 ' V F 3 ' 3 ' G R O U P V F 1 • e ' - ' A * V F 3 •*• G R O U P V F 1 S E T 2 0 * T O V F 3 ' 5 1 • 0 > G R O U P 1 1 9 '*• 2 0 * ' l * G R O U P 1 1 9 ' 5 ' , ' 6 ' 2 0 * ' 2 ' G R O U P 1 1 9 • l ' . ' T ' . ' S ' 2 0 * > 3 ' G R O U P 1 1 9 • 2 , , , 3 ' , * ' 2 0 * « *• ""' ~ S E T V H 3 T O '0' G R O U P V H 2 M ' - ' 9 ' V H 3 ' 1 ' G R O U P V H 2 ' A ' - ' M ' V H 3 ' 2 ' G R O U P V H 2 « 0 ' , • « V H 3 ' 3 ' H E A D I N G T H I S I S A N A N A L Y S I S 3 F T H E I N T E R V I E W D A T A . T I T L E G E N E R A L P O P U L A T I O N C H A R A C T E R I S T I C S . T A B L E S S I Z E = * » 1 6 2 2 - * 3 S I Z £ * 8 « 1 6 2 * - * 3 S I Z E « 8 M 6 2 0 1 , 2 1 5 - » 3 S U E « 8 » f l 2 0 1 - 2 * T I T L E D E P F N D E N T V A R I A B L E » 1 M A P S C A L E P A R T I S C A L E . S I Z E = l 6 M 6 3 0 - * 3 S I Z E = 1 6 * B 3 D - 2 * S I Z F = 8 * 1 6 3 1 - * 3 S I Z E * 8 * 3 « 3 1 - 2 * JlwtU'eil^f I1 M * P 5 C * L F P * R T 1 1 C O N S T A N T V S V A R I A B L E I N T f N S t T V V S I N S E T M A P . I AHL c 5 5 I Z e = * * I 6 V 5 1 - * 3 f 3 5 T I T L E D F P E N P E N T V A R I A B L E 0 2 M A P S Y M B O L - L E G E N D T Y P E .  UBLE$ tullV.lt llV-lf 2 4 9 ' ^ 5 U F ^ > 3 ? 3 B - 2 1 5 . 2 1 8 5 1 Z E = * » * 3 B - * 2 S I Z E . * « 1 & * 2 - * 3 SUZ***B 3 8 - 2 3 9 , 2 * 0 . 2 M . 1 2 3 S I 7 E = 8 « I 6 2 * 0 - * 3 S I Z E = 8 * 1 6 2 « l - * 3 , 2 * 9 , * 2 " " • - - ... T I T L F D E P E N D E N T V A R H B L E « 3 1 A " J N I T V A R I A B L E S P A R T I D I F F 6 » E N T 1 A T 1 N G C P I T E R t A . T A B L E S S I Z E = 8 * 1 6 V 3 1 - * 3 S I Z £ = 8 * 1 & 2 5 8 - 2 * 9 S I Z E = 8 * 1 6 2 5 3 - * 2 T I T L F O E P F N D E N T V A R I A B L E # * M A P J N I T V A R I A B L E S P A R T I I I N F O R M A T I O N L A C K I N G . T A B L E S S I Z E « 8 « 1 6 2 6 - 2 0 1 T I T L E D E P E N D E N T V A R I A B L E # 5 S O I L C L A S S I F I C A T I O N S Y S T E M . T A B L E S S I Z E = 8 * 1 6 2 0 * - * 3 S I Z f c - B * B 2 0 * - 2 * , 1 2 0 S I Z t " « 8 * 1 6 1 2 0 - 1 1 8 T I T L E J E P F N D E N T V A R I A B L E » 6 I N T E * P * E T I V F M A P L F G E N D S . T A B L E S S I Z E * S « l 6 V 6 1 - * 3 S I Z E = 1 6 * 8 3 1 5 - 2 * 9 S I Z E * 1 6 * ^ 3 1 * - 5 0 : T I T L E 3 E P E N 3 E N T V A R I A B L E # 7 M A P P R E S E N T A T I O N P A P T I - I N T E R P R E T I V E M A P S . T A B L E S S I Z E = 8 « 1 6 3 2 1 - * 3 S I Z F = 8 » 1 6 3 2 2 - * 3 S I Z E = * « 1 6 1 2 3 - * 3 . T I T L E D E P F N D F N T V A R I A B L E » 8 M A P P R E S E N T A T I O N P A P T 1 1 - I E G E N O t M A P B A S E . a N D T O P O G R A P H Y . T A B L E S S I Z C = * * 1 6 1 2 5 - * 3 S I Z E = * * B 1 2 5 - 2 * . 3 R StZE = 1 6 M 6 3 2 8 - * 3 S I 7 C = 1 6 * 1 6 3 2 9 - * 3 S I Z F ' 1 6 » 1 6 3 3 0 - * 3  r o THIS IS AM AN4LYS!S OF T*E INffc'VIEW P A T * GEULBAl E-P.ULaUQ.bl C 0 a ' . 4 i I t i l i I l - 5 , ' , r r t,, ai-AS-i-ic I S B L : n c US-CC^JIL ice 221 vs _c_ ir.r * 3 » > - F^EOUTNCY T4BL" I ZFORES SILVIC F ISH** ZERO T - T I 1 2 i CF3RES P ANGER 4 5 ENG 6 RFCFA 7 PEP»EC P R E SP LA| 9| 0 1 0 0 1 11 5 6 7 0 0 6 5 0 6 0 4 0 3 01 51 1 47 I 5 6 8 6 5 6 4 3 H 48 HORIZONTAL PERCENTAGE 1 ZFORES SILVIC F I S H U ZERO T - T I I 2 3 CF3RES RANGER 4 5 EMG 6 PFCEA 7 PEO*EC 8 RFSPlAl 91 01 .00 .00 103.30 11 1 0 . 6 * 12 .77 I f . 8 9 .30 .00 12 .77 1 0 . 6 * . 0 0 12. 7T . 0 0 8 .51 . 0 0 6 .38 .001 10.641 1 47 — I 10 .42 12.50 16.57 1 2 . 5 0 10.42 12.50 8 .33 t. 25 10.421 48 V!=*TTCAL PfcKlfcNTAGfc 1 ZF3*ES SILVIC F1SH»W ZERO T - T I 1 2 3 CFORES RANGER 4 5 ENG 6 RECPEA 7 PEO»FC 8 RFSPlAl 91 0 | . 0 0 .00 12.50 1 1 1 0 0 . 0 0 100 .00 8 7 . 5 0 . 0 0 .00 1 0 0 . 0 0 1 0 0 . 0 0 .00 100 .00 . 0 0 100.00 . 0 0 100.00 . 0 0 1 1 0 0 . o o l 2. 08 97 .92 I 5 6 8 6 5 6 4 3 51 48 NO CHI SOUAftE C0F = OI THERE IS ONLY INE CELL a i v - A a i a i E l a f l L E OF QEC ISUN. I C : z»» vs j c a ICC 431 — FREQUENCY TABLE 1 ZFORiS SILVIC FISH*W CFORES RANGER ENG RECREA PED»EC RESPLAl ZERO T - T I 1 2 3 4 5 6 7 8 ° l 01 0 0 1 L ES DAY 2 1 5 3 3 WEEKLY 3 1 0 2 2 MONTHY 41 0 1 0 0 0 2 3 3 2 1 0 0 4 1 0 0 2 1 1 0 1 2 0 01 31 11 1 1 1 26 14 4 LE SH ON 5 1 0 0 2 0 0 1 0 0 01 3 1 5 6 8 6 5 t 4 3 51 48 J \ - • — " O r THIS !S AM t'dLYS'S O c T - l " IWAMcil t i A U o T H I 5 ! 5 AM A'UL VST 5 O c T-T IHT " l i t * D 4 T 4 I ZFJRiS SUV'C cIiHt* LFCSi:S RANG" F\IO R TC j f i "FPtrr. »F<P|.A| ZERO T-T| 1 2 3 4 5 6 7 8 9| 6| .00 .00 133.33 Too Too 730 Too Too Too j 1 L fS DAY 21 19.23 11.54 11.54 7.69 11. 54 1 5.3B 7.69 3.85 1 1 .541 26 WEEKLY 31 .00 14.29 14.29 21.43 14.29 7.14 7.14 1*.29 7.14| 14 10N THY 4| .00 25.00 .00 25.00 .00 .00 25.00 .00 25.00) 4 LISMON 51 .30 .00 oo.57 .30 .00 33.33 .00 .00 .001 3 V~ T6T4T "12 . 50"ToTbT~50 10.42 12.50 §733 6T2T r67~42 I 48 ~~ VERTICAL PERCENTAGE I ZFTR^ S SHVIC FI5H»W CFORES R ANGFP ENG CECR *z A P ED*f:C ° F <Pt. A | ZERO T-TI 1 2 3 4 5 6 7 8 91 61 .oo Toil T<rrT6 Too Too Too Too Too Tool 2. os L ES DAY 21 103.00 50.00 37.50 33. 33 60.00 *6.67 50.00 33.33 60.001 54.17 WEEKLY 3 1 .'JO 33.33 25.00 50.00 40.00 16.67 25.00 66.67 20.001 29. 17 MONTHY 4| .00 16.67 .00 16.67 .00 .00 25.00 .00 20.001 8.33 LESMON 51 .00 .00 25.00 .00 .00 16.67 . 00 .00 .001 6.25 1 5 b 8~ 6 5 6 4 3 5J 48 PEARSON'S CHl-SOUARE» 21.21 L1KFLTH00D RATIO CHI-SOUA»E» 22.76 0F» ;-HPR08 - 3. 62673 CHIPR3B = 0.53440 GUTTHAN' S LAMBDA^  0.09B34 a i . i a i i i E i a a u OF M U E B E « I J I & I C C 2011 vs J O J ccc 43> FREQUENCY TABLE I ZFJReS SILVIC F1SH»W CFORES PANC-EE FNG RFCRFA PE0»FC PTSPLAl ZERO T-T| 1 2 i 4 5 6 7 8 " o| 0FFIC= II 3 * 5 3 2 3 2 1 4| 27 BOTH 21 1 2 1 3 3 3 2 0 Ol 15 FILLO 31 1 0 2 0 0 0 0 2 II 6 j 5 4 8 <T 5 6 4 3 Tl 48~~ HORIZONTAL PERCENTAGE I ZF3RES SILVIC FISH*W CF.HES RANGER ENG PECREA PFD+EC RESPLAI ZERO T-T I 1 2 3 4 5 ft 7 8 9| OFFICE II 11.11 14731 13.52 lTTTl 7741 fTTTl 7741 37TB 14.61 I FT BOTH 21 5.67 13.33 6.67 20.00 20.00 20.00 13.33 .00 .001 15 FIELD 31 16.67 .00 33.33 .30 .00 .00 .00 33.33 16.671 6 I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 48 24 INVALID- 100.00 X<5 52.T8X<1 •"THIS I!> AN ANAL/SIS U> !Hb A.VI• !••>••« O VERTICAL °=RCFNTAGt ZERO T-TI I | Z F 3 R F S < » L V ! C = IS-t»W C F 1 U S FAMG^R ENG R F C » E A P F O * E C C E S P L A | II 8:8 S& ti* Kit «^ *H •« .00 23.00 .00 .00 .00 -01 BOTH F I t .LD 31 20.00 I 5 PFAPSON'b CHI-SUUAPE' CHIPROB = 3.22373 GUTTHAN'S LAMBDAi 0.C6556 3 6 5 6 * T9T9I LIKFLIHCDl) l>AUJ I Hl-SOUA»b = 56.25 31.25 00 66.67 20.001 12.50 51 «8 C HI P R 3 B a U A B J . A . I £ I A B L E O F E S C 1 L ICC 215 1 v:> dun ICC '31 FREQUENCY TABLE I ZFOR=S SILVIC FiSH*«l CFORES RANGER ZERO T-TI 1 2 3 * 5 ENG REC'FA PCD*CC RESPLAl 6 7 8 91 LOW II 1 1 2 H I G H 2 1 1 1 0 NOTANS 3 1 3 * *> "6T 11 A l I 51 H O R I Z O N T A L P E R C E N T A G L I ZFDR5S SILVIC FISH** CFORES PANG5P ZERO T-TI 1 2 3 * 5 ENG PECRTA PFD+FC RFSPLAl 6 7 8 91 SlPa « «:si « ™ 0 » 10.421 | 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 VFRTICAL PERCENTAGE I ZFORES SILVIC F1SH*W :F31ES 0 ANGER 1 2 3 ENG RFTRFA PFP+EC RPSPLAl 8 9| 16.67 25.33 16.67 .00 16.67 33.33 4 5 6 7 00 .00 25.00 .00 .001 If.67 25.00 66.67 20.001 20.00 P E A R S O N ' S C H I - 3 Q U A R E * : H I P ° ? B -11.13 3.83415 ~" 6 5 6 4 LIKELIHOOD RATIO CHt-50UAR?= CHIPRDB = 13.68 0.62347 G U T T M A N ' S LAM8DS= 0.05355 6 10 32 48 6 10 48 Z E R O T - T | LOW l l 20.00 N O T A N S If " : o o ir.s; 75:30 ~~4 T~ 5f ~T6~~TWALID- 100.00 t<5 33.33*<I 12.50 20.33 48 DF = K3 16 I N V A L I D - 96.30 * < 5 37.04«<1 J THI S ts AN ftr4<VLv5I S dP lie UT"A*\Z* DATA " f l _ a a i a i £ lattLt OF HUEBEdUlK. (CC 201) vs DECISION ICC 241 FR EOUFNC Y TABLE ZERO T-TI OFFICE 1 I BOTH 21 FIELD 31 L ES CAY <d cEKLY MONTH Y LFSMONl 4 51 13 9 4 1 26 3 6 6 _2_ 14 31 Ol — 31 HORIZONTAL PERCENTAGE I ZERO T-TI LFSOAY WEEKLY MONTHY LFSMONl 4 51 OFFICE II BOTH 2 1 FIELD 31 3.70 48.15 .00 60.00 .00 66.67 2<!.22 40.00 33.33 I 2.08 54.17 29.17 27 15 6_ 48 14.81 11.111 27 . 00 .001 1 5 .00 .001 6 8.33 6.251 48 VERTICAL PERCENTAGE | LESDAY MEEKLY MONTHY LFSMONl ZERO T-T| 0 2 3 4 51 ¥ — OFFICE II 100.00 50.00 42.86 100.00 100.001 56.25 BOTH 2 I .00 34.62 42.86 FIELD 31 .00 15.38 14.29 I 1 26 14 .00 .001 31.25 .00 .001 12.50 4 31 48 PEARSDX'S CHI-SOUARE = 6.93 LIKFLIHPriD RATIO CHI-SQUARE' 9.54 DP- 6 INVALID- T5.00 «<5 25.00t<l CHIPROB = J.32708 GUTTMAN•S LAMBDA* 0.00000 CHIPR08 * 0.14435 tiT C—TT m—» M « I V T r r r- • • U* I THI5 1!: AN A N A L V b l * C E 'HI: INI..RVU* VC\\ O LEEE-QEUt _&14a_; 11 141 -CSLI 1 SCALE UlSifiilllE U&Li 01- UEIIN. (CC 3UI *S JCfi (CC *3) FREOUFNLV I AHL b~ I I c W S S I L V I C F I S H * * C F O R h S " A N G F F ZERO T - T I 1 2 3 A 5 C N G P E C ' F A P F D * C C c ^ S P L A l G R O U P l 0 1 1 I G R 0 U P 2 21 G P 0 U P 3 31 G R O U P * * l 2 * 3 A | I t 7 8 91 0 0 0 01 3 0 0 0 1 1 0 1 31 1 3 2 I 1 0 1 0 1 1 1 0 0 01 6 * • 3 51 2 J> " 2 6 11 2 1 * 8 H O R I Z O N T A L P E R C F N T A G f c I Z F O R i S S ' L V I C F I S H * * C F O R E S R A N G E R Z E R O T - T I I 2 3 4 5 E N G R F C R E A P P D * E C R E S P L A l fc 7 8 9 | 01 . 0 0 5 0 . 0 0 . 0 0 . 0 0 5 0 . 0 0 G R O U P l II . 0 0 G R 3 U P 2 21 1 9 . 2 3 GF.0UP3 31 . 0 0 G R O U P * * l . 0 0 2 * 3 A | 16 . 6 7 1 1 . 5 * 9 . 0 9 . 0 0 . 3 3 1 9 . 2 3 2 7 . 2 7 . 3 3 3 3 . 3 3 1 5 . 3 8 . 0 0 . 3 0 . 0 0 1 5 . 3 8 . 0 0 . 0 0 1 0 0 _ 5 0 . 0 0 3 . 8 5 " . 0 9 . 0 0 . 0 0 . 0 0 . 0 0 1 . 0 0 . 0 0 . 0 0 . 0 0 . 0 0 1 0 0 . 0 0 . 0 0 .oo 2 7 . 2 7 5 0 . 0 0 . 0 0 . 0 0 3 . 8 5 1 8 . 1 8 . 0 0 . 0 0 . 0 0 1 1 1 . 5 * 1 9 . 0 9 1 5 0 . 0 0 1 . 0 0 1 2 6" 2 6 11 2 1 j 1 0 . * 2 1 2 7 5 0 1 6 7 6 7 1 2 . 5 0 1 0 . * 2 V E R T I C A L P E R C E N T A G E I Z F O R E S S I L V I C F I S H * * C F O R E S R A N G E R Z E R O T - T I 1 2 3 * 5 12.50 8 7 3 3 6 . 2 5 10.*2r F N G R F C R E A P E O + E C R E S P L A l 6 7 8 9 | * 8 01 G R O U P l II . 0 0 . 0 0 G R 0 U P 2 2 1 1 0 0 . 0 0 G R O U P 3 31 G R O U P * * l . 0 0 . 0 0 1 6 . 6 7 1 6 . 6 7 5 0 . 0 0 1 6 . 6 7 . 0 0 . 0 3 . 0 0 6 2 . 5 0 3 7 . 5 0 0 0 . 3 0 3 3 . 3 3 6 6 . 6 7 . 0 0 . 0 0 2 0 . 0 0 . 0 0 8 0 . 0 0 . 0 0 . 0 0 . 0 0 5 0 . 0 0 1 6 . 6 7 1 6 . 6 7 . 0 0 Z f 3 ft| . 0 0 . 0 0 . 3 3 . 0 0 . 0 0 1 6 . 6 7 . 0 0 . 0 0 . 0 0 7 5 . 0 0 2 5 . 0 0 . 0 0 . 0 0 . 0 0 3 3 . 3 3 6 6 . 6 7 . 0 0 . 0 0 . 0 0 1 . 0 0 1 6 0 . 0 0 1 2 0 . 0 0 1 2 0 . 0 0 1 . 0 0 1 *. 1 7 1 2 . 5 0 5 * . 17 2 2 . 9 2 * . 1 7 2 . 0 8 51 * 8 P C A F S D N ' S C H I - S O U A R F = C H I P R O f l = 4 5 . 6 * 3 . 0 5 5 7 5 L I K F L I H O O D R A T I O C H I - S Q U A R F = CHIPROB = 4 5 . 3 3 0 . 0 5 9 3 3 D c = GUI I H A N ' S L A H B ' J A * 0 7 1 8 9 6 4 K3 3 2 I N V A L I D - 1 0 0 . 0 0 * < 5 6 4 . 4 * * < 1 '6 • HI!, 15 AN AMALVSIS OP He N H v U W DiTA MmiftlE MILS 01= QEIU ICC 331 vs DEUSicCi (CC ? M EREOJEMCY TABL E | L esUylHuiTr MONTHY LESMONI ZERO r-TI 0 2 3 4 5| GR0UP1 II 0 * 2 o 0 I GRCU°3 31 1 — 5 3 f §- ff GR0UP4 41 0 3 0 o 01 5 *• 0 0 J o x j I 1 26 14 4 " 3 | 48 HJMl/fWAl Mt^ tl-NTAGE ~ ZERO T-TI 0 L " S D 4 2 V H " E K L 3 01 .00 100.00 .00 .00 nnl r, Ji!«OyPLJJ .00 66.67 33.33 .00 '.00 6 G R O U P T T I T O O ^ O T O O ~ T H T ^ — T T 6 9 f r f i A GB0UP3 31 9.09 45.45 27.27 18 18 00 ft GR0UP4 4| .00 ,00.00 .00 "ioo '.OO L \ «°° -00 .00 1 00.001 1 J 2.08 5 » . 1 7 _ ^ 9 _ ^ _ ^ 3 J _ ^ 2 j j f ^ ^ ^ 8 VERTICAL PERCENTAGE 7FRP T T! „ L E S D A R ' E T K L V H W T H Y LESMONI itKU r - T | 0 2 3 4 5 | 01 .00 7.69 .00 .00 .00 1 IT GROUP 1 11 .00 15.38 14.29 . 00 • 00 1 •^1 i 12.50 GR0UP2 21 .00 50.00 64.29 50.00 66.671 •>& 1 7 GR3UP3 31 100.0 0 19.23 21.43 50.00 • 001 i i 22.92 GR0UP4 41 .00 7.69 . 00 .00 . 00 I ^•17 2*3 Al .00 .00 .00 .30 33.331 1 1 T b ^ * * J P ^ ^ ^ f f " GUTTMAN'S LAMBDA* 0.02500 Tins I!> >N avj.MY^ T$-cr-), J.J,.^,.,., „ ho *<5 55.00*<1 I HI; I i 4,1 1>J I U O ' J L'l :<»i :>. L (• T A ULy.eBI.aIE IfiHU; OF S.EUIC.1 (CC 31) vs ifla (CC 4?) FREQUENCY TABLE I Z^ORrS SILVIC FISH** CFORFS SANGER ZERO T-TI 1 2 3 4 5 TNG RFCR-;' prn*rx FESPL'AT 6 7 8 <»| 01 1 on II 20K 2 1 5 OK 3 1 OTHER 51 01 01 _3I_ II II I HORIZONTAL PERCENTAGE 51 I ZFOR=S SILVIC FISH** CFORES RANGER ZERO T-TI 1 2 3 4 5 ENG RECREA PFD*EC RESPLAl 6 7 8 91 0 1 .00 10K II 5.88 20K 21 11.76 .00 17.65 11.76 .00 17.65 17.65 .30 17.65 .00 50.00 5.88 17.65 50.00 17.65 5.88 .00 11.76 11.76 .00 5.88 5. 88 50K 31 33.33 OTHER 51 11.11 .00 11 .11 .00 22.22 33.33 22.22 .00 .00 .00 11.11 .00 .00 .00 11.11 .001 .001 17.651 33.T3T 11.11 I I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 VERTICAL PERCENTAGE I ZERO T-T| ZFOR?S SILVIC FISH** CFORHS RANGER 1 2 3 4 5 ENG RECREA PFD*EC RESPLAl 6 7 8 9| Ol .00 10K 11 20.00 20K 21 40.00 .00 50.00 33.33 .00 37.50 37.50 .00 50.30 .30 20.00 20.00 60.00 16.67 50.00 16.67 .00 50.00 50.00 .00 33.33 33.33 50K 31 OTHER 5 1 20.00 20.30 .00 16.67 .00 25.00 16.67 33.33 .00 .00 .00 16.67 .00 .00 .00 33.33 .001 .001 60. 001 20.00 I 20.001 I 5 PEARSON'S CHI-SQUARE' 2 17 17 3 9 48 2 17 17 3 48 4.17 35.42 35.42 to ON 6.25 18.75 17.25 6 5 6 4 3 51 48 LIKELIHOOO RATIO CHI-S0UAPE= 22.88 OF = 24 INVALID- 100.00 l<5 41.67t<l CHI PROB 0.83807 CHIPROB 0.52692 GUTTMAM'S LAMBDA* 0.10444 r THIS IS AN ANALYSIS OF THfc lNlcRVIc* U/T4 ~THlS IS AM ANALYSIS' OF T H t INIMVUW u m o U m u i E I4BU OF SELECt (CC 31) vs u t u i K W <cc 24) FREOUENCY TABLE . . j LFSOTY WEfcKLY MDNTHY LESMONI ZERO T-TI 0 01 0 10K 1 I 0 20K 2 1 .._L 51 ol 21 II 2 17 17 50K 3 1 0 2 OTHER 5 1 0 7 I 26 1* 31 4 8 HORIZONTAL PER CENTA3E j "LFSOAY-^ teKLY MONTHY LESMONl ZERO T-TI 0 2 0| .00 10K II .0° 2 OK 2 1 5 ._BJL 50.00 41.18 5 2.94_ 50.30 41. 18 23^ 53 , 6 o.001 5.88 11.761 lt.76_ 5.881 00 50K 31 .00 66.67 33.33 OTHER 51 .00 77.78 11.11 11.11 .001 .001 2 17 3 9 I 2.08 54.17 29. 17 8.33 6.251 48 VERTICAL PERCENTAGE^  1 T=SOAY WEEKLY MONTHY LFSMONl ZERO T-TI __ 0 2 3 « 3.85 7.14 .00 .001 26.92 50.00 25.00 66.671 3 4 . 67 28.57 50.30 33^ 33_L 01 10K 11 20K 21 .00 .00 100.00 4.17 35.42 35.42 .00 . 00 I 6.25 OTHER 51 .00 26.92 ^[...lllll ^ 3 ) ^ 26 14 l-o PEARSON'S CH1-S0UARE = " C H I P R Q O = 5.67 LIKELIHOOD RATIO CHI-SOWE* 6.78 OF. 9 I N V A L I D - 8 1 . 2 5 * < 5 3 1 . 2 5 » < 1 0.77381 CHIPROB = 0.66161 GUTTMAN'S LAMBDA* 0.06249 128 O J •a) X ] M U J zi •si ZI u.< H ZI M <) </«-Zlo •Jo Ulu. J O •ana MUI Zll-I ;uj« ;uj<l ictcd i ! t | i j * *+ V O o i o m m V | CO 77.7 l D _i < > Z CD in I eo I m m | CO m m 1 CO cr cr \ CO cr cr i CO >»• 1 1 1 * <M 1 «* fn 1 rn | J fn J II o 1 a j J D 1 1 • — — — •) _ , : ... . — — . «a a i 1 m < cr l Of. | < a o o I m <. cr i «-f- ^ 1 0 | ro _ J i _ J I CC ft | <t - J o o 1 o o ^ 1 I -J 1 ro —" CL | O. i • • l a • • o INi a. I 1 CL 1 • • ( A 1 1 to 1 cc «n i O «o O O; | • CO VJ 1 I \r> i O «-H C U- 1 1 U J I on | U J CD (M 1 or- U J f ' I LL I f-t Ot ' I l OL | <c Or. 1 1 a 1 CJ GO 1 fn O 1 m LU 1 f*- O | m u cc | o o 1 rn o U CO | rg 1 m CJ CD | m «—' in U J • | l <o O | fsl U J o o 1 Ui t 1 U J ( oo + • 1 • + • • + I i • | • • O | 1 o I <o | o o l tl II C 1 1 O 1 OJ Ul | 1 u. LU o 1 u. U J 1 f U' 1 CL I 1 a O- 1 QC cc CL 1 1 a l <T c. i l fn -a | •4 O 1 < r- 1 O* O 1 m •c r* o o 1 <4- Ot •a r- | fn ^4 | -a r- | cr U- I it' 1 CO O I m U J o o | a CL U J 1 1 t. J j*> •-* ni Of 1 or t • • 1 a. • • or 1 1 C 1 t_J 1 1 o f CO CO (_) o j i X LJ 1 1 L_> | r- —• 00 LU 1 U J U J o UJ 1 1 U J j O 1 1 CC or. 1 X a l l CC 1 (J3 *0 ' •o © 1 o o * 1 *n o o O O I o o O 03 1 »T ro | O 03 | 03 (M o z Z 1 fn O m z o o | D Z 1 1 Z 1 ro ro in u. 1 • * U J • • U J 1 1 ;LJ 1 • • I 1 n^ rg o | K m 1 1 O ro ro i 1 f-* o j •a -* 1 1 ro -i—t I —t J a 1 1 o. m in O I i" a m 1 -« © rsi o o 1 ^ Q cu m < - * 1 4ft CL to | 03 — ro L. j U J I o U J o o j O o U J | f iu: | ro «-< O o I * o • • L J O 1 1 O | Z • z I O z o X Z 1 1 Z I O O < I < 1 »H •n o | icq < 1 1 i< 1 "-^  *^ *—' o. I Of «—" I -1 Cl a 1 I 0L | u. 1 I to •O O 1 <0 to 1 m o o LO ^> o o 1 O to <*• tn — i 03 1/> •* 1 ro o 'JJ U J 1 fn O tn U J o o i i LO LU 1 1 J J J | CO —* ITv ry or t • • or: • • _ J > nr 1 1 of 1 r-i 1 C 1 m rg c o 1 | n i i O 1 ro -H ro LL. t u. i »-* Li_ o I LL. 1 1 LU 1 —< ^ —* O 1 <_> —4 j •ft O 1 t O 1 m X m r«- —i 1 cO 1 v3 rrt r— o o I CO tn -« >-n r- -4 i CO 3c m i + I m m Xi • m in • CO •V 1 1 + 1 o> —« 03 X 1 X i * * • X • • 1 in m X 1 1 X 1 • • * to 1 i/> j LO ro in 1 r- o 1/t 1 1 to 1 f- -4 J3 »u •-• 1 <-H m CD i-4 • u J —  u. 1 O LL U, 1 o *5 LL 1 1 O LL. 1 m ^ 1 <o fw U N I -4 fn i o S O N m r- D -M m O ro OD O 1 «C OD t-1 o ru 1 CO O ! o 5 — 1 .-» m m « fn <c Z «- 1 m o > 1 U J > 1 • * N- > * • 1 ti H • .a > 1 1 U J > 1 • • i • X' -J 1 U _i 1 m i ro 7 - J U J D «H U J - J 1 1 U 1 I r%j - J «-* 1 a «-« 1 PM m i •-, J J 00 t or CO _f 1 1 a I ~* E LO 1 U J LO 1 •X LU CO LO 1 1 U. LO 1 CL 3C => or n D < a. I 1 r^t — l/> «—« I in o 1 in 1 — o 1 <M i-' LO O G 1 Cl a a. a K to —« «* 1 m LO «-4 | 03 -4 UJ _j a* I -« o t a. U J o o 1 LA Uf " J 1 1 r-i _i I ^ V- of i •a or Of* • 1 1 X £ -1 V Of | 1 «ff or t • • • • _> n i »- n f 1 O - J m n 1 a z fXf o rn i i *- n i t o r u- i Z LL 1 1 o 1 X < •at z U- 1 t Z LU 1 J J KJ • I D *o J 1*0 1 H U. Kd | | n f»o i D i M 1 z> l i •sj | 3 — —. • • — — . + p» w — • + — — V M- — — > ~ — • 4 /> ui cr • — JU t- 1 —« r\ i DC »- I —* <M ac »- 1 ~4 f\ 1 B M U J *- I H N 1 or »- I •-4 ro v t f-> | jj t c «eft »v t i i O 1 I 1 i/i C i X t- 1 LO O > K 1 CO C 1 rt a «-« u. 1 LO O 1 X *~ 1 to O 1 LU Z 1 J- z 1 U J 2 to E ed 1 LU Z 1 UJ z o 1 > I c 1 > O 1 >• | UL - O 1 >- 1 O 1 > Of a GC •a - CL 1 1 CL | U J u I J J U J U J | | LU | 1 1 •s* a. jta fsJ | | •si | r T H I S !s AN ANALYSIS OF T H - i N r * v i r * rti\ VERTICAL PERCENTAGE I ZFTRES SILV'C FISH»rf CFCUKS RANGFR TMG R f C T A P f P + EC PFSPLAI ZERO T-TI 1 2 3 * 5 6 7 8 91 Y£S 1 I 80.0 0 TOO.00""dT.To 3 373 3 80 . 0 0 66.67 75.~0O 66767 " 8070 01 BT7T5" NO 21 20.00 .00 1 2.50 16.67 20.00 33. 33 25.00 33.3? 20.001 18.75 I 5 6 8 6 5 6 4 3 51 48 PEARSON'S CHI -S OU E- 2^98 LIKELIHOOD RATIC CHI-SOUAPE* 3.92 0F« CHI PROS "= 0.9351 9 ~ CHIPRDP = 0.86462 GUTTMAN'S LAMBOA* 0.02040 8 INVALIO- 94.44 *<5 27.78*<1 FREOJENCY TABLE I ZF:1RES SILVIC FISH»M CFORKS RANGF.R ZERO T-TI 1 2 3 4 5 ENG RFCRFA PFD*FC RCSPLAI 6 7 8 9| 01 1 INSET 1 I VARfCC 2 1 BOTH 31 21 ~3T 01 Ol I 8 51 M 3 R I Z O N T 4 L PFUCENTAGc I ZF3RFS SILVIC FISH*W CF31ES RANGER ZERO T-TI 1 2 3 4 5 {HG REC'FA PED*EC RFSPLAI 6 7 fl 9| Ol 12.50 INSET II 1 3 . 0 ^ .00 13. C4 .33 26.09 12.50 13.04 12.50 4.35 12.50 4.35 25.00 8.70 .00 4.35 25.001 13.041 VA^ACC 21 BOTH 3 I .00 16.67 18.18 16.67 9.09 16 .47 18.18 .00 13.18 16.67 27.27 16.67 .00 .00 9.09 16.67 .001 .001 I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 23 11 6 48 8 23 11 6 48 VERTICAL PERCENTAGF I ZFD*iS SILVIC FISH*W CFORES RANGER ZERO T-TI 1 2 3 4 5 FNG PFCRTA PFD+EC RESPLAI 6 7 8 91 Ol 20.00 .00 .00 IS.57 20.00 INSET II 60.00 50.00 75.00 50.00 20.00 VARACC 21 .00 33.33 12. 50 33. 33 40.00 16.67 50.00 .00 40.001 16.67 16.67 50.00 33.33 60.001 47.92 50.00 .00 33.33 .001 22.92 BOTH 31 23.00 16.67 12.50 .00 20.00 16.67 .00 33.33 .001 PEARSON'S CHI-SQUARE' ;rttPRrm = 13.55 0.63276 LIKELIHOOD RATIO CHI-SQUARE* C H I P T P = 3 51 17.08 0.38064 GUTTMAN'S L AM HD A= 0.10200 12.50 48 DF. 16 INVALID- 100.00 t < * 40.74*<1 o 'THIS IS AM A N A U S U W T-lfc I N I . - U l r d I t U BUMIUE MBLE OF 4£CUMCI ICC 34. vS BDItiEB <CC 35. FREQUENCY TABLE I ZERO T-TI YES 1 I NO 21 YES 1 M3I 21 351 II 45 3 361 4R H3RIZONTAL PERCENTAGt | YES N3I ZERO T-TI 0 1 21 "YETTT 15756 6-67 77.781 45 NO 2| .00 66.67 33.331 3 ZERO T-TI | 14.58 10.42 75.001 VER TICAL PERCENTAGE 0 48 I YFS 1 NJl 21 YES II 100.00 60.00 97.221 NO 21 .00 40.00 2.781 93.75 6 .25 O PEARSON'S CHI-SQUARE' CHIPROB = 361 48 4.32 LIKELIHOOD RATIO CHI-S0UA»F= 0.03554 GUTTMAN'S LAMBDA- 0.12500 CHIPROB 16.43 0.00009 0F = 1 INVALID- 7 5 . 0 0 * < 5 2 5 . 0 0 * < 1 .BMMltXE IMIE OF ICC 36. VS fiDTUEE ICC 35. FREQUENCY TABLE ZERO T-TI _VJS_ 1 NJ_L 21 YES II 6 NO 2 I 1 I 7_ 29| 71 361 39 9 48 HORIZONTAL PERCENTAGE | Y^S NOI ZERO T - t l 0 1 2l^_ YcS II 15.18 10.26_24.JJ6l_ N 0 ~ 2 r ~ T r . i l 11 .11 77 .781 . f -| 14.58 1 0. 42 75. 001 39 48 / THI5 \l W. ANALYSIS It THc INT"3I/1!:<J DATA VERTICAL PERCENTAGE I Y^ S MJI ZERO T-TI 0 1 21 * YES 11 85771 80700 90756l ~BTT25 " ND 21 14.29 20.00 19.441 18.75 I 7 5 361 48 PEARSON'S CHI -SQUARE* .JJ LIKELIHOOD PATIO CHI-SQUAR f» 7.3B DF_=_ CHIPROB* 0.57299 CHIPROB * 0.006 5 7 GUTTMAN'S LAMBOA* 0 . 00030 a m s i a i e MALE OF EBEEEJEUE I:C 37 > v s flpiuEB. ice 351 , FREQUENCY TABLE 1 YES M31 ZERO t-TI 0 1 21 Ol 0 1 71 B INSET 11 2 3 1B| 23 VARACC 21 3 0 81 11 BOTH 3 1 2 1 31 6 1 7 5 361 48 HORIZONTAL PCS CENI AGb I Y?S NOI ZERO T-TI 0 1 21 »-0| .00 12.50 87.501 8 INSET 11 8.70 13.04 78.261 23_ VARACC 21 2T.27 .00 72.731 11 BOTH 31 33.33 16.67 50.001 6 | 14.58 10.42 75.001 48 VERTICAL PERCENTAGE  j ~ YES N3I ZERO T-TI 0 1 21 0| .00 20.00 19.441 16.67 INSET 11 28.57 60.00 50.001 47.92 VARACC 2 1 42.36 -00 22.221 22^92. BOTH 31 23.57 20.00 8 .331 12 .50 | 7 5 361 48 PEARSON'S CHI-SQUARE* 1.82 LIKELIHOOD RATI3 CHI-SOUA°E* Z.65 OF* ; HI PRPB * 0.4052 5 CHI fi.?.? GUTTMAN'S LAMBDA* 0.00000 1 INVALID- 50.00 »<5 25.00«<1 U> 2 INVALID- 66.6T *<5 33.33*<1 0 132 V kr O O O I 1 Q j • o a •a p-LJ ft' CJ UL m a. O -4J LJ L J CO a. *n OJ -i LU ;H o I - H O 7 > < «a LX Z l -LJ] OJ LO La m l.D LJL1 LL* rr-- _J O -> I I I > LL, -J r > LJ <—| _ — U 31 m 2T 33 JJ? >4 OJ i X ^ >H JJ t-i </> i I i OJ _l <t CI SI a sn 114 u a) _< as xt SIM MUI SB W uJ <n C»M ui«a £ rn I © r\l[in ~t O f\J to to * IN m o t-o o - z r 2" LU O I N- N I ~» rt , I © m L l • . ! " - I u a> | O CO 00 I O « CD L l< r~ I o ut o 1 0 - 0 0 O o I O <0 CDfcO ~ i o r- ec O >0 in o « -. O CO O N O »_ I O IM I O rt ; z r : O LU ( Z i/> v 1 CD CM fM CC 1 CO 1 o * * c t 1 • • • 1 CM m IA r- II 1 rr* m f\ ! u. o 1 o o o o 1 1 o o o o >c 1 • • • • o rM 1 © o • O I + «o m CM p-. m I o m n m 1 (*' d I o m cn m 1 • • • • 1 rn m rn II n 1 m cn m LJ OL CC *a o 1 o o o o 1 •* >^ cxr 1 o o o o o a i • • • • l ir> m 1 x 1 r- fM o X i_> 1 O W N O t <o 1 o m wo o D 1 • • • • I m •£> o r-1 m »-* u\ •a or 1 o o o o 1 in o 1 o o o o D 1 • • • • j o o o X i * CM ^ _J 1 O rn O f* 1 -D tt i <-» m o J3 I • • • • —J i m o o i m m -H o « i O O O O 1 CO O O lA A • o • • • • fM CO —« 03 fM J5 - 1 O O O O O O O O o t-o o rt z O LU O Z Crt o LU or ac < o O cc 0 a. rt 1 T rt O I o I HI b l b AN ANALYSIS 'JF T He 1 Nl - < </ I t ^ r n r H T " T 4 O a l Y A a i a l E IfiBLS OF ilMQQLLtEE (CC 381 VS 5.EXEi4NIIiS <CC 2491 FREQUENCY TARLE I LOW H1SH >|}TA^SI ZERO T-T| 0 1 I 31 Ol 0 0 1 Ol 1 NONCON II 0 8 8 II 17 SfcMlCJ 21 1 12 4 OJ 17  CONNOT 3 1 0 7 6 Ol 13 I 1 27 19 II 48 HORIZONTAL PERCENTAGE | LOW HIGH NOTANSI  ZERO T-TI 0 1 2 31 0| .00 .00 100.00 .001 1 NONCON I I .00 47.06 47.06 5.881 17 SEMICO 21 5.88 70.59 23.53 .001 17 CONNOT 31 .00 53.85 46.15 .001 13 I 2.08 56.25 39.58 2.381 48 VERTICAL PERCENTAGE I LOW HIGH YDTASSI ZERO T-T| 0 1 2 31  0| .00 .00 5.26 .001 2.08 NONCON I I .00 29.63 42.11 100.301 35.42 SEMIC3 21 100.00 44.44 21.05 .301 35.42 CONNOT 31 .00 25.93 31.58 .001 27.08 1 I 27 19 f l 48 PEARSON'S CHI-SOUARI?- 4.13 LIKELIHOOD RATIO CHI-SOUAHE* 4.*8 ;HIPR08 = 0.38952 CHIPROB - 0.34496 GUTTMAN'S LAMBDA* 0.08332 LO DF ' 4 INVALID- 3 3 . 3 3 * < 5 3 3 . 3 3 K 1 THIS I S AN A N A L Y S I S rF T H C JrgTiRVI>IH D » T V amsLa iE U B L S O * aELustauttss. icc 2*°t v* 'toeiietai.EN.c.': ice F R F Q U E N C Y T A B L E T 5 , / 1 Y F S Nul ZERO T-TI 0 1 21 01 0 1 J l 1 LOW 1 1 1 22 41 27 HIGH 2 1 0 15 41 19 WANS 31 0 1 01 1 1 1 39 81 48 HORIZONTAL PERCENTAGE 1 YES NOI ZERO T-T| 0 1 2l 01 .00 100.00 .001 1 LOW 1 1 3 .TO 81.48 14.811 27 HIGH 21 .00 78.95 21.051 19 NOTANS 3 1 .00 100.00 .001 1 1 2.08 81.25 16.671 48 VERTICAL PERCENTAGE 1 YES NOI ZERO T-TI 0 1 21 01 .00 2 . 5 6 .031 2 .08 LOW 11 100.00 56.41 50.001 5 6 . 25 HIGH 21 .00 38.46 50% 001 39.58 NOTANS 31 .00 2.56 .331 2.08 1 1 39 Ul 48 PEAR SDN'S CHI-SQUARE" .46 LIKFLIHOOD RATIO CHI-SOUARE- .63 0F=> 2 INVALID- 6 6 . 6 7 * < 5 3 3 . 3 3 * < 1 CHIPROB = 0.79507 CHIPRPB = 0.73565 GUTTHAN* S LAMBDA* 0.00000 - : . . -. i r THIS I? Afi 4N4LVS IS nF THE INTERVIEW DATA •THIS IS AM ANALV!>I:> IT THfc IMIfc-tvltH iJATTr aiy.ft.aL6.iE t i a t e O F siaBQimt F R E Q U E N C Y T A B L F 38) VS E.SC1L «CC 215) I Z E R O T - T I LOW 1 H I G H NOTANSl 2 31 Ol NONCON I I SEMICO 2 1 1 I 121 _}J>_L 1 1 7 1 7 C O N N O T 3 I 91 1 3 10 321 48 H O R I Z O N T A L P E R C E N T A G E | L 3W Hjj5H_NJJ_*_lSj_ Z E R O T-T| 1 31 01 . 0 0 N O N C O N 11 5 . B 8 S E M I C O 21 2 3 . 5 3 C O N N O T 3 1 7 . 6 9 . 0 0 1 0 0 . 0 0 1 2 3 . 5 3 7 0 . 5 9 1 1 7 . 6 5 5 8 . 8 2 1 2 3 . 0 8 _ 6 9 5 2 3 l _ 1 1 7 1 7 1 3 I 1 2 . 5 0 2 0 . 8 3 66.671 V E R T I C A L P E R C E N T A G E | LOW H I G H N U T A N S I Z E R O T - T I 1 ? iL 48 Ol . 0 0 NONCON 11 1 6 . 6 7 SEMICO 2 I 5 6 . 6 7 C O N N O T 31 1 6 . 6 7 . 0 0 3 . 1 3 1 2 . 0 8 4 0 . 0 0 3 7 . 5 0 1 3 5 . 4 2 3 0 . 0 0 3 1 . 2 5 1 3 5 . 4 2 3 0 . C O 2 8 . 1 3 1 2 7 . 0 8 I 6 P E A R S O N ' S C H I - S Q U A R E * C H I P R O B = 1 0 32 1 4 6 2 . 8 1 L I K E L I H O O D R A T I O C H I - S Q U A R E = J . 5 9 3 2 1 C H I P R O B * G U T T M A N ' S L A M B D A * 0 . 0 6 5 1 9 2 > 7 L O F * 4 I N V A L I D - 6 6 . 6 7 * < 5 . 0 0 * < 1 3 . 6 1 0 4 P tHis is AN A N A L Y S I S T T H - INI - U U . . . c » ™ amuaiE taaLi O F aiao-aed ice J P I VS usaiL nc 21m F R E Q U E N C Y T A B L F . L J H H I G H N O T A N SI ZEFO T-TI I 2 31 31 1 0 Ol 1 NONCON 1 1 5 7 51 1 7 SEMICO 21 4 fl 51 17 CONNOT 3 1 1 6 61 13 1 11 21 l b l 48 H O R I Z O N T A L P E R C E N T A G E I Low H I G H N U T A N S I ZERO T-TI 1 """"2 " 3 1 0| 100.00 .00 .001 1 NONCON II 29.41 41.18 29.411 17 SEMICO 21 23 .53 47.06 29.411 17 CONNOT 31 7.69 46.15 46^15j 13_ I 22.92 43.75 33.331 48 VERTICAL PERCENTAGE | LOW HIGH NOTANS I ZERO T-TI 1 ? 3J _ 0| 9.09 .00 .001 2.O B NONCON II 45.45 33.33 31.251 35.42 SEMICO 21 36.36 38.10 31.251 35.42 CONNOT 31 9.39 28.57 37.501 27.08 — i n 21 i 6 i * 8 PEARSON'S CHI-SOUARE= 2.55 LIKELIHPOO PATIO CHI-SOL'APE = CHIPROB » 3.639bi CHIPROP. = GUTTMAN* S LAM80A= 0.03570 TT 2.81 D F = j.59275 4 INVALIO- 44.44 *<5 .00f<l ON o THIS I S AN A N A L Y S I S O F THI- iHTmu-H D O T * B l l t a a i a L E T a Q L E O F iXHBQLUEE ( C C 33) vs HiEEJSEEaifcMCE « C C *2) F R E Q U E N C Y T & B L E T 8 Z F R O T - T | YES 1 NOl 21 01 0 1 01 1 N O N C O N 1 1 1 13 J l 1 7 S E M I C O 21 0 1 6 11 1 7 C O W O T 31 0 9 * l 1 3 1 1 3 9 81 * 8 H O R I Z O N T A L P E R C E N T A G E | Y E S NOI Z E R O T - T I 1 21 01 . 0 0 1 0 0 . 0 0 . 0 0 1 1 N O N C O N I I 5 . 8 8 7 6 . * 7 1 7 . 6 5 1 1 7 S E M I C O 21 . 0 0 9 * . 1 2 5 . 8 8 1 1 7 C O N N O T 3 1 . 0 0 6 9 . 2 3 3 0 . 7 7 1 1 3 _ I 2 . 0 8 8 1 . 2 5 1 6 . 6 7 1 V E R T I C A L P E R C E N T A G E | Y E S NOI Z E R O T - T I 0 1 2JL * 8 0| . 0 0 2 . 5 6 . 0 0 1 2.08 N O N C O N I I 1 0 0 . 0 0 3 3 . 3 3 3 7 . 5 0 1 3 5 . * 2 S E M I C O 21 . 0 0 * 1 . 0 3 1 2 . 5 0 1 3 5 . * 2 C O N N O T 3 1 . 0 0 2 3 . 0 8 5 0 . 0 0 1 2 7 . 0 8 1 P E A R S O N ' S C H I - S Q U A R E * C H I PR OB = 3 9 BI * 8 3 . 2 1 L I K E L I H O O D R A T I O C H I - S O U A R E * 3 . A 1 0 . 1 9 9 1 0 C H I P R O R = 0 . 1 7 9 * 7 DF = 2 I N V A L I D - 50.00 t<5 . 0 0 K 1 G U T T M A N ' S L A M B O A * 0 . 0 8 1 0 6 o THIS IS AN ANALYSIS OF THE INTERVIEW PATA _ m _ H I _ U a i s OF ___!:a--_l-_C._ C C 42) V* JCB «CC 43) FREQUENCY TABLE "| ZCIliS SILVIC H S H » W C F O U S F ANC-FP rK'G PI-CP^A pro*7C Pi-sTi*) ZERO T-T I 1 . 3 4 5 6 7 8 9 1 01 0 0 0 0 0 0 0 O i l 1 YES II 5 4 6 ? 4 4 4. 3 4| 39 N3 2| 0 2 2 1 1 2 0 0 01 8_ 1 5 6 8 6 5 6 4 3 51 48 HORIZONTAL PERCENTAGE I ZFDRES SILVIC FISH+H .FIHES RANGER ENG RECRFA PED*FC "ESPLAl ZERO T-TI 1 2 3 4 5 6 7 8 9J 01 .00 .00 .00 .00 .00 .00 .00 .00 100.001 1 YES 11 12.82 10.26 15.38 12.82 10.26 10.26 10.26 7.69 10.261 39 N3 2 1 .00 25.00 25.00 12.50 12.50 25.00 .00 .00 .001 8 I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 48_ VERTICAL PERCENTAGE I ZFOR.S SILVIC FISH«-M CFO^ES RANGER E^G RECRFA PED*FC PFSPLAI ZERO f-TI 1 2 3 4 5 6 7 8 9| 01 .00 .00 .30 .30 .00 .00 .00 .00 20.0OJ 2.08 YES II 100.00 66.67 75.00 83.33 80.00 66.67 100.00 100.00 RO.OOt 81.25 NO 2 1 .00 33.33 25.00 16.67 20.00 33.33 .00 .00 .001 16.67 I 5 6 8 6 5 6 4 3 51 48 PEARSON'S CHI-SOUARF. 5.94 LIKELIHOOD RATIO CH1-50UARF= B.20 PF -CHIPROB = 0.65602 CHIPFOB = 0.41434 GUTTMAN'S LAM80A* 0.00030 Co CO INVALID- 94.44 »<5 27.78t<l TH IS Is AN ANALYSIS CP T H - i.r .^v i.n DATA au&ai-iE 14-1,- OF sx_a_i__-_ tec 3«i vs' ____c_.___A-i-_ ice 2391 FREQUENCY T A B L E "TO I ZERO T - T | VIRY 1 LOW 2 M JD 3 HIGHl 41 Ol NONCON II SEMICO 2 1 CONNOT 31 1 T I 1 11 H3RIZONTAL PERCENTAGE 0 2 _1J_ 5 ' II 1 51 21 1 17 17 18 Ol - — • -181 13 48 I ZEPO T-TI" VERY LOW MQ3 HIGH I 1 41 Ol NONCON 1 I SEMIC3 21 CONNOT 3 I .00 .00 .00 7.69 .00 .00 23.53 53.85 .00 100.301 11.76 88.241 64.71 11.761 38.46 .001 I 17 17 13 I 2.08 22.92 VFRTICAL PERCENTAGE 37.50 37.501 48 < 1 VERY LOW MOD HIGHl a « z ZERO T-TI 1 2 3 41 3 z 01 .00 .00 .00 5.561 2.08 0 NONCON 11 .00 .00 11. 11 83. 33 I 35.42 z SEMICO 21 .00 36.36 61.11 11. I l l 35.42 CONNOT 3 1 100.00 63.64 27.78 .301 27.08 1 1 11 18 18l 48 PEARSON'S CHI-S0UARE = CHIPROB = 38.23 0.00000 LIKELIHOOD RATIO CHI-S0UA»E« CHIPPOB = 43.40 0.00000 DF = 6 INVALID- 66.67 *<5 2 5 . 0 0 K 1 GUTTMAN'S LAMBDA» 0.54208 o f THIS I S AN ANALYSIS Of Tut INTEWIEW t)AT» —21 301 vs SEKLLCtifCIflllv: <CC 2*01 amaLaiE I B B L S O F S.Y.HB.QLUP.L (^ c FREQUENCY TABLE < >— 1 ZCRO T-TI L3W 2 MOO 3 HIGHl * l 01 NONCON 11 SEMI CO 2 1 0 1 0 0 12 2 1 1 * l 151 1 1 7 17 CONNOT 31 0 9 * l 13 1 1 23 2*1 *8 HORIZONTAL PERCFNTAGfc 1 LOW MOD HIGHl ZERO T-TI 2 3 t l 01 NONCON 11 SEMICO 21 CONNOT 31 .00 5.68 .00 .00 .00 70.59 11.76 69.23 100.301 23.531 88.2*1 30.771 1 17 17 1 3 1 2.08 *7.92 50.001 *8 VERTICAL PF.RCSNTAGF | LOW MOD ZERO T-TI 2 3 HIGH | *! 01 N O N C O N 1 1 SEMICO 21 CONNOT 31 .00 100.00 .00 .00 .00 52.17 8.70 39.13 * . 1 7 l 16.671 62.501 16.671 2 .08 35.*2 x 35 .*2 27.08 1 PEARSON* S GUTTMAN* S 1 23 2*1 CHI-SOUARE* 17.77 CHIPROB = J.031*9 LAMBDA* 0.***2* *8 LIKELIHOOD RATIO CHI-SQUARE* 1 9.*8 CHTPROB * 0.00072 OF* * INVALID- 33.33 *<5 33.33t<l — - - . -• — - • ~ — o o T H I S 15 AN A N A L Y S I S - P THb I N T E R V I E W DATA 38) VS. CjQM-UIfiii-- < f C 2 4 1 ) "7? H i - i a i a i E TABLE OF s.xaacuL_E_ < : c FREQUENCY TABLE I ZERO T-TI Ol NONCON 1 I SEM1C3 21 CONNOT 31 V ^ Y LOW MOO HI3HI 1 7 3 41 — _ — • 0 0 0 1 1 1 6 10 01 1 8 8 Ol 0 1 0 121 2 15 18 131 1 17 17 I HORIZONTAL PERCENTAGE 13 48 I ZERO T-TI V.RY LOW MOD HIGH! 4 T 01 NONCON l l SEMICO 21 CONNOT 31 .00 5.88 5.88 .00 .00 35.29 47.06 7.69 .00 130.301 58.82 .001 47.06 .001 .30 92.311 1 17 17 13 | 4.17 31.25 37.50 27.081 VERTICAL PERCENTAGE I VERY LOW MOO HISHl ZERO T-TI 1 2 3 4j_ 48 0| .00 .00 .00 NONCON I I 50.00 40.00 55.56 SEMIC3 21 50.00 53.33 44.44 CONNOT 31 .00 6.67 .00 7.691 2.08 .001 35.42 .001 35.42 92.311 27.08 I 2 15 18 PEARSON'S CHI-SQUARE* 43.01 CHIPROB = 0.030JO GUTTMAN'S LAMBDA* 0.44044 131 48 LIKELIHOOO RATIO CHI-SQUARE* C H I P R O B * 48.59 0. 00000 DF* 6 INVALID- 66.67 «<5 25.00SO o T H I S Iii AN A N A L Y S T S OF 1-1. 1 M - * V I•:<) T T r r FREQUENCY T A B L E 3 81 VS LIKE ICC 1 2 3 ) I Z E R O T - T I * ANY JNfcMAPl 1 2 1 01 N O N C O N 1 I S E M I C O 2 1 CONNOT 3 1 II l o l 81 1 9 8 1 2 7 1 H O R I Z O N T A L P F R C F N T A G E | M A N Y O U f c M A P I Z E R O T - T | 1 2 1 I 4 . 1 7 3 9 . 5 8 5 6 . 2 5 1 V E R T I C A L P E R C E N T A G E | M A N Y 3 N E M A P I Z E R O T - T I 0 1 2 1 1 1 7 1 7 1 3 4 8 0 | . 0 0 . 0 0 1 0 0 . O O l 1 N O N C O N II 1 1 . 7 6 2 9 . 4 1 5 8 . 8 2 1 1 7 S E M I C O 2 1 . 0 0 5 2 . 9 4 4 7 . 0 6 1 1 7 C O N N O T 3 1 . 0 0 3 8 . 4 6 6 _ U J 4 1 1 3 _ 48 0 | . 0 0 . 0 0 3 . 7 0 1 2 . 0 8 N O N C O N I I 1 0 0 . 0 0 2 6 . 3 2 3 7 . 0 4 1 3 5 . 4 2 S E M I C O 2 1 . 0 0 4 7 . 3 7 2 9 . 6 3 1 3 5 . 4 2 C O N N O T 3 1 . 0 0 2 6 . 3 2 2 9 . 6 3 1 2 7 . 0 8 I 2 P E A R S O N ' S C H I - S O U A R E * C H I P R O B * 1 9 2 7 1 4 8 1 . 3 6 L I K E L I H O O D P A T I O C H I - S O U A P * * 1 . 3 6 0 . 5 1 0 9 1 C H I P R D B = 0 . 5 1 0 5 ° G U T T M A N ' S L A M B D A * 0 . 0 6 3 8 1 THIS IS AN ANALYSIS OF T H r INfi^VIFW T A T A "7A BlV.ARlaT.£ IAB.LE OF MQUCa i^aLALlY.t (CC 239 1 V? J.QB. (Cc FREQUENCY TABLE 431 I ZFDR-S SILVIC FISHH. C F 1 H E S F AN&FF ENG RCCPTA P[D*rC RESPIA| ZERO T-TI 1 2 3 4 e 6 7 8 91 VERY 1 I 0 1 3 0 0 0 0 0 01 1 LOW 21 2 0 1 3 2 2 0 1 01 11 MOD 3 1 0 4 6 1 1 1 1 0 41 13 HIGH 41 3 1 1 2 2 3 3 2 11 18 1 5 6 8 6 f, 6 4 3 •=1 48 HORIZONTAL PERCENTAGE I ZFORES SILVTC FISH+W IFOIES P ANGER ZERO T-TI ENG REC'EA PED*EC F.FSPLAI ~ 9 T 8 VERY 1 I .00 J 00.00 LOW 2| 18.18 .CO MOD 31 .00 22.22 HIGH 41 16.67 5j,56_ .03 9.09 J3.33 3 .5b .30 27.27 5.56 JJL,_U_ .00 18. 18 5. 56 11.11 .00 18.18 5.56 16.67 .00 .00 5. 56 _16.6 7_ .00 9.09 . 00 JJU_U_ .001 .001 22.221 _5.56 .L I 10.42 12.50 16.57 12.50 10.42 12.50 8.33 6.25 10.421 VERTICAL PERCENTAGE I ZFORF.S SILVIC FISHtW CFORES PANGFP ZERO T-T I 1 2 3 4 5_ FNG RFCRTA PED»EC RESPLAl 6 7 8 VERY II .00 LOW 21 40.00 MOD 31 .00 HIGH 4| 60.00 16.67 .00 66.67 16.67 .00 12.50 75. 00 12.50 .on 50.00 16. 57 33.33 .00 40.00 20.00 40. 00 .00 33.33 16.67 50.00 . 00 .00 25.00 75. 00 .00 33.33 .00 66.67 1 11 18 J 8 . 48 .001 2.08 .001 22.92 83.001 37.50 20.001 37.50 I 5 6 8 PEARSON'S CHI-SQUARE* 31.75 CHIPROB * 0.13308 GUTTMAN'S LAMBDA* 0.24276 51 48 LIKELIHOOD RATIO CHI-SOUAPT* CHIPROB = 33.72 6.08957 DF« 24 INVALID- 100.00 *<5 30.56*<1 THIS IS AN ANALYSTS OF T H l IM'dJVIfcw DATA' "2^ TH15 15 AN ANAL V5T 5 HF T h _ I^r.H<VlEw DATA T 5 amai-lE'T-tt_= OF <cc zVb'lvs ___ <CC A ? I F R E Q U E N C Y T A B L E I ZFOR-S SILVIC FISH*H CFtMES R ANpef. ZERO T-TI 1 2 3 4 5 FNG RfCRSA RFD + EC T S P L A I 6 7 B 91 LOW 2 1 MOD 31 HIGH 4 I Ol 21 31 1 23 24 I 8 HORIZONTAL PERCENTAGE I ZF3R.S SILVIC FISH»W CF03ES RANGER ZERO T-TI 1 2 3 4 5_ 51 FNG RFCRTA PFD»FC FFSPLAl 6 7 8 9| 48 LOW 21 MOD 3 I HIGH 4| . OO 8.70 12.50 .00 .00 4.35 13.04 20.83 20.83 .00 100.00 13.04 8.70 12.50 8.33 .00 .00 17.39 17.39 8.33 .00 .00 8.70 4.17 .001 8.701 12.501 1 23 24 I 10.42 12.50 16.57 12.50 10.42 12.50 8.33 6.25 10.421 48 VERTICAL PERCENTAGE I ZFORES SILVIC FISHtW CFO*ES RANGER ZERO T-TI 1 2 3 4 5 ENG RECREA PF D* EC <»ESPLA| 6 7 8 9| LOW 2 I .00 .00 .00 .00 20.00 .00 .00 .00 .001 2. 08 MOD 3| HIGH 41 40.00 60.00 16.67 8 3.33 37.50 62.50 50.00 50.00 40.00 40.00 66.67 100.00 33.33 .00 66.67 33. 33 40.001 60.001 47.92 50.00 1 5 6 8 PEARSON'S CHI-SQUARE' 17.51 6 5 6 4 3 51 48 LIKELIHOOD RATIO CHI-SOUARF' 15.08 DF« 16 INVALID- 100.00 *<5 33.33*<1 :HtPROB = J.35293 GUTTMAN'S LAMBDA' 0.14058 CHTPPDB ' 0.51915 r THIS !<» AN ANALYSTS rr T H _ T^THVI.W TAT\ THIS 15 AN A N A L Y S T S ' CF T H . 1NT.1VT.W L ' A M O ai__at'_iE ialLE OF CJ__-_L-LUL: i c e 2<-n vs joji ( f x 4 ? i FREQUENCY TABLT ZERO T-TI VERY 1 I LOU 21 MOD 31  ZFOR.-S STLVIC FISH»W CFORCS RANGER 1 2 3 4 5 ENG =<rC'FA PFO*FC OTSPLAI 6 7 8 9| HIGH 4 1 II 21 21 "or 2 15 IB I 8 51 HORIZONTAL PERCENTAGE I 7F.1RFS SILVIC F1SHM CFJRES RANGER ENG RFC" E A PED+EC RESPLAl ZERO T-TI 1 2 3 4 5 < 7 8 9| VERY 1 I 50.00 LOW 21 .00 K 3D 3 1 11.11 HIGH 41 15.38 .00 13.33 5.56 2 3.08 .00 13.33 27.78 7.69 .00 20.00 16.67 .00 . 00 6.67 11. 11 15.38 .00 13.33 5.56 23.08 .00 6.67 11.11 7.69 .00 50.001 13.33 13.331 .00 11.111 7.69 .001 | 10.42 12.50 16.67 12.50 10.42 12.50 8. 33 6.25 10.42 1 VERTICAL PERCENTAGE I ZF3R3S SILVIC FISHtW CFORES P ANGER ZERO T-TI 1 2 3 4; 5_ ENG RECREA PED»EC PESPLAI 6 7 8 9J_ VERT II 20.00 LOW 2 I .00 MOD 31 40.00 HIGH 4| 40.00 .00 33.33 16.67 50.00 .00 25 .30 62.50 12.50 .30 50.00 50.00 .00 .00 20.00 40. 00 40.00 .00 33.33 16.67 50.00 .00 25.00 50.00 25.00 .00 66.67 .00 33.33 20.001 40.001 40.001 .001 51 PEARSON'S CHI-SQUARE' ;HIPROB * 22.42 0.55436 LIKELIHOOD RATIO CHI-S0UARE« CHIPROB « 26.1 0 0.34798 13 48 2 15 18 13 48 4.17 31.25 37.50 27.08 48 - P -(_n OF. 24 INVALID- 100.00 *<5 3 0 . 5 6 J O GUTTMAN'S LAMBDA* 0.14280 146 a l «• C _> a 0 a. U> rt 1 I rt o I >l : _ l _J _ l o o -o o o o IT. o I * rt r-o o o o o o n o I rt r^ t cn I CI 2) 21 X» <0 i i t — I H N m I I »- I > I o I _ o o a i u CC I > Ul I <- I I I I O I X I I I I o o •* <n| I if- r\i in or o T I • i I o r> o >o I I P h f * . I I i o r- o o i o •© © o ! I • I -o I m I I I CD l o I • I I IM I I K l rt «\j m •&] • I t- i v -x a I i or _ o o O I — CC I > X UL I IS X —< rNI —I CD tn m -o r- r- m CM m o m ro m FM O O O O o o o o e_ CD < c O cc o o_ in rt or b-ui I > i -I —« rsj m *J I >- _ O I I a i J I " I I > -I _> <_ o This Is A N A N A L Y S I S CF '<L arm IF* L » n Tit alV-AUAIE UttLd OF u a M E . I U i <CC 241. VS £aEEXEEE.I.EtiC.E ire 421 FREQUENCY TABL^: I ZERO T-TI Y C S 1 NOI VF»Y II LOW 2 I HOD 3 I H I G H tl 2 1? 9 01 21 _2J_ tl 2 15 18 13 - f — I 39 HORIZONTAL PERCENTAGE | Yrs 81 NOI 48 ZERO T-TI 0 1 21 VERY 11 .00 100.00 '00\ 2 LOW 21 6.67 80.00 13.331 15 POD 31 -00 8 8 ' 8 9 H ' l i l 1 8 HIGH 41 .00 69.23 ...30.21 j U-| 2.08 81.25 16.6/1 VERTICAL PERCENTAGE | YES NOI ZERO T-TI 9 I 21-48 VERY II .00 LOW 21 100.00 MOD 31 .00 HIGH 4| .00 5.13 30.77 41.03 23.08 .001 25.001 25.001 50.001 4. 17 31 .25 37.50 27.08 81 I 1 39 PEARSON'S CHI-SQUARE' 2.67 CHI PROB = 3.44755 48 LIKELIHOOO RATIO CHI-SOUARE= 2.80 CH1PR0B » 0.42594 DF' 3 INVALID- 62.50 X<5 1 2 . 5 0 K 1 GUTTMAN'S LAMBDA = 0. C5404 o THIS I S AM - N U v 5 f 5 OF T - l _ JMT.« IML . J DATA QEEt_Q£_I _4_ISBL£ ti 14. U_U _4-14Bl-_ ____! .IE.EtS.L_Il.Il-_ CEIICS!. _i__ai&__ I_UL- OF _ a i s i _ _ _ <cc 2.11 jca ice 431 FBFOU_NCV TAf>LR " " I Z F O ° . - S S I L V I C F I S H * W C F O R E 5 R A N G E R TNG P F C F F A P.n*rC F T S P L A I Z E R O T - T I 1 2 3 4 5 6 7 8 9 1 LOW 21 0 0 2 0 0 2 1 0 1 1 6 HOD 3 I 2 0 3 0 3 1 1 0 3 1 1 3 ' H I G H 4 1 3 6 3 6 2 3 2 3 "~TI 2 9 I 5 6 3 6 . 6 4 3 5 1 4 8 H O R I Z O N T A L P E R C E N T A G E I Z F Q R E S S I L V ' C F I S H » W . F 3 1 5 S R A N G E R E N G R F C R E A P E D + E C R E S P L A I Z E R O T - T I 1 2 3 4 5 6 7 8 9 1 LOW 2 I . 0 0 . 0 0 3 3 . 3 3 . 0 0 . 0 0 3 3 . 3 3 1 6 . 6 7 . 0 0 1 6 . 6 7 1 6 MOD 3 | 1 5 . 3 8 .00 2 3 . 0 8 . 0 0 2 3 . 0 8 7 . 6 9 7 . 6 9 . 0 0 2 3 . 0 8 1 1 3 H I G H 41 1 0 . 3 4 2 0 . 6 9 1 0 . 3 4 2 0 . 6 9 6 . 9 0 1 0 . 3 4 6 . 9 0 1 0 . 3 4 3 . 4 5 1 2 9 j 1 0 . 4 2 l ' - . l i o - I b T b l fT.To 1 0 . 4 2 . 2 7 5 0 8 7 3 3 6~.T5 1 0 . 4 2 1 4 B ~ V E R T I C A L P E R C E N T A G E I ZFOR ? s S I L V I C F I S H » W _ F O < E S R A N G E R E N G R E C ' E A P E D * E C R E S P L A I Z E R O T - T I 1 2 3 4 5 6 7 B 9 I LOW 2 I T O O 750 2 5 . 0 0 . 0 0 TOO 3 3 . 3 3 2 5 . 0 0 . 0 0 2 0 . 0 0 1 1 2 . 5 0 MOD 31 4 0 . 0 0 . 0 0 3 7 . 5 0 . 0 0 6 0 . 0 0 1 6 . 6 7 2 5 . 0 0 . 0 0 6 0 , 0 0 1 2 7 . 0 8 H I G H 4 1 6 0 . 0 0 1 0 0 . 0 0 3 7 . 5 0 1 0 0 . 0 0 4 0 . 0 0 5 0 . 0 0 5 0 . 0 0 1 0 0 . 0 0 2 0 . 0 0 1 6 0 . 4 2 I 5 6 8 6 5 6 4 3 5 | 4 8 " E A F S J V ' S _ H 1 - _ U U A < « : = Z 7 T J 1 L 1 K . I L I H U U U R A T I O L H I - . U L ' A R - = 2 7 . 4 1 TJF~-C H I P R O B = 0.1 3 2 2 9 C H T P R O B = 0 . 0 3 7 0 8 G U T T M A N ' S L A M B D A * 0 . 1 0 1 6 4 4> CO 1 6 I N V A L I D - 1 U U . U U Kb 3 3 . 3 . U 1 o THIS 15 AN AN 4L VS IS OF 'Hfc H I = < V I L W alV.AaiAI£ IfiBU O F SLQES I C C 2 3 2 ) V S JCB I C C * 3 » FREQUENCY TABLE T " l F O ' ^ " S S I L V I C F 1 3H+-W C F O R h S P A Nf- h P ZERO T - T I 1 ? * * o T N G RFiCR' • " P T f F C " RFSPL 'AT 6 7 A 9| LOW 21 0 0 0 0 1 1 1 POO 3 1 1 2 1 0 2 1 2 HIGH 4 I 4_ 4 7 6 2 4 Z_. 0 0 3 01 01 51 2 9 37 48 "TO HORIZONTAL PERCENTAGE I ZF33=S SILVIC F1SH»H CFORES RANGER ZERO T-TI I 2 3 t 5_ FNG RECREA PFO»EC RESPLAl 6 7 8 9 J LOW 21 .00 .00 .00 .00 50.00 50.00 .00 .00 .001 MOD 31 11 .1 1 22.22 11.11 .00 22.22 11.11 22.22 .00 .00 HIGH 4| 10.81 10.81 18.92 16.22 5.41 10.81 5.41 8.11 13.511 I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.1? 10._«2J_ VERTICAL PERCENTAGE I ZFORES SILVIC FISH*-* CFORES RANC-FR ZERO f - T I 1 2 3 4 5 ENG P.FCRFA PFD»FC RESPLAl 6 7 8 91 LOW 2 1 .00 .00 .00 .00 20.00 16.67 .00 .00 .00 .00[_ • obi I 5 6 PEARSON'S CHI -SQUARE-8 1 6 . 3 9 2 9 37 48 4.17 18.75 MOD 31 20 00 33.33 12.50 .00 40.00 16.67 50.00 HIGH 4| 80.00 66.67 87.50 100.00 40.00 66.67 50.00 100.00 100.001 77.08 • -- ^ ^ ^ ^ 5 | ^ LIKFLIHOOD RATIO CHI-SQUARE' 16.93 OF-C H I P R O B = 0.44672 GUTTMAN'S LAMBDA- 0.03920 CHIPFOB 0.39012 16 INVALIO- 96.30 t<5 5 1 . 8 5 X 1 r T H I S I S AM A N A L Y S T S O F T H h I N T S ^ V l t * O t T A r THtS IS AN ANALYSIS OF 'He INT:-XVIhW PA TA B i m i a l S I4.aLi 0 p EQE-S.LEI.UUi <CC 253 IV5 dOAICC 431 FREQUENCY TABLE I /FORr:S SILVIC FISHM CFORLS PANOFR ZERO T-TI 1 2 3 4 5 VERY 1 I LOW 21 HOD 3 I HIGH 41 2 3 E N G RFC.: A PED*FC I ^ S P L A T 6 7 8 91 *•-" 6 2 i 11 1 2 1 21 i 6 o !_!_ 4 0 1 H : I T 16 12 13 48 HORIZONTAL PERCENTAGE ZF 0 RES S t l VIC FISH»rf CFJ<E5 RANGER ENG RECREA PEO+FC F F SPLA| ZERO T-TI 1 2 3 * 8 91 VERY 11 .00 .00 i8.57 14.29 .00 .00 LOW 21 .00 6.25 31.25 12.50 12.50 6.25 HOD 31 25.00 16.67 8.33 16.67 16.67 8.33 HIGH 41 15.38 23 .08 .33 7.69 7.69 30.77, 28.57 12.50 . 00 .00 14.29 14.291 6.25 12.501 .00 8.331 7.69 7.691 7 16 12 13 48 | 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 VERTICAL PERCENTAGE „ „ „ . . , I ZFOR-S SILVIC FISHtW CFORES RANGER ENG REC° E A PEO«-EC RESPLAl ZERO T-TI 1 2 3 4 5 6 7 f VERY 11 .30 .00 25.00 16.57 .00 .00 50.00 LOW 21 .00 16.67 62.50 33.33 40.00 16.67 50.00 MOD 31 60.00 33.33 12.50 33. 33 40.00 16.67 .00 HIGH 4| 40.00 50.00 .03 16.67 20.00 66.67 .00 33.33 33.33 33.33 .00 20.001 40.001 20.001 20.001 14.58 33.33 25.00 27.08 1 5 6 8 PEARSON'S CHI-SQUARE- 27.62 CHIPRPB = 0.27600 LIKILIHOOD RATIO CHI-SOUARF-CHI PROB = 51 48 33.70 0.08997 DF* O 24 INVALID- 100.00 *<5 27.78X<1 GUTTMAN'S LAMBDA- 0.19432 151 I 0* •* ! I CD I CJ rt in " J , I co • m: i I I I I I »-< li-ed O 34 I I-Ci Z -U J > O _ l LU V C L o I j i 1 | i •-4 I I i V rn m • m cn tr> V •* o o • o o 1 c M •J < > *o 1 CD m CD r- | CO >* (\J m i ** | • • • l • | a* 4- | H | m m | J 1 a _ | 1 + __ ——• — + r | O m <* 1 (M o O O I I o tr> in i -* « J i o O O | CO f-i • * • i a a i B * • 1 r» cc 1 o _ | O *n 1 Q O | cr i <-H 1-> 1 4P4 UJ 1 * « | CO r-1 Of 1 CM o i m o 1 C I LT* o « | m O r- | m o t m O <c i CM LU 1 m O -O 1 1 * * • i • 1 • • 1 l m r- I O 1 m <o 1 M II 1 f° l UJ 1 m <£ 1 LU i a l er CO • < c; - t O 1 m < r- 1 o O O 1 o i ONU> i m U-: 1 o O O 1 o a i • • • i fit 1 • • i/) i in ~* i CD O I in m ' I X UJ l IM f - o 1 1 a i X 1 l_> © I o cr «*- o <_J 4) o O O I o r— to z o o O r> t • • • UJ • • I ir> *-. o o >-1 w+ W in M\ < CL m i o n CM o o o IP D i o IA un UJ o o o O • • • o • • • CJ | o - * © 7 o o X 4 * o #-i 1 a _ i 1 1 m o m o o m i m o CM UJ •e o m • • • •V • • i m CM O m CO I m •—« —i LL. O i f> m i m CM n> r- , c o o 1 ® LT\ -A I m t n « 1 1 IT m o 1 • fM I • • • 1 • X | • • I m CO I m _3> r-1 <£ (/> 1 <\ CM m JK t tr\ f\j 1 — M t •O N 1 LL. o CO • 1 LL r j M l O O • -t 1 C C U N 1 c o o 1 <£> ,—' I ON in I u - -a * -  c ) o o • • 1 K > 1 U II » to —< 1 ft J Z -J o o 1 LL O 1 __• «rtl 1 -_ LU m m I a tE I t LJ CO 1 < C 1 C_ CT I) r4 j O O 1 (T 1 *N J U J l/l H 1 c > o c I m c CL < 1 ON _ 1 4 r a uj 1 e 3 O O i f ON o 1 • • • 1 * a • • • i ' X CD 1 lf\ N-  c > -ID o o l > 1 f 4 < LL 4) •& I 3 <I | \ U I L. —J 1 l/> + + -- K — — - — — 4> — V > 1 1 rr »-UJ 1 1 r 1 s Z 1- 1 _t O I > >- | o i 1 c 1 O O (_> i i i 5 cn 1 v i z O 1 _ 1 _ H i O J 3L 1 CI t-or 1 X • i ac UJ X  < 1 u : j r— X* . Kj j i 1 Q o . o THtS t S AN ANALYSIS 0^ THc 1NT:RVTCW DA TA all£6il4I£ IAflLi OF YiG'ilStlUM ICC 255 ) VS JOE (CC FREQUENCY TABL: 431 I Z F C H H S S I L V I C F I S H t r t C F ' J ' . F S P ANGER ZERO T-T| 1 2 3 4 5 ENG "FCRFA PED*EC PCSPLAl 6 7 8 9| VERY 1 I LOW 2 | HOD 3 I 01 II 21 HIGH 4 T 37 I 5 6 8 6 5 HORIZONTAL PFRCFNTAGC I ZF33JS SILVIC FISH»W CFORES RANGER ZERO T-TI 48 1 FNG RECREA PFDtEC PFSPLAI ~ 9 T 8 VERY II .00 .00 .00 .30 .00 100.00 .30 .00 .001 1 L3W 21 .00 .00 .00 .30 .00 .00 .00 .00 100.001 1 HOD 31 22.22 .00 33.33 .00 .00 .00 11.11 11 .11 22.221 9 HIGH 4| 8.11 16.22 13.51 16.22 13. 51 13.51 8. 11 5.41 5.411 37_ I 10.42 12. 50 16. 67 12. 50 10.42 12.50 8.33 5.25 10.421 48 VERTICAL PERCENTAGE I ZFOR«S SILVIC FISH*W CFOIES RANGFP ZERO T-T| 1 2 3 4 5_ ENG RFCPFA PEO*FC PESPLAl 6 7 8 9 | VERY II .00 .00 .00 .00 .00 16.67 .00 .00 .001 2.08 LOW 21 .00 .00 .00 .00 .00 .00 .00 .00 20.001 2.08 MOO 31 40.00 .00 37.50 .30 .00 .00 25.00 33.33 40.001 18.75 HIGH 4| 60.00 100.00 62.50 100.00 100.00 83.33 75.00 66.67 40.001 77.08 IS3 PEARSON'S CHI-SQUARE-CHI PROB = 8 26.78 3.31*63 48 LIKELIHPOO RATIO CHI-SOUAPE- 23.29 CHIPROB = 0.50299 OF" 24 INVALID- 97.22 «<5 63.89t<l GUTTMAN'S LAMBDA- 0.0588O o BlSteaiatS taBLE OP auEElLiaL <c: 256 1 vs J2£ (CC 4?i FREQUENCY TABL c  F T F O R F S S I L V 7 < T F I S H * . < C F O U S RANGFF ENG P F f , : c A PFn»FC R F SF t A I ZERO T-T| 1 2 3 * 5 6 7 8 91 LOW 2 1 0 2 0 1 1 6 2 0 Ol 6 MOD 31 2 2 * 1 3 0 1 0 I I 1* HIGH 41 3 2 * * 1 6 1 3 4| 28 _ I 5 6 8 6 5 6 4 3 51 48 HORT ZONT 4L PERCENTAGE I ZFOR5S SILVIC FISH»W CFORES RANGER "NG REC*FA PED*CC RFSPLAl ZERO T-TI I 2 3 * 5 6 7 8 9j LOW 21 .00 33.33 . 0 0 16.57 16.67 .00 33.33 .00 .001 6 MOO 3| 1*.29 1*.29 28.57 7.1* 21.*3 .00 7.1* .00 7.1*| 1* HIGH *| 10.71 7.1* 14.29 1*.29 3.57 21.43 3.57 10.71 14.29| 28 I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 48_ VERTICAL PERCENTAGE I ZFOR = S SILVIC FISHtW CFORES P ANGEP ENG RFCPFA PECM-FC RESPLAl ZERO t - T | 1 2 3 4 5 6 7 8 91 LOW 21 .00 33.33 .00 16.67 20.00 .00 50.00 .00 .001 12.50 MOD 31 40.00 33.33 50.00 16.67 {0.00 .00 25.00 .00 20.001 29.17 HIGH 4| 60.00 33.33 50.00 66.67 20.00 100.00 25.00 100.00 80.001 58.33 1 5 6 9 6 5 6 4 3 51 48 PEARSON'S CHI-SQUARE- 22.35 L1KELIHCPD PATIO CHI-50UAPE- 25.40 DF-CHIPROB * 0.13173 CHIPPPR - 0.06290 GUTTMAN' S LAMBDA- 0.11662 16 INVALID- 100.00 «<5 33.33«<1 r T H I S I S AN iMALYlPTOF T H = I N T v i ' « l"STA~ o T H I S 1 5 AM A N 4 L Y S I 5 D P Tn c I S J T ^ V I N fATA " a t m i f t i f iaaL£ O F a ^ i i i J i «cc 257) vs jia ire 431 F R E Q U E N C Y TftRL"  ~ \ ZF3^1S SILVIC FISHfW CFD?r:S 5 ANC-CF FMG ' E T T A PEO+FC F CSF L A| ZERO T-T I I 2 3 4 5 6 7 P 91 VERY I I 1 0 0 1 1 1 0 0 II 5 LOW 2| 0 2 1 2 1 1 0 0 01 7 M JD 31 1 1 1 0 1 0 2 _ 1 01 7 _ HIGH t| 3 "3 6 3 2 A 1 2 41 29 I 5 6 8 6 5 6 4 3 5 1 48 HORIZONTAL PERCENT AGE I ZFQVIS SIL V TC FISH»W CFORFS PANOER f:N G RECRC A prD»*-C PFSPLAI ZERO T-TI 1 " 2 3 4 5 6 7 8 9| VERY II 20.00 .00 .00 20.00 20.00 20.00 .00 .00 20.001 5 LOW 21 .00 28.57 14.29 28.57 14.29 14.29 .00 .00 .001 7 MOD 31 14.29 14.29 14.29 .00 14.29 .00 28.57 14.29 .001 7 HIGH 4| 10.34 10.34 20.69 10. 34 6.90 13.79 6.90 6.90 13.791 V) I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 48 VERTICAL PERCENTAGE I ZFORES SILVIC FISH»W CFORES PANGFP ENG RFCREA PED»EC RESPLAl ZERO T-T| 1 2 3 _4 5 6 7 8 9J VERY II 20.00 .00 .00 15.67 20.00 16.67 .00 .00 20.001 10.42 LOW 21 .00 33.13 12.50 33.33 20.00 16.67 .00 .00 .001 14.58 MOD 31 20.00 16.67 1£.50 .00 20.00 .00 50.00 33.33 .001 14.58 HIGH 4| 60.00 50.00 75.00 50.00 40.00 66.67 50.00 66.67 80.001 60.42 j 5 6 8 6 5 1 4 3 5 i 4~1T PEARSON'S CHI-SQUARE- 17.o5 LIKFLIHCOO RATIO CHI-SOUAPE* 22.11 OF-CHIPPOB * J.82033 CH1PPPB - 0. 57331 GUTTMAN* S LAMBDA- 0.05082 -T 5 24 INVALID- 100.00 *<5 69.4**<1 155 z z on o rvj rtjrt is U|cc * ! - I u> <3 or t- _ O ISJ of o a •3* rt) COJ o I - A r t i ! V M pg CM • p-m V » O o • o o rH j o M < > ro ,0 -© m m 1 CO 1 o © CO CM 1 CO r\j 1 + 1 trv IP o •> i  • • • • 1 I 1 *M l\J P- P- | H | _ i ^  cJ_*fr 1 LL ! c 1- m a i CM 1 O O O O | IT <0 m o ro i ^ t O O O O | <o t • • Cv 1 • • • • 1 CO m ^ m P- 1 o LO 1 O O O O 1 t >C m L U | CM -i* CM CM | P- O » CC 1 I CM OJ O r- O o 1 ut O CO 1 O m O P- | ro d o O O 1 cu I o m o o i • • • • 1 • • i • • • • I CC I *0 a 1 m <© | II H rH j w 1 m <C I LL i a. i i LT CC C J p- O o 1 <2 r- 1 O O O O I «* X) a o o o o t cn LL- 1 O O O O I o CL rr 1 • • « • 1 rr. 1 co o I LO in i 1 X rH LJ i r\; p- i (_) I CC i j X LJ N O B O 1 o O •£> i P- o o m i 4) rf> o o l tn 3T i >o O o pn i o • • • i • Lw l • • • • i 4> m <X> 1 CM I <o o m i p-•-. ro | rt Ui m | < 1 I I or P- o a •f 1 «M I o o o O | iTl c *0 O <£> o 1 <4-LU I o o o o | o • • a • l • O l • • • • i CJ 4) p- m 1 o z | o O O | X rH 1 "H | IM N sO | | cd \ | | 1 | LJ p- rn co in 1 o lO •* 1 r- m m p- i •O «*> f*l m i in -JJ l «n ro CO sO I • • • rr i • » • • i -J •O rn 4T> * l <M O I v. m ro o i — i m -t Li. i w m («i H i l L J i i -o O O CO i r- 1 -A i n o o 3 i m p-o o m O 1 <o 4> 1 o o O O 1 in • • • t • X i • » • • i rsj Lfl o 1 o to i ur\ m i O J in CM 1 —* 1 r\l | 1 U- 1 » o O o N tr o 1 o s l_) CM 1 O P- O f\ | CM o o r- 1 IT\ 1 O O rn | • • • • > 1 • • • • | II II CO 1 <N< z -J I >c o m i LL' o "rH r\j 1 ^ LU I.* 1 M in m i rr CC j O LO j j C; | ad j i OL 11 i P- O Cr •4- 1 r\j LU CO »4 1 O O O O 1 in o CL «5 l * O J_) o 1 a U_! 1 O O O O 1 to O 1 • • - » 1 • Of t • • • • 1 1 X CO 1 -O r- 1 O o 1 O O O I r-t o X 1 rX 1 rH < L 1 CM PJ -O 1 X •a 1 o KJ 1 1 •J 1 r" 1 1 to to r-| tt »- 1 — i rsj m 1 •> •> I LU 1 1 i z 1 > X o X j > r- 1 >• » O X 1 o < 1 _ O c (_> 1 1 or o o o t LO X 1 LW -1 X M I o t 11 J t N 1 or r-1 > X oc 1 > X I « h-| LU LL =) •sj I a o VOVNV3 Ml lOVH ( TH15 15 A N A N A L Y S I S *Hz INT ZI* 1-* U ! i mv.fiP.latH t f l B L i 0" Aie^CL (LC 253) VS E.tLLy£5Jll&S (CC 2*91 FREQUENCY TABLE >— 1 LOW H13H VDTAMS1 ZERO T-T 1 0 1 2 31 VERY 11 1 3 2 01 ' 6 LOW 2 1 0 5 1 01 6 MOD 31 0 7 5 1 1 1 3 HIGH 4 1 0 12 11 Ol 23 1 1 27 19 1 1 48 — HORIZONTAL PERCENTAGE 1 LOW HI3H NOTANS1 ZERO T-TI 0 1 2 31 VERY 11 16.67 50.00 3 3 . 3 3 .301 6 LOW 2 I .00 33.33 16.67 .00 1 6 MOD 3 1 .00 53.85 38.46 7.691 13 HIGH 41 .00 52.17 47.83 .001 23 1 2.08 56.25 39. 58 2.081 48 VERTICAL PERCENTAGE 1 LOW HIGH NOTANS1 ZERO T-TI 0 1 2 31 VERY 1 I 100.00 11 .11 10.53 .30 1 12.50 LOW 21 .00 18.52 5.26 .001 12.50 MOD 31 .00 25.93 26.32 100.001 27.08 HIGH t | .00 4*.** 57.89 .001 47.92 1 1 27 19 11 48 PEARSON' S CHI-SQUARE- 4.62 LIKELIHOOD RATIO CHI-SQUARE- 4.72 OF- 6 INVALID- 66.67 « < 5 3 3 . 3 3*<1 CHIPPOB = 0.59476 CHIPPOB = 6.581 94 GUTTMAN' S LAMBDA- 0.02272 -- - -*. — — — I 157 u. o 2 3 K-1 u l oJ XI Jl b cc •a o => a. a o. w. l I o C J o T I CD m o CD I m m c © 1-4 UJ _l - 4 ,4- I'M I CD >o in cr !o I C *- l/> cH 3 4 I Mo I o *± .o i n ' t . o I ~* m I I m m • I CD -I o o I o o o X j»- I _* r\j m I >- X o x I a o c o I u j 1 -I > X I I T . I IM I • I CO I I i in in o o I ivi I M o m -< in M I I O O O O I O O O O o o . _ , . i IM m •* I I in J I -I > X < o 0 a. to 1 X •-i o X 3 o THT? li AN ANALYSIS OF THK. INI :*VI.-W vt.fi. " QEELtiQtSlI ltaiI.4tiL.ii 44 34E J U L V.4E.L4ELES EAEI11 ItlEUSfAILCi! LLLlLM t i m u i i E l a s t ; OF \.^v±n <cc 2ti v< utiiEEuDEii <cc 2011 " 7 ^ > — -FBEOUFNLY 1AULfc~ 1 prF T r<z ROTH FICL.H ZERO T-TI 1 2 31 01 SOIL S 11 1 10 1 11 31 31 2 24 V EGETA 21 3 1 31 4 TCFFAI 31 4 0 31 HYOFOL 4| 2 1 31 6 CLI MAT 5 1 7 0 01 7 TOPO 6 1 0 1 01 1 1 27 15 51 48 HORIZONTAL PERCENTAGE 1 OFF ICE BOTH FIELD! ZERO T-T1 1 2 31 01 50.00 50.00 .001 2 SOILS 11 41 .67 45.83 12.501 24 VEGETA 2| 75.00 25.00 .00 1 4 ~ T ERR AI 31 100.00 .00 .001 4 HYDROL 4| r i THAT f1 33.33 1 00. 00 16.67 . 00 50.001 . UO 1 6 7 TOPO 6 1 4 W • W W .00 100.00 .001 1 1 56.25 31.25 1 2. 501 48 -VERTICAL PERCENTAGE 1 n r c r r f ROTH F l i L D I ZERO T-T1 1 2 31 •-01 3. 70 6.67 .001 4.17 SOILS 11 37.04 73.33 50.001 50.00 VEGETA 2 1 T ro D A 1 3 1 11.11 14.81 6 .67 . 00 .331 . 00 1 B .33 8.33 HYOFOL 4| 7.41 6.67 50.331 12. 50 CLIMAT 51 25.93 .00 .031 14.58 T0P3 61 .00 6.67 . J u l 2.OB • 1 27 15 al '6 rv.—. l i m | . n BIT—rTB—T/t TB—QQT/ 1 PfcAUSJN'S CHl-S'JUAm- = " 21.59 LIK FX. I HP no 0JTT0 CHI-iOUAWE* 21.63 UF« 10 !NV°LIU— BB.O^ %*.r» J D i O f t M CHIOROB = 3.31739 CH1P0OB = 0.00874 GUTTMAN * S LAMBDA = 0.C7140 00 159 V 5 D — ui !t-t CO 1/1 • 'l/l L O • <a o Ljt CM J - l -I 1-1 e t a iire/i M J LL| Li. J O ca M _ l of) a* :>l>-t a - i S)5i a i ^ an < C | _J 1 a i 1/7 1 U 1 a l fM O < O CO | LL 1 • 1 a i u. i O. 1 o © < r- 1 U. 1 It 1 O 1 LL . 1 a : l (M O Z I ti ; i < i o o i i a. m o < IX Lrt U.' rV Li-Li — CM 1 1 o -< L. m — I CM o| 1- I " >M| I I I- I X „ • I J * CL I UJ I INI I I I I I I < cr i I to o o o o o o o o o o I eo CM I I fM I «r i i «n —« I m • I * r- i co I I : I o o ol«*i I o - 1* I m • I • , ~ I IM IM I ~ i e o o i T Q/ I ' J U I — > I T UJ I i cr o o I o o o i o o o I o o o r» o o M f l O O I r- O o!r-| N O OIlTi I • • • • I r- o m m j fM IM fM) I CO o o I - o o l I CM I I I O I et I IXI I M I I _ i fM cn] • l o l l O O O I m I CM I I m I >!* I O > 1 I fY'»M m cn I cr •* c*i I • • • • I tv o co cc I CM i n I | o o o o I o o c o o : o o o o © © o r- m o o m o o < o O o-V> *— I X o o I »- > a i •U I > I uj I I j o i n | O O ' J u i o i _i st = : o THI5 IS AN ANALYSIS CiF Trie IKiT-^vlHw DATA Bl«4B.lat£ IaflLC O F CSSCUULUl (CC 2 04 1 vs DCUS.IQN. ICC 24) FREQUENCY TABLE ? 1 L rSDAY WEhKLY MONTHY LESMOMl ZERO T-TI 0 2 3 4 •5 1 LOW 1 I 6 10 0 1 01 11 MOD 21 0 : 2 1 01 5 HIGH 3| 0 1 3 0 01 4 NtV'R 4| 1 13 9 2 31 28 1 1 2b 14 4 31 48 HORIZONTAL PFRCENTASc 1 LESDAY WEEKLY iDNTHY LESMONl ZERO T-T| 0 2 3 4 51 LOW 1 1 .00 90.91 .00 9.09 .001 11 MOD 2 1 .00 40.00 40.30 20.00 .001 5 HIGH 31 .00 25.00 75.00 .00 .001 4 NEVER 4| 3 .57 46.43 32.14 7.14 10.711 28 1 2.38 54.17 29.17 8.33 6.251 48 VERTICAL PERCENTAGE - - - — 1 LESDAY WEEKLY MONTHY LESMPNI ZERO T-TI 0 2 3 4 51 LOW 1 I .00 38.46 .00 25.00 .001 22.92 MOD 21 .00 7.69 14.29 25.00 .001 10.42 HIGH 31 .00 3.B5 21.43 .00 .001 8.3 3 NEVER 41 100.00 50.00 64.29 50.00 100.001 58. 33 1 1 26 14 4 3 1 48 PEARSON'S CHI-SQUARE* 13.34 LIKELIHOOD RATIO CHI-SQUARE* 16.80 DF * 9 INVALID- 81.25 X<5 3T.5 0 f < l :HIPRQB = 3.14711 CHIP'DP * 0.05174 GUTTMAN'S LAMBDA = 0.04878 --. — — — ' o fHIS ti AM A N U Y S ' S Cfr ^ 1NT.:*VICW PAT4 aiwatfiis taaU OF CSSCULILU.: ICC 204 I VS BDBLBUI <cc 1201 FRHOU'NCY TABLF ZERO T-TI LOW 1 j MOD 21 HIGH 3| NOTIMK 0 1 0 0 0 NEVER 41 10 12 13 HORIZONTAL PERCENTAGE I NOT IMF 1J31E 17 3K 6 21 WELL DONTNOl ZERO T-TI _5T LOW 1 I MOD 21 HIGH 31 NEVER 4| .00 9.09 9.09 T2.T3 9.09 .001 .00 20.00 20.00 60.00 .00 .001 .00 .00 .00 .00 100.00 .001 3.57 35.71 2B.57 21.43 3.57 7.141 11 4 2B I 2.08 25.00 20.83 35.42 12.50 4.171 48 VERTICAL PERCENTAGE | NOTI ME NOON; ZERO T-T| 0 1 4 OK 3 WELL DONTNOl 4 51 LOW II .00 8.33 10.00 47.06 16.67 .001 MOD 21 .00 8.33 10.00 17.65 .00 .001 HIGH 3| .00 .00 .00 .00 66.67 .001 NEV=R 41 100.00 83.33 80.00 35.29 16.67 100.001 22.92 10.42 8.33 58.33 10 I 1 12 PEARSON'S CHI-SQUARE* 41.72 ;nl"RnB * 3.33334 17 21 48 OK WILL DONTNOl 2 3 4 51 _ _ _ _ • 1 B 1 O l 11 i 3 0 Ol 5 fl n 4 Ol 4 8 6 1 21 28 LIKELIHOOD RATIO CHI-SQUARE* CHIPRDR = 32.26 0.00132 DF. 12 INVALID- 85.00 *<5 30.00t<l GUTTMAN'S LAMBOA* 0.26000 162 ( i n : Y K O O • o m o 0 4 crl O —' o f- ui © o o o o o O o o *M C  o r»| o 00 o o O CD O O o ; 2 * 1 1 X Z O o z iii' v _j n z 0 -1 z • o UJ »_ 1 cobm N O h I © o co •» u-i ~ J m o CM ^ C\i CSJ Cl » O O O O O » o o rs o o CL C  < o 3 o 0 0. l/l 1 X rt u X 0 CJ I P - © rr\ 1 o ^ o o o o o » rt cvj m ^  iri at UJ scr _j 0 r z o._i z rt o Ui t— t- o xz o z o z o o VOVNV3 Nt |g*N 163 .2) V IU -33 t/j LU or o u. o to —• C X o o o m o o o tr m o r- ro o m f» o in «n O m o O m o ;o CM o| iO <*% Oj O *n m O Lf\ O m O • • • fNJ «H CD -I fNJ O ^ f-i r N >o • • • rvi Ln « v o o t/l LU •X u l C D u.! Z _J Ui rt LJ LO rt < L. 4 0> IA 00 rt m rvj rt O O O O O O O O O O Oi B O O M m o o <c m i ^ r - N o o > o o o ! > o o o 1 I o o o i r\j * ru f*J m X O I LU _i X rt OL I > -' UJ I »l I «0 CO LU Or* CO < O Z> x 0 a. tA rt 1 X rt I 1 O vo*NV3 N I lavn THIS is AN ANALYSIS THC IHCRVIEW DAT* Bl 'mia i i i I4BUd OF (CC 5131 v s jca ICC A3 I FPfcQUFNCY TABLF X R ZPOR.TS S I L V I C F i SHf W CPCHZS R ANOF'P cNG o r e 0 , F A P r n t |.c F«SPIA 1 ZfcRO T-T 1 1 2 3 4 c 6 7 8 91 LOW 21 1 2 0 2 2 2 1 1 Ol 11 MOO 3 1 7 t 2 2 2 1 1 1 1 1 13 HIGH 41 2 3 6 2 1 3 2 1 41 24 1 5 6 8 6 r 6 4 3 51 48 HORIZONTAL PERCENTAGE 1 ZFORES SILVIC F1SH»W :FD<ES RANGER ENG PECRE A PEO*EC RFSPLAI ZERO T-TI 1 2 3 4 5 6 7 8 91 LOW 2 1 9 .09 18.18 .00 18.18 19.IP IB. 18 9. 09 9.09 .001 11 MOO 3 1 15.38 7.69 15.38 15.38 15.38 7.69 7.69 7.69 7.69| 13 HIGH 4| 8.33 12.50 25.00 8. 3 3 4.17 12.50 8.33 4.17 16.671 24 1 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 48 VERTICAL PERCENTAGE 1 ZFORiS SILVIC F1SH»W CFORES RANGFR ENG RECRFA PED»EC PESPLAl ZERO T-TI / 1 2 3 4 5 6 7 8 91 LOW 2| 20.00 33.33 .30 33.33 40.00 33.33 25.00 33.33 .001 22.92 MOD 3| 40.00 16.67 25.00 33.33 40. 00 16.67 25.00 33.33 20.001 27.08 HIGH 41 40.00 50.00 75.JO 33.33 20.00 50.00 50.00 33.33 80.001 50.00 1 5 6 8 6 5 6 4 3 51 48 PEARSON'S CHI -SQUARE* 9.37 LIKELIHOOD PATIO CHI-SQUARE* 12.18 DF = 16 INVALID- 100.00 t<5 M . 1 1 K 1 CHIPROB = 0.89743 CHIPROB * 0. 73226 GUTTMAN'S LAMBDA* 0.04686 ON 165 o o o o o o o lltv I <«• I I I I I I + — I | I M I IT . 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I CL 1 t • • • I • CL i « • • • i m »* to I V, 1 O lT\ —« 1 o to t o o O | • <o LL UJ 1 m rH rH 1 rH Lw | fNJ ^ * | P- CO u. 1 OL 1 j a. • | PJ <M LJ CO 1 -« -< o ft O CO 1 O LT* O CO i L J CO | ft m O ft 1 en d «U 1 Ul 1 o o C rD 1 fNJ UJ I f t f> O ft j 1 • • • • • 1 • *> 1 • • * • 1 O 1 . a o P- m 1 <o a 1 ft m ft j H It LL' ILt in I 1 m i n f t | LL' a 1 O- 1 1 a. 1 | a cc 1 1 « I O O O 1 O CO 1 ft < p- 1 O O O O 1 D a u 1 LLt 1 O O in CD 1 ft LL 1 O O O O 1 O CL 0*. t or 1 • • • • or I • • • • 1 to ft LJ 1 LJ ir- PU m I co U I m o ir. i 1 X L'.> 1 LL' • rH 1 U J 1 r\j m r j | V ° a 1 CL 1 a. 1 i X 1 LJ o <c j : D m >o O o 0 s m <o 1 o t O P- O m | *•> z i : O * f- r» i m z j o O O f l t o LL 1 1 LL, • • • • LU t • • • • 1 I : i rsi | O f i 1 1 ! 1 1 H i n n i | 1 1 1 1 a LJ •LJ 1 i ; a - » - H ft tt\ CC If. o o" m m 1 fNJ cc m I O O O O | m o L'. i UJ 1 •* L L I O O O O | o i • • • • O 1 • • • • 1 u •3 ffl z i z 1 o z 1 O O O | X r- LJ} i 4 rH 1 rH | fNJ fM <£• | H c IT i CC j a. 1 1 —J D i Lv. to V -4- t D lM rt m «o to O c o in m 1 o to «*• 1 O M K O I *r XT X > LU i LU O ft fNJ 4) l ut L J i n ft j j o i f« JJ rr i • • • • or t • • • • 1 - J — CD i a m -o r- 1 fNJ O I m UJ O i > m LL i rH * H 1 ~H LL i m ~ H m i V O i I_> LJ JJ .m i -H — i o m m O CD V o co m o 1 * - T ro 1 O O O O | CO m sO o + i •*• O O N O I -rt 4V 1 n m m o i t p-—» _> T i X • • • • I • X i • t • • i O m •-/> i 1/1 m - H ; i «n LO 1 P* PNI 1 P J m JJ M i LU - H CM ft 1 *H H H I m sfi i • eg X LL i •J> LL | LL l i -"5 «-H F- ran < P-UJ O f\J i O rH <f O fj* in f i 1 o O U fNJ 1 O *^ P- 1 f> M t— i Z *~ O tsi \f\ 1 m *s * - 1 o <o o o 1 rH 29 > i UJ > • • • • I • r- > 1 • * • • j n ti • i-4 U _J i O _J *»• so m 1 r j Z -1 | tO •£> 1 LU o •> _ l t— i or fNJ 1 rH UV | -H -H O | or co N- LL CC to i UJ to | o to < n 1/1 O «c i a * j Iff D or It r- CO -H i O f t ft M 1 If* to o o> in 0 0 1 fNJ ED to - 1 O O O O 1 in o a <J _J i i L" i _j '» O «o f*- co 1 «• C LU 1 O O O O 1 CO f/ n *T _ i V rr i *T Cf • • • • I • rv t • • • • t I I 3T Z rrf O O i r- O IP* co m 1 o - J O 1 O O O 1 o <fl z UL i Z LL rH I rn < I LL 1 I M «o rsi i X < ;M L - •Si t O *KJ O o _ J X> i | •*4 1 1 1*0 to tf UJ o + r- — — Lb t— i »t a t— H N f O * Cf r - l f i rsi rn 4- I m r OT 1 i o 1 | Ul | 1 1 T z f> :f« LL tw i >• j o X X t— >• _f O X j > I- 1 > X O I 1 a < ed i a C o K U D O | I a o o o l to LO <d O i LU -J 4. rn O tu _ J X *H | o 1 U J —1 I 1 or P-i-H »1 OL. i > X CC ' > x j or 1 > X I < *-X - J LLi i L U | UJ 1 1 UJ 3 r- :CO) i 1 1 CL i j J 1 o 167 M U. O oJ I no cn <£i i M (*1 I O O O • O O O C O O O O C O I r- com ty t rt o cn 4 i • • • • I <r r- rttti | ro cn cn 1 I O O O O O O O O c o I 1X rt I ro ff cc; t-i UJ <n u. Li-ed «a JX t— i rt ro m . I I Q I UJ -< stii o <o ir. ct> O » l - rt O o lf"i o o o no o I IT. I I I CD I O I • I ro I I I D X • x o X D O O , J l -X 03 03 03 CM m *n j 4 «<c m I • • • • I r» cr cr m I co CM cn I o o o o ( O O O O I I I * I rt CM m * I I I I > 1 C I I I or o O O I LU -J X rt I I > X I I I I p-1 m l/\ m • Lf\ m • o not II (| • LU o or UJ «i a or II O a •a */* •~* r* 1 X en o X X <o (_> — J CO *n : T z o < *-< 1-LU Z) a. i o • o ( ( 168 2 I a !<-4 - H ul _ ~ <M , < -I-- I ca a ) 3 4 I (-o j X I I O I " X I o X O O rt o i O O en r-[ I O O co r I O O rt co] I . . . I or I rt I I |>- I rt fsj m ^ I > 1 O I I cc O o u> I li* — i 2. rt 1 > x UJ I CQ d it 3 I O O CO O I I <o >o in cv I I tn ir\ rt r- t (\j rvj £ I I I I I I I © © -0 I i © o tn ( •t in I in I I rt IM m i l l I y I o I l I a o c~ J i I LW «J X •— | I > X I I I o o V O V M V J HI l a v i i -THIS IS AM ANALYSIS 'He l U l K V l t w ua'A o a i " ! ? ! i t i t ! " " aawtue* «cc 321. vs jua ccr 43. ZERO T-TI VERY 1 I LOW 2 1 :UCY TABLE ZFUR-S SILVIC F1SH*« CFORES PANOFP 2 3 * 5 1 "NG RFCHEA PFD+FC PCSPLAt 6 7 8 9| Ol 1 I MUD 3 1 3 1 2 HIGH 4| 2 3 5 41 Ol 51 19 21 48 HORIZONTAL PERCENTAGE ZERO T-TI VERY 1 I LOW 21 MOD 3 I ZFOR-S M L V I C FISH*W CFORfcS P.ANGtP " l 2 . 3 * 5 .00 .00 100.00 .00 28.57 .00 15.79 5.26 10.53 .00 28.57 10.53 .00 14.29 10.53 ENG R F C E A PFDtFC PESPLAI 6 7 8 91 — — — f -.00 .001 .00 14.291 .00 21_.0_5L .00 14.29 21.05 .00 .00 5. 26 1 7 19 HIGH 4| I 10.42 12.50 16.67 12.50 10.42 12.50 8.33 6.25 .001 10.42 I 21 48 VERTICAL PERCENTAGE I ZFCJR^ S S I L V J C J L L S * r S PANGFP ZERO T-T | I 2 3 * 5 ENG RECBEA PED*EC P F S P L j l 6 7 8 9 F VF RY 11 LOW 2 1 .00 .00 .00 33.33 12.50 .03 25.00 .00 33.33 33.33 .00 .00 20.00 16.67 40.00 66.67 , 00 .00 25.00 .00 .001 .00 20.001 .00 80.001 2.08 14. 58 39.58 » - - - - 3 5, 48 PEARSON'S CHI-SQIJA3F* 3HIPR0B = 24.55 LIKCLIHrOCi RATIO CHI-SQUARE* 27.72 CHIPPOB = 0.27187 DP = 0. 43046 24 tNVALTD- 100.00 «<5 4 7 . 2 2 K 1 GUTTMAN'S LAMBDA* D7TTTOT -*~1 o THIS IS AN ANALYSIS CF T H - : I N T . . 3VTHW P A T / y > 5 1 BAY.AU. A I L laaui OF Qcj;wae (cc 322. vs JDB (CC '31 FREQUENCY TABLE < >— 1 zFar-:s SILVIC FiSH«-W CFO-*;;S KANCFF !;NG ptc+cc FESPLA | ZERO T-TI 1 •j 3 4 5 6 7 8 °l LOW 2 I 1 1 7 0 2 0 0 2 Ol 8 KOO 3 1 0 0 3 2 0 0 ? 1 3 1 11 HIGH 4 I 4 5 3 4 3 6 z 0 21 29 1 5 6 9 6 «5 6 4 3 51 48 HORIZONTAL PERCENTAC-c PESPLAl 1 ZFORES SILVIC FISH*W CFORES PANOrR ENG RPCRCA PED*FC ZERO T-T | 1 •> 3 4 5 6 7 8 91 LOW 21 12. 50 12.50 25.00 .00 25.00 .00 .00 25.00 .001 8 MOD 3 1 .00 .00 27.27 18.18 .00 .00 18.1 8 9.09 27.271 11 HIGH Al 13.79 17.24 10. 34 13.79 10.34 20.69 6.90 .00 6.901 29 1 1 n.47 12.50 16.67 12.50 10.42 12.50 8.33 6.25 10.421 48 VERTICAL "ERCF.NTAGfc 1 ZFORES SILVIC FISH** CFORES RANGFR ENG RECREA PED+FC RESPLAl ZERO T-TI 1 2 3 4 5 6 7 8 91 LOW 21 20 .00 16.67 25 .00 .00 40.00 .00 .00 66.67 .001 16.67 POO 31 .00 .00 37.50 33.33 .00 .00 50.00 33.33 60.001 22.92 HIGH 41 BO.00 83. 33 37.50 66.67 60. 00 100.00 50. 00 .00 40.001 60.42 1 5 6 a 6 5 fc 4 3 51 48 PEAFSON* S CHI-SOU ARE' 25.85 LIKELIHOOD RATIO CHI-SOUARE= 32.1 2 DF* 16 INVALID- 100.00 X<5 2 5 . 9 3 * < 1 ;HIPROB = 3.055 01 CHIOOOB = 0.00973 GUTTHAN•S LAMBDA = 0.10164 - - - — m u t g i i r r r t n i ,- OF 1 T K ^ ICC 1231 VS JOB ICC 43 1 FREQUENCY TABLE 1 ZFOR.:S SILVIC FISHt-W CFORES FANPEF ENG RFCRFA PCD»EC PCSPLAl ZEPO T - T l 1 2 3 4 c 6 7 '8 9| Ol 1 0 0 0 0 0 1 0 01 2 MANY 11 1 7 4 2 2 1 2 3 21 19 ONr'MAP 2| 3 4 4 4 3 fi 1 0 31 27 1 5 6 3 6 «; 6 L. 3 51 48 -> * — — o I s THIS IS AN ANUYj'.S I"- I Hi: i ^ i ^ V l r U LI1H HORIZONTAL P^ RCFNT AG* I z^ o^ -s *ILVTC FISH*K CFP*ES PAMHI-P VNG P.'-CD-:A Pf=n*rc FF.SPLAI ZE*0 T-TI 1 " 2 3 4 5 t T R P| > — 01 50.1/U .00 .00 .00 .00 .00 50.00 .00 .001 2 MANY I 1 5.26 10.53 21 .05 10.53 10.52 5. 26 10. 53 15.79 10.531 19 ONE/MAP 2 1 11.11 14.11 14.31 14.81 11.11 18.52 3.70 .00 11.111 27 — 1 10.42 1 2.50 16.67 12.50 10.42 1 2.50 8.33 6.25 10.421 48 Vc'TTCAL "cRCTNTSG1: 1 ZFOR<ES SILVIC FISH»H CFO?ES P ANGER ENG RECREA PFD*EC FESPLA | ZERO T-TI 1 2 3 4 5 6 7 8 31 20.00 .00 .00 .00 .00 .00 25.00 .00 .001 4.17 MANY t 1 20.00 33.33 50.30 33.33 40.00 16.67 50.00 100.00 40.001 39.58 ONEMAP 21 60.00 66.67 50.00 66. 67 60.00 83.33 25.00 .00 60.001 56.25 1 5 6 8 ft 5 6 4 3 51 48 . - -PEARSON'S CHI-SQIJARE= 7.57 LIKELIHOOD RATIO CHI-SOUARE = 8.82 DF= 8 INVALID- 100.00 *<5 •00*<1 CHIPROB = 3.47709 CHIPROB = 0.35757 GUTTNAN * S LAMBOA* 0.08770 QEEE.tiQE.rJI Y.AB.J.ABL.E t a 8AE eiiES.SjltALIQN E4P.I -UrUiGEttOdlAe BASUAiW IQEQfiBAEUI BlVArUAIE TABLE OF LEGENUSIZE ( CC 1251 VS JOE ICC 431 FREQUENCY TABLE 1 ZFORSS SILVIC FISH*W CFORES RANGER ENG RECREA PEO*EC PESPLAl ZERO T-TI 1 2 3 4 5 6 7 8 9| o| 0 1 1 0 0 0 1 0 01 3 LARGE 11 3 2 4 1 2 3 2 0 1 1 18 SMALL 21 2 3 3 5 3 3 1 3 4| 27 1 5 6 8 6 5 6 4 3 51 48 HORIZONTAL Pf;RCEM'r»Ge; 1 ZFORES SILVIC FISH** CFORES PANGFP FN G R F C T A PEDfFC FESPLA 1 ZERO T-TI 1 2 3 4 5 6 7 8 91 . — — —— —— * — — — 01 .00 33.33 33.33 .00 .00 .30 33.3? .00 .001 3 LARGF 11 16.67 11.11 22.22 5.56 11.11 16.67 11.11 .00 5.561 18 SMALL 21 7.41 11.11 11.11 18.52 11.11 11.11 3.70 11.11 14. 81 1 27 1 10.42 12.50 16.67 12.50 10.42 1 2.50 8.33 6.25 10.42 1 48 I — — — — I THIS IS AM ANALYSIS Ul- H u i .Jl „i <l I ctl U i 1 4 VE'TICAL oiRC'NTar." I ZFOR-S SILVIC FISH4-W CFOfVS FANC-F? fMG RFCR^A o r p t F C FFSPLAl ZERO T-T| 1 2 3 4 5 6 7 8 91 ^ " b l .oo f i 7 6 7 lYTso Too Too To o~ Yr.oo Too Too i isTTs LARGE II 60.00 3 3. 33 50.00 16.67 40.00 50.00 50.00 .00 20.001 37.50 SMALL 21 40.00 50.00 3/.50 R3.33 60. 00 50.00 25. 00 100.00 80.001 56.25 I 5 6 8 6 5 6 4 3 5 1 48 PERSON'S CHI-SOUAR^ 77TT2 L 1KI L IHCOD PATIO CMI-SOI!AP.r» BT77 BTNVALIO- 100.HO *<5 .00T<1 CHIPRnf) * 0.53507 CHIPRnR = 0.40740 GUTTMAN'S LAM BDA= 0.08925 aiV.fllU.aIE I a B l i OF LEGLt/QSiZE (CC 12 51 vs DECISION < CC 2') FREQUENCY TABLE I L uSOAY WEEKLY MONTHY LESMONl ZERO T - f I 0 ? 3 4 51 01 0 1 1 1 01 3 LARGE 1| 0 11 4 1 21 18 SMALL 2 1 1 14 9 2 11 27 I 1 26 14 4 31 48 HORIZONTAL P cPfFNTAGE I LcSDAY WEEKLY MONTHY LESMOM ZERO T-TI 0 2 3 4 5| Ol .00 33. 33 33.33 33.33 .001 3 LARGE II .00 61 .11 22.22 5.56 11.111 1_8 SMALL 2| 3. 70 51. 85 33.33 7. 41 3. 701 27 I 2.08 54.17 29.17 8.33 6.251 48 VFRTICAL "HRCeNTAGF | LrSOAYWcfcKLY MONTHY L FSMQN | ZERO T-T| 0 2 3 4 5 1 Ol .00 3.35 7.14 25.00 .001 6.25 ' L A R G E 1 1 .00 42.31 28.57 25.00 66.67 1 37.50 SMALL 21 100.00 53.85 6 4 . 2 9 50.00 33. 331 56. 25 I 1 ?6 14 " 4 3l 48 PEAPSnN'S CHI-S0UARF= 1.55 LIKFLIHOOC PATIO CHT-SQUAT= 1.55 OF» 3 INVALID- 50.00 *<5 .00«<1 3 HI PRPB = " 0.67584 CHIPPOB = 0.67469 GUTTMAN'S LAMBDA= 0.02702 o ~5> THIS IS AH ANALYSIS »c l-MTuHVirn lift I A B i m i a l E IfiBLS HF LtGl'JUiLtL (CC 125 1 VS IIBHCLIiEi- ICC 381 FREQUENCY TAHLc ZERO T-TI "TiONCON SE11C3 C JNN 3 T I 0 '. 2 31 LARGE 1 I SMALL 21 1 3 13 Ol 81 51 3 18 27 I 1 17 17 131 48 HORIZONTAL PERCENTAGE | NONCON SEMICO CONNOTI ZERO T-T I 0 1 ? 31 Ol .00 66.67 33.33 .301 LARGE II 5.56 33.33 16.67 44.441 SMALL 21 .00 33.33 48.15 18.521 3 18 27 | 2.08 35.42 35^42 27.081 48 VERTICAL PCRC.ENT AGE | NONCON SEMICO CONNOTj ZERO T-TI 0 1 _9_L .00 11.76 2 5.88 31 .00 I 6.25 LARGE II 100.00 35~.29 17.65 61.541 37.50 SMALL 2 1 -00 52.94 76 .47 38.461 56 .25 I 1 0 0 PEARSON'S CHI-SOUARF= 17 17 131 48 5.56 LIKELIHOOD RATIO CHI-SQUARE* ~ ~ C H I P S I B = 0.06048 GUTTNAN' S LAMBOA= 0.17776 C H I P 0 ( J B 5.75 0.05494 DF * 174 I r- ec m I |u; co | o © O IC "f I « « (M N W * A I <M: to ^ I ~ "5 rO 1 ~. O <M U r- • O ro -»j • 1 H I -« O r\i I I J -J X. LU I r- co ro O j 1 CM o "0 O o o o o o o o © cr o co o r\j o t-o o 0 - 0 C M LO I rg 1 ro 1 GC OS I gr CM m 0 | l ^ ) CO o \ ro o 4- CM in in c c rsj m cr o o o cr o C D o CM O O •4- m m l 1 - 4 - I Ai] 1 t- o s o I cr o C D o r-t o ro O ^ CM ro •4" • X O I Ol j £ > I a *n] Lb O z < co »*- an r-m 4> o <c * O r " < O O O O C O O C O O O O O O O O O O O O O O O O f- r— m ro JJ >o m m o o o c (M CM o- rg r~ O r- r-O o >n I O in r-CM ro ! 1 o 0 o Mt or c o I O O f- ro O O O O O O O O o o —t CM m o-V X O X a o o J r- CO -D • cr CM rO <\> in s r> 0 0 r-•—• I 11 • It O O-2D «3 C D CC I O 0. < O 1 X C _ X X < */) t/J Z r> < tn X cc *-<. 1-LU z> a. 0 c 175 SI rt rt CM L H §1 X U J ~ H O L L LJ _l LU CO O *M| eo r- *** <o I : o o o * o o P-m in er o OO 1 OOI lA l\l O O! OO'SIO tn CM co in o o <o u*v m o r- r-n N i n o i n i n «o tn o o in o 0 0 * 0 f i r\i m V I O X a : 0 a o tt. u> U J o f t < L L p- eo r\j m o in » t m • • • • «o »t m m f i f i rn O O O O 0 0 0 0 O O O O IM CM c\* o o P- m o o <c m O O O O O O O O P- P- O P-<£ <C O <ti P- O m O it) 3 1*1 O r~» o o e> in o in n rg tr> p» rt r\ j m fNj O O O r-O O O O O O in in O O O O 0 0 0 0 rt fM m ^  cy O L5 L U _J X. rt co m CM CM < o X; or O C L I X +• 0 X C J o o X o o »OVNY5 Nl »0»W T H I S t S A M A u V i , I b, I* r ' ' l l i ' - ^ 1 ^ " * ' * " 5 1 ttivlsuuc i a a u i JF CCUUR i t c 330. vs JDQ <rr. 431 FF Z OU^NCY T A R L i ZERO T-TI V E R Y 1 I 1 LOW 21 0 r T ^ T n r f i T v T c F i i H * * C F U H S R t N G k - R 1 2 3 « 0 0 1 1 0 1 0 1 M n n 3 1 1 2 1 A 1 HIGH 41 3 « 3 * 1 -NG a r c s " * P."D» rC c r f P l A l " ( 7 P "?l 0 ~ " 11 * 0 II 7 0 01 10 1 I 31 — t-51 25 43 HORIZONTAL PERCENTAGE „ . . , „ I_JFJTRJVS_SLLVIC FiSHfw C L ^ . E L - L ^ - H i L TERO T-TI 1 2 3 1. ENG RtT*£A P<;D»FC °FSPLA| 9l 8 VERY II LOW 21 MOO 3 I HIGH 4| 16.67 .00 10.00 12.00 I 10.42 .00 .00 20.00 12.50 .30 14.29 40 .00 12.00_ 16.67 . 00 10.00 16.00 16.67 14.29 10.00 9.00 16.67 ^2.86 .00 8.00 16.67 14.29 10.00 4.00 .00 .00 .00 12.00 16.671 14.291 .001 12.00j_ 6 7 10 25 16.67 12.50 10.42 12.50 VERTICAL PERCENTAGE | ZFORES SILVIC FISH*H CFO»ES RANGER f « n T-TI 1 2 3 ± — ± - 1 . 8.33 6.25 10.421 E^G R F C F A PED*FC RESPL&I 6 7 8 ? J „ I 5 PCftPSDN'S CHI-S0UARP = ;HIPROB = 8 22.15 GUTTMAN'S LAM30A= 0.57034 0.09522 48 VERY 1 I 20.00 .00 .33 \Z M'M LOW 21 .00 .00 12.50 .00 20.00 50 0 0 6 ^ H^H l\ 11:11 22:2? I?:SS U:2? l ^ ^ i ^ J ^ o ^ ^ ^ ^ ' • ^ c 4 3 5 I 48 LIKELIHOOD RATIO CHI-SOUARE- 26.70 D c-CHI PROP = 0.31823 ON 24 INVALIO- 100.00 X<5 50.00TO T o "THIS 1S AN ANALYSIS HF T H E jNfE^VIfcW DATA B u a u t i E n a u OF cuaiaud «cc 1 3 2 1 vs J O B ' <"cc 4 3 1 PPEOUF'NCY TABLE  TT^iR 7 VTrLv rc f=ioh*w cF - " . r s F A N G ( : F ZERO T-TI 1 2 3 * • P N G ° F C B " A PCP*EC o^SPLAl 6 7 R .91 Ol 0 YES 1 I * NO 2 I L Ol 41 1 I 2 40 6 I 48 HORIZONTAL PERCENTAGE I ZFO^S SILVIC FISH*W CFORES P ANGFR ZERC T-TI 1 2 i * *-ENG RECREA PEO+EC PESPLAl 6 7 8 ILL 01 .00 50.00 .30 .00 .00 .00 50.00 YES II 1 0 . 0 0 10.00 2 0 . 0 0 15.00 1 0 . 0 0 1 2.50 NO 21 16.67 16.67 .00 .30 16.67 16.67 ^ ;----;----~-';;7^ ~';;^ o"~7^ ;2 1 2 . 5 0 8 * 3 3 — 6 ^ 5 10 .421 48 . 0 0 . 0 0 1 7.50 5.00 10.001 .00 16.67 16.671 2 40 6 Ve"f5S«;rS;S!cEF.SH« CFORES RANGER ENG R E C E A PED-EC RESPLAl ZERO T-TI _ 1 _ J 3 * I b_ I ! 11 * ~ | " II 77 . 1 1 , .r»n .00 .00 25.00 .00 ._00.L 4 . 1 7 4 8 I 5 6 PEARSON'S CHI-SOU ARE* 8 4 .54 LIKELIHOOD RATIO CHI-SQUARE* 6.38 DF * CHIPROB * 0.80453 GUTTMAN'S LAMBDA* 0.02272 TIME FOR TABLE PRINTING: 1.747 CPU SEC.  CHIPROB « 0.60594 "5* 8 INVALID- 8 3 . 3 3 « < 5 4 » . 4 4 « < 1 , „ , X X X X X X X X X X X X X X X X X , X X X . X A , X X X X X X X X X X X X X X X m ^ ^ ^ ^ • _ d ^ . ^ a U . . . 2 . . F 3 U . . . 3 . . ^ 1 7 7 0 8 7 3 6 W T O - A P ^ O T R O ! F>3 NO. 6*5 02 1 J M V . O S T Y l.i 178 A APPBJDIX III QUESTIONNAIRE AND INTERVIEW DATA 179 * X Lu or x X li •Li > O • • 1— oc UJ X z X LU L," LO - z x» II U- Z *- If fM LL ^ » c ~ mi « fx c . * c f *-lO • :U" r to UJ J ? lO *5 — C Ii II LO rsj II f TV r* » rs. OL * u-u a x LU <r i -5" — 1l C :fX c LL " rv U' IT «M Z m it » IS CL - > Lw *» « < C fM -Ci' • |i o r LL' o IT O L- - •— rt f\ II lU- _ l < V I/* p to o — » |ii — u " J f 7 i f C * —1 > c • z jU.1 U. —. f* II ii Ii » _i L. O •» r.: *.* e f-rv - =1 T +t> II > — >. L- II IT p -.0 r . ;ii — • r' b iv-7 r. E • c -F t - J — P-- u- p b t _i z r" n * * .Ll fM 1U-rt £ L * !C LL U-» rt It > LO*~ *- c — Z » -J UJ « — L5 X l - U C 3 w - J L n n I/O IT LO M C * U — H LL-II • Vc to 4 u. < a r - o CM — v C - -J r-J II - I -» a i t J u. L L e to t II C. 1/ t li :|l IT- ll <|i II <* L rt * rt f- «» l_ rs. r . r i r. *M T" C C » - II r t/i g c < LL P U* LJ Or !H • |l t U* LO cc it pr a r- fM ; *> to "t C < — < K > to ^. it u z ui a —• < O. LJ LL U CL LU a li <• h H r — ill 0 — fM rf- <# rt rt W- fM ro | ry Z C. II t- r. In » kr> v |r: z * LL i r: IT.' tr, * I ® f l U •* ' ^ II IA II >^  I ^ t • C rg M . HP- <M — I * . I f N I — I i » * f ^ IM II ... a. H «r> > C I i. i_> i - u Z a. — it iii * C u. • * > > o • « « t - i v z •* c • u * — ^ I IM I • m r\ , _ • — p- > > P rv m 0-o ^- — P II .II II M C IU m > > > m . a rM f = p . C I I — C Z II a • E IT. l/l • LU _ fp c z fM ITN • * •> f, IT * * •* H :ll i r-. > > > & > > > u. > L. > c > 1 •J p LL ^ --J r. I a — * c IT *" fcO or 1 It X u H r-c * * 4 —1 > V t r- •4- < . r X II X n > to II -* > • • • L." « > - > f D y - a T •J c a fM «C O ^ UJ LV - 1 fn to LJ to _ i f-UJ JJ t-1 o 1 k# • -J *- « f^ J fl*. « II — V-- H rt- .11 > ^ LL > ^4 UJ S. II K 7 n K c > « LL v 5 cc — UJ > O > ». • > F z • tU LT, a » «l > « -J r • *M fM »/l »— u ^ t < •4 J UJ O r z c 1 — >- v*. 1  H »o c o CM tr. M M > • H r • rt fM »— IM - C II H H * > > > c i - p if t ? r» > > LL.' LU z z P.I > I I X s < « c z z • * CM f l w. ir a * s IM c * a «-< c f p 4. m fM O * r> — c • or if. O * • « fv • a in B?. or • C < p 4> p» ffi C P 15 S * - a IM r ip . > > fM O LL: > > F M X p-C c c c If ^ O P u c M P tac L X 5? Z IT K g > z LU II z • II r~ fn - I c-D — r I H M Ifi X v _j V" IC LP N p _ IM • P -i p r> .1 ; P _ «: if K J • p c. p IT — —' P II II • P 4. P lO — P^ IM — > P P I L b U . I L t i l l « « « «t z Z z z f'. Z u u l < to I •XT •I I - CL I p |>.— u - u. *- a < • o < *^ < r J Z I II H * JM P . — n LP . > E k ( if' — ~. t* p. > — 1^ u r i. a « z z fM • p - > i. ' L. » r « M Z Z I p l/l • ! « II r -— c • _J • u- i m 0. C. • «c < i * T . X U L iS Z 2 O , x S C 5 = p-> u. U L r c H II C c < C >« - - > z fc z L J u II > • X »— r . o c X z II c • L J X x 1/ 1— L . u or c ro n II a •m « 2 r»- li ~7 • • •> r* LP L J L . 1 > <• LL rt f 1/ •3 II lO > II » l< li • P— m rM fM •> • 7_ a Z to LL r_ L-' li. > O. to L_ z C. Z Z r u-II z li II II m m • rt * » r or . f r* r «•* ; LP. i_ LL i. & l c •c «-Z X X Z o NAME VTI i v f * r 4 n s , L 4 r s , F « ; o s , i 5 P S , r i s o s , L 6 o s . r ™ s , L T o s i )| SF.T ? f l TP r i R n u n ?] •(,»-•»• ?->i r.Bnijp ?i ip i .<|i ?r i H > t,vrvj" 1^ '—' ?"] 141 SF T VIC" Tp mi r.prn'p VKI M •-•V (1< GRnu° VKI •">•,' • VK3 ' 3* SFT V*3 TO I;MHUI' VF1 M"-' i ' v?i »1» T.RPIIO VF 1 • yf •) 1 j l GROUP vr 1 t^t_ i7« V r i • V - ~ C R pnp--vr T — - T 7 i V F '' 4 • CROUP v r i • • v " '5' SET ?"4 Tp »0» —r.Ritup 1 iv ?m »1» '•.ROUP 119 '5','A' 2C4 '2' r.RPijP 119 • l ' . ' T ' . ' R * 2P4 »3> TJPfJtro m» »?*;'?•,' » 2P4 »*T-SFT VH' TO r.ROUP VH2 • l * - , 9 « VH3 i;uilU» VM2 »»•-• H» VMj ' > ' GROUP VH2 • P ' , ' • VI3 . „ MF&PING THIS IS AN ANALYSIS OF THE QUEST IONAIRF AND INTERVIEW DATA COMBINED. T1TLF r^NERAL POPULATION CHAP ATJTER 1ST If. S . TABLFS SIZ r=4*16 22-43 S lZ r = R*l<S 24-43 SIZF*8*16 201 .215-43 S IZE -B»B 201-24  IIILF UbPENUhNI V at I HHI b «1 MAP SL * l F P APT | SCAtE. SI7F = 16*16 30-43 S!?F=lA*fl 30-24 SIZE=B*16 31-43 SI7F=B*B=31-'4 _ _ -—n-rrrDFPFNPFNT VBRIABLE »I «ap scatF PART II CONSTANT VS VARIABLE INTENSITY VS INSET MAP. TABtFS S!2r=4*lft V51-43.35 ] Z ' ^ n ^ ^ ^ ^ ^ ^ ^ > ^ *H-4 2 S l « - , * l > 42-43 S.ZE,4«B 38-239,240,241,12 3 SI7F=8»16 2^9-4' StZE = P-»16 240-43 ST7F=B»I6 741-43,24<»,4? TITLE OF PENDENT vapiABLF » ' MAP UNIT VARIABLES PARTI DIFFERENTIATING CRITERIA. TABLiS S1/>«M»H- V3I-4* M>f^B*T6 ysfl-JHO Si 7F=q*l fe. 253-42 T1UF PF PENDENT VARIABLE «4 *AP UNIT VAPIABLES PARTII INFORMATION LACKING. TABLFS S IZE=*»16 26-2^1 -TTT t rWFNT1ENT VA"IABir §5 SOIL riASSTFICATtOM SYSTCM. TABLCS <:|ZE = B*l6 2"4-43 SI'.F = B*8 204-24, 120 SlZF=B*lft 120-118 TITLE DEPFNPFNT VAPIABLF #«, INTPRPPETIVF MAP LEGENDS. _ la ' l i r s SIZI^B'Ife IIM-M M/I*1>.«M H*-?* 1* SI7r-Mfe»2 » 4 - 5 » TITIF OFPENPENT VARIABLE »7 MAP PRESENTATION PAPT I- INTERPRETIVE MAPS. TKmtS S'TTF =R*1 F T»1-«3 SIZF=R*lf. 3??-43 *!/r=4*16 123-43 • TITir 1FPFN»FNT V API APL E «8 "AP PPPSrNTATION P«PT II -L C.FNP ,1AP BASE,AND TOPOGRAPHY, TARirS S t / . r -4»1h 125-4' S i ; f = « " ' l.'5-;-4,VI TARl - - " ST TTS IF* Vi "3 7 »- 4 3 s i ;r=-i*» I e 321-43 SI7F=!6*16 33 r-43 181 182 u j n XI L/1 O . — I I I < Cf I o o l u er i — ori a |i LU' | « r i o m D « i o c or u~ i o c LL i I R LS I v. : i c « • . I X l u ~ i c e f o » i - II Lu a I O <M C O I O — O I • • • I O M IT I * — in I I O S S O a> o :# m i o rt e C O T~ O * O C l " O O * c C c e « c c * c c ' c I— p« o. o r- o •If " c l o o > o a — C C IT. S T E L J U J ro I— > - J rt lo I D O l « rt « lu r- r. c I C rt r*. i m o O o o in «** I I > I O rt 0- I rt C C C I rt rt rt | C D kr r- r» ; | ro \r. f t r -e rt * 05 m er E c c k_.CC, E c c o o if- co m l fc * <o I rt h r . o i f t c c ' « p e c ; p c o : z er ro er r-• o C D o r o c o n II LU or re « C U*> rt X 4> IT * IT. • C 0 c. r\ r | n i, fa cr. g C b s p a " x o 183 o o 4/1 < I — *M J>| L . - I > O I t — I I ' CM C I c-I CM I I IT. IT. I O lt-> C I C ' W I i o IM C C I : C C I T . ir — — ! i j j cvcn ; i — of i n l c C — (T- CV' O : I C C « t / . l cc X c i — c cr k ' I — | CM ui H Ul K M fe! I L r H r . r : l i f O I t L (. H C \ p 4 ^ o _ _ _ ••. — «v _ o <o r sj O m e> i t Vs r- c i rn • • • • I < M o I *-C M m i *>•* O o O o C *M f - c c r- m • • • * IM m «M (M (M IM o JJ C. CM IM I a, I C * I Z I o o * r.: i c c-• c c. i o o I o c p-c — i f-r I C J O I C VJ I I O CM O * IM r\ O If* C l o ir< m O i l " I * CM Jf.Cn I CM I I o " " n i cc C JJ m I Jf • • • l • IT m m i r. c r e u-. c o tfroc I m I T i n c I e r • • • i • fc CM * O I r-— »~ I I I ko c * m i c k i f o r i —' [ • • • • I * p CM cr. r". i c> iff u- o r c c r i • . • I r ! i i • > V Z I c J i g I A k . £ i r I b'UlDL I J X X J I o o w « u. — > c. u u-u z c luu. c r- cr r- * CM cc fn if. O CO c r- 0* fn If. m* - 4 C O O O C J C C If* If* if* u if* * cr cr o * m o c-• • • • • H Y J J cr a * m o l * « i " « c O cf er Jr cr o ir* r*. p* r- if« iC m f < « • • • • • to m J J J J * |u JJ * JJ rv e t o mr- c c t o m J J c ; i i b Jf ec j ; r-E * r- i r JJ * r- ir. JJ JT CM — r jf — l> JT — ec CM f . • • • • r f i i * IT CM r» r- cn r- r o C p m J) c. c m J J O o & c t; tr- o o r o f » CM l»: tff If* • v >. z , i -J s c kr t *r I- T L</> l u E b r u i m O t . c o c o c • c o * C J a. cc < c a c I 3 X u x u u z o C O T 3 184 i < I rg £ (\t I P I U " -« »o i cr i :rr — cH ' r~ i uj ro ol : ir. I iff ft.1 c . •» i v c r.\ L> CM i ec c or b, — > c a. — i < er - J e. > o I Z - I o «| ro cr r-cr m . t r C «r cr C ro ro tn oc o C f - C ro * J f er c — ir* r- o cr 03 c 15 « z a «r z 3 i n o lc - i r r « U-»- o z c tu u. Uvro , U . 3 B I IkE U- I i. ro I ~ » C t - o r U J x c U J i- - J - c u. U . C O « -u. u. c. i ro n —i o o c o cr er u c C J p o o p c M r> c o - mmt :> u-3 « c r" — 3 ' in < u. — > c z : VI o} UJ o ro > U J C _ i u CO csl O r -03 O = ! I o— * — a tr I ~ M m n ui J ST - «2 C t-c-z V d V M T J Ml BOVf* 185 LL D- > f- 1 •P -J 1 Z * 1 L? PVJ 1 X~ 1 T— Jt 1 iA LL -> fV 1 < 1 C' rr t v i LJ L- 1 T —J • f- LL. 1 C O — • LL •C 1- 1 o t- •> 1 Z M- J LT LUZ • LJ < LO a 1 LU . C ' a : < Z —J ' < «3 L_* Z rt ** a ; »-a i — > *-187 188 189 ,Z if V p-2* y c LU-LL f •* cr LU, ">• •< c-XT. U. IX X fl Lv <_*<L U <C L-C t- > 7L. lyO LU i — Lv V"! a • LU C _J a «3 J" «3 « u • sr f-«r »- «— — a' f 10 L. - 1 — >; *•* •> 1 c 1 oc X Iv r-r, tr cr . • • • O • o c «*> ^  ic e e llf 4T IT IP*. If 0" kr go 4- f- *r c |4* p» IP tv * c Lr 45 * 4? p . IT W o- o* if p- u. W 45 J rr c • f k fv CL IT C Lf • L c . o i - . c c t o c t- c c - N r « ' C L C u O X M II LL' c a rr « c ro D or c? o-tr 1 X — *—* p- c •< 1* rv p-i IC c f-1-J it*. C A£ i - i r • c •43 C Kr C CXI s IM 3 LU to •<t -4. io.1 l«9 I--v i — j * M as; «/i 3D to •not Ml P* u JLJ t q -*a pu«a tr> Hod P-IJ o W«l i I IT 4M0- I O I O-> I f l in « i m ** u. - i > c I tZ — i a , < L/l i LU I a ' U CP I o P-« P I ~ IP O 43 I O P-a if I c » | M r V. 4T I C * U *M I C C P I I irsj i In I 33 B -I/I b i tf 45 Z If L/l L- IA L-er u. z u 6 X I P 43 0-O — O fl I • • • i S2S I O C  0* ODK O — * * C —' <^  I O m C-C C C IMT C *^ r**. C * T O C IT c *N* p-c *v r-. C M f r. C  f~. o M c C tr c C- *- fM P P * I o p if in I n • • • I P I iv. c* cc I L P <z u. — > c p " PP c — « t r o. i« t-!LU :cc K> luj |K IC? 45 3r < x l_) P4 > z c LL' U-U l>4 a ?~»-U . I > C IPI P-> P" IM • • PJ P-P- IM fp p- i er 4f IT. I fM Ip- fi ri i 43 c p-. 45 I IP. if", n p- l et St. " i 45 i -bi fJ b< i r . L > C c. J — l i -i/i O l u. Z •• ll II Ll 1" O _ D C 3 o. 190 HI BO*" 191 V m o c UJ ; T C < *— *c c L-' *T > a LL' ' L ; t-z. i a « J > u or *— +• «c :r ? LU l/l « I If- ^ ^ > C l_' CO I ' f\) IT ^| O * i «M -« e kr. I .CMIM «* y — i —• t-r — £ a O o > -J u — l> m 0? N C m fn CM o* IT f\J Off lr> f v cr ir cr cr - c l r * C n| «o m c w 0*'O *-« <c o cr r>-cr r-i n cr t r :Mr- r -ir\o * cc i r c - i cjr 1* CM CM • 1 • • • a o cr K IT ir er ^ in c- r- c f - o i l t L > e s s > o a — • or fM u LU CO e z UJ m «T or in c LL LP m z < ex r- tr. **• U-fM u «-• LL f l * CO + • X if LL c IM _ *— • > r I/* IV O tn M If '«C LL. +- a ;z c fLu U. ice ku. O CC fi. • «J •a *-> — — > r* IM «£» tn in m tM c cr * eo tr rn m co «r cr cr - -ooo er r n c r c r c o o o c e* w w m cr — .c m rv tr m c^  CM m r - m r*. m »c r* * f. * m * ev rsi ^  >^ rv «f I T vo c r ^ m m o fv CM -f if r-if or c v o c c c I- U l I lu Ul- I i/i « c i z a a. I » B I > z » n LU or x < c 3 a 9 CL */* — I I I U U c c c i z M- u z z 3 V0VMW3 Ml SMWI* 192 1.1 OJ p P. I c * 4 kA —. I t PJ C IN! Z I - ^ — H IT. »C' C" I C —' I . * p-\ i . . • I I T p. i er r -* «o i — c P - i - I er • a • I • c r ' P rr o * IP i * O — P J j «p p z . •a u. p v z r, i 5 c c c 1 § 5 c c o o -z z 3 LPO c VOVMV3 Wl BOtH 193 194 m — • Z z If IT • • i kr r- cr K * c- cr i , ' f- X cc — — r C I t- u I u u r t/i «a r z o- E — < > . UJ C C = C < c g§: V — t I U J o C c a cr *c — C — t o t . in — I X — u X u ir sr o > i a. ' u I LL.' * o e CD T ca U J a O J - » >i r-c m > • S-u i er U 4 T ! U - I l-> a — ' 03 Z) u i >H OJ t/l JH: OJ _ J XI crt - i cr ca 3 3 ad ca •31 «01 XH, IU4 I « M U J . o Z » rH u . u l «i a 3 — ".a ul " i Ol X u i - I O ) C O m-* r- f\J i in < u — > o i — « e>| i o I m I ru (9 « Z X cr. * X U CNJ > <Sin t~ t/> i s -«-)C 6S ta. E O V — c o « u > c tf> C ry cn O "J O » . *o cr : c CM x o ir i r cn c* if. CM '• r i p e / * IT r i M c IT cr c c cr * CM c « c- cc i C 4 j i r . c c r i f < r rn c in c c. — CM r> Z D r c u e u r- r Z E Z r u-u Z i n u ;a IT \U-iw :Z >0C VI < : u-•cc c L- U If) < u. — > c e> — « cr or u> i ' I if • > UJ LT fc/» — « U. C r c UJ u U K a *- UJ c r e. • « cr « r. cn i i I I O W O CJ I u \ c r if i o r r o i o CM r- c I c* * e CM CM — CM I- o o «* O cr, |m c er :ec CM CM ;cn c r-1 J3 c <c £ . " Ir. r m i f c, fcu. f > • i c cc ir C cc L - r - r c l cc U . « ; r> <r I — Lo «© m 1 fc- r- cn . Lr JJ r r- I I X C c o r « L-~ a a a c — I X — o X u u^ z c c 195 V M CI f-l IM • 4/ C c —• er a e « v 3 a; 0 o. */> "-1 X C O c i I/- -z i . D LJ I Z f [ T r t IM IT 4> I 0 -0 X X IM: c ' 43 O I*" r l * IH c c c o c c IM co <v f . C O C U o o c o r c •-O l_' o ei u. c-Z l ^ u c c c • r. II II UJ Ox B <3 C 3 a. O D. l/l * -I X x u u z o z « z 3 13 I c i THIS IS AN ANM.YSIS nr m i S U U N M F F ANO iwrfRvirw ••»•» O l i M f f l l H I LAflLE Cf BEIEHAMIXBS t r r >*o. \ s SliErtEEWEUCL (CC F (tr QUPLNCyT^BlE jEnn T-TI 1 PI LOW 1 I HIGH ?l MM HNS i l , * rr l -NOT ?l II * ?s| 11-. •>5| 111 I 10 15"» 7^r ' i i 7Fnn t- i I 01 inn i r HIGH 21 NOTANS 31 HORIZONTAL PERC ENT AGF I — P ! NOI -rr | *.35 69.13 26.??! VFR T I C » L PERCENTAGE I y r s lFBn T-T| »-1 MO I 21 01 LOU I I "HIGH 21 NIT AN S ? l 20.00 .63 1.6*1 *0.00 53.*6 *0.98l *0."0 *5.2B 57.3M .00 .6? .001 T -TVT PFARSON'S CHI -SOUAPE-1 - ' CHIPROB = ".2112-1 GUTTMAN'S L»HBO<r* 0.05->ST 1 2 30 50.00 25.01 25.001 * 3.51 7*.56 21.931 H * 3.60 6*.«6 31.531 111 .OC lOO.C^ .QOl L 230 l .T* *<}. 57 * f t . ? 6 .*3 C3> -6TT W • 3.09 L IK EL I HOOO RATIO CHI -SQU»PE» CHI PR'IB 3.36 0.1B*50 2 INVALIO- 33.33 *<5 33.33XO 197 ( ( ( ( ( « ' ' ' ' ' " ' 8 c 2 I r. er i C M ( M I (v i r~. oc i I i — i fu CP c | P- x ZC> U I P -U I a Ui a > J O «- -p-z o - i ^ P« CM i i - I z c I C u a * cc I > *•* CO — 1 ) kt m We-*1'* — f\, p. m CC- *-« G C r © o c • . • • • r»i f\j i% - i - 4 . - IM cn i ! t t O t -„ 1 1 : u o i • : x £ u t | 3 u C -i 1 \ E tc w II- IP. z O T • D » fc *7 > »-i E pj o- I M p-ip I M ic I P D> - r r- eo N i f N O P - IMP! Z P P O P C * r i C U i C Z |/>k/ II u, o C — - 4 • .p. IP IP ;PJ » « w o. cc « o 3 a: 0 o. «- — 1 X x u u o c c m tr - r IM • ip. if - f IN. O S . l/l p» I X 3 .VWTMV9 M> MVM t . 1.98 v — U 3 3 a o 2? r < s UJ X CD U J t - t * — i i r — c c 3 « M S T e C U U J « -z X LLC o z UJ a z J C B X r»tv cr * m f- r- « • . • • • I M c er in or rn r j fM ee rn oj cr m c c m ^- m m •» v <r LO m z O X IM U J 4 i T C H • • • • :i — r- — C I * f\J I K ec i r cr I • • • • I ir «f rs. | ^ ir> I ( IN. r«- n CD »0 C O ^ ft > Z D r c u e z z i e t c Z y. u er — J3 — n u U J CL C < c 3 a co. I X e c c x r- -»r-H ;D LU 4u 3 DC cua l/l — • x — u x • [ THtS K AN ANALYSIS • » THE QUEST ICNM PH Ann INTERVIEW OATA COMB I NED Ol D I X K l f l l C - l i B U n r - S i l B 3 0 L l I C C <<T " l V< i J i E L X E E l l E t C E ICC -.21 FREQUENCY TABLE ^vn; rTTTl 7FRO T - T I 0 t ?l r r — — 5 " " i M i° NHNCON II 1 21- 1 3 1 SFMICn 21 1 TT I M P-. CUNNUI J l " I — r IP I S P "T6T 611 T B 231 I * . 3 5 6P.11 2 6 . 5 2 1 •-• VESTICJl"PERCENTAGE' | Y P S NO| ZFRO T-TI 0 1 ? l 230 —— — • 01 50.00 5.f? 9.8-.I B.26 NONCON 1 I 10.00 15. T2 21.311 16.96 SEMICO 21 10.00 *B.-.3 26. 231 40.87 CONNOT 31 30.00 30.B2 *2.62| 33.PI T T 5 T PEARSON'S CHI-SQUAPF" CH! PPOR = 611 7.81 0.319 TP LIKELIHOOO RATIO CHI -SQUAPE= CHIPROB * 8.0-. o . o i ni OF" GUTTMAN'S LAMBDAs 0."5950 _. .. . . — T A T A \ 2C ( THIS IS AN ANALYSIS OF T l l L JU r ST ' U N A 1 Ht nNll n i t » V U K . 1 H I Y S t l l f i i r i a B U HF 54EC!ie£E.l!;LjC£ <CC F RF HI IF NT V TAPI F 4 ? i vs joe cc < i -> 1— z r n P H M L V K ZERO T-T| r 1 2 FTsTtfW 3 CFPRF5 4 5 F.NG 6 PCCRF A 7 prefer, rt r-TSPLA 9 P1VFS1 I l n IA| r - j - ? - n 1 VFS 11 0 13 12 NO 71 r 2 1 r <> i ?! 6 0 1 7 13 1 19 11 i 4 1 1 24 3 I IC 0 21 251 131 10 159 61 ; I 7 15 71 1 8 2R 30 31 6 ?fl 11 401 7 30 , H n m n r r n . PTRCENTAI,F | ZFORFS S It VIC ZERO T-T| 0 1 » FISH»M 3 CFCRFS 4 P ANGER 5 FNG 6 RECRE * 7 PEG*EC 8 RESPLA 9 PIVFSl I1CIAI • 0| 70.OP .00 10."0 res 11 .00 B. l« T.*5 NO Z \ . O f 3 . 7 R 13.11 .on 8.81 6.56 1C.00 13.21 9. 8* .00 10.69 21.31 10.00 11.95 18.03 10.00 2.52 1 .64 10.00 15.09 4. 92 10.CC 6.29 .00 70.001 15.721 21.311 10 159 61 i • i i I .87 6.57 9.13 7.83 12. 17 13. 13.48 2.61 12.17 4 .78 17.391 230 VERTICAL PTRCENTAGE I ZFORES SILVIC 7FW1 T-T| P 1 7 F 1 SHt-W 3 CFCRES RANGER 5 FNG 6 PECRE * 7 PEO*EC B RESPLA 9 PIVFSl I10IAI 01 inn.OC .CT 4.76 . r t n 3.57 •on 3.23 16.67 3.57 9.09 5.001 4.35 TVS 1 1 H5TF7—57.1* NO 21 .OP 13.33 38.10 77. 7A 22.77 75. M 21. 43 56 .67 A3. 33 61.29 35.48 66.67 16.67 85. 71 10. 71 90 .SI .00 62.501 32.501 69. 13 26.52 O O . 1 2 15 21 IR ?fl 30 31 6 28 11 «0| 230 PFARSflN" S CMI-SOUARF- 17.OR 1 IK EL IHOnO RATIO CHI-SQUARE* 20.24 DF * 9 INVALID- 75.00 X<5 .nn*<l LHIPHUH = n.U,774 CHlPROB = 0.01657 GUTTHAN'S LAMBDA- O.OOOOP t _ _ -- — • THIS IS An ANALYSIS OF TI'F OUTST IflNA I P<~ ANO INTFBVirW RATA CnMHIMGD o —THT5 ts *N ANALYSIS nf TUF ouHsTir.NAipr ANO INTFRVITW RATA COMHI NEO B m E I S T F T i D L E Si«Q0LIl!I£ <CC 'fl 1 vs uQMXMbQiflliyr. <cc n i l ID F R E Q U V r Z E R O T - T | F N C Y T A B L E V F P V KJW 1 7 urn. 3 mr.Hi 4 | * — 7 7 44 31 3 7 1 1? 1 ~ ' 1 9 3 9 9 4 - - — n r N O W O N 1 1 S E M I C O 2 1 * 5 r 3 A 3 5 CnNNOT 1 | I T - 3 3 7 8 * l T R • 1 B 7 3 B 6 5 3 1 7 3 0 1 .... _ H O R I Z O N T A L P F R C E N T A G F 1 V E R Y L O U M O O H I G H l 7EW1 T - T | 1 I 3 4 | O l TONTON 1"| S E M I C O 2 1 C O N N O T 31 2 1 . 0 5 2 6 . 3 2 . 0 0 . 0 1 4 . 7 6 3 7 . 7 3 1 2 . 8 2 4 2 . 3 1 3 6 . 8 * 1 7 . 9 5 " 4 6 . 8 1 3 5 . 9 0 1 5 . 7 9 | B 2 . * 5 | I 1 . 7 0 | 8 . 9 7 1 1 9 3 9 9 4 T 8 • 1 7 . 8 3 3 1 . 7 4 3 7 . 3 9 2 3 . 0 * 1 7 3 " V f f T T C ' I L P E R C E N T A G E I V E R Y L O M Z E R O T - T | 1 2 M O O 3 H I G H l 4 | O l N O N C O N 1 I S E W T C O 7 | C O N N O T 3 1 2 2 . 2 ? 6 . 8 5 . O C . 0 3 77.27' 4 7 . 0 5 5 5 . 5 6 4 5 . 2 1 8 . 1 4 8 . 1 4 5 1 . 1 6 3 7 . 5 6 5 . 6 6 | ftf.381 7 0 . 7 5 1 1 3 . 7 1 1 8 . 2 6 1 6 . 9 6 4 0 . 8 7 3 3 . 9 1 • — 1 nr~ • if 8 6 7 3 0 P F A R S O N * S R I I T T M A M t C C H | - S Q U A R F » 0 8 . 7 4 C H 1 P P C B « ' O . O T O O O 1 A M A D A s 0 . 7 7 B 5 T L 1 K F L 1 H O 0 0 R A T I O C H I - S Q U A R E * C H I P P O R * 9 5 . 0 6 0 . CO000 D F = 6 — • to O 202 P-I p-i o o z I P o • o •* c 43-u UJ C L C C « o 3 a : 0 o. i / i — 1 I -a n ,a 3 a a i—i -n LJ - cc z < z If * C H I iv iv kr i § a i cc p j rn TI •>-> L I I i I f . e o z o O u u — z z O u. Z i -o «tr * i P J C O f m C o cc r c t-) o > LV O : I P o c i c X P - - J z u. r u. 5 ? * L> »" r P e if <o — * — O c O c; c c o c C p C </ I o o c o If C O f Z C P -C t, o £ • § O UJ c o 9 SB 3 rx 0 o. 1S> p -1 S — u Z ' u z o , 3 o t o T H U I S AN A N A L Y S I S nr I H F Q I IFST I O N A I R F A N I T M T F K V K W H A T A C H M H I N F O F R E Q U E N C Y T A B ! E 1 7fttv->* ERO T-T I 1 Tnir 1 M | G H | -.1 Ol NONCON 1 I SEMICO ?| t I 4 26 ft ] fl '.3 I rnrwI 3 I ftl 31 20 i I 15 43 11 101 I 94 230 I T H D T - T HORIZONTAL PERCENTAGE I VFPY L O U MOD — 1 2 T HIGHl — - r r 0| 31.58 5.2ft t IT. 2 6 3 5 . 9 0 SEMICO 2| 5.32 77.66 CONNOT 31 .OC 2.56 31 .58 31.5R| -WTT5 7.69| 45.74 21.781 5.13 9?.3 l| 19 39 9-, 7 8 6.52 18.70 3".87 43.91| 230 VERTICIL PFWCFNTAGr I WERT LOW MOO HIGHl ZERO T - T I 1 2 3 4 1 .» Ol 40.00 2.33 8.45 NONCON I I 26.67 32.56 25.35 SFMtCO ?j 33.33 60.47 60.56 CONNOT 3 I .00 4.65 5.63 5.941 8.26 2.9T| 16.9ft 19.801 40.8t T1.29| 33.91 T PEARSON'S CHI-SOU»RE» CHI PR OR « T i -l l 5.55 ".oono" T O T T 7 3 7 T LIKELIHOOD RATIO CHI -SQUARE" CHI PROR = 131.66 o.orcoo 0F = 6 INVALID- 25.00 «<5 ,0C«<1 GUTTMAN'S L*MBOA<= 0.38610 204 o ea «/1 I C M , a) c 1st M Bi 5fe z : O i x u * I- > z z u-« l _ T z o X CO t CM in rv. PA • '• • • • c m PA cn r" » . — — U r- IM cn - Z O t-p i_> C ? C — z X (CZ z J- r i b o C C <f cc 1 c p* r.ff h 1 cr, 1 CV i - ! cc CM gf CM 1 p* If P - P B • «* ; • • • • i • • M <T CC CM 1 <M cn cf * -o in z c O v -a z «- « « z M < * r ^ rj c e c* c cn cn cn I C CC rn CC If I C _ o- •# X cn P - CM r i M i r cn CM p-Z C t-C U C * £ z o u. c Z lfl U a cc CM IM n H Ui ex co < c 3ec a a . i / i _ I X x ° c 3. n N SE D K O B . 1/1 .-I x — u z 3 O C 205 Lw I un < i L . — 1 > C I a — i i p. i n r-i c Cf l -< l e. i i C N r a I O CO 1 LU 1 * 1 C 1 LU 1 C 1 c cr — i ! < r . 1 u. 1 CC 1 U 1 LL! 1 cr i L 5 -S 1 L- 1 — cr 0 ca cc i r LL. W z u. i ; — 4 21 a X r * I k_ IM I — «i a c < r* © t i — • *-— f - ru IT,I t> J» C a I > l i . I , I i cjm « i i «rfr> co i — u> t» i/> ££ U C 0- LL i r — r - < c co «• «9< o • » H o m er oH c m r - o l r r i « # t rv rn * p-•» '» C H c *• r - er c c < o •. • • • I-z CJ 5" X I « U l r - > ir> I lf> ||M « IT I i » i r r i r I «• <r « m i • • • • i »-. «c * r-I — fc o o i " | c e o I — ! I h i - ; I « -cr • B O X a B c c u - I ST — > . X V M O * i -» : « r -i * u i I • - cc I I M O I • i a i I CO (9 « i •» z or «n & z < or if « i U . U l IM m * » * I e r n o I • • • • i r - -« p. m | f n fn f u I I • I O O ( J O I i n I M P m I O Ce « CC O » 0 " -C — IM i n « « « «. • • • • « <6 « O rn rn • «c .n cr r v i r IM m fr, c c r" m c o -e m • • • • — 3 CM IM c a* *-c «M — w • • • • er rn cc m rn IM « C C O r -f . I - O « « »- IM «r r i f < c m " h r rn rg rg C O C O c o c o o 4 - i UL o >• » c I L J l -• er rn • o m m Lu a x « o S e c e ? a i/". — I x X cr rv 3 * X u O : if CL o 'THIS IS AN ANALYSIS nr THE -IMrSTiPHMHF ANf» INTrPVirw nATA CCH'UNED -BIX5B.I5IE T i B i r SWltQ^JUiailVt »cc ?*'> vs JOB FPFQUENCY T TABLE 7F«T> T-T I r - VFBT 1 I 7 LOW 71 f TI 0 7fOO?5 <; ? L V I (' USH.W CFOPFS P AN Of » I 7 3 1 5 HIGH A I r T ~ T T 7 Q "TT 0 2 15 13 FNG 6 6 1 19 11 PFCPFA 7 i i 5 0 PE0»rc 8 1 2 11 15 RF SPLA 9 PIVFSl (1 0 IA I 0 * l 0 21 7 161 5 181 R 1 1 I 15 2 1 18 28 30 31 78 11 <>0I 111 730 HOR | ZONT AL ^ ™ ^ L V , C F.SH-W CFCP.ES RAN PER ZEHH '-II r ' 2 ' 5 ? ENG FECREA PEC»EC RE SPLA PIVFSl a U O I A I VFRTICH PFRCFNTARE „ , , „ . „„ , „ | 2F0RFS SILVIC FISH»W CFORES RANGER ZERO T-T | 0 1 2 \ Z 5-ENG RECREA PEC»EC RE SPLA 6 7 8 9_ PIVFSl UOIAI VFRY II LOW 2 1 — HDD 31 HIGH *| 100.00 .00 .or .00 5.V> .00 .00 .00 16.67 .00 .OC .00 7. 1* 6.67 3.23 .00 7.1* . OC *6.6T 33.33 50.00 32.1* 50.00 61.29 83.?3 39.29 18. 18 46.67 61.90 * * . * * 6C.71 *3.33 35.*8 .00 53.57 81.82 *5.0C| *8.26 .0? 6.67 *.76 10.001 3.*8 5.001 *.Tfl *0.00l *3.*8 1 7 n rr— PEARSON'S CHI-SQUARE* 37.09 CHIPROB -= 0.09313 GUTTMAN'S LAMBDA- 0.^6213 — r n Vi 3 1 6 z i I IK TI I HOOD RAT 10 CHI-SQUARE" 39.32 CHIPROB « 0.05913 24 11 *0I 730 DF = 2 7 INVALID- 5 7 . 5 0 » < 5 3 2 . 5 0 J < 1 .'.1 4 ho O 207 208 c C c I- • 2 0 9 ( ( < < i ' 21 Z z * r- to i P-I • t " I f ; I IP ' Mi I <p - -r t rsj r -p o c I —-r p- o • o — M r - — ' " ' D P I t • • • • o p o — * F-«- * rn IP, — f~ • • • • fP N P P CO J5 |l PJ | — PJ P-I > * C I S C O i u. _/ x O Cu z r - > I — > »-_ P N _ m IP PI o • fj» CJ C II II UJ . a. az « c Z3 a. OC I/I — I z 1 ° u c c 4 p P m I u- — cn C • CC If • cc cn CC a ip p- r or IM re cn i c c c o c o o o « • • • S o o c p* p- m j ~ co ri * I V X O X I oc C C c"* l\J —' • -c C- Pi f- II II o i l i / i I X x ° 210 ZJ I U — W 4 as; •«i so CJCI -1 - J > rn «n — I I p- -o i o « 0" I O <\lI. fU I !fU • i C f- I - 4 X P" • (J p. > I ru V •— i c :, I — P — _ l/l <I t o 0 0 C O I — — I O C if > O I • a — i 4/ ru -« I ;C pi m IP I I —I P * to C P-c c to o» to 45 O I O CO IP i c. o r-l o P~ I f i l l i p I C 0" p I  45 45 i b p 45 X I -+ r ip I jc c cc I tV PI IP «  4f g o cc C c i h e * I C O P. I • • • . • : « I : I i i > l o o t j P . c o P- II P - P 4 I z ! 15 >- • • * c J tt C O c L _i z I m i f P rt i I PI 43 C  -41 I * ir. o c (M 45 O O O <J e I A c o o C I m o CU = 1 o rt i / \ N| O — m r\jl «•* O C O n e c l »*» f - r— o| <0 •£ <C C :M C tr o —> 5 cc " i o a. a kr. i m m i*» K c w *N* « C P - o- c O P " cr c C C 43 4f 4 P |p P-1 C - C J P C ! c c c e o o c c o c c o p-1 p a N m (- I *• * C I a C C o c o c X — c -< 45 O ru • ir e c. oo. i/i — I X p - o X i_> D o " THIS 1 s KN AMALVSIS" fiF T'(F OIIFST I0NAIRF Min INTFRVIFW OATA GQMHINEO 31 B r n H I A T T T a B I E TF SL02E <cr 25->i VS JQQ (r.c 43 1 FREQUFNCV TARLE * 1 /FHPFS ZERO T-TI 0 1 S TL V IC 7 F ISH»H CFCRFS 3 4 P ANGER 5 CNG 6 PFCREA PEO*EC 7 8 RESPLA 9 P I V F S l I10|A| < VEPT I | 7 r L O W 21 n ? M O O 3| o 2 0 ] 8 r 0 i o 1 6 n 1 5 0 1 1 1 0 0 7 2 9 0 0 0 Al 01 81 "7" 8 4 2 nii.H * | u i i ! Z 16 7 2 74 2 9 3 1 7 11 2e| I 73 1 2 15 2 1 1 8 7 8 30 31 6 2 8 .11 A 0 | 2 30 HORIZONTAL PERCENTAGE 1 ZF1RFS SILVIC F ISH»W CFORES RANGER ENG RECREA P E O E C RF SPLA PIVFSl i t w I-T i n i 7 3 A 5 6 7 8 9 I10IAI VFP» I| 7 B . 5 7 .00 ~ i.nw 2 | — . o r - 25 .rn ion 31 .oo «>.76 HIGH A| .Of 6 . 3 6 .00 .00 .00 17.50 12.50 .or 1 9 . 0 5 2 . 38 1A. 29 6 . 9 A 9.25 12.72 ,nn 12.50 11 . 9 0 1 3 . 8 7 .00 1 2 . 50 2. 3 8 1 6 . '6 1A.79 .00 .66 25.00 A . 7 6 2 1 . 4 3 1.73 9.83 .00 .00 .OC 6 . 3t 57.1A) .001 19.051 16.181 7_ 8 A2 173 1 .87 6.5? 9.13 7 . 8 3 12. 1 7 1 3 . 0 4 1 3 . A 8 2.61 1 2 . 1 7 A . 7 8 17.39| 2 30 VERTICAL PERCFNTAGE 1 ZFDRFS SILVIC ZERO T-T| 0 1 2 FISH» W CFORES 3 4 RANGE" 5 FNG 6 RECREA PE0»EC f 8 RESPLA 9 PIVF S l I10IAI VERY 1 | 100.00 .03 LOW 2 | .00 13.33 «tO0 31 .00 13.33 H H H A| .no 73.33 .00 A.76 38.10 57 . 1 A .00 .00 5.56 .00 5.56 21. A 3 88.89 78. 57 .00 3.33 1 6 . 6 7 8".00 .00 3.23 3.23 93.55 1 6 . 6 7 .00 .00 7 . 1 4 33.33 32. 14 5C.O0 6 0 . 7 1 .00 .00 . 00 100.OC 1C.00I .00| 20 . 001 7 r.oci 3. O A 3 . A 8 I 8.26 75.22 1 2 15 71 In ?8 AO 31 6 2 8 11 AOI 2 30 PEARSON'S CHI-SQUAPE-CHIPROB * 5".46 0.?04P7 LIKELIrOOO PATIO CHI-SQUAPE* 49.81 CHIPROB = 0.00483 OF » 27 INVALID- 65 .00 «<5 42.50K1 GUTTtMN'S LAMBDA* 0.016 * A ... - -^ -< ho I - H i s ts AN ANALYSIS P r T'tr QUEVTTpt.Alpr AN1 INTFKV1FW OATA CO"HlNEI) o —BUEnK'XWtE DTEKE5.IC.L0DE ICC 'R>3» v. I Q B tcr 4 3 I FRfOUFNCY " B I E ^ , , h ^ f f ^  k f t N l L „ T IERO T-T | TFTT-rr r - J G P.FCRTA "EO»FC RFSPLA 6 T 8 1 L O W 2 1 M O D 3 1 H I I H i| r~ TT 6 3 2 5 2_ 14 2 6 1 3 1 0 1 8 9_ 1 0 PIVFSl 11 0 I n | «•-1 T | 5 6 | 4 1AL 1 131 I 15 ? l 11 2 8 3C 3 1 2 8 1 1 4 0 1 H C R j M I N T A l " ^ C E N T A G E L V I C F ^ R A N P E R  * j ?j 5 i? is 7 8 9 ( 1 0 I A I ENG PFCREA PE0»EC RESPLA P I V F S l ZFW) i -11 T r H-JJJI :°cco . . . a — 8 . T O 1 2 . 2 4 1 8 . 0 6 1 3 . 0 4 " 4 . 0 8 . 0 0 4 . 3 5 ~ U . 3 3 1 2 . 5 0 4 . 3 5 1 0 . 2 0 5 . 5 6 3 0 . 4 3 1 1 2 . 2 4 1 1 9 . 4 4 1 *-I . 8 7 6 . 5 2 9 . 1 3 7 . 8 3 1 J . 1 7 1 3 . 0 4 1 3 . 4 8 2 . 6 1 1 2 . 1 7 4 . 7 8 1 7 . 3 9 1 V W | r C a t ^ ^ " " s i L V I C V l S H « CFCRES RANGFR CNG RECREA PEC*EC RESPLA P I V F S l ZERO T-T| P 2 3 4 S__ 6 T 8 9 " 0 , H .CO 3 2 . 1 4 3 6 . 3 6 3 5 . O u ! H7GH 4 1 . 0 0 4'o.pn' 5 7 . ' 3 8 1 6 . 6 7 6 C . 7 . 4 6 . 6 7 3 2 . 2 6 1 6 . 6 T 3 5 . 7 1 9 . 0 9 3 2 . 5 0 1 T "T5~ 7 T -nr 7 T -T7T ~3T "TtT 1 1 2 3 49 _ 7 2 _ 8 6 2 3 0 2 3 4 9 T2 8 6 2 3 0 9 . 0 9 1 7 . 5 0 1 1 0 . 0 0 VFRY II 1 0 0 . 0 0 6 . 6 7 . 0 0 1 1 . 1 1 7 . 1 4 6 . 6 7 6 . 4 5 5 0 . 0 0 3 . 5 7 [ni 2 0 0 f " 1 4 . 2 9 4 4 . 4 4 2 1 . 4 3 1 6 . 6 7 1 9 . 3 5 1 3 . 3 3 2 - . . 5 T 4 5 . 4 5 " . 0 0 2 1 . 3 0 . . O C 5 3 . 3 , 3 3 . 3 3 2 7 . T R 1 0 . 7 1 3 0 . 0 0 4 1 . 9 4 ^ . ^ 0 3 2 . 1 4 3 6 . 3 6 3 5 . 0 0 | 3 1 . 3 0 2 3 0 7} r—' PEARSON'S CHI-SOUARE* CHIPROB = 5 1 . 7 7 IIKFLIHOOO RATIO CHI-SQUARE* 5 3 . 1 7 rj.niJar CHlPROP =" 0 . 0 0 1 9 6 OF = 2 7 INVALID- 5 0 . 0 0 » < 5 2 . 5 0 K 1 GUT THAN* S L A M B D A * 0 . 0 6 9 6 9 o f THIS l< AN A N A L Y S E S n r T H r " U F .TtOMIPr ANO I N T F R V I F W OATA CrPHINEO 33 m a s i J i E 1 4 B I E r r stiiLEficiQ!: tec ? ^ \ VS JUB <CC ' 31 FREQUENCY TABLF _ cj > r-^ ZFnors s i L V K . ZFRn T - T | r i ? FT5H»W 3 CFr»Fs" 4 P ANGER 5 FNG 6 P F C R C A 7 P F O » F C 8 RFSPLA 9 PIVFSl 11 J 1A | V E n r i T — 7 r * LOW 71 0 2 n N n n 31 o 5 i n r •x 8 r 1 10 i ' 7 11 0 3 S 1 0 1 0 1 2 0 0 5 41 41 81 8 1 6 6 ? H I C H 4i r * 11 7 p 1 6 1 9 4 25 6 2 4 | 137 1 2 15 21 18 78 __30 31 6 28 .11 4 0 | 7 3 0 HORIZONTAL PERCENTAGE I ZFORES SILVIC FISH»W CFCPES RANCER ENG RECREA PEC*EC RESPLA PIVFSl 7FHTT T-TI P 1 2 V 4 5 6 7 8 9 ( 1 0 I A | V E O Y 11 25.00 .00 .00 - LOW Tt . O C " 12.5C .00 won 31 .00 T.?5 1 4 . 4 9 HIGH * | .00 5.R4 8 . 03 .oo TB. 75 1 1 . 5 9 5.11 .00 6 . 25 14.49 12.41 12.50 12.50 15.94 11.68 .00 18.75 13.04 1 3 . 8 7 12.50 .00 1.45 2.97 .00 6.25 2 . 9 0 18. 25 .OC • C O 7 . 2 5 4 . 3 8 50 . 0 C I 2 5 . 0 0 1 11 . 5 9 1 1 7 . 5 2 1 B . .. . . 1 6 69 1 3 7 1 .87 6.5? 9.13 7. 83 12. 17 1 3 . 0 4 1 3.48 2 . 6 1 12.17 4. 78 1 7 . 3 9 1 2 3 0 VERTICAL PERCENTAGE I ZFORFS SILVIC ZERO T-TI 0 1 2 FISH»W 3 CFORES 4 P ANGER 5 TNG 6 RFCREA 7 PEC*FC 8 RESPLA 9 PIVFSl I10IAI VFRY 11 100.00 . 0 " .00 LOW 2 1 .00 13.33 .00 — Win 3| . P C 33.33 4 7 . 6 ' HIGH 4 | . O C 53.33 52.38 .00 1 6 . 6 7 4 4 . 4 4 3 8 . 8 9 . 0 " 3.57 35.71 6 0 . 71 3.33 6 . 6 7 3 6 . 6 7 53.33 .00 9 . 6 8 29.03 61 .29 1 6 . 6 7 .00 1 6 . 6 7 6 6 . 6 7 .00 3. 57 7.14 89. 2 9 .00 1 0 . 0 0 1 . C C 1 0 . 0 0 1 4 5 . 4 5 20 .0C| 54 .55 60.001 3 . 4 8 6 . 9 6 30.00 5 9 . 5 7 | 2 1 5 71 1« 21 30 J l 6 2 8 11 4 C | 2 3 0 PEARSON'S CHt-SOU»»F« 42.03 LIKFLIHOOO CHIPROB = 0 . 0 3 7 6 7 G U T T H A N ' S LANBDA* 0 . 0 1 7 9 0 RATIO CHI-SQUARE- 43.57 CHIPROB = 0.6»291 0 F « 2 7 INVALID- 6 0 . 0 0 »<5 2 7 . 5 0 I < 1 — ' " - - -• o THIS IS" KH ANALYSTS OF TUT OHF ST 1 V,U M RF *'|rj INT F KV I CW DATA CHMBIN^D D W a r i f i i n s B L E CF « E C C I 4 H 2 ' J i r e ' 5 5 i v< J O B ( c c •"•«•?» CPFQUENCY TA BI r •ft* 3*1 / > — 7.FRP T-T| r /Fint"; I i n v T r ? 3 c p r u t ' s 4 RANGCP 5 FNG fa RECRF.A 7 PEOtF.C fl RFSPLA 9 PIVFSl « 1 0 1 A | ; •I'-s V " r i t LOW 2 I _ y-. 0 0 I 3 p. 0 n 1 5 " r v r 4 1 r, 6 >• ? 7 1 0 1 0 1 12 0 2 3 * l 41 101 10 11 53 l l H|l\H a j II \ 1 i « ; 1? 24 73 ?1 4 I s 6 22t 1 56 1 7 15 ? i 18 20 30 31 6 7fl 11 401 2 30 HOP f b W I-I 20NTAL PERCENTAGE ZFOPFS S11 VIC F1SH»W CFCRES RANGER - 7 1 5 «T T " TNG R6CREA PEC+EC RE5PLA PI V F S l 9 11 0 1 A | v p p y 1 1 2 0 . 0 0 .00 .00 . 0 " . 0 0 10.00 2 0 . 0 0 10.0.1 .00 . 0 0 4 0 . 0 C I 10 - t o w 71 . o o - - ~ « i . o < r " •00 "— 9 . 0 9 . 0 0 . 00 1 8 . 1 T . 0 0 " 9 . 0 9 1 8 . 1 f> 3 6 . 3 6 | 11 " t o 3 1 .00 5 . 6 6 3 . 7 7 9.43 7.55 11 . 3 2 1 3 . 2 1 l . f t j 22.64 5.66 1 8 . 8 7 1 53 HIGH 4 | . 0 0 7 .ns 1 2 . 1 8 7.69 1 5 . 3 8 14. It. 1 2 . 8 2 2 . 5 6 9 . 6 2 3 . e s 1 4 . 1 0 1 1 5 6 1 . 8 7 6 . 5 2 9.1 3 7 . 8 3 1 2 . 17 1 3 . 0 4 1 3 . 4 8 2 . 6 1 1 2 . 1 7 4 . 7 8 1 7 . 3 9 1 230 VERT ZERO T-T I C A l " PERCENTAGE 7F0RFS SILVIC FISH»H CTCRES RANGER ENG RECREA PEC'EC RESPLA PI V F S l 0 1 2 3 4 5 6 7 3 9 I101AI ro i—1 VFR Y 1 LOW 2 MOO 3 HIGH 4 100.0" .OC .00 .00 .00 3.33 6.45 16.67 .00 .00 .00 6.67 .01 5.56 .On .00 6.45 .00 3.57 18. 18 - .00 20.CD 9.5? 27.78 14. 29 20.00 22.58 16.67 42.86 27.27 .00 73.3? 9^.40 66.67 85.71 76.67 64.52 66.67 53.57 54.55 1 0 . 0 0 1 4.35 1 0 . 0 0 1 4.78 2 5 . 0 C I 23.04 55 . 0 0 1 67.83 1 2 T5 7 T -PFAPSON' S CH1-SQUARF* 36.88 CHIPROB = 1.09717 GUTTMAN'S L AH BO Aa 0.01536 I' :. •t TTT TT LIKFL IHOOP PATIO CHI-SOUAPr* 39.43 0F = CHIPROB * 3.05 77 1 l a 11 4 C I 230 27 INVALID- 65.00 t<5 27. 50»<i, i i 215 IT C  O • <— IM X r . I cr •31 »H U —I UJ u. C U l ui c 2 UJ o — -T i r i. * c er C C a. j «. u. cr a u. uo>om O if, CJ* u f in * O O l A C N C r»» c r> fcf M r*-o m c c c o O 15 I u. j r — k o t IM •* ec CM 1 lf> — p. ir\ CM 1 <0 CM ^ 1 f 1 -.PA * 1 * a < 1 c o o o m u. 1 c c e o • > o 1 • • • • mm tr- cr IT* © •—i a — <«i * CO •c 1 o w r- * . j 1 O O IM « & 1 t/i rr p- m UJ CM -0 QC t U cr 1 c o -o -t ••4 UJ 1 o c cc — * 1 rg a 1 p. 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Set §1- *« ru tv r a u. 7 o T c ir ^ • • • • IM -o et i «t in m ir -< n — • !• • • r- K> c c ' C t ' l C O < > * C X a c c is u. — > O a — c o <o «o «-• <M IT rj O g » 4 r\t t/l cr i*- v m o N o ^ ! •# * O . — cc c I • • • c 3 r\ P N C i c -c c c c IT fv C • • • • r. * tt' r-t») rsj ir. •O *v f\* c r- »r <c * cr co o c t t o o c c • • • • o c fu m ur >• x e z a a c u V c •4- IP m c • ^ IT rn :f\j N II UJ < C D a O CL 4/! ~ -I X x^ c ' c c tr ic IP p-• » C CP H II u. O K «J CJ 0 c l/l •— 1 z — u z u T < 217 cn «a a. — > o & •— < cr-a. • 51 «n !c5 i W a c i t c c _ IT I I = 1 UJ Ofc I - c & I |SJ * - ~* I « <c N m ; m CD • • • IT C f • : • CNJ ; * ,0 w- • * iy> j ir «^  p- er rsj im a «o IM t> o « ~ rs. i c. — t c < * tr p- o c •X' r-» f» <C O o c w I T o c o i >- ft e i I or C o e b I UJ aJ Z " s i > x > o a. — x UJ °-i « p -• or. rsj u ! UJ a CO o o or ir ft r u < > U J U . be 6 ' > i * o - o O C I O O I 5 0* < O C 9 « I o m —* m o er -* »*• i c M H I T c* *- o m c O •c o tM ^ SC I fr. I*» f"- m " #n <C >C m •# f- f*\ f - er ir • • • • r* r- ou * «~ fM »T C *M «0 «M «- «i. C «0 «N * r- r- «o er or c I I ! V O o o o a cc < c ~ oc oo. vi — c o c x. S o S£ V. i-I X x p U 1 o TCTINV} HI 218 • ( ( i f r « — : — Li 'cd •en lei Ol H 2 * a, c K "2 r* * C fc _! I o z o 8 I C o n- c © C fn C o r e , ir Jn — i ir K ri o P» »D l f l OD f i i o r « • t i n * i r e u m • • f e e • > » c I ! a e o e u r-•o c — r - » m ir, c e o c f e c e •M — in ^ * m O f I m r- f- c IM -C •t 4 •* CC. — c a a • • • • « * « CM — rn * t , O O C J I c c c o i • • • • I v x c x a o c c > ! X i z •O * i -C IT • O er ^i cc cc < c 3 Of 0 a cr * -1 X H c r ^ l ~ n n LU er cc « C 2 i 0 c. in i— 1 X 5° w 1 *4 - «i P I N I d * I o in CC 2 z vavwvs MI W N 219 UJ Z 'f\J or IV •LV «? «: *^ a =J _~T Ul U. M a1 > LU CJ u. vi b-1 CJ <3 311 z V" a L «r Z c •» U" LL CI a & 21 r f- C N » c t e c i e> t - « -& z o K. m • * • rn tr * IT f~ P-« ft P -K P IP. . c O ' r 1 Lu C* i **> IT I •- •« LL • I > K > cr i e> if* 1 <c it *\» r* m o cc ir «o r «-* * • • • • r- _ rv « r c « . LC fs; *NJ INi P CI o c o o o o n o o c c o —« — o n n LL' CL £ <f o 3 ar V? »-I X 5" o o £ C ~ — c = a 0 a vi *-1 I — o I ; - » f. or — IM r> * I 3 p ie c u o T < s o 220 r § xi ;u> •El CU a u-• u l -3^ =3 •H o 31 n u .CJ erf <r> UJ CJ =1 .ja-unt ei v _i > a «1 — —4 C dj rj <:i 91 c n~ i r. ru - U J • Ir pn o iwr »c ! l c ' » I* * 4 IM V fn •» IP O J — _ l I- O . < c « o. L ft F C ; PC c <-t-L U > «J • 1 1 " ' I-' o 8. I m I iv • » f CC c r . - C » • • • C m rn er . _• * c « c I t r - c — » • • . • • • c — r cc w r i ^ IM • r» r - * c I \ ftj ifl >fl c I i o tr c r . r • «r »5 «3 « r - f ; — IM c. # ir. i f l « r j r j r « c < — L U a CC X O O C C D — V ' U J U J V —I > tr- X u i u-— u. 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H It OL X < c O c t o n un — X <• fM , * m i c « c cc tM - I rn IT m | f-_ r Ic c •» i — 1 C » - M r » IT « i i / l « — j r e •J r- « D « a 1— UJ cc e x o K O O cc U CO LU LU r J M P > *- C U. m fM tM «e c - o I -C C 5T r o T H 1 ! , 11 AN UNalVS'S (tt T^r ^'IrSTlCNAiRF AMI INTrB, VTr w nATA f CHBI NEn 41 O l Qi^mrai XSHI5BUE !5 5.2 IL CL»SS1EJC4IIC U S^IEtV E u i f t U f l l E ISBLE OF cSiCUIlLill <cr ?>4i \ s jtja ICC 4* 1 TTflTT Z F 1 R F S n 1 0| 7 1 LOW 1 I 0 5 FBFOUFHCV ZERO T -T I s n v i c FiSH.W r.rr.p.rs P A N G E R FUG RECREA PCC»CC RF SPLA PIVFSl HH1I f\ HIGH 3 1 NFVFR 4 I I 1 5 ? 3 4 5 6 7 H ; _ . - - - ' - - - — - - - - - - - - - - - - - -P 7 0 4 3 1 0 n 3 4 7 3 2 ?. 5 7 11 7 11 0 5 <V 3 3 0 1 1 7 3 6 R 11 1 ? 13 2 1 - i . — u . - . — - - - - - - - - - - - - - - - - - - - - - -?1 10 28 30 31 6 20 9 (10 1*1 1 41 IB 5 101 47 1 71 49 1 71 43 3 _ 121 73 11 401 2 30 HUM/UNI »l PtMLFNI *'".t ZERO T -T | 31 tOH I I ZFTRES SILVIC F!SH»W CF0RFS RANGER 1 2 3 4 5 U . U .OP 5.56 10.64 HUH 2 1 HIGH 3 1 NEVER 4| I -nro im-.00 ft.9(3 .00 8.22 .00 12.77 9. 30 B.22 11.11 6.38 4. IIH 6.98 10.96 . 0 0 8.51 77.45 27.22 14.B9 14.21 6.98 .00 13.70 16.44 ENG 6 16.67 6. 38 27.45 PECREA 7 5.56 4.26 .on" PE0»EC 8 .00 4.26 10.2V RESPLA 9 5. 56 10.64 2.04 PIV F S l (10IAI 22 . 221 21.281 2.33 17.81 2.33 2.74 46.51 1.37 2.33 4. 11 .87 6.52 9.13 7.83 12. 17 13.04 13.48 2.61 12. 17 4. 78 14.291 16.281 16.441 17.391 18 47 49 43 73 230 7ERC V P " I I T - T I 0 1 LOW 1 I WIT) 21 HIGH 31 NEVER 4 I I T E H . P E R T F I ' A I J F ~ 2FHRFS SILVIC FISH*W CFCRES RANGER P 1 2 3 4 5 FNG RECRE* PEC»EC RESPLA 6 7 8 9 100.00 .OC TOT" 6.67 33.33 .00 28.57 73. H 1 11.11 16. 6T r r r •oc .00 2 0 . O P 43.00 19.05 28.57 21 T T 16.67 44.44 18 .00 14.29 31. 71 1 0 . f\ 35. 71 28 13.33 23.33 2T.33 . 0 0 4 0 . 0 0 30 9.68 9.68 "35.48 3.23 41.94 31 16.67 33.33 T T " 16.67 33.33 9.09 45.4? .00 7. 14  17.86 9.09 71.43 9.09 3. 57 27. 21 28 11 PIVFSl (1 0 IA | 10. 001 25.001 17.501 17.501 30.001 «.. 401 7.83 20.43 21.30 18.70 31. 74 2 30 to P1AHSUN 5 CW-SL'Un"! . CHIPROB • R?.66 O.ionio LiKFLinnnn R A T I O C H I - S Q IMP E » CHI P R O B ' 85. 4d 0 . 0 0 0 0 0 CF« 27 INVALIO- 45.00 »<5 .00«<1 KOI THAW S" TA-1BTJA* 0.12680 f THIS T l AN >MAlVS|f ffF Tlir OijrST I 0 N A I P C AMn I M T F R V I T K D A T A r . r n U n C 0 o THm m AN A N A L V S I S I n * fturslUJKMi'g Arm i m i R v i r w T A T A I N - . - - B m n s t r r n t r C F C S S C U I I L I H 'cc vs D L C I S I D N K . C ? M FREQUENCY T A B L E ^ ^ ^ „ , | M U I > H | W | r 1 2 ? * *>' - r H F R O T-TI r LOW i i r MOO 2| P — H I G H 3 NFVFR 4 | r i • -t ii r ' F R O T-T I --fr-L O M 1 I H O D ? I mi.n 31 25 T O - r r i "7 4 1<J 41 117 T T 1 * 6 22 ri 51 TT Tl 301 18 47 49 4 1 71 -2*3"" H 0 R W 0 N T " ' T ^ U S W m i U I »L LLWII 0 1 5.56 .or .no -rotr 16. 67 12.77 2.P4 II .63 5.48 — r e i T , T 53.19 8.51 61.2? 16.33 /5.5H 56. 16 51 ,pr 16.671 """•" 1C.64 14.89| 10.20 10.2O| 1 •".'••> NEVFR 41 1.37 I .87 8.?6 50. P 7 VERTICAL PERCENTAGF TETDl 2 6.84 1.37 i.64 r m >.<"« 117 16.93 19.18 17.39 IB.fc'll 9.59| 9.57 11.041 8.22 18 47 49 —TTT 73 230 ZFRO T-T| 0 1 — o r 50.00 15;79 LOU 1 1 .00 31.58 HOD 2 1 .00 5.26 NEVFR 4| «0.00 2 l ! o 5 -------— t 7 19 3 4 51 _ _ _ „ 7.50 .00 10.001 7.83 10 .00 22.73 23.331 20.43 20.00 2 2.73 16.671 21.30 7 7» Z / 26.6 71 18.70 35)0" 27. 27 23.331 31.74 • — — -40 ?? 301 230 r o r o r o PF ARSON'S CHI-SOU ARF« • LHIPHIIH • -T.IblS .V LIKELIHOOD RATIO CHI-SUU>RE« CM I P R O D • 18.20 0.10934 D F « 12 INVALID- 25.00 l<5 '00«<1 CUTTHAN'S LAHR0A» 0.0124? TMI5 I "! AN ANALTSIS^ OF INE n,|FST I O N A T P F ANO I M T c q v n r n P A T A C O M B I N E D O ffUKlHE IfiBLE rr C S S C U I l L l I i : CC ?f*l vs uGJEuUl ir.c i z o i " , > r Z E R O T - T I FPFQUENCT TAPLF LOW TI MOO 2 I HI'.H i | NFVFR 4 I HPT |Ml PK UFlL •rtCNTNOi r 1 2 •* 4 5| . - - — • - f - -" V 3 7 '•' fl 1 o l IB ? 6 7 33 4 o| 47 2 b S 31 5 P | 40 "J 3 71 4 11 1! 3? 11 3 01 41 43 73 - t 4 43 73 115 31 ~ 41 23"" HCR 17.0NTAL PERCENTAGE 1- NOTTMTr NOTJTE DK HE 11 OflM TNUI 2ER0 T - T | 0 1 2 3 4 51 01 27.22 16.67 n V V T " «4.44 5.56 .001 TOT IPW II 4.26 12.77 4.26 70.21 8.51 .001 47 MOO 21 4.08 17.24 10.70 63.27 10.20 .001 49 HIGH 3| 8T9U 16.28 9TT0 25.58 41.86 . W l T T NFVFR 4| 4.11 28.77 13.70 43.84 4.11 5.48| 73 I 6.09 18.70 10.on 50.00 13.4R 1.741 230 1 ho VERTICAL PERCFNTACF  1 RUl I ME NUI'Nt OK kE LL IJUNINIII ZERO T-T| 0 1 2 3 4 51 01 -7H.5T 6.98 P.70 6.96 3.23 .OOI 7.8* LOW II 14.29 13.95 8.70 ' 8 . 7 0 12.90 .001 20.43 MOP 21 14.29 13.95 21.74 26.96 16.13 .001 ?1'3_2 HIGH 31 21.43 TrJTTI TT7TS TTTF 5TT1J5 TdTT 18.70 NFVFR 41 21.43 48.84 43.48 27.83 5.68 100.00| 31.74 "-" | 14 4? 23 115 31 41 ?30~ PEARSON'S CHI-SOU ARE* 58.01 LIKELIHOOD RATIO CHI-SUUARE° 53.28 0F« 12 IKVALIO- 35.00 |<5 l ? i°P«l-— : I.HIPHUH O.Ol'OO:) CHIPROB ' 0.C0100 GUTTMAN'S LAMBDA* 0 . i r i ? 0 7> 224 3 U U n- I p- ,» •I « ' k e c . n r . i - l c — r. l i . 1 I- I LU u. i i r c i - c a. I r fc lu I '. C Z ~ i i r k - J o 1 U: I- : 3 Z O c o > U-' z j | •» r r I -> •» :l p- >£ c- O rr. 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IP — 1 X X o c o c cr c O b . i/i •— I X p- o X • u c l/l o «r ct 3 o vcnrMV 3 HI aavm 227 228 I < I gr gj UTll c Ui l-» ca P u I <f *• »-' IO < t rr. 4T-to if I flV f 4-L . * t r «* cr p r I ET. *t f -• rv | •— rr, r\ ^ ~ • « J K c *- I »* f » f i B t t. at-u. — > c I- in u . 2 U J U- CL u o 6 — m * rg INJ IN) O CM ng rg CO — — * CO p4in o n M |(M lA *C* rg j — n-c ic co rr i r i r o m g- — O * tr> — no sr it. ier c r-— CI cc * C r-"" C o er r-\ r c — r» * C K N I T IT f c c SI i r rv. cr t r t O t - f e e m er o co i r o n " i r c - * i — rg i r I e o o cr i n « m u. —I • > o r* - e v -er z in >• t » C I O D C C > : x 19 m •H U. r - C t iliu Lx r— O O O W I o o i r i n i O i n rg rg — — — gj I lfl CP l -cc in c r er cr- cr m rg c rg * t>. r » rn m gs ga n . m e er r- » e r . i r — gr cc r-— CM IT. o IN. c er O rg cc cc go c r rg r* r n i r g gf * O — r- r- o •C -c *C c o e e o o o o c * e 1 C t IS J i -lt* i - e n n UJ a cc « c S e r O t i n — I X o c o o • i r IT — » t n 'ti UJ cc rr « C => B O C . i n — I X x ° U : Z c o 2 2.9 •.«-». «— o-< JT >•«-iO-* Itn u ion •>i 5. n r <c — J ll— *J la: i < > Iz It— f L 1— o Ic-Q-l &1 0+-— * I r-fv C l I . I »• i c tr c c . A -c c t fu *r 45 43 M f v ec i r . • 1 • • • IM r- «M -n IM «r * m r - * C. 45 45 r- • - CC • • • • 45 i f IT CM 45 IT. IT 4? E c X - ! — U *r- «H • u a cc 4T C C r-I B « f I <~ — 45 — CM c . * J V * C X a O c c cc i -CMlC. • r-CD F • II LU 91 g 3S •y — I X « -u 3 u> . * j- ui Sr c a «T u-b 230 p- •» •-3 ta I •» t-l In — i •£ c o • P l i 1 r» r- * o Ui> U IM In C <" w~ ¥* ~ & f- — C , it 0* IT c f n o- — o r> o •» • . • • • ir f > — m v * e x C -i r- c u/ 15m • m u fC k v m cc Cf er o C I — Ui u. u-c z — ; cv U.' ' > s; t z> o o o z vavwva MI wenm 231 T'lF ill)»=;T 10KAf * E AN" IN"n.VlFW HATA CMMIJ1NFD ajvanflif iflBit nr CHCgjg ice v i v. JOE C?X « IS rPFiuFwr.r T A B L E 1 / I M H H M L V I C <MSH»U r,rcpr«; gAMCF]" cur, R E C R F A PEt»Ei". R E S P L A P T V F S ! r!E RTt T - T | C 1 7 3 4 5 6 7 8 9 <10IA| -VF°T - T 1 7 n n n ? T 1 I I i 5| 1 5 LOW 7 | f 7 7 4 3 5 0 3 4 1 11 77 » n p 1 1 0 1 6 ». 6 7 7 2 1 5 6 q j 6 5 HI'-.H Ai P rz n « r r PI n 5 § I TTI ITR " I 2 1 5 7 1 I B ? 8 3 0 31 6 2 8 11 401 2 3 3 HOR I ZONTAL PERCFNTAOE I Z F O P F S S I L V I C FISH»vj C T C R F S RANGER FNG R F C R E A PEC+EC R E S P L A P I V F S l tmil I-I | n I •> 3" 5 5" 6 f B 9 I I O I A I V F » * I I 1 3 . 3 3 .00 . 0 0 . 0 0 1 3 . 3 3 . 0 0 2 0 . 0 0 6.67 6.67 6 . 6 T 3 3 . 3 3 1 \9 Ltmrt .oo—9.o<7 «r.09—rB.ifl n . 6 4 2 T V T 3 . o o . t o " T S . i a A . 5 5 4.551 22 " 0 0 31 . 0 0 1 . 5 4 <».23 9 . 2 3 9 . 7 3 ! 0 . T 7 1 0 . 7 7 3 . 0 8 2 3 . 0 8 9 . 2 3 1 3 . 8 5 1 65 H I G H 41 . 0 0 9 . 3 8 1 0 . 1 6 6 . 7 5 1 3 . 2 8 1 4 . 0 6 1 6 . 4 1 2 . 3 4 6 . 2 5 2 . 3 4 1 9 . 5 3 | 1 2 8 i f j u i j i - i . j f I . 8 7 6 . 5 7 9 . 1 3 7.83 1 2 . 1 T 1 3 . 0 4 1 3 . 4 8 2 . 6 1 1 2 . 1 7 4 . 7 8 1 7 . 3 9 1 2 3 0 I /F f lPES S I L V I C F ! S H * M C F C R E S RANGER ENG R E C R E A P E C * E C R E S P I A P I V F S l ZERO T - T I 0 1 2 3 4 5 6 7 8 9 U O I A l  • |UI>i l II .4 _ — — VERY I I 1 " 0 . 0 0 . 0 " . 0 " .Of 7.14 ,r 1 0.68 1 6 . 6 7 3 . 5 7 9 . 0 9 1 2 . 5 0 1 6.52 LOW 71 . O C 1 3 . 3 3 9 . 5 7 2 7 . 7 ? 1 0 . 7 1 16 . 6 7 .CO .CO 1 4 . 2 9 9 . G 9 2 . 5 0 1 9 . 5 7 •»f»r> 31 ;f»P 6.67 28.57 3 3 . 3 3 2 1 . 4 3 2 3 . 3 1 2 2 . 5 8 13.33 " 5 3 . 5 7 54.55 2 2 . 5 C | 2 8 . 2 6 H I G H 4| .OC B O . 0 0 6 1 . 9 0 4 4 . 4 4 6 0 . 7 1 6 0 .on fc7.74 5 0 . 0 0 7 8 . 5 7 2 7 . 2 7 6 2 . 5 C I 55.65 i 2 n rt m TIT V T — n 5 21 n 4 c i 730 P E A R S O N ' S C H I - S Q U A R E " 43.66 L t K F L I H O O O R A T I O C H I - S O U A R E * ? 0 . 7 5 0 F » 2 7 INvALIO- 6 0 . 0 0 1<5 1 0 , 0 0 * < 1 - C H I P R r R = 0 . 7 7 7 4 3 C H I P R O B = 0 . ) 137 7 G U T T N A N ' S L A N R O J U 0 . 0 6 « 4 0 ro to ro 233 F 234 > c 0, — • C I • IM i • es ( tc i f r.• • I c •C IP r-IXi C U C CL U . r c r ** ^ i i r c~ o U 6! T I • c li ' _j ;i < ;i « * ii in ii H ; i N « i i «- a IT CP ; I a ir ; l t? z t ; r j t c N — i r- i > ;l . . _ i ] l * " — I * C L I , c c < 1 O o (T u. 1 o c > o 1 • • « rt o c CL j 1 •** itn < • 1 er i o —I 1 o m rt: B o. | • • • U, 1 o UJ 1 t IT. a CD 1 tr i*- cr UJ 1 ir. •O LO * 1 • • • If* M0 1 o • c t tr p-a 1 I »r « 1 C i c UJ 1 w a. 1 u 1 o o N 11 UJ 1 IT UJ a c « c S c : o c . I X cre-pt o c i n -I X — X z o l/l ! o ! 3 3 I I in CP jl — o Z IP . 5 ; I P cc I — I > IM i *r f p IP i - i i i > » 3 p *r «-i a) •a X a C — M C J z c m cc r-—« JL. Ur m «o * »C f\.' f\ •V cc lf\ r- C i*. * C- o-C- rt fS, | z 2 3 5 l!= L" C — < U kn o I T ( m r »' C I k. r c i e fee- c i * it C "I C I ' • II C jl I T !l — f.' * k J • C J • Cf < < a l l _ l V • II II LL' let SC I* c p tt I II II p ct p a I tr<-' o — — U J t- I a , i • H C c I **• J, * -<— ^ I CD ff m f-K " i l C O * • o in f S c I T I ff L B mj I er < I « I i j i y i I t I M t r-I - I SI LU | fc I I " H I rv LP c « c r - u r z U - ' O u z a 4 >C <C I tC o* t • in ir l I M T . I in gr i > » o I • r- c i • > • i ' K O I ts> in i r n-j n in I • I -o I I rg • I I - I M | i m I • • I I er a» r - r» n II LU a cr « c so-o n •— i rr c c X => or O b LO 5* — * 4 -T C : e vcrrgv» MI aavw 236 i i * « i - « * p p i i i i l« C I C> ~ O f |« f- i r ^ c r _ l n ir i r i M f r c ! *~ L r ' P Ei o * 0" IT. i*, o = | 2 - 2 I : C I T o I jCJ CD C» I : • • I . rn •o I <r i*» e •* P * 6? ! l i n e ; i _ I O C I V IT I • • I r\j p* I . I I I o <* I o o ?|5 i . lu er I O *- O I iC IT- Okf l — er i :ru O p -O I . I ' • 4 I *M I I i <r I *n i *•* i I P K* a 1 t f I c if* I c * I o n -i c I i * -I i sr i p- sr rt *0 If CO i f " C cc r rr e * Or-I BP I 'ei I '• " I I 1 c 5 . I c * s O I C I I*. I •S I c I r- - J H = l i p- I r • I c * 1 ; K I* I k c i t b c i i * . Ic C I r- IT I rv; C C c 3. pn CO -. r-L c • e> 2ES P i t * r b o c * IS p O I X I p. i p ru V S a C « — K i IS I c c I c c ' I or-I c <c i c c i I I : e I : O I , " I , p p* rvi J 5 I p-O I N I ! X I u J i - «/> . i w i m w i P " 237 i <S* <i ( . — 45 cr U. — I > O I a — I I {« ir 1 'O m ml' i c r e - in < C I o '45 t O m IT . — I 145 — I m l/> 4» I ic — <nk L M I C C r* 45 45 < CM fnl« I o I m I «M I C I p r-< o- b (_ I > o i • • o cu u>[iri 'f c o ckr c t = I S K R C i I-I j c rri I ; . . . I — «e L> cc l |C ir cr el- • i t " ' p rr I to m I jc or i cr — ' I CD ir I r-• I • m i j> & r- i tr if. rr, If C I c o r I C IT I , IS r 3 L . l ti> t U j t t fe?: is. t ! -2 c I t>o-I C 45 I D I • i m 2 * - rv • • D 1/5 r, CM - O V K r — IT t m i s l t , — r* I I £ I X I I I m c- IT • rri 45 l|/l < I D U O - I m c ui > O I • • I— — I IM I f l N |_l | O IM IM U> O O I :C i I Jf. rri l ;C m O CC O 45 b LT I O I*"' C I c r-I C IT* C 45 c *• O o o 45 • I IM — C 4" o r K iu i o kc 45 i <r i m I X u.1 o c o c I C O I I C C ' U 45 I — — t — I C < r-. I c r o ! i "I o ru l c C I c o te-opc I I >- X => f O -to -I r. c : cc ' r • .Tf OVMV9 •*' -WW* 238 , H | S |S HH ANAL V S I ^  HI- I M> I • !.| i M I W 11": i M ^ n v t t w I'AIA U.M'UNFO ZERO T-T I rRtj»,FNCr SI L V I L l l ^ A U I I L'll .. ..WI.J.M TNI p E H M PFC*!2!*. RI-SML* PI V F S l «, r 1 9 l i r i A l *•-t-r VES 1 I 11 7 IS IB ND ? I P * 4 r n 4 ~ 1 22 T 4 ' 23 4 2 24 7 II 321 _IL 1* 182 34 I 21 1« 28 30 31 28 11 401 230 H P H | ! l ' N l . l T W W « ^ u i t F ! SH*V) CFT-RES R ANGER 2ER0 T-T I - - - - - • • i > • 0 1 ENG P.FCRF A P E O F C RESPL* P I V F S l j, 7 8 U O I A I n| 14.29 .Or 14.29 YFS 1 I .0" 6.^ 4 8.24 HO 71 i O P — I t . T 6 — t T V T r -| .RT e>.*!2 .00 9 . 89 - . n n .OC T.14 28.57 14.24 14.29 13. 19 12.09 12.64 2.20 12. 19 11.71. 20.SI 11 .76 . f T S.Bfl 9.13 T.83 12.1T 13.04 13.48 2.61 12.1? .OC T.14I 4.95 l T . s e l 182 5.S8 20.591 34 — . -4.T8 IT.39| 230 ENG RFCREA P E O E C RESPL* P I V F S l 6 1 8 9 1101*1 VER TIC *L P " « N T » G F s | L v i c C F C R F , R R N G E R - t ! W - T * r | O 1 2 3 * I _ _ "'Z^^'"^"^"^'^ frinrgi * c 1 1 ' * , , , , > P 0 l«_7c, 7^.31 12.90 . r > '.14 18.18 1T. 5" I . 0 0 2.501 6 . 0 9 NO 21 .00 26.6T - ? t« 71 PEARSON'S CHI-SOUARr= « . < " IB 7R 2"<.33 3 0 1 7.90 31 T9.13 14.78 78 U 401 230 • CHIPRCP fjt«ti»a,n3 L IK FL 11-000 RATIO CHI-SQUARE" — CHII'HUH S-12.20 rj.?ni4J 0F = 9 l u w A l t n - 5 0 . 0 0 « 5 5 . 0 0 . K 1 GUTTNAN* S L»"BO»« 0.0«00r T l f E FOP TAPLE PRINTING: 1.P63 CPU SEC. 240 A P P E N D I X I V S U F M A R Y O F R E S O L U T I O N S 24 1 SOIL-VEGETATION-LANDFORN MAPS USER SURVEY: SUMMARY OF RESPONDENTS DESIRES AND RESOLUTION OF HYPOTHESES PLEASE ANSWER QUESTIONS/ ADD COMMENTS AND RETURN 242 HYPOTHESIS #\: MAP SCALE C h o i c e s : < I : I 0 , 0 0 0 , 1 : 2 0 , 0 0 0 , 1 : 5 0 , 0 0 0 , > I : 1 0 0 , 0 0 0 T h e r e were t h r e e methods used t o a s s e s s d e s i r e d map s c a l e A l l t h r e e s t r o n g l y I n d i c a t e d t h a t 1 :20 ,000 s c a l e was t h e most d e s i r a b l e . T h e s e c o n d most d e s i r a b l e s c a l e was < I : 1 0 , 0 0 0 . C o n s i d e r i n g t h e t r e m e n d o u s d i f f e r e n c e in c o s t , 1 :20 ,000 i s u n d o u b t e d l y t h e b e t t e r c h o i c e . p , , 0 „ , t , n n o f H y p o t h e s i s , ! : » . . . t . t l o n - s o , I - I , „ d f or. . . p s an a p p r o p r i a t e l e v e l o f d e t a f o r t h a t s c a l e Comments : 243 S u r f i c i a l D e p o s i t SI ope A v F G e t c . I 0-2.5% B bFG e t c . 2 2.6-5% c 3 6-9% A 9 -15* ( R . A . B . t e r r a in u n i t ) ( R . A . B . s l o p e cI a s s e s ) Q u e s t i o n : Would you r a t h e r have d i f f e r e n t c o n n o t a t i v e i t e m s t h a n s l o p e and I a n d f o r m / s u r f i c i a I d e p o s i t ? YES • NO • If Y E S , what? Comments: 244 HYPOTHESIS » 4 : USE OF SOIL CLASS I F ICAT ION C h o i c e s : H i g h I m p o r t a n c e Low i m p o r t a n c e P e d o l o g i s t s were t h e o n l y g r o u p w h i c h f o u n d s o i l c l a s s i f i c a t i o n h i g h l y i m p o r t a n t . A l l g r o u p s were s a t i s f i e d w i t h t h e i r p r e s e n t l e v e l o f k n o w l e d g e . R e s o l u t i o n o f H y p o t h e s i s tiA: S o i l c l a s s i f i c a t i o n s h o u l d be b a c k -g r o u n d o n l y . It s h o u l d be in t h e r e p o r t , but d o e s no t seem n e c e s s a r y on maps . Comment s : 245 HYPOTHESIS 16: C h o i c e s : DER IVAT IVE MAP LEGENDS ExampIe ExampIe ExampIe Examp le 4 S u i t a b i I I t y Su I t a b i I I t y Su i t a b l l I t y .+ I m p o r t a n t S u i t a b I I I t y + I i m i t a t i o n + I I m i t a t i o n v a r i a b l e + I i m i t a t i o n + a l I p o s s i b l e I i m i t a t i o n s 2W 2 2W c / t d2W c / t : I ,2 A l l g r o u p s e x c e p t p e d o l o g i s t s had a s t r o n g p r e f e r e n c e f o r e x a m p l e 4, t h o u g h 3 was a l m o s t a s d e s i r a b l e . More e x p e r i e n c e d i n d i v i d u a l s had a h i g h e r p r e f e r e n c e f o r e x a m p l e 4 . R e s o l u t i o n o f H y p o t h e s i s #6: When a management p r e s c r i p t i o n i s made a i l v a r i a b l e s w h i c h c o u l d p o t e n t i a l l y be l i m i t a t i o n s s h o u l d be i n c l u d e d In t h e r e p o r t a n d / o r l e g e n d . T h i s s h o u l d be t i e d i n w i t h t h e r e s u l t s o f t t ie n e x t h y p o t h e s i s Comments : 246 HYPOTHECS: MAP PRFSFNTAT ION : ^ F N ^ F B) HAP BASE TYPE C h o i c e s : A) l a r g e l e g e n d smaII l e g e n d B) a e r i a l p h o t o map p l a i n l i n e map c o l o u r map C) c o n t o u r s no c o n t o u r s Legend s i z e . T h e r e was no o v e r a l l p r e f e r e n c e o f l e g e n d s i z e . Some g r o u p s p r e f e r r e d a s m a l l l e g e n d and some a l a r g e l e g e n d . L e s s e x p e r i e n c e d p e o p l e p r e f e r r e d a s m a l l l e g e n d ; more e x p e r i e n c e d p e o p l e a l a r g e l e g e n d . No r e a l r e s o l u t i o n was p r o v i d e d . C o l o u r maps were most s t r o n g l y p r e f e r r e d , but no t t o o much more s t r o n g l y t h a n a e r i a l p h o t o g r a p h s . A e r i a l p h o t o b a s e maps were p r e f e r r e d by p e o p l e w i t h more e x p e r i e n c e . C. C o n t o u r s were d e s i r e d by 84% o f r e s p o n d e n t s . T h e i n t e r v i e w e r s b a l k e d a t c o n t o u r s on t h e maps s a i d t h a t t h e main p r o b l e m who was l e g i b i I i t y R e s o l u t i o n o f H y p o t h e s i s #8A: Rpc;t-) Lut i on o f H y p o t h e s i s fy8B: No r e a l r e s o l u t i o n . Maps s h o u l d be p r e s e n t e d on o r t h o -p h o t o s . C o l o u r maps a r e more d e s i r e d , but more e x p e n s i v e and l e s s u s e f u l . W i t h more e x p e r i e n c e a e r i a l p h o t o s w i l l p r o b a b l y be more d e s i r a b l e b e c a u s e o f e a s e o f f i e l d l o c a t i o n and r e c o r d o f h i s t o r i c v e g e t a t i o n . L i n e map p r o d u c t i o n w i l l a l l o w f o r i n - h o u s e m a n u f a c t u r e o f c o l o u r e d maps . Comment s: , 247 SUMMARY OF PROPOSED MAPPING METHOD S o i l - v e g e t a t l o n - l a n d f o r m maps s h o u l d be p r o d u c e d a t 1 :20 ,000 s c a l e w i t h i n s e t maps a t 1:5000 s c a l e o f more s e n s i t i v e a r e a s ( d e f i n e d p r e s e n t l y a s m a j o r s t r e a m s ) . and Map s y m b o l s s h o u l d be l a n d f o r m c o n n o t a t i o n s . o f t h e s e m i - c o n n o t a t i v e t y p e w i t h s l o p e Man u n i t s s h o u l d be s e p a r a t e d p r i m a r i l y by s l o p e c l a s s e s . p o s s i b l e i t e m s f o r d i f f e r e n t i a t i n g map u n i t s w i l l no t be used s u c h - t h e y w i l l be s u p e r i m p o s e d ( s e e d i a g r a m b e l o w ) . S I ope u n i t s V e g e t a t i o n and m o i s t u r e sub u n i t s w i t h r a n g e s o f o t h e r p e r t i n e n t p r o p e r t i e s g i v e n in l e g e n d and r e p o r t . P r i m a r y d i f f e r e n t i a S e c o n d a r y d i f f e r e n t i a S o i l c l a s s i f i c a t i o n w i l l no t be e m p h a s i z e d , bu t i t w i l l be i n c l u d e d . A l l c o n f u s i n g n o m e n c l a t u r e w i l l be pu t i n t o t h e r e p o r t . The i n t e r v i e w e e s made i t q u i t e c l e a r t h a t o b f u s c a t i o n was one o f t h e most d i f f i c u l t p r o b l e m s in u s i n g t h i s t y p e o f map. D e r i v a t i v e maps s h o u l d no t be p r o d u c e d , but t h e y s h o u l d be an o p t i o n . The l e g e n d and r e p o r t s h o u l d c o n t a i n enough i n f o r m a t i o n so t h a t a d e r i v a t i v e map c a n be p r o d u c e d I n - h o u s e by u s e r s . Any I n t e r p r e t a t i o n s made s h o u l d be a c c o m p a n i e d by a l l r e l e v a n t i n f o r m a t i o n t o f a c i l i t a t e t h e f a b r i c a t i o n o f a d e r i v a t i v e map l e g e n d o f c o n s i d e r a b l e d e t a i l . S o i I - v e g e t a t i o n - I a n d f o r m maps s h o u l d be p r o d u c e d u s i n g an a e r i a p h o t o b a s e w i t h c a p a b i l i t y o f l i n e map p r o d u c t i o n f o r d e r i v a t i v e map-m a k i n g . C o n t o u r o v e r l a y maps s h o u l d accompany e a c h s i t e map. Comment s: 

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