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A proposed framework for analyzing the spatial organization of urban open space networks : case studies:… Shakibi, Negin 2015

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A PROPOSED FRAMEWORK FOR ANALYZING THE SPATIAL ORGANIZATION OF URBAN OPEN SPACE NETWORKS Case Studies: Tehran and Isfahan, Iran  by  Negin Shakibi B.Arch., Tehran University, Iran 2008 MLA., Shahid Beheshti University, Iran 2011  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF ADVANCED STUDIES IN LANDSCAPE ARCHITECTURE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Landscape Architecture)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  March 2015  © Negin Shakibi, 2015 ii  Abstract  Urban Open Spaces (UOS) are among fundamental urban elements that can have a profound effect on the quality of social life and on the physical and/or mental health of citizens. UOS system is composed of individual components and a network that connects these spaces. UOS system, just as any other sustainable systems, should be more than the sum of its parts and should exhibit interdependence among its components. Therefore, there is a need to study these individual UOS as interconnected elements and, in other words, as part of a network. To date, most studies have focused on the quality of UOS as individual urban elements. Studies specific to the role of public participation on design and the planning of certain UOS, quality and quantity assessments of certain case studies, and behavioral studies are all good examples of current studies on UOS. However, a holistic view of these spaces as connected urban elements is missing from most urban studies.  The UOS networks of some Iranian cities such as Tehran and Isfahan have faced numerous changes through time. While some cities such as Isfahan have had the opportunity to adapt to new conditions, these sudden changes have caused some abnormalities in the UOS networks of other cities such as Tehran. Moreover, the cultural and historical sensitivities of these cities amplify the complexity and importance of analyzing the UOS network. Despite the need to study UOS networks, only a few of the valuable studies conducted in Iran have addressed the importance of UOS as connected networks of individual elements. Therefore, the aim of this study is to illustrate an attempt to fill in some of that gap by proposing a framework for analyzing the spatial organization of the UOS networks. This Spatial Analysis Framework (SAF) focuses on how individual UOS connect, interact and interdepend upon one another at a greater spatial scale. Finally, using the proposed SAF, the UOS networks of two selected case studies from the historic cores of Tehran and Isfahan have been analyzed and compared. The results of this analysis have been used to refine the proposed SAF. iii  Preface This  thesis  is  original,  unpublished,  intellectual  product  of  the  author,  N. Shakibi.   Case study analysis in Chapter 4 was also designed, carried out, and analyzed by the author solely using publicly available sources.  iv  Table of Contents  Abstract ................................................................................................................................ ii Preface ................................................................................................................................. iii Table of Contents ..................................................................................................................iv List of Tables ......................................................................................................................... x List of Figures ....................................................................................................................... xv List of Charts .................................................................................................................... xxvii List of Abbreviations ........................................................................................................ xxviii Acknowledgements ........................................................................................................... xxix Dedication .......................................................................................................................... xxx Chapter 1: Introduction ......................................................................................................... 1 1.1 Three Distinct Scenarios of a Street ................................................................................ 1 1.2 Problem Statement ......................................................................................................... 8 1.3 Scope of Study and Thesis Statement ............................................................................ 9 1.4 Research Goals and Questions ..................................................................................... 11 1.5 Thesis Overview and Research Methodology ............................................................... 12 Chapter 2: The Urban Open Space (UOS) Network ............................................................... 15 2.1 UOS ............................................................................................................................... 16 2.1.1 UOS Definition .......................................................................................................... 16 2.1.1.1 UOS Domain ...................................................................................................... 16 2.1.1.2 Public Space Definitions .................................................................................... 17 2.1.1.3 Effective Factors in Defining UOS ..................................................................... 19 2.1.1.4 Openness, Accessibility and Ownership of UOS ............................................... 19 v  2.1.1.5 Social Values of UOS ......................................................................................... 21 2.1.1.6 Health Values of UOS ........................................................................................ 22 2.1.1.7 Diversity of Activities and Functional Values of UOS ........................................ 22 2.1.1.8 Comprehensive Definition of UOS .................................................................... 23 2.1.2 UOS Typology ............................................................................................................ 26 2.1.2.1 Functional Classification of UOS ....................................................................... 28 2.1.2.2 Scale and Domain of Influence of UOS ............................................................. 39 2.1.2.3 Morphological Classification of UOS ................................................................. 43 2.1.2.4 Privacy Gradient of UOS.................................................................................... 46 2.2 UOS Network ................................................................................................................ 53 2.2.1 UOS Network as a Complex System.......................................................................... 54 2.2.2 Network Attributes ................................................................................................... 57 2.2.2.1 Proximity ........................................................................................................... 57 2.2.2.2 Connectivity ...................................................................................................... 58 2.2.2.3 Complexity ........................................................................................................ 60 Chapter 3: The Proposed Spatial Analysis Framework (SAF) for UOS Networks ..................... 61 3.1 The Key Evaluation Questions ...................................................................................... 61 3.1.1 Is It Enough? .............................................................................................................. 63 3.1.2 Is It in the Right Place (the main focus of this study)?.............................................. 63 3.1.2.1 Are the Different Types of UOS Well-distributed in the Network? .................. 65 3.1.2.2 Is the UOS Network Spatially Integrated? ........................................................ 65 3.1.3 Is the Program/Context Appropriate? ...................................................................... 65 vi  3.1.4 Is It Well-designed? ................................................................................................... 65 3.1.5 Is UOS Modification Suitable? .................................................................................. 66 3.2 The Proposed Evaluation Matrix (EM) .......................................................................... 66 3.3 The Proposed Spatial Analysis Framework (SAF).......................................................... 69 3.3.1 The Main Focus of the SAF........................................................................................ 69 3.3.2 Effective Scales of the SAF ........................................................................................ 69 3.3.3 Indicators Employed in the SAF ................................................................................ 71 3.3.4 The Three Stages of the SAF ..................................................................................... 71 3.4 Indicators and Metrics .................................................................................................. 72 3.4.1 Theme Indicators ...................................................................................................... 72 3.4.2 Indicators .................................................................................................................. 72 3.4.3 Metrics ...................................................................................................................... 73 Chapter 4: Applying the SAF to Case Studies ........................................................................ 91 4.1 Why Tehran and Isfahan? ............................................................................................. 92 4.2 Context .......................................................................................................................... 95 4.2.1 Tehran ....................................................................................................................... 96 4.2.1.1 Location ............................................................................................................. 96 4.2.1.2 Important Urban Features ................................................................................ 99 4.2.1.3 Population ....................................................................................................... 106 4.2.1.4 Land-use .......................................................................................................... 109 4.2.1.5 Urban Structure and Street Network.............................................................. 111 4.2.2 Isfahan ..................................................................................................................... 114 vii  4.2.2.1 Location ........................................................................................................... 114 4.2.2.2 Important Urban Features .............................................................................. 117 4.2.2.3 Population ....................................................................................................... 125 4.2.2.4 Land-use .......................................................................................................... 129 4.2.2.5 Urban Structure and Street Network.............................................................. 130 4.2.3 Context Comparison ............................................................................................... 133 4.3 The UOS Palette .......................................................................................................... 136 4.3.1 Tehran’s UOS Palette .............................................................................................. 138 4.3.1.1 Parks and Gardens .......................................................................................... 140 4.3.1.2 Natural/Semi-natural Features ....................................................................... 144 4.3.1.3 Sports Fields .................................................................................................... 146 4.3.1.4 Civic Spaces ..................................................................................................... 148 4.3.1.5 Streets and Corridors ...................................................................................... 151 4.3.1.6 Incidental Spaces ............................................................................................ 154 4.3.2 Isfahan’s UOS Palette .............................................................................................. 157 4.3.2.1 Parks and Gardens .......................................................................................... 159 4.3.2.2 Natural/Semi-natural Features ....................................................................... 162 4.3.2.3 Water Edges .................................................................................................... 165 4.3.2.4 Sports Fields .................................................................................................... 167 4.3.2.5 Civic Space ....................................................................................................... 169 4.3.2.6 Streets and Corridors ...................................................................................... 172 viii  4.3.2.7 Incidental Spaces ............................................................................................ 175 4.3.3 UOS Palette Comparison ......................................................................................... 177 4.3.3.1 UOS Fraction Comparison ............................................................................... 177 4.3.3.2 UOS Diversity Comparison .............................................................................. 177 4.3.3.3 UOS Per Capita Comparison ........................................................................... 179 4.3.3.4 Morphological Taxonomic Comparison .......................................................... 180 4.3.3.5 UOS Palette Analysis: Conclusions .................................................................. 186 4.4 UOS Networks ............................................................................................................. 187 4.4.1 Proximity ................................................................................................................. 188 4.4.1.1 Proximity: The Tehran Case Study .................................................................. 189 4.4.1.2 Proximity: The Isfahan Case Study .................................................................. 191 4.4.1.3 Proximity Comparison ..................................................................................... 193 4.4.2 Network Pattern ..................................................................................................... 196 4.4.2.1 Network Pattern: The Tehran Case Study ...................................................... 198 4.4.2.2 Network Pattern: The Isfahan Case Study ...................................................... 199 4.4.2.3 Network Pattern Comparison ......................................................................... 200 4.4.3 Neighbourhood Scale Case Studies ........................................................................ 203 4.4.4 The Privacy Gradient ............................................................................................... 208 4.4.4.1 Privacy Gradient Comparison ......................................................................... 213 4.4.5 Network Connectivity and Complexity ................................................................... 216 4.4.5.1 A Comparison of Network Connectivity and Complexity ............................... 219 ix  4.5 Proposed Enhancement Strategies ............................................................................. 223 4.5.1 Proposed Enhancement Strategies for the Commercial Case Study of Tehran ..... 225 Chapter 5: Discussion ......................................................................................................... 233 5.1 Thesis Contribution and Significance .......................................................................... 233 5.2 The Potential Replicability of Findings ........................................................................ 238 5.3 Limitations................................................................................................................... 239 5.4 Possible Future Research Directions .......................................................................... 241 Bibliography ....................................................................................................................... 243  x  List of Tables  Table ‎2-1 Public Space in different academic disciplines (table by the author; data adapted from Varna & Tiesdell (2010, p. 577)) ................................................................................................... 18 Table ‎2-2 Summary of all defining factors and keywords used in various definitions around UOS (table by the author) ..................................................................................................................... 25 Table ‎2-3 Dimensions of successful UOS (adapted from Francis (1991, p. 99), edits by the author) .......................................................................................................................................... 29 Table ‎2-4 Typology of traditional and innovative UOS (table adapted from Francis (1991, pp. 78-79); edits by the author) ............................................................................................................... 32 Table ‎2-5 UOS types and sub-types (table adapted from Francis (2003); edits by the author) ... 33 Table ‎2-6 UOS functional form typology (data adapted from Stanley et al. (2012, pp. 1095-1107); table by the author) ........................................................................................................... 35 Table ‎2-7 A proposed form-functional typology of UOS (table by the author) ............................ 39 Table ‎2-8 Woolley’s UOS typology base on the scale and social distance from home (data adapted from Wolley (2003, pp. 76-149); table by the author) ................................................... 40 Table ‎2-9 UOS scales and form-functional typology (table adapted from Stanley et al. (2012, p. 1094)) ............................................................................................................................................ 42 Table ‎2-10 Connectivity patterns of UOS networks (table by the author; data adapted from Marshall (2005) ............................................................................................................................. 59 Table ‎3-1 Proposed indicators and metrics used in the SAF (table by the author) ...................... 90 xi  Table ‎4-1 Residential population and population density of each district within Region 12 of Tehran (table by the author; population densities adapted from (Region12 Tehran-Municipality Official Website, 2014)) .............................................................................................................. 107 Table ‎4-2 Day-time population of Tehran’s study area (table by the author; Region 12 day-time population adapted from (Region12 Tehran-Municipality Official Website, 2014)) ................. 108 Table ‎4-3 Day-time and residential populations of Tehran’s case study (table by the author) . 108 Table ‎4-4 Land-use types, areas and proportions in Tehran’s case study (table by the author) 110 Table ‎4-5 The residential population and population density of each neighborhood within Isfahan’s case study (table by the author; population densities adapted from Isfahan Municipality Portal (2014)) ......................................................................................................... 127 Table ‎4-6 The day-time population of Isfahan’s case study (table by the author) .................... 127 Table ‎4-7 The day-time and residential populations of Isfahan’s case study (table by the author)..................................................................................................................................................... 128 Table ‎4-8 The land-use types, areas and proportions of Isfahan’s case study (table by the author) ........................................................................................................................................ 129 Table ‎4-9 Form-functional types and sub-types of UOS (table by the author) .......................... 137 Table ‎4-10 The existing types of UOS in Tehran’s case study (table by the author) .................. 139 Table ‎4-11 The fraction of the Parks and Gardens type and sub-types in Tehran’s case study (table by the author) ................................................................................................................... 140 Table ‎4-12 Fraction of the Natural/Semi-natural type and sub-types in Tehran’s case study (table by the author) ................................................................................................................... 144 xii  Table ‎4-13 A fraction of the Sports Fields type and sub-types in Tehran’s case study (table by the author) .................................................................................................................................. 146 Table ‎4-14 A fraction of the Civic Spaces type and sub-types in Tehran’s case study (table by the author) ........................................................................................................................................ 148 Table ‎4-15 A fraction of the Streets and Corridors type and sub-types in Tehran’s case study (table by the author) ................................................................................................................... 151 Table ‎4-16 A fraction of the Incidental type and sub-types in Tehran’s case study (table by the author) ........................................................................................................................................ 154 Table ‎4-17 Existing types of UOS in Isfahan’s case study (table by the author) ........................ 157 Table ‎4-18 Fraction of the Parks and Gardens type and sub-types in Isfahan’s case study (table by the author) ............................................................................................................................. 159 Table ‎4-19 Fraction of the Natural/Semi-natural type and sub-types in Isfahan’s case study (table by the author) ................................................................................................................... 162 Table ‎4-20 Fraction of the Water-Edges type and sub-types in Isfahan’s case study (table by the author) ........................................................................................................................................ 165 Table ‎4-21 Fraction of the Sports Fields type and sub-types in Isfahan’s case study (table by the author) ........................................................................................................................................ 167 Table ‎4-22 Fraction of the Civic Spaces type and sub-types in Isfahan’s case study (table by the author) ........................................................................................................................................ 169 Table ‎4-23 Fraction of the Streets and Corridors type and sub-types in Isfahan’s case study (table by the author) ................................................................................................................... 172 xiii  Table ‎4-24 Fraction of the Incidental type and sub-types in Isfahan’s case study (table by the author) ........................................................................................................................................ 175 Table ‎4-25 A morphological comparison of the “Parks and Gardens” type in Tehran’s and Isfahan’s case studies (table by the author) ............................................................................... 180 Table ‎4-26 A morphological comparison of the “Natural/Semi-natural Features” type in Tehran’s and Isfahan’s case studies (table by the author) ........................................................................ 181 Table ‎4-27 A morphological comparison of the “Water Edges” type in Tehran’s and Isfahan’s case studies (table by the author) .............................................................................................. 182 Table ‎4-28 A morphological comparison of the “Sports Fields” type in Tehran’s and Isfahan’s case studies (table by the author) .............................................................................................. 183 Table ‎4-29 A morphological comparison of the “Civic Spaces” type in Tehran’s and Isfahan’s case studies (table by the author) .............................................................................................. 184 Table ‎4-30 A morphological comparison of the “Streets and Corridors” type in Tehran’s and Isfahan’s case studies (table by the author) ............................................................................... 185 Table ‎4-31 A morphological comparison of the “Incidental Spaces” type in Tehran’s and Isfahan’s case studies (table by the author) ............................................................................... 186 Table ‎4-32 The proximity to all types of UOS in the Tehran case study (table by the author) .. 189 Table ‎4-33 The proximity to all types of UOS in the Isfahan case study (table by the author) .. 191 Table ‎4-34 A comparison of the UOS network patterns in the Commercial areas of the Tehran and Isfahan case study (table by the author) ............................................................................. 201 xiv  Table ‎4-35 A comparison of the UOS network patterns in the Residential areas of the Tehran and Isfahan case studies (table by the author) .......................................................................... 202 Table ‎4-36 Comparison of UOS network patterns in Institutional areas of Tehran’s and Isfahan’s case studies (table by the author) .............................................................................................. 203 Table ‎4-37 A comparison of the Connectivity and Complexity from the Commercial neighbourhood scale case studies of Tehran and Isfahan (table by the author) ....................... 219 Table ‎4-38 A comparison of the Connectivity and Complexity of the Residential neighbourhood scale case studies of Tehran and Isfahan (table by the author) ................................................. 221 Table ‎4-39 A comparison of the Connectivity and Complexity of the Institutional neighbourhood scale case studies of Tehran and Isfahan (table by the author) ................................................. 222 Table ‎4-40 The report card of the Commercial neighbourhood scale case study of Tehran (table by the author) ............................................................................................................................. 225 Table ‎4-41 The effects of applying the first enhancement strategy  (i.e. the modification of UOS types from the “Incidental Spaces” type to “Parks and Gardens” and “Civic Spaces” types) on the effective domain of the UOS in the case study of Tehran (table by the author) ................. 229 xv  List of Figures  Figure ‎1-1 First scenario: Boozarjomehri St. (Later named Panzdah-e-Khordad St) in late Naseri Era (1848-1896) located in between governmental, religious and commercial functions (base map adapted from Motamedi (2002); edits by the author) ........................................................... 1 Figure ‎1-2 Sabzeh-Maidan Plaza as a central public gathering area (image adapted from Shahidi (1993, p. 216)) ................................................................................................................................. 2 Figure ‎1-3 Second scenario: Pandzdah-e-Khordad St. has turned into a vehicle dominated street (diagram by the author) .................................................................................................................. 3 Figure ‎1-4 Crowded narrow sidewalks and heavy car traffic in Panzdah-e-Khordad St. (image by Krapf (2006), CC BY-SA 2.5 (http://creativecommons.org/licenses/by-sa/2.5/deed.en), via http://commons.wikimedia.org) .................................................................................................... 4 Figure ‎1-5 Third scenario: a short section of Panzdah-e-Khordad St. has been closed to car traffic (diagram by the author) ....................................................................................................... 5 Figure ‎1-6 New design of the Panzdah-e-Khordad St. (all images by the author, 2011) ............... 6 Figure ‎1-7 Panoramic view of Sabzeh-Maidan plaza (image by the author, 2011) ........................ 6 Figure ‎1-8 Sabzeh-Maidan Plaza, as an empty open space and entrance of the Grand Bazaar of Tehran (image by the author, 2011) ............................................................................................... 7 Figure ‎1-9 Research methodology and thesis overview diagram (diagram by the author) ......... 14 Figure ‎2-1 UOS domain (diagram by the author) ......................................................................... 17 Figure ‎2-2 Typology of UOS (diagram by the author) ................................................................... 27 xvi  Figure ‎2-3 Helen Wolley’s Domestic, Neighbourhood and Civic UOS typology (2003, pp. 76-150) (diagram adapted from Zhang (2011, p. 63)) ............................................................................... 40 Figure ‎2-4 Access to open spaces in certain distances from home (diagram adapted from Barton et al. (2010, p. 113)) ...................................................................................................................... 41 Figure ‎2-5 Morphological taxonomy of UOS in comparison to landscape ecology components (diagram by the author) ................................................................................................................ 43 Figure ‎2-6 Si-O-Se Pol or AllahVerdi Khan Bridge as an example of a linear UOS (image by Andrea Thompson Photography, adapted from Bing Wallpapers (2013)) ................................... 44 Figure ‎2-7 Morphological taxonomy of patches in landscape ecology (diagram adapted from Forman & Gordon (1981, p. 736)) ................................................................................................ 45 Figure ‎2-8 Morphological types of UOS in Isfahan, Iran (diagram by the author) ....................... 46 Figure ‎2-9 Alexander’s diagram of the intimacy gradient in a building (diagram adapted from Alexander et al. (1977, p. 613)) .................................................................................................... 47 Figure ‎2-10 Robinson’s seven degrees of privacy in a territorial gradient (diagram by Hank Liu; adapted from Robinson (2001, p. s2.3) ........................................................................................ 47 Figure ‎2-11 Privacy gradient patterns in UOS networks (diagram by the author) ....................... 49 Figure ‎2-12 Degrees of publicness of a certain UOS type (Alley) at two different scales; neighbourhood and block scales (diagram by the author) ........................................................... 51 Figure ‎2-13 The effect of physical accessibility on the degree of publicness of a certain UOS type (diagram by the author) ................................................................................................................ 52 xvii  Figure ‎2-14 Controlling the visual accessibility with lattice windows in Ameri House, Kashan, Iran (image by Maryam Nademi) CC BY-SA 2.0 (https://creativecommons.org/licenses/by-sa/2.0/), via www.flickr.com ......................................................................................................... 53 Figure ‎2-15 Visual accessibility to and from Naghsh-e-Jahan Square, Isfahan, Iran (image by Nicola e Pina Iran 2008) via www.panoramio.com ...................................................................... 53 Figure ‎2-16 Components of UOS networks as complex systems (diagram by the author) .......... 55 Figure ‎2-17 Connectivity of components in UOS networks as complex systems (diagram by the author) .......................................................................................................................................... 56 Figure ‎2-18 Interdependence of components in UOS networks as complex systems (diagram by the author) .................................................................................................................................... 56 Figure ‎2-19 Outside variables affect UOS networks as complex systems (diagram by the author)....................................................................................................................................................... 57 Figure ‎2-20 Connectivity patterns of UOS networks (diagram by the author) ............................ 59 Figure ‎2-21 Two examples of regular networks which consist of one type of connectivity pattern ((a) regular tributary network and (c) regular grid-base network) in comparison to complex networks (b) which consist of two of more types of connectivity patterns (diagram adapted from Marshall (2005, p. 153)) ....................................................................................................... 60 Figure ‎3-1 Five key evaluation questions regarding the spatial aspects of urban open spaces (diagram by the author) ................................................................................................................ 62 Figure ‎3-2 The proposed Spatial Evaluation Matrix for UOS (diagram by the author) ................ 68 Figure ‎3-3 The proposed Spatial Analysis Framework (SAF) (diagram by the author) ................. 70 xviii  Figure ‎3-4: Proposed Indicator outline including A: Theme Indicator, A1: Indicator, A1-1 Metric (diagram by the author) ................................................................................................................ 74 Figure ‎4-1 The historical structure of Islamic cities in Iran (diagram by the author) ................... 92 Figure ‎4-2 Official urban divisions in Iranian cities (diagram by the author) ............................... 96 Figure ‎4-3 The location of Tehran’s case study (diagram by the author;  base map adapted from GoogleEarth ©2014 DigitalGlobe) ................................................................................................ 97 Figure ‎4-4 location of the Tehran’s case study in Region 12 (diagram by the author) ................ 97 Figure ‎4-5 The main streets and squares surrounding Tehran’s case study (diagram by the author) .......................................................................................................................................... 98 Figure ‎4-6 Tehran’s case study includes the historic core of the city (base map adapted from Bayat (2010); edits by the author) ................................................................................................ 99 Figure ‎4-7 The Grand bazaar of Tehran (diagram by the author) .............................................. 100 Figure ‎4-8 Construction of the first four streets of Tehran on the ruins of the Golestan Palace walls in the Late Naseri era (base map adapted from Motamedi (2002); edits by the author) 101 Figure ‎4-9 Former and current boundaries of the Golestan Palace (diagram by the author) ... 102 Figure ‎4-10 Shahr (City) Park (diagram by the author) ............................................................... 102 Figure ‎4-11 Panzdah-e-Khordad Street (diagram by the author) ............................................... 103 Figure ‎4-12 The pedestrian-dominated section of Panzdah-e-Khordad St. and the main locations on either sides to which it is connected (diagram by the author) ............................................. 104 Figure ‎4-13 Governmental Complex (diagram by the author) ................................................... 105 Figure ‎4-14 Imam Mosque (diagram by the author) .................................................................. 105 xix  Figure ‎4-15 Imam Mosque, one of the three elements that form the Islamic triangular urban structure (diagram by the author) .............................................................................................. 106 Figure ‎4-16 Land-use types and proportions in Tehran’s case study (map by the author; data adapted from Bayand Consultants (2006)) ................................................................................. 110 Figure ‎4-17 The development of the street network pattern of the historic core of Tehran (figure adapted from Sabri & Hamidi (1998); edits by the author) ............................................ 111 Figure ‎4-18 The current conditions of the street network in Tehran’s case study (map by the author) ........................................................................................................................................ 112 Figure ‎4-19 The street network in different parts of Tehran’s case study (map by the author) 113 Figure ‎4-20 The selected case study in the city of Isfahan (diagram by the author; base map adapted from GoogleEarth ©2014 DigitalGlobe) ....................................................................... 114 Figure ‎4-21 The main streets and square around Isfahan’s case study (diagram by the author)..................................................................................................................................................... 115 Figure ‎4-22 The continuous pedestrian access in the historic areas of Isfahan (diagram by the author) ........................................................................................................................................ 116 Figure ‎4-23 The primary and secondary historic cores of Isfahan in the study area (diagram by the author) .................................................................................................................................. 117 Figure ‎4-24 Isfahan’s Historic Bazaar, between two historic cores of the city (diagram by the author) ........................................................................................................................................ 118 Figure ‎4-25 The linear pattern of historic bazaars and the elements of a neighborhood centre in Iran (diagram by the author) ...................................................................................................... 119 xx  Figure ‎4-26 Chahar-bagh Boulevard (diagram by the author) ................................................... 120 Figure ‎4-27 Garden-Palaces along Chahar-Bagh Boulevard (diagram by the author) ............... 121 Figure ‎4-28 Naghsh-e-Jahan Square (diagram by the author) ................................................... 122 Figure ‎4-29 Naghsh-e-Jahan Square, enclosed by commercial (bazaar), religious (Imam Mosque) and royal (Ali-Qapu Mansion) functions (base map adapted from (Ardalan & Bakhtiar, 1973, pp. 98-99); edits by the author) ........................................................................................................ 123 Figure ‎4-30 Imam Mosque on the south side of Naghsh-e-Jahan Square (diagram by the author)..................................................................................................................................................... 124 Figure ‎4-31 Zayandeh-Rood River, and the AllahVerdi Khan and Khaju Bridges (diagram by the author) ........................................................................................................................................ 125 Figure ‎4-32 The area, residential population and population density of the regions and neighborhoods within Isfahan’s case study (diagram by the author; population densities adapted from Isfahan Municipality Portal (2014) ) .................................................................... 126 Figure ‎4-33 Land-use types and proportions in Isfahan’s case study (map by the author; data adapted from (Bavand Consultants, 2003)) ................................................................................ 129 Figure ‎4-34 The transition of the urban structure and the street patterns of Isfahan (diagram by the author; data adapted from (Danesh Nama Journal, 2009) (Karimi & Motamed, 2003)) .... 130 Figure ‎4-35 The current condition of the street network in Isfahan’s case study (map by the author) ........................................................................................................................................ 132 Figure ‎4-36 The street network in different areas of Isfahan’s case study (map by the author)..................................................................................................................................................... 133 xxi  Figure ‎4-37 Examples of the morphological taxonomy of three different UOS types (diagram by the author) .................................................................................................................................. 138 Figure ‎4-38 The existing types of UOS in Tehran’s case study (map by the author) .................. 138 Figure ‎4-39 The map and morphological taxonomy of the Parks and Gardens type of UOS in Tehran’s case study (map by the author) ................................................................................... 140 Figure ‎4-40 Pocket parks in Tehran’s case study (map by the author) ...................................... 141 Figure ‎4-41 Green squares in Tehran’s case study (map by the author) ................................... 141 Figure ‎4-42 Green public courtyards in Tehran’s case study (map by the author) .................... 142 Figure ‎4-43 Gardens in Tehran’s case study (map by the author) ............................................. 142 Figure ‎4-44 Urban parks in Tehran’s case study (map by the author) ....................................... 143 Figure ‎4-45 The map and morphological taxonomy of the Natural/Semi-natural type of UOS in Tehran’s case study (map by the author) ................................................................................... 144 Figure ‎4-46 A lake in Tehran’s case study (map by the author) ................................................. 145 Figure ‎4-47 A pool in Tehran’s case study (map by the author)................................................. 145 Figure ‎4-48 The map and the morphological taxonomy of the Sports Fields type of UOS in Tehran’s case study (map by the author) ................................................................................... 146 Figure ‎4-49 Outdoor Sports Fields in Tehran’s case study (map by the author) ........................ 147 Figure ‎4-50 Map and morphological taxonomy of the Civic Spaces type of UOS in Tehran’s case study (map by the author) .......................................................................................................... 148 Figure ‎4-51 A Plaza in Tehran’s case study (map by the author) ............................................... 149 Figure ‎4-52 Roundabouts in Tehran’s case study (map by the author) ..................................... 149 xxii  Figure ‎4-53 Public squares in Tehran’s case study (map by the author) .................................... 150 Figure ‎4-54 Central courtyards in Tehran’s case study (map by the author) ............................. 150 Figure ‎4-55 Map and morphological taxonomy of the Streets and Corridors type of UOS in Tehran’s case study (map by the author) ................................................................................... 151 Figure ‎4-56 Pedestrian streets in Tehran’s case study (map by the author).............................. 152 Figure ‎4-57 Sidewalks in Tehran’s case study (map by the author) ........................................... 152 Figure ‎4-58 Pedestrian corridors in Tehran’s case study (map by the author) .......................... 153 Figure ‎4-59 Map and morphological taxonomy of the Incidental Spaces type of UOS in Tehran’s case study (map by the author) .................................................................................................. 154 Figure ‎4-60 Vacant open spaces in Tehran’s case study (map by the author) ........................... 155 Figure ‎4-61 Parking in Tehran’s case study (map by the author) ............................................... 155 Figure ‎4-62: Storage spaces in Tehran’s case study (map by the author) .................................. 156 Figure ‎4-63 The existing types of UOS in Isfahan’s case study (map by the author) ................. 157 Figure ‎4-64 The map and the morphological taxonomy of the Parks and Gardens type of UOS in Isfahan’s case study (map by the author) ................................................................................... 159 Figure ‎4-65 Green public courtyards in Isfahan’s case study (map by the author).................... 160 Figure ‎4-66 Gardens in Isfahan’s case study (map by the author) ............................................. 160 Figure ‎4-67 Urban parks in Isfahan’s case study (map by the author) ....................................... 161 Figure ‎4-68 Map and morphological taxonomy of the Natural/Semi-natural type of UOS in Isfahan’s case study (map by the author) ................................................................................... 162 Figure ‎4-69 A river in Isfahan’s case study (map by the author) ................................................ 163 xxiii  Figure ‎4-70 Streams in Isfahan’s case study (map by the author) ............................................. 163 Figure ‎4-71 Pools in Isfahan’s case study (map by the author) .................................................. 164 Figure ‎4-72 Map and morphological taxonomy of the Water-Edges type of UOS in Isfahan’s case study (map by the author) .......................................................................................................... 165 Figure ‎4-73 River fronts in Isfahan’s case study (map by the author) ........................................ 166 Figure ‎4-74 Stream side alleys in Isfahan’s case study (map by the author) ............................. 166 Figure ‎4-75 Map and morphological taxonomy of the Sports Fields type of UOS in Isfahan’s case study (map by the author) .......................................................................................................... 167 Figure ‎4-76 Outdoor sports fields in Isfahan’s case study (map by the author) ........................ 168 Figure ‎4-77 Playgrounds in Isfahan’s case study (map by the author) ....................................... 168 Figure ‎4-78 Map and morphological taxonomy of the Civic Spaces type of UOS in Isfahan’s case study (map by the author) .......................................................................................................... 169 Figure ‎4-79 Plazas in Isfahan’s case study (map by the author) ................................................. 170 Figure ‎4-80 Roundabouts in Isfahan’s case study (map by the author) ..................................... 170 Figure ‎4-81 Forecourts in Isfahan’s case study (map by the author) ......................................... 171 Figure ‎4-82 Central courtyards in Isfahan’s case study (map by the author)............................. 171 Figure ‎4-83 Map and morphological taxonomy of the Streets and Corridors type of UOS in Isfahan’s case study (map by the author) ................................................................................... 172 Figure ‎4-84 Bridges in Isfahan’s case study (map by the author) ............................................... 173 Figure ‎4-85 Sidewalks in Isfahan’s case study (map by the author) ........................................... 173 Figure ‎4-86 Green pedestrian streets in Isfahan’s case study (map by the author) .................. 174 xxiv  Figure ‎4-87 Map and the morphological taxonomy of the Incidental Spaces type of UOS in Isfahan’s case study (map by the author) ................................................................................... 175 Figure ‎4-88 Vacant Open Spaces in Isfahan’s case study (map by the author) .......................... 176 Figure ‎4-89 Parkings in Isfahan’s case study (map by the author) ............................................. 176 Figure ‎4-90 An example of a circle with a radius of 400 m, representative of areas with proximity to a certain UOS within a less than 5-minute walking distance from the UOS (diagram by the author) ............................................................................................................................. 188 Figure ‎4-91 The proximity to all types of UOS in the Tehran case study (map by the author) .. 189 Figure ‎4-92 The proximity to each type of UOS in the Tehran case study (map by the author) 190 Figure ‎4-93 The proximity to all types of UOS in the Isfahan case study (map by the author) . 191 Figure ‎4-94 The proximity to each type of UOS in the Isfahan case study (map by the author) 192 Figure ‎4-95 The Dominant UOS network pattern in the Residential, Commercial and Institutional areas of Tehran’s case study (diagram by the author) ............................................................... 198 Figure ‎4-96 The Dominant UOS network pattern in the Residential, Commercial and Institutional areas of the Isfahan case study (diagram by the author) ........................................................... 199 Figure ‎4-97 Three neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) .......................................................... 205 Figure ‎4-98 An aerial view of the three neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (base map adapted from GoogleEarth 2013; edits by the author) .......................................................................................................... 206 xxv  Figure ‎4-99 The existing UOS in neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (base map adapted from GoogleEarth 2013; edits by the author) .................................................................................................................... 207 Figure ‎4-100 The privacy gradient of the existing UOS in neighbourhood-scale Commercial case studies of Tehran and Isfahan (diagram by the author) ............................................................. 208 Figure ‎4-101 The Privacy gradient of the existing UOS in neighbourhood-scale Residential case studies of Tehran and Isfahan (diagram by the author) ............................................................. 209 Figure ‎4-102 The privacy gradient of the existing UOS in neighbourhood-scale Institutional case studies of Tehran and Isfahan (diagram by the author) ............................................................. 210 Figure ‎4-103 The privacy Gradient of the neighbourhood scale cases of Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) .............................. 212 Figure ‎4-104 The dominant privacy gradient patterns from neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (diagram by the author) ........................................................................................................................................ 213 Figure ‎4-105 Network Connectivity in neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) .............................. 217 Figure ‎4-106 Intersections in neighbourhood scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) ..................................... 218 Figure ‎4-107 Connectivity of the UOS networks in the Institutional case studies of (a) Tehran and (b) Isfahan (diagram by the author) ..................................................................................... 223 xxvi  Figure ‎4-108 A modification of the existing “Incidental Spaces” in the commercial area of Tehran’s case study to the “Parks and Gardens” and “Civic Spaces” types. (a) The Commercial neighbourhood scale case study of Tehran, (b) Existing “Incidental Spaces” within the commercial area, (c) Modifications of the “Incidental Spaces” to “Civic Spaces” and green central courtyards, (d) Proximity to low quality “Incidental Spaces” before the modification, and (e) Proximity to high quality UOS following modification (diagram by the author) .................. 227 Figure ‎4-109  (a) The effective domain of the “Parks and Gardens” type before modification, (b) The effective domain of the “Parks and Gardens” type following modification, (c) The effective domain of the “Civic Spaces” type before modification, (d) The effective domain of the ”Civic Spaces” type following modification, (e) The effective domain of the “Incidental Spaces” type prior to modification, (f) The effective domain of the ”Incidental Spaces” type following modification (diagram by the author) ........................................................................................ 228 Figure ‎4-110 Proximity to all UOS including the “Incidental Spaces” (a) before and (b) after modification, and the increase in the effective domain of all UOS excluding the “Incidental Spaces” (c) before and (d) after modification (diagram by the author) ..................................... 231  xxvii  List of Charts  Chart ‎4-1 The residential and day-time population density in Tehran’s and Isfahan’s case studies (chart by the author) ................................................................................................................... 134 Chart ‎4-2 The land-use proportion in Tehran’s and Isfahan’s case studies (chart by the author)..................................................................................................................................................... 135 Chart ‎4-3 Comparison of the UOS fractions in Tehran and Isfahan (chart by the author, based on Tables 4-10 and 4-17) ................................................................................................................. 177 Chart ‎4-4 A comparison of UOS per capita in Tehran and Isfahan (chart by the author) .......... 179 Chart ‎4-5 A comparison of proximities to each type of UOS in Tehran’s and Isfahan’s case studies (chart by the author) ...................................................................................................... 193 Chart ‎4-6 The percentage of the study area with an effective proximity to a certain UOS type in Tehran’s and Isfahan’s case studies, downward trend (chart by the author) ............................ 194 Chart ‎4-7 Percent of the study area with effective proximity to certain UOS types and total UOS, before and after applying the enhancement strategy (chart by the author) ............................. 230  xxviii  List of Abbreviations  UOS: Urban Open Space(s) SAF: Spatial Analysis Framework EM: Evaluation Matrix   xxix  Acknowledgements  I owe particular thanks to my dear supervisor, Professor Ronald Kellett, who opened my eyes to a whole new possibilities in academic world and advised me patiently throughout my research with his endless support and fatherly guidance.  I offer my enduring gratitude to Professor Cynthia Girling, Committee member of this thesis and my supervisor in elementslab, who welcomed me to UBC and her research team since the first days with her support and guidance, and taught me the real meaning of teamwork throughout my work experience in elementslab.  Special thanks to Marta Farevaag, PFS principal, who kindly accepted to join the supervising committee of this thesis, and for her valuable guidance and thoughtful comments throughout this research.  I would like to thank the faculty and staff at UBC, who have inspired me to continue my work in this field, and also my fellow MAS(L)A students in Ponderosa B Office, who created unforgettable memories for me.   And, I would like to express my sincere thanks to my parents, whose have supported me unconditionally throughout my years of education, both morally and financially. Without their support this would not have been possible. I would also like to thank my family, especially my beloved sister Negar, whose has been a wonderful and inspiring role model for me since day one. Last but not least, I thank my adorable and talented niece Helia, who filled my life with vitality, joy, and happiness.  xxx  Dedication  This work is dedicated to my dear mom, who has devoted her entire life to her children, supported me generously with her heart of gold and inspired me to pursue my dreams with passion.   1  Chapter 1: Introduction 1.1 Three Distinct Scenarios of a Street Panzdah-e-Khordad Street (Former Boozarjomehri Street), 1896, Tehran This street is located in the central part of the old city inside the city walls and was built on the ruins of the palace walls. This street is located in between three main urban elements, a palace, a bazaar and a mosque, and welcomes a variety of people including vendors, buyers, residents and staff of the palace, and residents of nearby neighbourhoods. (Figure 1-1)  Figure ‎1-1 First scenario: Boozarjomehri St. (Later named Panzdah-e-Khordad St) in late Naseri Era (1848-1896) located in between governmental, religious and commercial functions (base map adapted from Motamedi (2002); edits by the author) 2  Sabzeh-Maidan Plaza, a central public gathering space, is located in the middle section of the street at the entrance of the Grand Bazaar. This plaza and Boozarjomehri St. form a public city centre and a venue for numerous religious, political, social and royal events and ceremonies. (Figures 1-1 and 1-2)  Figure ‎1-2 Sabzeh-Maidan Plaza as a central public gathering area (image adapted from Shahidi (1993, p. 216)) Panzdah-e-Khordad Street, 1996 About a century later, walking on the sidewalks of Panzdah-e-Khordad St. (former Boozarjomehri St.) has become a entire different experience. New buildings have been added to both sides of this street. On the north side of the street, many government buildings and offices, including a bank and a courthouse that received a huge number of visitors have been built.  Numerous shopping centres and shops have had been added to the south side of the street. (Figure 1-3) 3   Figure ‎1-3 Second scenario: Pandzdah-e-Khordad St. has turned into a vehicle dominated street (diagram by the author) At the same time, the residential population of nearby neighbourhoods has increased dramatically. With increasing numbers of visitors, despite the need to have more space for pedestrians, most of the street width has been dedicated to motorized vehicles while only two narrow sidewalks have been defined for pedestrians.    Panzdah-e-Khordad which used to be a central public gathering area is now a vehicle dominated street which suffers from heavy traffic. The noise, air pollution, and lack of proper pedestrian pathways, have created an unpleasant experience for visitors. (Figure 1-4) Although this street is located in the historic core of the city, there is no evidence of being close to the historic Golestan garden-palace.  Every historic buildings, including the Grand Bazaar and the palace are now located behind all those government office buildings and modern shopping centres, disconnected from the street. Sabzeh-Maidan Plaza has lost its function as a public gathering space and has turned into a chaotic forecourt as an entrance to the Grand Bazaar.  This street which formerly connected important urban elements is now tearing the historic heart of the city apart, through heavy traffic and lack of proper pedestrian access.  4  It seems that the historic core of the city did not have the chance to adapt to the new changes. The fast-paced changes that have been imposed to this area, has corrupted the experience of spending a pleasant afternoon in a public city centre.   Figure ‎1-4 Crowded narrow sidewalks and heavy car traffic in Panzdah-e-Khordad St. (image by Krapf (2006), CC BY-SA 2.5 (http://creativecommons.org/licenses/by-sa/2.5/deed.en), via http://commons.wikimedia.org)  Panzdah-e-Khordad Street, 2011 About a decade later, this street has faced another evolution. However, this time, new changes were aimed to enrich the pedestrian experience and enhance the public life in the central core of the city.  To reach this goal, a short 400 meter section of Panzdah-e-Khordad Street, has been closed to motor vehicle traffic. (Figure 1-5) 5   Figure ‎1-5 Third scenario: a short section of Panzdah-e-Khordad St. has been closed to car traffic (diagram by the author) In this section, over half of the street’s width has been dedicated to pedestrians while limited access has been provided for vehicles including emergency vehicles, horse-drawn carriages, loading carts and bicycles. (Image 1-6) Although the new pedestrian experience in Panzdah-e-Khordad St is pleasant, it is very short and ephemeral.  This 400 meter pedestrian dominated section of the street is not connected to any other urban open spaces or historic urban features such as Golestan garden-palace. The pedestrian experience is very unpleasant at the ends of this space, where the users are confronted with heavy vehicle traffic (Image 1-6) 6   Figure ‎1-6 New design of the Panzdah-e-Khordad St. (all images by the author, 2011) As part of the new development plan, new paving, bicycle stations and new street furniture including benches, garbage bins, and planters, have been added to this section of the street and to Sabzeh-Maidan plaza. (Figures 1-7 and 1-8)  Figure ‎1-7 Panoramic view of Sabzeh-Maidan plaza (image by the author, 2011) But, what is the problem with the new design-development plan that has been applied to the historic Pandzdah-e-Khordad St. and Sabzeh-Maidan plaza? What is missing from the new development plan? 7   Figure ‎1-8 Sabzeh-Maidan Plaza, as an empty open space and entrance of the Grand Bazaar of Tehran (image by the author, 2011) Although the result of the new design was positive and promising, it is still far from ideal. The problems within the new development plan of Panzdah-e-Khordad St. might have many aspects and therefore can be addressed from different points of view and lenses.  Interpretation, evaluation, and diagnosis of the problems require a constructionist research approach. In a constructionist research, “…knowledge is generated though the interaction between the investigators (and their society) and a reality (or realities) that exists but that can never be known independently of the presumptions of the investigators (Deming & Swaffield, 2011, pp. 8-9).” Therefore before diagnosing the problems in this case, it is important to introduce researcher’s point of view and the specific lens through which the subject will be studied. 8  Having lived in the metropolitan area of Tehran for nearly 25 years, I was always interested in issues regarding the urban environment and specifically the transitional processes of urban structure in historic parts of cities such as Tehran. I have always been interest in analyzing and studying the spatial aspect of urban spaces and specifically urban open spaces.  Holding a BArch and a MLA degree from two architecture schools in Tehran has equipped me with the right skills to go deeper into analyzing spatial problems within urban environments. Therefore, considering my personal interest and skills in addressing problems in the spatial qualities of urban open spaces, the problems within the new development plan of Panzdah-e-Khordad St. will be looked at from a spatial lens.  Spatially looking at these three scenarios, it seems that the main concern in the new development plan has been on enhancing the design and program of the street as an isolated element and an individual urban open space. In other words, by blocking the two ends and designing the inner part of this isolated section of the street, the new development plan was only focused on a small portion of the problem without looking at this issue from a broader scale. The street, although a better place compared to its former condition, is still not connected to other UOS nearby and therefore is not part of a larger system of urban open spaces.  It seems that this enhancement lacks a strategic vision and a holistic view of the overall condition of this street within a larger network of UOS. 1.2 Problem Statement The main issue regarding most random enhancement strategies that have been applied to UOS is often a lack of strategic vision. “Hundreds of citizens groups have organized across the continent in the last twenty-five years, dedicated to the protection and planning of open-space lands. They are concerned about […] lack of a strategic vision about open space in relation to new growth (Erickson, 2006, p. 6).” This issue can be categorized into the following four main problems;  Lack of studies on UOS as a network of connected and interacting elements: It seems that most development plans have focused on the quality of UOS as individual 9  urban elements. However, a holistic view of these spaces as connected elements in the city is missing from these types of developments. UOS systems as a whole, like any other sustainable systems should be more than sum of its parts and should exhibit interdependence among its components. Therefore there is an urge to analyze the interaction and interdependence of these spaces from a broader scale and predict the consequences of any modification in the system before applying a small scale enhancement strategy to individual UOS.  Lack of practical strategies for diagnosing  spatial problems of UOS networks Instead of applying random enhancement strategies, there is an urge to diagnose the problems within the system or network. Therefore, proposing a proper analysis framework which can be used for diagnosing the problems in UOS network is really important. Any alteration to existing conditions without first diagnosing the main problem could be a waste of time, energy and money.  Lack of practical strategies for enhancing the spatial qualities of UOS networks: any modifications in UOS networks should be based on comprehensive analysis. General and random solutions are not suitable for different situations. The most effective solutions are site specific and should be based on analysis. For instance, while certain areas of a city might need more UOS, other parts of the city may need modification in the function or design of existing UOS and all these problems require different approaches, actions and enhancement strategies.  No priorities in enhancement: After diagnosing the problems and choosing appropriate enhancement strategies, appropriate phasing needs to be determined. Applying enhancements in random order can cause loss of time, energy and money. 1.3 Scope of Study and Thesis Statement UOS can be looked at from different scales, from the large scale or city scale to neighbourhood scale to individual block scale and to human scale.  In each scale some aspect of these spaces can be studied.  As Gehl explains in his book Cities for People, “… urban design and 10  city planning can be described as work involving several very different levels of scale (2010, p. 195).” To date, most studies have been focused on the quality of UOS as individual urban elements. Current research on UOS includes studies about the role of public participation in design and planning of UOS, quality and quantity assessments of certain case studies, and behavioral studies.  Most research advances have results from studies of individual open spaces. Some of these studies are focused on traditional types of open spaces such as neighbourhood parks, playgrounds and urban plazas while some others are more focused on innovative open spaces such as waterfront parks and redesigned neighbourhood streets. (Francis, 1991) However, a holistic view of these spaces as connected elements in the city is missing from most urban studies. Therefore, there is an urge to study UOS as a network. UOS in the city, as a whole and as a system, should be more than sum of its parts and should exhibit interdependence among its components, just like any other sustainable system. Therefore, there is an urge to look at these individual UOS as connected elements and in other words as a network.  Accordingly, this research aims to study the spatial organization of UOS as a network and therefore will not zoom into issues of individual UOS in closer scale such as design criteria, behavioral studies, and on-site observations. Considering the focus of this research which is on UOS as a network of connected elements, the suitable scale would be the larger scale which includes “holistic treatment of the city (Gehl, 2010, p. 195).”  Moreover, this study is focused on historic neighbourhoods of Iranian cities. The cultural and historic sensitivities of the chosen neighbourhoods limit the possibilities for intervening in these spaces, which amplifies the importance and complexity of this analysis. Although there have been many valuable studies about UOS worldwide and about UOS in Iranian cities, only a few have addressed the importance of UOS as connected networks of 11  individual elements.  Therefore, the goal of this study is to start filling some of the gap, by providing a method for spatial analysis of UOS as a network. To sum up, the focus of this study is on how individual UOS connect, interact and interdepend with one another at a greater spatial scale. Accordingly the following thesis statement has been defined: In order to study the spatial organization of UOS, these spaces need to be analyzed as a complex system or network of connected, interacting and interdependent elements in the urban context. 1.4 Research Goals and Questions The primary goal of this research is to diagnose flaws in the spatial organization of UOS networks in historic neighbourhoods of Tehran and Isfahan. Accordingly, the secondary goal of this research is to design and propose a framework for analyzing the spatial organization of UOS networks. Eventually, the attempt is to apply and test the proposed analysis framework on two selected case studies from the historic cores of Tehran and Isfahan, and compare the results.  To design a framework for analyzing the spatial organization of UOS networks, three research questions have been defined. Q1: How do UOS networks work? Before developing a spatial analysis framework, we need to know what an UOS network system is and how it works. Recognition of UOS as individual urban elements and the network system is the first step before the analysis process.  - What is UOS? - What is UOS network? - What are the different types of UOS? - What are the different spatial attributes of UOS networks? Q2: How can the spatial qualities of UOS networks be evaluated? Any evaluation should start with defining key evaluation questions. Next, indicators need to be defined in order to analyze different aspects of UOS networks. Thirdly, an evaluation framework can be created to 12  analyze the spatial aspects of UOS networks.  The evaluation framework is the main part of the research presented in this thesis. - Why is it important to study UOS as a network of connected elements? - Why is it important to study the spatial qualities of UOS networks? - What is the proper scale that UOS networks need to be looked at? - What are those indicators and metrics that need to be taken into account while analyzing the spatial qualities of UOS and UOS network? - What are the outside factors that affect the spatial qualities of UOS networks? - How can the analytical layers be assembled to design a proper spatial analysis framework for UOS networks? Q3: How can the spatial qualities of UOS networks be enhanced? The final step of the analysis is to propose suitable enhancement strategies. Before applying any enhancement strategy, it is essential to diagnose the problems and set the priorities accordingly.  - What is the problem in the UOS network? - What is the appropriate enhancement strategy? - What are the priorities for enhancing the spatial qualities of the UOS network? - And finally, is the enhancement strategy suitable considering all the restrictions and sensitivities of the context? 1.5 Thesis Overview and Research Methodology This study is organized in 5 chapters. The thesis outline is as follows; (Figure 1-9) Chapter 1: The first chapter starts with three scenarios of a single street and its transitions through time. Personal experiences along with literature reviews have been used as information sources for defining the problem statement and the main scope of this study. This chapter continues with the classification of research goals, research questions, and research methodology. Chapter 2: The second chapter presents existing definitions, typologies, and attributes of UOS and UOS networks as found in existing resources and literature. In this chapter,   I critique 13  the existing definitions and typologies in order to present a new definition and classification system of UOS which suits the requirements of this research.  Chapter 3: In this chapter the goal is to design and propose a framework for analyzing the spatial organization of UOS networks. To do so, this chapter starts with posing five evaluation questions. Next, the Spatial Analysis Framework (SAF) which looks at spatial organization of UOS within a network is introduced. In addition to the SAF, indicators and metrics has been defined, listed and classified in the last section of this chapter.  These indicators and metrics will be used later in chapter 4, for analyzing the spatial organization of UOS networks in the case studies. Chapter 4: This chapter starts with testing the proposed SAF on two case studies from the historic cores of Tehran and Isfahan. As a result of these two tests, the framework (SAF) which is presented in the third chapter has been refined and modified. The analysis starts with the general understanding of the context of each case and continues with comprehensive analysis of the UOS palette and UOS network. To analyze and compare both case studies, available resources including master plans, reports, aerial photos and drawings have been used as input data. Moreover, certain attributes have been measured using a spreadsheet-based calculator and maps. Qualitative and classification methods have been used to study other spatial aspects of the UOS networks such as network pattern and morphological taxonomy of UOS.  Chapter 5: The final chapter includes a short overview of this research followed by a discussion of the significance of this research to future UOS development. The last section of this chapter outlines the limitations of this research and further research opportunities.  14   Figure ‎1-9 Research methodology and thesis overview diagram (diagram by the author) 15  Chapter 2: The Urban Open Space (UOS) Network The role and importance of Urban Open Spaces (UOS) as urban elements affecting public health and the social life of citizens has been the subject of considerable research among urban planners, designers, architects, and landscape architects. UOS can effectively alter the experience of living and strolling in different cities around the world. The quality of these spaces, the way they are organized in the city, their types, shapes, sizes, and many other aspects of these spaces can influence the overall quality of life in different cities. While UOS in some touristic cities such as Isfahan-Iran might be in harmony with the historic structure of the city (Karimi & Motamed, 2003), the spatial qualities of these spaces in other cities such as Tehran (Rismanchian & Bell, 2013), might not possess similar spatial conditions as Isfahan. But, what defines these differences in the spatial qualities of UOS in different cities or different neighbourhoods of a city? To understand the concept of UOS and to study the spatial qualities of these urban elements, the following questions have been studied and the result of this study has been presented in this chapter; UOS:  What is an UOS?  What are different types of UOS?  What are the spatial attributes of UOS?  What are effective scales of UOS? UOS Network:  What is an UOS network?  How does an UOS network operate?  What are the spatial attributes of UOS networks?  What are the factors that affect the spatial qualities of UOS network?  By what method can the spatial qualities of UOS as a network be improved? 16  2.1 UOS In order to analyze the spatial organization of UOS as a network, and before going through the analysis process, it is essential to understand the general concept of UOS and UOS networks. Therefore, the above mentioned questions need to be studied comprehensively. In brief, this chapter aims to study the following terms about UOS and their networks:   UOS definition  UOS typology   UOS network definition  UOS network attributes and operation 2.1.1 UOS Definition  What is an ‘UOS’?  How does it differ from a ‘Public Open Space’?  What is an ‘Open Space’?  What is a ‘Public Space’? While searching for an accurate definition of UOS, the above mentioned terms appear many times in different sources. Since these terms are interdisciplinary, they should be looked at from different points of view and perspectives. Researchers have presented a variety of definitions in this field. The effort is to critique these definitions followed by presenting a comprehensive definition of UOS that not only includes the valuable concepts and ideas from existing definitions, but also fulfills the requirements of this research and personal intentions of the author in dealing with UOS. 2.1.1.1 UOS Domain Before defining UOS and their qualities, and to be able to effectively differentiate these terms, one should first define public space domain, open space domain, public open space domain and UOS domain spatially. Pubic Space domain: Focusing only on the spatial aspect, Carmona et al. define public spaces as “all those parts of the built and natural environment where the public has free access. It 17  encompasses: all the streets, squares and other right of way…the open spaces and parks; and the public/private spaces where public access is unrestricted (2008, p. 5).” (Figure 2-1) Open Space domain: Public space is different from open space, as Stanley et al. explain the differences between these two domains by focusing on the accessibility to these spaces: “Although public access is common for many types of open spaces, open space does not automatically imply public access (2012, p. 1091).” From the same source, open space is defined as “any urban ground place regardless of public accessibility that is not roofed by an architectural structure (2012, p. 1089).” On the other hand, public space may include spaces with different degree of openness including open, semi-enclosed and enclosed spaces. (Figure 2-1) Public Open Space domain: ‘Public Open Space’ is where public space domain and open space domain overlap. (Figure 2-1) UOS domain: Generally, UOS is an urbanized form of public open space. (Figure 2-1)  Figure ‎2-1 UOS domain (diagram by the author) 2.1.1.2 Public Space Definitions Scholars from different points of view and backgrounds have defined public spaces differently by focusing on a few of the numerous aspects of these spaces, including social, spatial, functional, and historical aspects. “Public space is receiving increased attention across the range of social science and humanities disciplines. Each discipline comes to public space differently, viewing it through a different lens and with particular interest and concerns to the fore (Varna & Tiesdell, 2010, p. 577).”  18  For example, while in political science the primary focus in studying public spaces is commonly on democratization and rights (Mitchell, 1995), (Mensch, 2007), others such as legal scholars focus on access and control (Ellickson, 1996) in public spaces.  (Table 2-1) Public Space Political Science - Democratization - Rights Geographers - Sense of place - Placelessness Anthropologists, sociologists - Historical construction - Subjective value of place Legal Scholars - Access - Control in public space … - … Table ‎2-1 Public Space in different academic disciplines (table by the author; data adapted from Varna & Tiesdell (2010, p. 577)) Public Space as a larger domain, which includes UOS, has numerous meanings and types in different societies, places, and times. (Low & Smith, 2006) In other words, there are three other factors that affect the concept, definition, typology and function of public spaces; Time, Society, and Location. Public space and Time: The definition, typology and function of some public spaces have changed through time. Mumford believes that “during the last generation a change has taken place in our conception of open spaces in relation to the urban and regional environment (1969, p. 13).” There used to be some types of public spaces in ancient cities which either does not exist anymore, or has been altered significantly, such as old squares in historic city centres which used to be a place for royal announcements. However, the function of this type of public space has changed through time. Also there are numerous types of public spaces in our cities which did not formerly exist. Through time, as the needs of the people change, new public spaces have been designed and added to our cities. Public recreational spaces such as public parks are examples of this innovation in public spaces. Public space and Society: In any society with different religions, cultures and economic conditions, a variety of public spaces might appear. For instance, while having a public space for a specific gender, such as women’s parks in Islamic cities might be essential, in other societies, 19  providing access to a diverse range of users might be one of the key requirements in designing and planning a new public space.  In other words, while providing free access to all public users is a must in some societies, in a different context, spaces with limited access to certain users can be still defined as public spaces. Therefore, the meaning of public space is not the same in different societies. Public space and Location: The context of a place itself may dictate a certain type of public spaces. Waterfront plazas, riverside walkways, a garden which generates more comfort in arid areas (e.g. Persian Gardens in deserts) are all good examples of how context and climate can dictate the types of public space needed in a specific location. Therefore, typology and existing types of public spaces might not be the same everywhere. Places with different contexts might have different types of public spaces. 2.1.1.3 Effective Factors in Defining UOS In order to present a comprehensive definition of UOS, it is essential to study a variety of definitions from different perspectives. Each definition covers some aspects of UOS. This part of the research is dedicated to introducing and critiquing these definitions from different aspects, including:  Openness, Accessibility and Ownership  Social values  Health values  Diversity of activities and functional values 2.1.1.4 Openness, Accessibility and Ownership of UOS From Gold’s point of view, UOS can be defined as land and water in an urban area not covered by cars or buildings, or as any undeveloped land in an urban area. (1980)  This definition solely focuses on the physical aspect and openness of UOS. It seems that “outdoor space” was the main concern in his point of view when mentioning that the UOS should not be covered with buildings. Moreover, he has pointed out that these spaces should not be covered with cars, and this part of the definition eliminates all those UOS which might 20  be accessible by cars, such as drive-in cinemas. Moreover, this definition encompasses all outdoor spaces regardless of ownership of these spaces. For example, a private garden, although an outdoor space which is not covered by cars or buildings, is not a part of UOS system as public users do not have free access to this outdoor space. This definition raises some other questions as well: what about mixed-use streets that provide access for cars and people at the same time? Are they excluded from UOS system? What about an unused inactive vacant lot? Simply because there is no car access and it is not covered with buildings, can it be a part of UOS system regardless of its social values? Some other scholars such as Lynch have defined UOS as publicly accessible open spaces, designed and built for human activity and enjoyment. This may include parks, neighbourhood playgrounds, community gardens, downtown plazas, streets and malls. This definition is drawn from the work of Lynch (1981), who argues that open space is open when it is accessible. This definition is an effort to look at different aspects of UOS simultaneously, including accessibility, openness and function. Other scholars, such as Francis, believe the focus in defining UOS should be on the accessibility, and not only on the openness of these spaces. He believes a more useful way of defining and categorizing UOS may be to distinguish between “accessible” and inaccessible” open space rather than simply “open” and ”closed”. (Urban Open Spaces, 1991)  The “publicly accessible” key phrase in Lynch’s definition also raises the question of to what “public” refers. Is it about the ownership or the user groups? Marcus & Francis discuss the ownership and accessibility of UOS in the introduction of their book People Places. In this book, they address “a variety of outdoor social spaces, including those that are; publicly owned and publicly accessible (neighbourhood parks, miniparks, some plaza space), often privately owned and managed but accessible to the public (corporate plazas, college campuses), and privately owned and accessible only to a particular group of users (residents and staff in elderly housing, children and staff in day care centre and patients, staff and visitors in hospitals).” (1990, p. 5) The last two groups are not “technically public, yet all contributes to a sense of public life, 21  allowing one to meet, view and converse with those other than one’s immediate family (Marcus & Francis, 1990, p. 5).” This definition clearly indicates that what makes an open space an UOS is the public life happening in that space and not solely the type of ownership. Users’ social life is more important in defining UOS compared to the type of ownership. Therefore, based on Marcus and Francis’s point of view, as long as an open space is accessible to public or semi-public user groups and it contributes to the public and social life of these user groups, it can be classified as UOS regardless of its ownership. 2.1.1.5 Social Values of UOS While some definitions of UOS are focused on the physical and spatial aspects of these spaces, some other definitions are focused on the values of these spaces, including social values. Some scholars such as Mensch focus on the role of UOS in providing a chance for social interaction among public users. Mensch believes that UOS are where individuals see and are seen by others as they engage in public affairs. (Mensch, 2007) This definition focuses on the social values of UOS and the need to communicate with other people other than the immediate family and private domain. Considering this definition, not all open spaces are considered as UOS, as people should be engaged with social affairs in these spaces. In other words, the public life and social engagement in an open space is the quality that forms a good UOS. The social function of open spaces in the city is to bring people together, and this mingling and meeting may take place under the pleasantest possible condition in the neighborhood. Public open spaces in the city should permit and encourage the greatest possible number of meetings, encounters and challenges between varied people and groups. (Mumford, 1969) Mumford also believes that socially speaking, too much open space may be a burden rather than a blessing. The quality of an open space, its charm and accessibility is more important compared to its gross quantity. (1969) In this definition Mumford tries to elaborate the importance of qualitative aspects of UOS. However, the quantity of UOS could be as important as its qualitative aspects, considering the needs of users on a broader scale. For example, while 22  a high quality UOS such as a sport field is a must in a residential neighbourhood, at the same time its quantity should be appropriate to the residential population of that neighbourhood so that it can provide proper service to all residents of that neighbourhood. Therefore, an open space which does not provide the chance of communication and social interaction for citizens cannot be considered an UOS. For instance, urban streets that are only designed for motorized vehicles are not part of UOS as these spaces do not provide the chance of social communication for citizens. However, pedestrian dominated streets which provide the chance of social communication for citizens are part of UOS. 2.1.1.6 Health Values of UOS Mumford (1969) believes that UOS have biological, economic and social functions and values. UOS and users have reciprocal effects on each other. While different UOS can affect the physical and mental health of its users, the livability and activeness of these spaces highly depend on the presence of diverse user groups at the same time. Putting it differently, users can affect the quality of UOS and UOS can affect the quality of life. These spaces play a vital role in creating healthier and more liveable communities. This concept has been well-reflected in Helen Wolley’s definition of UOS: “Urban Open Spaces are one aspect of the urban environment that is of great importance in daily life for people living in urban areas (2003, p. 2).” In another definition, Stanley et al. elaborate on the role of UOS on the mental health and social development of citizens: “Sociospatial contact between different ethnic and class groups generates the healthy psychological, social and political development of urban citizens (2012, p. 1090).” Mumford (1969) also points out the effect of UOS on physical and mental health of citizens. In a section of a book Small Urban Spaces he emphasizes open spaces as biological and psychological necessities in the city that act as refugees from the noisy, crowded, and dusty urban hive.  2.1.1.7 Diversity of Activities and Functional Values of UOS Some other scholars emphasize activities and functional values of UOS from users’ point of view as being an arena that allows for different types of activities encompassing necessary, 23  optional and social activities. (Gehl, 1987) Although not a comprehensive way of defining UOS, this definition has some valuable points. Gehl focuses on the diversity of activities which gives users the right to choose between different options. Diverse activities that have been defined for an UOS can effectively attract a variety of user groups and eventually the presence of various users can keep the space active and alive. 2.1.1.8 Comprehensive Definition of UOS One of the best and most comprehensive definitions of UOS has been presented in a report by Urban Task Force chaired by Lord Rogers; “Public space should be conceived of as an outdoor room within a neighbourhood, somewhere to relax, and enjoy the urban experience, a venue for a range of different activities, from outdoor eating to street entertainment; from sport and play areas to a venue for civic or political functions; and most importantly of all a place for walking or sitting-out. Public spaces work best when they establish a direct relationship between the space and the people who live and work around it (Urban Task Force, 2003, p. 28).” The keywords and key phrases of the above mentioned definition that covers most aspects of UOS are as follows:  ‘Outdoor’ refers to the openness of UOS.  ‘Within a neighbourhood’ refers to the scale and domain of influence of UOS.  Somewhere to ‘relax’ and ‘enjoy’ refers to the role of UOS in users’ mental and physical health.  ‘Different activities’ and functions refers to diversity of activities and functional values of UOS   ‘A place for walking or sitting’ not only refers to the physical health but also to the importance of providing pedestrian experiences in UOS.  ‘Relationship between the space and the people’ refers to the reciprocal effect of UOS on users, the importance of public engagement, and the important role of users in UOS. 24  What is significant in this definition is that the type of ownership is not a determining factor, and what matters is the quality of public life in these spaces. To summarize, there are a variety of definitions for UOS, each of them covering some aspects of UOS including social, health, functional and aesthetical values, ownership and users, openness, land cover, visual/physical accessibility, etc. In order to define UOS comprehensively, defining factors and key words of all the previously presented definitions have been collected and categorized in a table as follows: (Table 2-2)  Defining Factors Key words Openness - Outdoor space  - Outdoor room - Open space, open field - Openness - … Land Cover - Not covered by cars or buildings  - Any undeveloped land - … Accessibility & Pedestrian Experience  - Accessibility - Physical/visual access - Control of access - Publicly accessible - Open and accessible - Not covered by cars - A place for walking or sitting out - … Ownership  - Publicly owned - Privately owned but accessible to the public - Privately owned but accessible to a particular group of users - … Users’ Rights - Designed and built for human activity - Publicly accessible - Democratic space - User engagement - Varied people and groups - Direct relationship between the space and the people - … Social Values - Social life - Sense of place - Sense of public life - Ability to meet, view and converse with the public - Permit and encourage meetings, encounters and challenges - Social interaction - Social communication - Engagement in public affairs 25  Defining Factors Key words - Bringing people together - Mingling and meeting - Important in the daily lives of people - … Health Values - Relaxation and enjoyment - Physical health - Psychological health - Livability - Creating healthier and more livable communities - Quality of life - Refugee from noisy, crowded, dusty urban hive - Biological necessity for open space - Sunlight, fresh air, free movement - Physiological needs for sight and smell of nature and open sky - … Location - In an urban area - In a neighborhood - Close to where people live and work - … Diversity of Activities - Freedom of choice - Different types of activities;  - Necessary, optional, social activities - Livable space - Activeness and livability - Outdoor activities, entertainment, sport and play areas  - … Diversity of Functions - Freedom of choice - Biological, economic and social functions - Civic and political functions - … Aesthetical Qualities - Charm - Aesthetical attraction - … Table ‎2-2 Summary of all defining factors and keywords used in various definitions around UOS (table by the author) Summarizing all important defining factors and keywords presented in the Table 2-2, and considering the scope of this research, UOS has been defined as follows: UOS is defined as an outdoor space which is accessible to public users. UOS with a variety of functions and activities is a social milieu that engages public users with the open space and offers a variety of values including; environmental, socio-cultural, economic, and aesthetical values. 26  The highlighted keywords used in the comprehensive definition of UOS have been adapted and extracted from previously presented definitions. 2.1.2 UOS Typology A successful UOS network consists of a diverse range of UOS with different types. While diversity of types is a defining indicator of a high quality UOS network, it is essential to study different UOS classification systems. As previously mentioned, UOS have many different aspects and by focusing on each of these aspects a new classification system can be defined for UOS.  What are the different types of UOS?  What are different methods of classification?  What would be those proper classification methods with consideration of the focus of this research?  There are numerous methods of classifying UOS. For instance, 1) classifying historical and existing types of UOS (Francis, 1991) (Mumford, 1969), and also 2) classifying UOS by focusing on the benefits of these spaces, including social, health, economic and environmental (Wolley, 2003) are two different classification methods that have been formerly used.  While different scholars have covered different aspects of UOS in their studies and projects, what is missing from most of these works is the spatial analysis and spatial classification of UOS. Since the focus in most studies has been on individual UOS, the main concern in these types of studies has been on quality assessments of UOS and behavioral studies of users in these spaces. Therefore, based on the focus of these studies, there are not many examples of spatial classification of UOS. In this regard, Stanley et al. believe “much of the theoretical work on the social significance of open spaces suffer from a lack of specificity on the spatial configuration, scale, and functions of different kind of urban spaces and is confined to a narrow historical framework (2012, p. 1095).” Therefore, although there are numerous methods of classifying UOS, considering the scope of this study, the main focus in classifying UOS is on spatial aspects of these spaces. Accordingly, in this research, the following categories have been defined and studied for spatial classification of UOS; (Figure 2-2) 27  Form-Functionality of UOS: One method for categorizing UOS is to look at their functions and forms simultaneously. For instance, a square is a form-functional type of UOS. The word ‘square’ represents a special form of a UOS with a certain function. -What are the different form-functional types of UOS? Morphology of UOS:  Another spatial attribute of UOS is the morphological aspects of these spaces. UOS can be classified by focusing on their morphology, sizes, and shapes.  - What are the different types of UOS considering their morphology? Scale and domain of influence of UOS:  UOS have different domain of influence or service coverage area in the city. While some types of UOS might serve a larger area, other types might cover a smaller area.  -What are the different types of UOS considering their domain of influence in the city? Privacy Gradient of UOS (public, semi-public, inbetween, semi-private): While walking from a main street to inner parts of the neighbourhoood and entering a semi-private forecourt, one is actually experiencing different UOS with a variety of publicness degrees.  -What are those factors that affect the publicness degree of UOS?  -Considering the publicness degree, what are the different types of UOS?   Figure ‎2-2 Typology of UOS (diagram by the author) 28  2.1.2.1 Functional Classification of UOS The quality of UOS network is directly related to the diversity of functions and spaces within that network. Jan Gehl in his book Life Between Buildings mentions that a good public space should present a broad spectrum of possible human activities. The more diverse the activities, the more attractive the public space would be for its users. (1987) -But how is it possible to categorize UOS by looking at their functions? -What are the different functional types of UOS? Different researchers have categorized UOS differently by looking only at their functions.  In a study held by Stanley et al. it has been mentioned that “A broad comparative perspective on urban history confirms that open space has assumed a tremendous range of forms and functions, with a variety of benefits for urban populations.” (2012, p. 1090)  Based on this statement, there could be two different methods of classifying UOS based on their function: 1- Classifying UOS by looking at their ‘Functional Values’. In other words, UOS can be categorized based on their benefits or value. For example, all UOS with environmental values can be classified under one group. This group with a single functional value may include various forms of UOS including parks, green spaces, storm-water mitigation sites, etc.  2- Classifying UOS by looking at their ‘Functional Forms’. For example ‘street’ is a type of UOS with a defined form and function. Functional Values of UOS: UOS designed for different purposes present a variety of functional values in the city, including social, political, economic, health and wellbeing, recreational, environmental, and structural functions. For instance, based on a quote from Mumford, “the social function of open spaces in the city is to bring people together (1969, p. 20).” He continues, “the great function of the city is to […] permit and to encourage the greatest possible number of meetings, encounters, challenges, between varied persons and groups, providing as it were a stage upon 29  which the drama of social life may be enacted, with the actors taking their turn, too, as spectators (p. 20).” Some other researchers such as Weaver have focused on recreational values of UOS. Playgrounds and parks are two examples of UOS with recreational values. This type of UOS provides the essential space for relaxation, rest and physical activities. As Weaver explains “Man does not live by bread alone. Recreation and beauty and the leisure to enjoy them are necessities (1969, p. 27).” He continues that there must be some facilities for “recreation, rest and relaxation that are available to all citizens in every walk of life. We must consider the urban citizen who wants his recreation within the city (1969, p. 24).” In another research, Francis presents some other functional values of UOS such as economic and environmental values.  Social functions:  Should be used by a variety of users including children, teens, and the elderly  Should allow for a variety of activities  People should feel safe and secure when using space  Space should be comfortable  Should afford opportunities for user involvement, control and manipulation  Space should be publicly accessible  Is democratic  Is loved by those who use it and live or work nearby Environmental Functions:  Provide opportunities for environmental learning  Includes opportunities for discovery, delight, and challenge  Should be ecologically healthy Economic/management Functions:  Should contribute economic benefits to the surrounding community  Is evaluated, redesigned and improved over time Table ‎2-3 Dimensions of successful UOS (adapted from Francis (1991, p. 99), edits by the author) 30  While some scholars classified ‘recreational’, ‘health and well-being’ and ‘social’ functions in separate groups, some others believe that ‘recreation’ and ‘health and wellbeing’ can be parts of the social functions. For example, in a report by Stiles, UOS have been classified based on their functional values into the following three categories: (Stiles, 2009, p. 10)  1) Environmental and ecological functions include:   Climatic amelioration   Noise screening   Influencing the hydrological cycle – storm water management   Providing habitats for wild plants and animals  2) Social and societal functions include:   Providing space and facilities for leisure and recreation   Facilitating social contact and communication   Access to and experience of nature   Influencing human physical and psychological health and well-being  3) Structural and aesthetic functions include:   Articulating, dividing and linking areas of the urban fabric   Improving the legibility of the city  Establishing a sense of place   Acting as a carrier of identity, meanings and values There are many more examples of studies around functional values of UOS. To summarize and considering the focus of this research, five main functional values have been defined for UOS: 1- Environmental: all those types of UOS which serve to the environment and possess ecological values, such as storm water mitigation sites. 2- Socio-Cultural: all those types of UOS that provide space for social, cultural and recreational events and activities. 31  3- Economic: although all different types of UOS possess economic values, some specific types such as amusement parks contribute more to economic benefits for the city in comparison to some other types.  4- Aesthetical: all those types of UOS that enhance the aesthetical aspects of urban life such as parks and other natural, semi-natural types of UOS which provide access to nature and scenic landscapes.   5- Structural: all those types of UOS which has been designed for urban planning and management purposes. A good example of this type can be green belts which serve as a boundary, preventing further urban development while retaining the undeveloped natural, or semi-natural environment. Functional Forms of UOS: Another way of classifying UOS by their functions is to look at functional-forms of these spaces. For example: the ‘street’ type represents all those UOS with linear forms that have been designed to accommodate vehicles and/or pedestrian traffic. Therefore ‘Street’ is a form-functional type of UOS. Francis (1991) classifies UOS by looking at their functional forms and categorizes these spaces into two groups; traditional and innovative UOS.  The brief version of this table has been presented in the following: (Table 2-4)   32   Type Traditional: Public parks  Neighborhood parks Playgrounds Pedestrian malls Plazas Innovative: Community open spaces Neighborhood open space Schoolyards Streets Transit malls Farmers markets Town trails Vacant/undeveloped open spaces Waterfronts Found spaces Table ‎2-4 Typology of traditional and innovative UOS (table adapted from Francis (1991, pp. 78-79); edits by the author) The functional forms of UOS are not limited to what have been presented in the Mark Francis’ table. Considering only the functions of UOS, there are still some missing types that can be added to this list. In 2003, in his new book Urban Open Space; Design for User Needs, he presents a more comprehensive version of UOS typology adapted from Carr et al. 1992 with new UOS types and sub-types. A brief version of the new table has been presented in the following: (Table 2-5) Type Sub-type Public Parks  Public/Central Park  Downtown Park  Commons  Neighborhood Park  Mini/Vestpocket Park 33  Type Sub-type Squares and Plazas  Central Square Memorials  Markets  Farmers Markets Streets  Pedestrian Sidewalks  Pedestrian Malls  Transit Mall  Traffic Restricted Streets  Town Trails Playgrounds  Playgrounds  School yards Community Open Spaces   Community Garden/Park Greenways and Linear Parkways  Urban Wilderness  Atrium/Indoor Market Places  Atrium  Market Place/ Downtown Shopping Centre Found/Neighborhood Spaces  Everyday Spaces  Neighborhood Spaces Waterfronts  Waterfronts, Harbors, Beaches, Riverfronts, Piers, Lakefronts Table ‎2-5 UOS types and sub-types (table adapted from Francis (2003); edits by the author) While this classification method is a more comprehensive version of the one before, it does not include all types of UOS, such as sports fields. In some cases this classification system also might not work properly as some UOS can be considered as more than just one type. For example, a small memorial park, which is clearly under the ‘memorials’ type, can also be part of the ‘public parks’ type, as well as ‘neighbourhood spaces’ and ‘community spaces’. These examples of multi-functional UOS make the functional form classification a bit more complicated. Stanley et al. propose seven types that are very useful in situating and analyzing UOS in different groups. The author also mentions that this typology is based on the conceptual tension between form and function of UOS (2012).  The summary of their functional form categories and a brief definition for each type has been presented in the following: (Table 2-6) 34  Type Definition 1- Food Production  All green spaces utilized predominantly for crops and livestock 2- Parks and Gardens  Parks and gardens are defined as partly landscaped, mostly green areas intended for social and recreational activities as well as aesthetic or display purposes, although historically these functions have been intertwined with food production. While some parks and gardens are highly specialized and institutionally designed for specific cultural functions, others have operated as multi-purpose spaces of social interaction, recreation, and ritual. 3- Recreational Spaces Recreational spaces involve functionally specialized green and grey spaces designed or used for leisure activities, such as sports or exercise. 4- Plazas  Plazas are defined as intentionally established open spaces framed by buildings on most sides and usually hard surfaced. Plazas can host a diversity of civic activities and tend to be multi-purpose. 5- Streets In both ancient and modern cities, streets functioned as pedestrian and vehicular corridors as well as crucial locales of social interaction, political demonstration, ritual, recreation, economic production, and trade. 6- Transport Facilities  Transportation areas represent spaces in which the transfer and distribution of goods is conducted close to forms of transport. This is a specialized functional category, and these areas vary based on the mode of transport. These spaces may include some marketplace functions, but marketplaces and shops may exist separately in plazas or buildings. 7- Incidental Spaces  Incidental space, also referred to as marginalized or amenity space, is defined here as any green or grey space located on the margins of other spaces or 35  Type Definition buildings that is either ignored or not intended for a specific use other than safety, visual amenity, or physical separation (Garde, 1999; Al-Hagla, 2008). These spaces are not easily amenable to either formal or functional classification. Table ‎2-6 UOS functional form typology (data adapted from Stanley et al. (2012, pp. 1095-1107); table by the author) This comprehensive functional-form typology of UOS not only includes the definition and examples of each type, but also information about the scale and green to gray gradient, which will be explained in the a later section of this chapter. Before going through this classification in more details there could be some critiques of this typology that are worth mentioning: 1) Although all UOS types in this classification system were supposed to be form-functional types, some categories such as ‘recreational spaces’ do not fit in the same classification system. The sub-types of recreational spaces in this classification have been defined as: Stadiums, Greenbelts, Beaches, and Playgrounds. But the question would be; is the recreational UOS type limited to spaces and fields for physical activities? What about the recreational aspect of visiting a garden, or walking down the pedestrian walkway?  Recreation is a quality, and as mentioned before, it is more like a ‘functional value’ of UOS.  Recreation can be the result of physical, mental or social activities and therefore recreational spaces should not be limited to those fields solely designed for physical activities. It seems that “Sports Fields” would be a better representation for all those spaces designed for physical activities including stadiums, playgrounds and other sub-types of “Sports Fields”, and it clearly presents a form functional category. 2) The transport facilities classification includes; harbours, airports, train stations, etc. ‘Transport facilities’ is not a valid title for a UOS type because it does not exhibit the social value of UOS. The reason people gather in harbours is likely to socialize and enjoy the view from a public plaza or pedestrian walkway designed adjacent to the water edge. This has nothing to do with transportation of goods or people which are happening in the same place. Therefore, all sub-types under this main type can be classified under other new UOS types. For example, 36  harbours and waterfronts can be classified under a new type of UOS called ‘Water Edges’. This type clearly represents aesthetical values and functional form of all UOS in this type, regardless of all other transportation activities which are not directly related to the social function of UOS. 3) The ‘food production’ type in this classification system includes agricultural fields, community gardens, etc. Although these subtypes are all part of UOS, ‘food production’ is not limited to these subtypes and can be found in other types of UOS such as gardens. For example, fruit trees are often found in Persian Gardens. There is a need to categorize the sub type of this group differently. Also, while concentrating on urban areas and the social values of UOS, some agricultural fields in sub-urban areas might not fit in this category as UOS. While considering the form-functional aspect of UOS, community gardens and other types of gardens can be categorized in a ‘garden’ type of UOS regardless of their different functional values. In other words, while the functional value of a community garden might be primarily economic value with some social value, in a Persian garden socio-cultural value might be of greater importance. However, considering the functional forms, these two subtypes fall into the same ‘garden’ type of UOS. 4) In Stanley et al. UOS typology, ‘incidental spaces’ includes natural, semi-natural spaces, empty lots and marginalized spaces between buildings. However, considering the similarities of natural and semi natural spaces these UOS can be categorized under a separate type and not as a part of incidental spaces. Additionally, the ‘incidental spaces’ type refers to those UOS including parking lots, storages, or vacant/undeveloped spaces in urban areas that have the potential to be modified and turn into valuable UOS in the future. Comprehensive Functional Typology of UOS: Considering all valuable aspects and critiques over the presented functional classification methods of UOS, a new version of this classification method which covers all aspects of this research has been presented in a form of a table. Table 2-7 represents a proposed form-functional UOS typology, including all types and subtypes of UOS.  Each type of UOS might have more than one functional value, which complicates the classification of UOS. Therefore, in order to distinguish ‘functional forms’ from ‘functional 37  values’, a new column has been added to this table called ’Functional Values’. This part with a few modifications has been adapted from other works (Francis, 1991), (Stiles, 2009), (Wolley, 2003) which have been previously presented in this section.  Each functional value has a certain degree of importance to each form-functional type;  High: demonstrates a strong relation between the functional forms and the functional value.  Moderate-High: demonstrates a semi-strong relation between the functional forms and the functional value.  Moderate-Low: demonstrates a moderate relation between the functional forms and the functional value.  Low: demonstrates a weak relation between the functional forms and the functional value. Seven different form-functional types of UOS have been defined in this proposed classification system. Each type has been colour-coded for future reference in the analysis process of the two selected case studies.   38  Functional Forms Functional Values Type Sub-Type Environmental Socio-Cultural Economic Aesthetical Structural The importance degree of each functional value  1- Parks and Gardens  - Urban parks - Neighborhood parks - Pocket parks - Gardens - Community gardens      2- Natural/Semi-natural Features1 - Agricultural fields - Water surfaces - Habitats - Woods - Wetlands - Meadows - Natural elements      3- Water Edges - Beaches - Waterfronts - Harbours - Piers - River fronts      4- Sports Fields - Outdoor sports fields - Playgrounds      5- Civic Spaces  - Squares - Plazas - Institutional open spaces - Forecourts - Courtyards - Entrances      6- Streets and Corridors - Pedestrian alleys - Pedestrian sidewalks - Pedestrian streets - Traffic restricted streets - Pedestrian linear corridors - Trails                                                           1 In the context of this thesis “Natural/Semi-natural Features” refers to any outdoor space which provides environmental and/or ecological benefits. This form-functional UOS type includes outdoor spaces that are consist of natural elements (plants, water, etc.) which can be interpreted as softscapes.    High Moderate-High Moderate-Low Low 39  Functional Forms Functional Values Type Sub-Type Environmental Socio-Cultural Economic Aesthetical Structural 7- Incidental Spaces  - Vacant, derelict, undeveloped open spaces - Parking lots - Storages      Table ‎2-7 A proposed form-functional typology of UOS (table by the author) 2.1.2.2 Scale and Domain of Influence of UOS UOS with different types and sizes, like other urban elements, provide service in different scales and have different domain of influence. For instance, while a local soccer field might provide service only in neighbourhood scale, a stadium works in a larger scale and provides service to a larger region. Put differently, the size and function of an UOS directly affects its service coverage area. Therefore, another method of categorizing UOS is to look at their effective scale or domain of influence.  Jan Gehl emphasizes the importance of the scale and spatial proportion of urban spaces and explains that the spatial relationship, scale and size of different places in cities have a crucial influence on peoples’ experience of place. He has also mentioned in his book Cities for People that in any urban space with human activities, especially in old cities, scale or spatial proportion is an important factor effective on peoples’ perception of space. (Gehl, 2010)  Woolley (2003), in the book Urban Open Spaces, categorized all types of UOS in three groups; Domestic, Neighborhood and Civic UOS. (Table 2-8) “Domestic urban open spaces are physically associated most closely with the home and socially are likely to be used mainly by the family, friends and neighbours. Neighbourhood urban open spaces are physically not directly related to the home but to the neighbourhood and community within which one lives. Socially, these spaces will be used not only by family, friends and neighbours but also, predominantly, by others within the community who are likely to live within the vicinity of the space, Civic urban open spaces, then, are those that are set within the urban context but which are, usually, physically farthest from the home or are places at strategic or specific locations. Such spaces 40  are more of a social mix where one is most likely to meet people from different walks of life and from a different physical part of the conurbation (Wolley, 2003, p. 75).” (Table 2-8, Figure 2-3) Table ‎2-8 Woolley’s UOS typology base on the scale and social distance from home (data adapted from Wolley (2003, pp. 76-149); table by the author)  Figure ‎2-3 Helen Wolley’s Domestic, Neighbourhood and Civic UOS typology (2003, pp. 76-150) (diagram adapted from Zhang (2011, p. 63)) Types Domestic UOS Neighbourhood UOS Civic UOS Sub-Categories  Housing  Private gardens  Community gardens  Allotments   Parks  Playgrounds  Playing fields and sports grounds  School playgrounds  Streets  City farms  Incidental spaces and natural green space  Commercial  Health and education   Transport  Recreational  41  Barton et al. (2010) have organized UOS by looking at their service coverage domain in the urban area and the necessity of providing access to different types of UOS within a certain distance from home. For example, there should be a neighbourhood park within 600 (target) to 1000 (maximum) meter distance from home. In other words, a neighbourhood park provides service to residential areas within a 600 to 1000 meter radius.  (Figure 2-4)  Figure ‎2-4 Access to open spaces in certain distances from home (diagram adapted from Barton et al. (2010, p. 113)) Stanley et al. (2012) classifies all different form-functional types of UOS, which have been presented before, in three groups based on their effective scale in the city. These three scales include city scale, neighbourhood scale and intimate scale. (Table 2-9)  42   Table ‎2-9 UOS scales and form-functional typology (table adapted from Stanley et al. (2012, p. 1094)) Considering all different methods of classifying UOS which has been based on their scale and service coverage area, three main scales of UOS has been defined for this research; Large scale: The city scale Middle Scale: The neighbourhood scale Small Scale: The block scale 43  2.1.2.3 Morphological Classification of UOS Another method of classifying UOS is to provide a morphological taxonomy of these spaces. Morphological taxonomy can be defined as organizing UOS by their shapes and forms. A very good example of the morphological taxonomy can be found in Landscape Ecology. Taxonomy of patches, open space corridors, and matrices adapted from landscape ecology can be used as a basis for a more structural view of UOS morphology. Landscape ecology “uses a taxonomy of patches, corridors, and matrices for understanding landscape patterns and processes (Erickson, 2006, p. 20).”  As with landscape components, UOS may also have diverse shapes and forms. In general, UOS can be divided into two groups based on their morphologies; (Figure 2-5) - Linear corridors - Patches (including large patches and scattered spots)  Figure ‎2-5 Morphological taxonomy of UOS in comparison to landscape ecology components (diagram by the author) 44  In biodiversity planning, corridors have two roles: corridors as movement routes or corridors as linear habitats. These linear corridors of diverse shapes are not only habitats for resident species but also connect two or more large blocks of suitable habitats or patches. (Erickson, 2006) Similarly, in an UOS network, linear UOS such as green ways, streets, and linear river-side parks not only work as individual UOS with a variety of functions, but also connect other forms of UOS. Therefore the linear type plays a critical role in the UOS network. For instance, a pedestrian corridor with a scenic view, while connecting different types of UOS along its path, at the same time serves the social needs and wellbeing of citizens by providing a venue for walking, enjoying the view, and socializing.  A very good example of this linear type of UOS can be found in Isfahan, Iran. Si-o-Se Pol is a historic bridge over Zayandeh-Rood River which connects the northern part of the city to the southern part. This pedestrian dominated bridge, which attracts hundreds of visitors daily, provides a chance for visitors to enjoy the fantastic view of the city and the river and also to walk, sit, and socialize. (Figure 2-6)  Figure ‎2-6 Si-O-Se Pol or AllahVerdi Khan Bridge as an example of a linear UOS (image by Andrea Thompson Photography, adapted from Bing Wallpapers (2013)) In landscape ecology, patches may have different sizes. “Productivity, nutrient and water flux, and species dynamics are all affected by the size of landscape patches […] the microenvironment in the centre of a tiny patch of woods differs strikingly from the centre of an 45  extensive woods (Forman & Gordon, 1981, p. 735).” Not only the size, but also the configuration and the numbers of patches in the landscape affect the overall condition of the landscape. “For example, a landscape with ten evenly-distributed large patches differs fundamentally in most ecological fluxes from a landscape with the ten patches clustered at one end (Forman & Gordon, 1981, p. 736).” (Figure 2-7)  Figure ‎2-7 Morphological taxonomy of patches in landscape ecology (diagram adapted from Forman & Gordon (1981, p. 736)) Similarly in an UOS network, non-linear UOS may have different sizes, configurations, and numbers. Considering the size, these non-linear UOS can be divided into two groups; large patches and small spots. Also considering the configuration of patches and small spots within the network, these spaces can be divided into three sub-categories: regular, aggregated and scattered patches and spots. (Figure 2-8)  46   Figure ‎2-8 Morphological types of UOS in Isfahan, Iran (diagram by the author) 2.1.2.4 Privacy Gradient of UOS Alexander et al. in the book A Pattern Language elaborates on the importance of intimacy gradient and sequences of spaces with different degrees of privateness: “Unless the spaces in a building are arranged in a sequence which corresponds to their degrees of privateness, the visits made by strangers, friends, guest, clients, family, will always be a little awkward (1977, p. 610).” 47   Figure ‎2-9 Alexander’s diagram of the intimacy gradient in a building (diagram adapted from Alexander et al. (1977, p. 613)) While Alexander focuses on the intimacy gradient and sequences of spaces in a building, similar gradient of privacy can be defined on a larger scale, including neighbourhood scale and city scale. Similarly, when discussing UOS, we are actually pointing to a group of spaces with different degrees of publicness. “Public space is not a homogenous arena. The dimensions and extent of its publicness are highly differentiated from instance to instance (Low & Smith, 2006, p. 3).” A primary street compared a local secondary access, a vast urban plaza compared to a forecourt of a residential building; they are all UOS with different degree of publicness. “How a society divides it space into public and private sphere and how this division controls movement from one place to another (Madanipour, 2003, p. 1)” can be considered as defining characteristics of the city. Robinson defines seven degrees of privacy in a territorial gradient: the public civic domain, the public neighborhood domain, the semipublic or collective domain, the semi-private domain, the private domain, the semi intimate domain and the intimate domain. (Robinson, 2001) (Figure 2-10)  Figure ‎2-10 Robinson’s seven degrees of privacy in a territorial gradient (diagram by Hank Liu; adapted from Robinson (2001, p. s2.3) What is important is that spaces with different degrees of publicness work as a connected network, allowing people to move between spaces in a gradient of privacy. Through a series of 48  spaces with different degrees of publicness, the autonomy of the resident within a small social group is provided and the individual is granted a large measure of control over time, space, activity and social interaction. (Robinson, 2001)  UOS, as one of the major spaces providing a chance for social communication, needs to be at the same time supportive of the user’s desirable level of privacy while providing the opportunity for social interaction. The desirable level of privacy depends on the scale and functions of UOS. Therefore, different type of open spaces can be used as regulators of privacy in citizens’ social life. UOS with different degrees of privacy form an important part of our everyday life; “if we monitor our individual everyday routines, one of the defining features of these routines is how we live in and pass through private and public spaces, and feel and behave accordingly […] Many aspects of our mental and behavioral states at each moment depend on whether we are on our own, with our intimate friends and relatives or in the presence of strangers (Madanipour, 2003, p. 1).” How UOS with different degrees of publicness connect to each other in a variety of patterns is the subject of this discussion. Therefore, in studying UOS networks it is essential to study all different patterns in which individual UOS with different degrees of publicness connect to each other in the network. Privacy gradient patterns affect the spatial qualities of the UOS network and eventually, as mentioned before, can be considered as defining characteristics of our cities. (Figure 2-11)  49   Figure ‎2-11 Privacy gradient patterns in UOS networks (diagram by the author) 50  Generally the following key variables affect the degree of publicness of different types of UOS:  Time  Culture  Scale  Design/program  Accessibility (visual/physical) The publicness degree of a certain UOS type might not be the same in all societies. The publicness degree of a certain UOS might differ from period to period. Not only time affects the publicness degree of a certain UOS type, but people’s perception of privacy and the importance of privacy gradients for different cultural backgrounds might be different. “Cities of all cultures, at all historical periods, are organized along some form of public-private lines, although the nature of this division, the meaning of and relationship between public and private spheres vary widely. […] public-private distinction has been a key organizing principle, shaping the physical space of the cities and the social life of their citizens. (Madanipour, 2003, p. 1)”  The degree of publicness of UOS is only definable in comparison to each other, and on a certain scale. While a UOS might be a semi-private space in a larger scale, the same space might be defined as a public space in a smaller scale. For instance, to get into the inner part of the traditional house in an Islamic society, which is dedicated to the family itself, one should pass through the public space (alley) towards the semipublic space (entrance) and in-between space (the central courtyard),  to semi-private space (the guest rooms) and finally the private space which includes bedrooms and kitchen. However, looking at the same Islamic residence from a larger scale (neighbourhood scale), the definition of public to private will change. In the neighborhood scale, the entire house will be considered as the private space, while the alley would be semi private space connecting private residences to the public main street. (Figure 2-12) As it has been mentioned before, the publicness degree of UOS is affected by different factors, and one of these factors is the program and design of that UOS itself. The design of a 51  UOS affects the publicness degree of different parts of an individual UOS. For example, an unregulated UOS with a variety of names (ie. Loose-fit, un-designed, smooth space, slippery, unconstrained, nomadic or migrant open spaces) provide opportunities for more privacy in public space by creating a feeling of self-direction among users. These spaces are not necessarily places with no rules but places where users can continually invent new rules. (Thompson, 2002)   Figure ‎2-12 Degrees of publicness of a certain UOS type (Alley) at two different scales; neighbourhood and block scales (diagram by the author) Another key variable which affects the publicness degree of UOS is the physical and visual accessibility to or from these spaces. Physical or “spatial accessibility means the ability to move safely and unimpeded from a starting point to a destination (Girling & Kellett, 2005, p. 77).” Spatial barriers, limiting or controlling the access to UOS, affect the way people perceive these spaces. As a result, spatial barriers and the physical accessibility to UOS affect the publicness degree of that space. For instance, a forecourt of an office building, with no physical barrier on the edges has a higher degree of publicness in comparison to the same space surrounded by buildings and bound by physical barriers. In other words, these spatial barriers which are 52  controlling the access to the above mentioned forecourt, affect the experience of users in this space. Users might experience a more private space while the forecourt is surrounded by spatial barriers. (Figure 2-13)  Figure ‎2-13 The effect of physical accessibility on the degree of publicness of a certain UOS type (diagram by the author) Moreover, change in the visual accessibility, to or from any UOS, affects the degree of publicness of an UOS. A very good example which demonstrates the effect of visual accessibility on the publicness degree of an UOS can be found in historic Islamic cities. In these cities, limiting the visual accessibility was always a practical method to create a more private space where needed. For example, in Islamic residential buildings, architects used lattice panels to cover windows. These panels allowed air to circulate, while limiting the visual access to inner parts of the house. Using this method, Islamic architects could control the desirable level of privacy in residential buildings. (Figure 2-14) Another example on a larger scale is the introverted Naghsh-e-Jahan Plaza which is surrounded by two-three storey buildings. Although the plaza is a public space, being surrounded by physical barriers imbues a sense of privacy in public by limiting the visual access from this plaza. In Naghsh-e-Jahan Square the eyesight is limited to the inner space. Although this is a vast plaza, 507m x 158m,  it feels like an inward semi-public space separated from the rest of the city. (Figure 2-15)  53   Figure ‎2-14 Controlling the visual accessibility with lattice windows in Ameri House, Kashan, Iran (image by Maryam Nademi) CC BY-SA 2.0 (https://creativecommons.org/licenses/by-sa/2.0/), via www.flickr.com  Figure ‎2-15 Visual accessibility to and from Naghsh-e-Jahan Square, Isfahan, Iran (image by Nicola e Pina Iran 2008) via www.panoramio.com  2.2 UOS Network “A city that invites people to walk must by definition have a reasonably cohesive structure that offers short walking distances, attractive public spaces and a variation of urban functions (Gehl, 2010, p. 6).” This cohesive structure can be interpreted as the UOS network, which 54  includes different types of UOS with a variety of functions. In other words, UOS Network is a complex system which is made up a large number of entities, including different types of UOS. Although any kind of complex systems includes a variety of components, having all the components is not enough to form a system. Connectivity among components is a vital factor that forms the system as a united whole. The more connected and integrated the components are, the stronger the entire system would be.  Similarly, in a UOS network as a complex system, there should be interconnection among all these individual UOS. Therefore, a good UOS network should exhibit interdependence among its components while having a dense web of connections among different types of UOS. Good UOS and a good connecting network are two side of a same coin, completing each other. (Gehl, 2010) In order to study UOS as a network of interacting components, it is essential to first study the components of this network. Accordingly, categorizing UOS into types (e.g. function, scale, morphology, and degrees of publicness), which has been presented previously in this chapter, is the first essential step in understanding the components of this networks. Moreover, to present a comprehensive definition of a UOS network, it is essential to study defining factors of a complex system. Finally, to have a deeper understanding of how UOS networks operate, it is necessary to study the main attributes of these networks including proximity, connectivity, complexity, and permeability. 2.2.1 UOS Network as a Complex System Comprehensive definition of UOS networks: UOS network is a complex system that is composed of interacting and interdependent entities including public users and individual UOS with different functions, scales, forms, and degrees of publicness. This proposed definition includes all aspects of UOS network including;  Network components: individual UOS and users  Diversity in UOS network: UOS with a variety of functions, scales, forms (morphology) and degrees of publicness, which was presented in the UOS typology section (see Section 2.1.2) 55  Donna Erickson, in her book Metro Green, introduces Homer-Dixon’s six defining factors of complex systems. Erickson believes that open space embedded in an urban structure should be considered as a complex system. (2006) Complex systems such as UOS network: 1- “Are made up of a large number of entities, components or parts (Erickson, 2006, p. 10).” In UOS networks components include individual UOS with a variety of types and shapes, and also public users. “Open space systems are composed of a myriad of interacting and interdependent entities, from human and wildlife to natural features and built structures. They inherently engage complex casual connections, both ecologically and socially (Erickson, 2006, p. 10).” (Figure 2-16)  Figure ‎2-16 Components of UOS networks as complex systems (diagram by the author) 2- “Contain a dense web of casual connections among components. The more casual the connections, the more complexity (Erickson, 2006, p. 10).” In fact, these connections form interdependence among individual UOS and make them something more than just a group of individual, isolated open spaces dispersed over the city. The more connected individual UOS are, the greater the probability of their endurance, and as a result the stronger the entire system will become. (Figure 2-17) 56   Figure ‎2-17 Connectivity of components in UOS networks as complex systems (diagram by the author) 3- “Exhibit interdependence among components (Erickson, 2006, p. 10).” In other words, any change in a component of a UOS network might affect other components as well. For instance, the privacy gradient of the network is strongly dependent on the sequence of individual UOS with different degrees of publicness. Eliminating any of these individual UOS might affect this gradient and produces a fragmented network with some abnormalities in the hierarchy and sequences of spaces with different degrees of publicness. (Figure 2-18)  Figure ‎2-18 Interdependence of components in UOS networks as complex systems (diagram by the author) 4- “Are not self-contained but rather are affected by outside variables (Erickson, 2006, p. 11).” A UOS network is not simply a cluster of individual UOS and public users in one area; there are many factors and forces that affect this system. As UOS networks are parts of a dynamic urban environment, changes in outside variables are inevitable. Components, effective forces, and outside variables as a whole create a complex UOS 57  network. For instance, a change in the population density as an outside variable might affect the performance of the UOS network. (Figure 2-19)  Figure ‎2-19 Outside variables affect UOS networks as complex systems (diagram by the author) 5- “Have a high degree of synergy among components; the whole is more than the sum of the parts (Erickson, 2006, p. 11).” When talking about a system, we are actually pointing to the interaction and synergy of components. Similarly, an UOS network is not just a group of individual UOS close to each other in a defined geographic boundary. Casual connections, interdependence, and synergy of these components, in addition to outside variables and forces, create a complete and complex system which is ‘more than the sum of its parts’ and components.  (Figure 2-19) 6- “Are nonlinear. A change in the system can produce effects that are non-proportional to its size (Erickson, 2006, p. 11).” Therefore, predicting the effects of any alteration in the system is not a simple task. This fact alone shows the importance of analyzing UOS networks precisely and with great detail before applying any alteration to the system.  2.2.2 Network Attributes 2.2.2.1 Proximity Proximity by definition means “nearness in space, time, or relationship (Oxford Dictionaries, 2014).” Proximity in a UOS network is about the need to be close to UOS within a certain 58  distance, which is convenient by walking. But why should we care about proximity as an important UOS network indicator? There has always been demand from citizens for more convenient parks, playgrounds, sports fields, and other type of UOS. Accordingly, Stanley et al. elaborate on the importance of proper proximity to UOS and state that “equitable access to public space, especially proximity to parks, is increasingly addressed as an environmental justice concern as well (2012, p. 1090).” What is the proper proximity to UOS? Surveys around urban parks indicate that the majority of users prefer to walk to parks on a regular basis only if parks are in less than 3-5 minute walking distance from where they live or work. (Thompson, 2002) Accordingly, in this research, proper proximity has been defined as nearness to any type of UOS within less than a 5 minute walking distance or 400-450 meter distance. 2.2.2.2 Connectivity Flows of movement are an important part of a dynamic system and any discontinuity of movements and fragmentation causes problems and defects in that system. For instance, in ecological science, protecting isolated natural fields is not enough. Connectivity of all these patches to allow movement between these habitats is also very important. An environmentally viable landscape has close or directly connected patches. (Erickson, 2006) In fact, the connectivity of each component of a system is an important factor that can guarantee the survival of the entire system. "The concern is with connections not only among ecosystems and landscape but also between people and elements of the built environment (Erickson, 2006, p. 19)." Therefore, connectivity is an important attributes of UOS networks. “Ample literature supports the idea that an urban landscape with high connectivity is more accessible, more humane, and indeed more democratic. Connected urban areas allow exchange among various social groups, democratizing the city in a spatial way. A connected urban landscape foster mobility, visual interest, and efficiency. Humans need to easily access services and amenities at the neighbourhood and city scale, and this access should not all be dependent on cars. The walkable, connected city is one that helps foster sound human-ecological health (Erickson, 2006, p. 24).” 59  As mentioned before in morphological typology of UOS (see section 2.1.2.3), linear UOS such as streets and corridors play an important role in connecting different types of UOS, and therefore are highly effective on overall connectivity of the UOS network. A good prototype for studying the role of linear UOS in connectivity of UOS networks can be found in studies of street patterns and street networks.  As with street networks, four different connectivity patterns can be defined for UOS networks, ranging from grids to tributaries. These patterns in order of connectivity are: grid-based, semi-grid-based, semi-tributary, and tributary patterns. (Marshall, 2005) (Table 2-10, Figure 2-20) Connectivity pattern Degree of connectivity Intersection types Grid-base High Mainly X junctions Semi-grid-base Moderate-high X and T junctions some cul-de-sacs Semi-tributary Moderate-low X and T junctions some cul-de-sacs Tributary Low Mainly T junctions and cul-de-sacs Table ‎2-10 Connectivity patterns of UOS networks (table by the author; data adapted from Marshall (2005)  Figure ‎2-20 Connectivity patterns of UOS networks (diagram by the author) Connectivity Index is one of several different methods for evaluating the connectivity of UOS networks. “The most common method requires determining the ratio of street segments to intersections, or the number of roadway “links” divided by the number of roadway “nodes” for a given study area. Cul-de-sacs are counted as a node. Index values typically fall between 1 and 2, with higher values indicating more connectivity (02 Planning + Design & Calgary Regional Partnership, 2013, p. 63).”  60  2.2.2.3 Complexity Although connectivity is a defining factor that affects the overall performance of UOS networks, a high level of connectivity does not always lead to a strong and preferable network. UOS networks need to demonstrate a balance of both complexity and connectivity.  In street networks, the degree of complexity can be defined as the total number of route types within that network relative to the total number of routes. In other words, “The more different types of route a network has, relative to the total number of routes,  the more irregular and complex it tends to be (Marshall, 2005, p. 146).” Similarly, as mentioned before, a UOS network might consist of more than just one network connectivity pattern. The more different types of network patterns a UOS network has, the more complex it tends to be.   Figure ‎2-21 Two examples of regular networks which consist of one type of connectivity pattern ((a) regular tributary network and (c) regular grid-base network) in comparison to complex networks (b) which consist of two of more types of connectivity patterns (diagram adapted from Marshall (2005, p. 153)) In summary, in addition to the four UOS typologies presented earlier in this chapter (functional typology, scale and domain of influence, morphological typology and privacy gradient) proximity, connectivity, and complexity are three attributes of UOS networks which will be used in the following chapter to form a framework for analyzing the spatial organization of UOS networks.   61  Chapter 3: The Proposed Spatial Analysis Framework (SAF) for UOS Networks This chapter presents a proposed Spatial Analysis Framework (SAF) as a tool for analyzing the spatial organization of UOS as a network of interconnected and interdependent elements.  This chapter begins by posing five key evaluation questions regarding the spatial aspects of UOS. However, the primary focus is the second key question. Accordingly, the proposed Spatial Analysis Framework has been designed to study the spatial aspects of UOS networks by focusing on the chosen key question. The second section of this chapter is dedicated to presenting an evaluation matrix, which is a combination of all five key questions along with indicators for spatial aspects of UOS. In other words, the evaluation matrix is a guide which articulates the importance of each indicator for each key question. Among all of the possible indicators, those which possess a greater import on the spatial analysis of UOS have been listed.  The proposed Spatial Analysis Framework (SAF), the main outcome of this dissertation, will be presented in the third section of this chapter. The SAF acts as a step-by-step method to detect problems regarding the distribution of different types of UOS within networks, and to study the integration of UOS networks, from the city to the local scale. In the last section of this chapter, indicators and metrics to be used during the evaluation process will be presented.  3.1 The Key Evaluation Questions In order to study and analyze the spatial aspects of UOS, five different questions need to be addressed (Figure 3-1). Each of these questions has been posed so as to cover a specific aspect of UOS in terms of different urban scales ranging from large or city scale, through middle or neighbourhood scale and small or individual block scale, to human scale.  62   Figure ‎3-1 Five key evaluation questions regarding the spatial aspects of urban open spaces (diagram by the author) As Gehl explains in his book; Cities for People, “…urban design and city planning can be described as works involving several very different levels of scale. There is the large scale which is a holistic treatment of the city […] this is the city as it is seen at a distance or from an aerial perspective. Then there is the middle scale, the development scale, which describes how the individual segments or quarters of the city should be designed, and how buildings and city space are organized. […] Last but certainly not least is the small scale, the human landscape. This is the city as the people who will use city space experience it at eye level (2010, p. 195).” Accordingly, in order to analyze different aspects of UOS, it is essential to study these spaces at different scales:  The large or city scale includes: planning and analyzing the spatial qualities of UOS as a network; The middle or neighbourhood scale includes: planning, detailed analyses, developing and applying enhancement strategies;  The small, block or human scale includes: design evaluations, quality and quantity assessments, public participation and user reviews. 63  While at the human scale the social qualities and role of public participation in designing a new UOS is strikingly important, at the larger scale, the spatial qualities of UOS are of greater importance. To study a network of UOS and how these individual elements connect and interact, it is essential to look at these spaces on a neighbourhood and city scale. In this holistic view, a wealth of information around UOS networks can be studied. Focusing on larger scales does not ignore the importance of the human scale and the role of public participation in planning, design and post-production evaluations. Each type of analysis simply requires its own scale.  Since this thesis looks at UOS as a network, the focus of this study is on the first three questions, and more specifically on the second question, “Is it in the right place?” This question seeks to understand the spatial organization of UOS and how it works as a network of interconnected and interdependent elements.  3.1.1 Is It Enough? At the city scale, the first question that needs to be asked is whether there are enough UOS in the study area, especially in terms of residents’ needs. The first key question looks at spatial issues such as: - Are there enough UOS per capita in the city? - Are there enough UOS of each type to meet the needs and preferences of the residents? For example, are there enough sports fields for children and adults in the city? - Are there enough UOS to adequately deal with the environmental issues of the city?  For example, is there a sufficient number of storm water management sites to meet demand during extreme weather events? 3.1.2 Is It in the Right Place (the main focus of this study)? The second question that needs to be addressed is whether the existing UOS are in the right place, given the urban context.  64  The urban context includes the location, historical background, land-use, dominant patterns, and important urban features which form the main characteristics of that particular city. The most effective scale for studying the spatial organization of UOS is the district/neighborhood scale.  This middle scale provides a better opportunity for studying the existing UOS, more precisely and in detail, within their urban contexts.   “‘Is it in the right place?” is the key question concerning the spatial organization of UOS in their urban context, and examines such spatial issues as: - UOS types and land-use:  Are the existing types of UOS appropriate to the dominant land-use and the needs of the user groups in the study area? For example, in a residential area, are there any neighborhood parks available and accessible to residents? In an institutional area, are there convenient and accessible gathering spaces available for users such as plazas and public courtyards? - Transitional process of UOS through history:  How were UOS organized in the past? What were the historically existing types of UOS? How were they interconnected? Was there a dominant UOS network pattern in the past and, if so, how did it transformed into its current condition?  - … Organization has been defined as, “The way in which the elements of a whole are arranged (Oxford Dictionaries, 2014).” Therefore to analyze the spatial organization of UOS, these spaces should be studied in relation to one another and as a whole.  To put it differently, studying the spatial organization of UOS requires the analysis, in the urban context, of these spaces as a complex system or ‘network’ of interconnected, interacting and interdependent elements. This idea forms the main statement of this dissertation. This key question -- “Is it in the right place?” -- includes two secondary questions: 1. Are the different types of UOS well-distributed in the network? 2. Is the UOS Network spatially integrated? 65  3.1.2.1 Are the Different Types of UOS Well-distributed in the Network? The goal in posing this second question is that of studying, within a network, the distribution patterns of UOS of different types, shapes and sizes.  To put it differently, in the middle scale (neighborhood/district scale), two different properties of UOS in direct relation to their distribution patterns, will be analyzed: - The typology of existing UOS - The morphology of UOS 3.1.2.2 Is the UOS Network Spatially Integrated? As has been mentioned in the second chapter of this thesis, a good network, just as any other complex system, should possess interdependent elements. The more interconnected and integrated the components are, the stronger the entire network will be. The goal in posing this question is to study the spatial integration of UOS networks.  The issues to be addressed in relation to this question are: - The proper proximity to different types of UOS in a neighborhood - The diverse UOS network patterns extant within a neighborhood - The effective connectivity between the different types of UOS that form a network - The privacy gradient of the UOS network 3.1.3 Is the Program/Context Appropriate?  On the smaller scale, this question examines the program that has been defined for each individual UOS. This program, in the context of its neighborhood, should be appropriate to the neighborhood’s framework and the needs of its users. 3.1.4 Is It Well-designed?  This inquiry focuses specifically on the design and spatial qualities of individual UOS at a smaller, more comprehensive scale.  66  There are numerous zoomed-in studies that have looked at the design and spatial qualities of UOS as individual spaces. Design criteria, quality and visual assessments, and behavioral studies are various examples of examining UOS and their spatial qualities as individual spaces.  Since this thesis sees UOS as networks forming part of a broader setting, answering this query is beyond the scope of this study. 3.1.5 Is UOS Modification Suitable? This question aims to estimate the opportunities and constraints involved in intervening in, modifying and enhancing the existing conditions of UOS. Changes and decisions should always be made in consideration of the context’s sensitivities. In order to prevent further damage, suitability studies should be conducted before implementing any decisions or modifications. For example, in a historic neighborhood, while enhancing the existing conditions of UOS, any modifications should consider the sensitivity and values of the area’s historical sites and buildings. While adding to the existing UOS might not be possible in historic neighborhoods, modifying the existing UOS or adapting plans to the existing conditions might be effective strategies. 3.2 The Proposed Evaluation Matrix (EM) The Evaluation Matrix (EM) combines all five key evaluation questions, which have been organized into a row at the uppermost level of the matrix; the matrix also includes a list of indicators for the spatial aspects of UOS. These indicators have been organized into a column situated at the left side of the matrix (Figure 3-2). The EM is a guide which articulates the importance of each indicator to each key question.  Among all of the possible indicators, those which are most important and effective for the spatial analysis of UOS have been listed. These indicators have been organized into three categories: context, UOS palette and UOS network. The context includes the indicators which are related to the situation of the study area. This includes its population, historic-cultural context, ecological context, land-use information and dominant street pattern of the study area. The UOS palette includes information about the fraction, form-functional typology and morphological taxonomy of UOS. The UOS network is the main category which includes such 67  indicators as the dominant network pattern, connectivity, proximity and privacy gradient of the UOS network. The main focus of this research -- studying the spatial organization of the UOS as a network -- has been highlighted in the EM.  68   Figure ‎3-2 The proposed Spatial Evaluation Matrix for UOS (diagram by the author) 69  3.3 The Proposed Spatial Analysis Framework (SAF) Based on the highlighted section of the proposed EM and in order to study and analyze the spatial organization of the UOS network, a comprehensive Spatial Analysis Framework (SAF) has been designed and presented in this section. The goal was to use the information and knowledge from the literature reviews (in the second chapter) to design a new spatial analysis framework. The draft version of the proposed SAF has been tested on two case studies which will be presented in the next chapter of this thesis. As a result of these tests, the framework has been edited, refined and further developed. The final version is presented in the following (Figure 3-3). 3.3.1 The Main Focus of the SAF  As has been mentioned in the first section of this chapter, the focus of this study is on the second key evaluation question, “Is it in the right place?” or, more specifically, “How is the UOS network organized?”. This key question aims to study two different spatial aspects of UOS networks: 1. The spatial distribution of UOS within a network (see 3.1.2.1), and 2. The spatial integration of the UOS network (see 3.1.2.2) 3.3.2 Effective Scales of the SAF  Since the goal in designing the SAF is to analyze UOS as a network, the effective scales by which to study the spatial qualities of the entire network of urban open spaces are the large and middle scales, including the city and local/neighborhood scales. “The neighborhood, not the individual building block, is now the unit of urban design (Mumford, 1969, p. 21).”  This framework has not been designed to study and analyze individual UOS but, rather, provides a holistic view over a network of UOS.  70   Figure ‎3-3 The proposed Spatial Analysis Framework (SAF) (diagram by the author) 71  3.3.3 Indicators Employed in the SAF Just as the Evaluation Matrix, all the effective indicators of this framework have been organized into three main categories, namely: Context, UOS Palette and UOS Network. Indicators have been demonstrated as separate analytical layers on the right side of the SAF. Moreover, all of these indicators along with the related metrics have been presented with more detail in the next section of this chapter (see sec. 3.4). 3.3.4 The Three Stages of the SAF  The spatial analysis framework is composed of three different stages, namely: input, diagnosis and output. As mentioned above, the data and Indicators used in each stage have been divided into separate analytical layers that have been presented in a column on the right side of the SAF.  The Input Data: The first stage is dedicated to studying the existing conditions and transitional process of UOS within the urban context. The input data include information about the location, historical background and transitional process, important urban features, population, land-use information and, finally, the dominant street network pattern. Diagnosis: Considering the input data provided, the second stage of the proposed SAF is dedicated to analyzing the spatial aspects of the UOS network and diagnosing possible deficiencies, disorders or abnormalities within that network. The diagnosis process is itself formed from two stages: first, that of analyzing the spatial distribution of UOS and second, that of analyzing the spatial integration of the UOS networks (see 3.1.2.1 and 3.1.2.2). The UOS Palette is the first category of the analytical information found within the diagnostic process. The UOS palette represents the existing form-functional types and also the morphological taxonomy of a UOS within a network.  By comparing the analytical layers of the UOS Palette with layers of urban context enables the researcher to diagnose issues regarding the spatial distribution of UOS within a network. Analytical data and indicators used in the UOS Network category include the proximity, network pattern, network connectivity, and, finally, the privacy gradient of the UOS network. By comparing these analytical layers with the UOS 72  Palette and the urban context information provided above, the researcher is able to diagnose issues regarding the spatial integration of the UOS network within the urban context.  The Output: The third stage of the SAF concerns the process of decision making. Decision making should be based on the results drawn from the diagnosis process. Most abnormalities in the UOS networks are the result of omitting the diagnostic stage and performing random decisions based on the trial-and-error method. Therefore, a comprehensive analysis and diagnosis are essential before making any changes to complex systems including the UOS networks. Based on the output data resulting from the analysis, three possible approaches would enhance the spatial aspects of the UOS networks, namely: infilling and adding more UOS to the existing networks, modifying the existing networks, and adapting the network to the existing conditions in order to cause minimal damage to sensitive areas while enhancing the existing conditions of the UOS networks. 3.4 Indicators and Metrics In order to evaluate and analyze the spatial qualities of the UOS networks, as well as to be able to formulate decisions concerning the existing conditions of the UOS networks, there is an  inevitable urge to focus in detail on the different spatial and physical attributes of these spaces and their networks. To make this happen, this section is dedicated to introducing and presenting all of the proposed indicators and metrics to be used in the spatial analysis of the UOS networks.  3.4.1 Theme Indicators Just as with the SAF, all indicators which will be presented in this section have been organized into three main categories. These categories, which from now on will be called “Theme Indicators”, are “A: Context”, “B: UOS Palette” and “C: UOS Network”, respectively. As has been mentioned above, these categories or theme indicators are essentially the three major steps that need to be taken in order to analyze the UOS as a network. 3.4.2 Indicators Among all of the possible indicators that are relevant to UOS analysis, those which are directly effective on the spatial analysis of UOS networks have been presented in this chapter.  73  Each indicator focuses on one spatial attribute of the theme. Moreover, each proposed indicator has been defined in relation to the content of this research.  3.4.3 Metrics In order to measure and evaluate the spatial qualities of the UOS networks, metrics have been assigned to each proposed indicator. These metrics include input and output data, calculation/evaluation methods and also the scales at which specific metrics are effective and measurable. Since the focus of this research is on the spatial aspects of UOS, using a combination of qualitative and quantitative methods in evaluating and analyzing the spatial aspects of UOS networks is inevitable. Therefore, the metrics have been rigorously selected from possible qualitative and quantitative ones in accordance with the proposed indicators. Theme indicators, indicators and metrics have been organized, with letters and numbers, into the following format (Figure 3-4): A: First Theme Indicator. A1: First proposed indicator in Theme Indicator A. A1-1: First metric assigned to the A1 Indicator. 74   Figure ‎3-4: Proposed Indicator outline including A: Theme Indicator, A1: Indicator, A1-1 Metric (diagram by the author) The following indicators and metrics have been also applied in the spatial analysis of the chosen case studies which will be presented in the following chapter (Chapter 4) of this dissertation. CONTEXT Theme Indicator A          Goal: Studying the urban context of the study area before analyzing the spatial aspects of UOS networks. INDICATOR A1: Population         Definition: the population of the study area, including the residential and day-time populations. METRIC A1-1: Residential Population Input:   total number of residents in the study area  Calculation method:  total number of residents in the study area Output: persons 75  Scale: District, Neighbourhood METRIC A1-2: Residential Population Density Input:   residential population (A1-1)   total area (A3-1) Calculation method:  total residential population divided by area Output: persons per hectare (A1-1 divided by A3-1) Scale: District, Neighbourhood METRIC A1-3: Day-time Population Input:   total number of residents, workers and visitors within a study area Calculation method: residential population plus estimated number of visitors and workers Output: persons Scale: District, Neighbourhood METRIC A1-4: Day-time Population Density Input:   day-time population (A1-3)   total area (A3-1) Calculation method:  total day-time population divided by area (A1-3 divided by A3-1) Output: persons per hectare Scale: District, Neighbourhood INDICATOR A2: Land-use          Definition areas dedicated to specific use, i.e. residential, commercial, institutional, touristic, green spaces, industrial and religious areas. METRIC A2-1: Land-use Area 76  Input:   area associated with each type of land-use  Calculation method: area associated with each type of land-use Output: hectares Scale: District, Neighbourhood METRIC A2-2: Land-use Proportion Input:   -area associated with each type of land-use (A2-1)   -total area (A3-1) Calculation method: area associated with each type of land-use divided by the total area (A2-1 divided by A3-1) Output: % Scale: District, neighbourhood METRIC A2-3: Duration of Activity Input:    duration of activity Calculation method:  duration of time within 24 hours in which the area with a specific land-use is active and used by residents, visitors or workers Output: hours per day Scale: Neighbourhood, Block INDICATOR A3: Total Area          Definition total area of the case study METRIC A3-1: Total Area Input:   area of the case study Calculation method:  total area within the boundaries of the chosen case study Output: area Scale: District 77  UOS PALETTE Theme Indicator B         Goal: Analyzing the spatial distribution of UOS within a network. INDICATOR B1: Morphological Taxonomy       Definition classification of different shapes and sizes of existing UOS METRIC B1-1: Morphological Taxonomy of Each Type Input:   patches of UOS within a certain scale  Method: tracing the shapes of different patches within a specific type of UOS, at a specific scale and in plan-view, and organizing all of these patches into a circumscribed square according to their sizes and shapes. Output: visual Scale: District, Neighbourhood METRIC B1-2: Grain size Input:   patches of UOS of the same type   Evaluation method: comparing the size of existing patches of UOS of the same typein the study area. Output: Fine (less than 0.5 hectare)/ Medium (0.5 to 2.5 hectares)/ Coarse Grain Size (more than 2.5 hectares) Scale: District, Neighbourhood METRIC B1-3: Diversity of Shapes and Sizes Input:   patches of UOS of the same type   study area boundaries  Evaluation method: comparing in the case study the variety of shapes and sizes of the same type of existing UOS . Output: Low/Moderate/High 78  Scale: District, Neighbourhood METRIC B1-4: Distribution Balance Input:   patches of UOS of the same type   study area boundaries  Evaluation method: the degree to which different patches of the same type of UOS are distributed over a study area Output: Low/Moderate/High balance Scale: District, Neighbourhood INDICATOR B2: Form-Functional Typology       Definition organizing the existing UOS within the study area into form-functional typology METRIC B2-1: UOS Types Input:   a list of existing UOS of different forms and functions  Method: categorizing all UOS within the study area into 7 form-functional categories (1-Parks and Gardens, 2- Natural/Semi-natural features, 3- Water Edges, 4- Sports Fields, 5- Civic Spaces, 6- Streets and Corridors, 7- Incidental Spaces) and presenting the result in the form of a table Output: table Scale: District, Neighbourhood METRIC B2-2: UOS Sub-types Input:   a list of existing UOS in the same type Calculation method:  organizing all UOS within the same type into sub-types Output: Table Scale: District, Neighbourhood 79  INDICATOR B3: UOS Fraction         Definition Percentage of the study area which is covered by UOS METRIC B3-1: Total UOS Area Input:   area of each existing UOS  Calculation method:  the total sum of the areas of all types of UOS Output: hectare Scale: District, Neighbourhood METRIC B3-2: Total UOS Fraction Input:   total UOS area (B3-1)   total area (A3-1) Calculation method: total UOS area divided by the total area of the case study (B3-1 divided by A3-1) Output: % Scale: District, Neighbourhood METRIC B3-3: Total UOS Per Capita Input:   total UOS area (B3-1)   day-time population (A1-3) Calculation method:  total UOS area divided by Day-time Population Output: square metres per person Scale: District, Neighbourhood METRIC B3-4: Area of Each Type of UOS Input:   area of all existing UOS of the same type   Calculation method:  the sum of areas of all existing UOS of the same type Output: hectares Scale: District, Neighbourhood METRIC B3-5: Fraction of Each UOS Types 80  Input:   area of each type of UOS (B3-4)   total area (A3-1) Calculation method: area of each type of UOS divided by the total area of the case study Output: % Scale: District, Neighbourhood METRIC B3-6: Area Per Capita of Each UOS Type  Input:   area of each type of UOS (B3-4)   Day-time population (A1-3)  Calculation method: area of each type of UOS divided by the Day-time Population (B3-4 divided by A1-3) Output: Square metres per capita Scale: District, Neighbourhood METRIC B3-7: Fraction of Each UOS Sub-type in the Same Type Input:   area of each sub-type of UOS   area of each type (B3-4)  Calculation method:  area of each subtype of the same type of UOS divided by the total area of the same type Output: % Scale: District, Neighbourhood METRIC B3-8: Fraction of Each UOS Sub-type in All Existing UOS Input:   area of each sub-type of UOS   total UOS area (B3-1) Calculation method: area of each subtype of the same type of UOS divided by the total UOS area Output: % Scale: District, Neighbourhood 81  UOS NETWORK Theme Indicator C         Goal: Analyzing the spatial integration of the UOS network.  INDICATOR C1: Network Connectivity Pattern      Definition The UOS network connectivity pattern is a simplified silhouette diagram which is representative of UOS of different size, shape and type, and their connectors. In a case study there might be more than one network connectivity pattern, considering the context of each part of the study area.  METRIC C1-1: Dominant Network Connectivity Pattern Input:   silhouettes of UOS and their connectors   land-use (A2) Evaluation method: dominant pattern of UOS networks in different parts of a case study with different land-use types  Output: Visual Diagrams (Grid-based, Semi-Grid-based, Semi-Tributary, Tributary) Scale: Neighbourhood, Block METRIC C1-2: Number of individual UOS in a Network Input:   number of individual UOS within a network Calculation method:  counting the number of individual UOS  Output: # Scale: Neighbourhood, Block INDICATOR C2: Network Connectivity        Definition the degree to which UOS are connected to each other in a network. Higher connectivity indicates the strength and cohesion of the network. 82  METRIC C2-1: Length of Pedestrian Pathways Input:   length of each pedestrian pathway  Calculation method: the sum of the lengths of all pedestrian pathways in a network Output: metres Scale: Neighbourhood, Block METRIC C2-2: Length of Streets Input:   length of each street  Calculation method:  the sum of the lengths of the streets Output: metres Scale: Neighbourhood, Block METRIC C2-3: Intersection Pattern Input:   T-junctions   X-junctions   loops and cul-de-sacs  Evaluation method: mapping (in plan-view) of all the different types of junctions which exist in the network of UOS Output: visual graph Scale: Neighbourhood, Block METRIC C2-4: Number of X-Junctions Input:   X-junctions  Calculation method: total number of existing x-junctions in a network Output: # Scale: Neighbourhood, Block METRIC C2-5: Number of T-junctions Input:   T-junctions  Calculation method:  total number of existing T-junctions in a network 83  Output: # Scale: Neighbourhood, Block METRIC C2-6: Number of Loops and Cul-de-sacs Input:   loops   cul-de-sacs  Calculation method:  total number of existing loops and cul-de-sacs in a network Output: # Scale: Neighbourhood, Block METRIC C2-7: Number of Links Input:   links (pedestrian pathway segments) Calculation method:  the total number of links Output: # Scale: Neighbourhood, Block METRIC C2-8: Connectivity Index Input:   total number of links (C2-7) total number of nodes (X-, T-junctions, loops, and cul-de-sacs) Calculation method: total number of links divided by the total number of nodes. “A score of 1.4 is the minimum needed for a walkable community (02 Planning + Design & Calgary Regional Partnership, 2013)” Output: score (ratio) Scale: Neighbourhood, Block INDICATOR C3: Complexity        84  Definition the degree to which an UOS network pattern exhibits complexity and is made up of a combination of different pattern types (Tributary, Semi-Tributary, Semi-Grid-Based, and Grid-Based). The more pattern types that exist in a network, the greater the complexity the entire network would possess.  METRIC C3-1: Number of Pattern Types Input:   dominant network connectivity pattern (C1-1)  Calculation method:  number of different network connectivity patterns within a single UOS network. Output: low (1 pattern type), moderate (2 pattern types), high (3 or 4 pattern types) Scale: Neighbourhood, Block INDICATOR C4: Proximity        Definition nearness to any type of UOS within a 5-minute walking distance (400-450 metre radius) METRIC C4-1: Effective domain of All UOS Types Input:   proximity map of all existing UOS  Calculation method: total area within less than a 5-minute walking distance from any type of UOS (overlaps should be counted once) Output: hectares Scale: District, Neighbourhood, Block METRIC C4-2: % of the Study Area with Effective Proximity to Any UOS Types Input:   effective domain of all UOS types (C4-1)   total area (A3-1) 85  Calculation method: effective domain of all UOS types divided by the total area of the case study Output: % Scale: District, Neighbourhood, Block METRIC C4-3: Area without Effective Proximity to Any UOS Type Input:   -total area (A3-1)   -effective domain of all UOS types (C4-1) Calculation method:  total area of the case study minus the effective domain of all UOS types Output: hectares Scale: District, Neighbourhood, Block METRIC C4-4: % of the Study Area without Effective Proximity to Any UOS Types Input:   -total area (A3-1)   -area without effective proximity to any UOS type (C4-3) Calculation method:  area without effective proximity to any UOS type divided by the total area Output: % Scale: District, Neighbourhood, Block METRIC C4-5: Effective Domain of All UOS Types Excluding Incidental Spaces Input:   effective domain of all UOS types (C4-1)   areas with proximity to only Incidental Spaces and not to any other type of UOS Calculation method:  effective domain of all UOS types minus areas with proximity to only Incidental Spaces and not to any other type of UOS Output: hectares Scale: District, Neighbourhood, Block 86  METRIC C4-6: % of the Study Area with Effective Proximity to Any UOS Types Excluding Incidental Spaces Input:   effective domain of all UOS types excluding Incidental Spaces (C4-5)   total area (A3-1) Calculation method:  effective domain of all UOS types excluding Incidental Spaces divided by the total area of the case study  Output: % Scale: District, Neighbourhood, Block METRIC C4-7: Effective Domain of a Specific UOS Type Input:   effective domains of all UOS the same type Calculation method:  the sum of the effective domains of all UOS of the same type (overlaps should be counted once) Output: hectares Scale: District, Neighbourhood, Block METRIC C4-8: % of the Study Area with Effective Proximity to a Specific UOS Type Input:   effective domains of a specific UOS type (C4-7)   total area (A3-1) Calculation method:  effective domains of a specific UOS type divided by the total area of the case study. Output: % Scale: District, Neighbourhood, Block METRIC C4-9: Area without Effective Proximity to a Specific UOS Type Input:   total area (A3-1)   effective domain of a specific UOS type (C4-7) Calculation method:  total area of the case study minus the effective domain of a specific UOS type  87  Output: hectares Scale: District, Neighbourhood, Block METRIC C4-10: % of the Study Area without Effective Proximity to a specific UOS Type Input:   total area (A3-1)   area without effective proximity to a specific UOS (C4-9) Calculation method:  area without effective proximity to a specific UOS type, divided by the total area of the case study Output: % Scale: District, Neighbourhood, Block INDICATOR C5: Privacy Gradient Pattern of the UOS Network    Definition The privacy gradient pattern of the UOS network is a diagram representative of how a series of UOS with different degrees of publicness/privacy connect with each other. In a case study, there might be more than one privacy gradient pattern, considering the context of each part of the study area.  METRIC C5-1: Privacy Gradient Plan Input:   a plan-view of all existing UOS  Evaluation method: a plan-view of all existing UOS and their connectors, marked with different colours. These colours are indicators of their degree of privacy (Public, Semi-Public, In-Between, Semi-Private, Private) Output: visual plan Scale: Neighbourhood, Block METRIC C5-2: Privacy Gradient Dominant pattern 88  Input:   privacy gradient pattern (C5-1) Evaluation method: a simplified tree diagram which represents the dominant patterns of public-to-private (1. diverse sizes of circles are representative of UOS with different degrees of publicness, and 2. lines as their connectors)  Output: visual-tree diagram Scale: Neighbourhood, Block For future reference, all of the above mentioned indicators and metrics have been listed in the form of a table, as following (Table 3-1): Theme Indicator Indicator Metric CONTEXT A1:POPULATION A1-1 Residential Population A1-2 Residential Population Density A1-3 Day-time Population A1-4 Day-time Population Density A2:LAND-USE A2-1 Land-use Area A2-1 Land-use Proportion A2-3 Duration of Activity A3:TOTAL AREA A3-1 Total Area UOS PALETTE B1:MORPHOLOGICAL TAXONOMY B1-1 Morphological Taxonomy of Each Type B1-2 Grain Size B1-3 Diversity of Shapes and Sizes B1-4 Distribution Balance B2:FORM-FUNCTIONAL TYPOLOGY B2-1 UOS Types B2-2 UOS Sub-types B3:UOS FRACTION B3-1 Total UOS Area B3-2 Total UOS Fraction B3-3 Total UOS Per Capita B3-4 Area of Each UOS Type B3-5 Fraction of Each UOS Type 89  Theme Indicator Indicator Metric B3-6 Area Per Capita of Each UOS Type  B3-7 Fraction of Each UOS Sub-type of the Same Type B3-8 Fraction of Each UOS Sub-type of All Existing UOS UOS NETWORK C1:NETWORK CONNECTIVITY PATTERN C1-1 Dominant Network Connectivity Pattern C1-2 Number of Individual UOS in a network C2:NETWORK CONNECTIVITY C2-1 Length of Pedestrian Pathways C2-2 Length of Streets C2-3 Intersection Pattern C2-4 Number of X-Junctions C2-5 Number of T-Junctions C2-6 Number of Loops and Cul-de-sacs C2-7 Number of Links C2-8 Connectivity Index C3:NETWORK COMPLEXITY C3-1 Number of Pattern Types C4:PROXIMITY C4-1 Effective Domain of All UOS Types C4-2 % of the Study Area with Effective Proximity to any UOS Type C4-3 Area Without Effective Proximity to Any UOS Type C4-4 % of the Study Area without Effective Proximity to any UOS Type C4-5 Effective Domain of All UOS excluding the Incidental Spaces C4-6 % of the Study Area with Effective Proximity to any UOS Types Excluding the Incidental Spaces C4-7 Effective Domain of a Specific UOS Type C4-8 % of the Study Area with Effective Proximity to a Specific UOS Type 90  Theme Indicator Indicator Metric C4-9 Area Without Effective Proximity to a Specific UOS Type C4-10 % of the Study Area without Effective Proximity to a Specific UOS Type C5:PRIVACY GRADIENT PATTERN OF THE UOS NETWORK C5-1 Privacy Gradient Plan C5-2 Privacy Gradient Dominant Pattern Table ‎3-1 Proposed indicators and metrics used in the SAF (table by the author) 91  Chapter 4: Applying the SAF to Case Studies This chapter details the process of testing the proposed SAF on two case studies that have been carefully chosen from the historic cores of Tehran and Isfahan. This chapter begins with a general understanding of the context of each case study, including information about its location, important urban features, population, land-use, urban structure, and street networks.  This chapter goes on to analyzing the existing UOS of each case study. The existing UOS, along with a variety of form-functional types and morphologies, will be organized into an UOS palette.  Moreover, to study the UOS networks with greater detail, three neighbourhood-scale case studies will be defined within each larger case study. At the neighbourhood scale, the spatial aspects of the UOS networks will be studied. This includes an analysis of certain network attributes, including proximity to diverse types of UOS, network patterns, privacy gradient, network connectivity, and complexity. However, in some cases, due to some differences in numbers, data, and calculations that have been reported in various sources, and also a lack of access to the updated data, the input data which will be used in the calculations might not be completely accurate. Considering the possibility for errors in the input data, what is most important to this research is the method that will be used to test, evaluate and refine the designed framework. In addition to the publicly accessible sources, 2-D maps, 3-D models, tables, and charts, will be used to study and compare the spatial organization of the UOS networks in the cases studies. Therefore, for both case studies, an attempt has been made to use the available resources, including Tehran’s and Isfahan’s master-plans, official reports, aerial photos, and available drawings, to attain the highest possible accuracy while utilizing personal assumptions in interpreting the available data.   92  4.1 Why Tehran and Isfahan? Both Tehran and Isfahan have been capital cities of Iran. Isfahan was the capital for more than 120 years (1598-1722) before the Qajar king of the time moved the capital to Tehran (1778-present). Despite important similarities in their historic urban structures, these cities are very different today in many aspects. One concerns the illustrative difference in approach each city has taken to its UOS networks, the subject of this study. These similarities and differences make comparisons of the UOS networks in these cities very instructive for this research. Both, for example, share very similar historical urban form patterns. As mentioned in the sources, “The city of Isfahan is the Iranian city par excellence (Karimi & Motamed, 2003, p. 14.1).” The major development pattern of Isfahan dates back to the beginning of the Islamic era, when the Islamic pattern and urban structure were implemented. In the early Islamic era (AD 950), mosques, as a new urban component, were added to Iranian cities, changing their urban structures. In this period, the three major components of the Islamic-Iranian city centres included a grand mosque, palace, and bazaar around a central square (maidan) (Pirnia, 2003) (Figure 4-1).  Figure ‎4-1 The historical structure of Islamic cities in Iran (diagram by the author) 93  Over six hundred years later, during the Safavid era (AD 1598-1736), new developments changed the urban structure of Isfahan. Many researchers refer to this era as the “Golden Age” in Iranian art, architecture, and urban design. The “Isfahan Style” in the above-mentioned three arts also originates in transformations that took place in the city of Isfahan during the Golden Era. During this period, urban design became very important (Pirnia, 2003). “A large number of new buildings and urban spaces were established, but more importantly, a great work of urban master planning occurred in this period (Karimi & Motamed, 2003, p. 14.5).” During this period, Isfahan, “Expanded enormously and its great old structure was complemented by massive new urban developments. These developments, which were in harmony with the older establishments of the city, transformed Isfahan to a unique Iranian city (Karimi & Motamed, 2003, p. 14.1).” These changes that transpired in Isfahan were all conducted in the name of its reaching a Safavid utopia. Therefore, the city of Isfahan is the paramount prototype of the Isfahan Style. Some of the characteristics of the Isfahan style that affected urban design and planning in Iranian cities and, most importantly, in the city of Isfahan are as follows (Pirnia, 2003): -Simplicity in forms and the use of rectangular forms in open spaces and buildings -The employment of certain proportions in the size of newly designed spaces -The use of humanistic scales in architecture and urban design -The innovation of a wide, multifunctional plaza, the Naghsh-e-Jahan, in the centre of the city  -The innovation of a linear garden-street called the Chahar-Bagh -The design of new neighbourhood centres, with public baths, water reservoirs, mosques, and schools, as public gathering spaces -Reapplying the ancient hierarchical sequences extending from public to private spaces in neighbourhood patterns. -The spatial integration and coherent connectivity of public spaces Tehran was originally formed on these same traditional Islamic-Iranian patterns; its historic centre also included the same components (Figure 4-1) as Isfahan. This similar urban structure 94  persisted up to the modern era, especially in the city’s central area. However, in the modern era, despite their needing to deal with very similar forces of change, including population growth, adaptation to modern lifestyles and advanced technologies, Tehran and Isfahan reacted very differently from one another. Changes in Isfahan were mostly in harmony with the historic structure of the city, which had the opportunity to adapt to the new changes (Karimi & Motamed, 2003). In the historical core of Isfahan, UOS have maintained their spatial integration, allowing walks through these spaces to continue to be a very desirable experience. Residents and visitors of Isfahan alike still prefer to travel on foot through the historic urban fabrics of this city. Despite the population growth and the growing number of visitors, this city still provides the proper amenities to pedestrians while connecting the important urban features through a network of UOS. However, in Tehran, the situation is quite different. Change there has been very rapid and the historic core of the city has not had the time to adapt incrementally to the sudden alterations that were superimposed on the urban structure. A grid street pattern was implemented which fragmented the historic fabric of this city, disrupting its formerly strong pedestrian connectivity (Rismanchian & Bell, 2013). As a result, despite all the similarities in the historic urban structures of these two cities, but due to their experiencing dissimilar transitional processes, they have ended up in different places with very diverse consequences for the spatial qualities of their UOS networks, especially in in their historic cores.  In order to illustrate these consequences for the UOS networks of Tehran and Isfahan, this thesis has studied and compared - The existing spatial aspects of UOS networks - The major spatial  problems of UOS networks - Suitable enhancement strategies appropriate to the diagnosed problems To undertake this study and comparison, it has been essential to define a middle scale case study from the similar urban contexts of each city. Accordingly, a 550 hectare case study has been defined in the historic core of each city. Each case study consists of a combination of 95  residential areas, historic commercial areas (the grand bazaar), institutional areas (palaces, governmental and office buildings, schools and religious centres) and green spaces. Moreover, these case studies have been selected so as to include the main three historic components (Figure 4-1) of Islamic-Iranian cities.  All of these similarities, in the context of these case studies, provide the opportunity to focus, with greater detail and more accuracy, on variables of the UOS networks. 4.2 Context Analyzing the context of each case study is an important part of analyzing and diagnosing problems within the spatial organization of the UOS networks. The context analysis includes information about:  Location  Important urban features  Population  Land-use   Street networks 96  Before introducing the location of each case study, it is essential to understand the urban division system in Iranian cities. Each city is divided into smaller regions, and each region itself is composed of smaller districts. These districts are also divided into smaller sectors and neighborhoods (Figure 4-2).  Figure ‎4-2 Official urban divisions in Iranian cities (diagram by the author) 4.2.1 Tehran 4.2.1.1 Location The selected case study in the city of Tehran is located in Region 12 (Figure 4-3).  This region includes six separate districts, and the chosen case study partially covers Districts 1, 2 and 3 (Figure 4-4). 97   Figure ‎4-3 The location of Tehran’s case study (diagram by the author;  base map adapted from GoogleEarth ©2014 DigitalGlobe)  Figure ‎4-4 location of the Tehran’s case study in Region 12 (diagram by the author) This case study, which is approximately 550 hectares in area, is confined on all four sides between the main streets and squares, with Jomhouri Street and Baharestan Square being located on the north, Mostafa Khomeini Street on the east, Molavi Street and Mohammadieh Square on the south and, finally, Vahdat Eslami Street and Hasan Abad Square are on the west side of the study area (Figure 4-5). 98    Figure ‎4-5 The main streets and squares surrounding Tehran’s case study (diagram by the author) This case study includes the historic core of Tehran (Figure 4-5). With the increasing population growth, the city walls surrounding the historic core expanded and, at the same time 99  this area experiences many changes which included structural and land-use alterations in some parts.   Figure ‎4-6 Tehran’s case study includes the historic core of the city (base map adapted from Bayat (2010); edits by the author) 4.2.1.2 Important Urban Features Since the historic core of the city is located within the study area boundaries, still numerous historic urban features can be found in this area. In order to have a better understanding of the context of the study area it is essential to study a brief history and major changes that have happened to these urban features through time.  Tehran’s Grand Bazaar: One of the most important urban sites located in this case study is the Grand Bazaar of Tehran. This historic bazaar, which was one of the first urban elements in the main city structure, is still, despite all of the major changes it has undergone, a very active commercial centre of this city, and the daily recipient of thousands of daily visitors.   Among all of the diverse types of modifications that this bazaar has experienced, including functional and structural changes and spatial expansions, of great import to this project are the alternations to the bazaar’s pedestrian network and its connectivity to the surrounding residential areas. The old commercial fabric of the bazaar was once more greatly integrated with the residential area; however, the bazaar has today expanded to the point that it is 100  surrounded by main vehicle-oriented streets and, as a result, the inner pedestrian network of the bazaar has lost its integrity with the residential fabric (Figure 4-7).  Figure ‎4-7 The Grand bazaar of Tehran (diagram by the author)  Golestan Palace: Another important urban feature located in the study area is the Golestan Palace and Garden.  This palace, which is the oldest historic urban feature in Tehran with about 400 years of history, was built originally in the Safavid era between the years 1524 and 1576, when it became the official residence of the royal family the of the Qajar era when the capital of Iran was moved to Tehran. This palace remained one of the most important locations for official ceremonies and receptions in the Pahlavi era. (Golestan Palace, 2014) The first four throughways dominating the streets of Tehran were constructed in the Naseri era (1848- 1896) over the ruins of the former palace walls (Figure 4-8). 101    Figure ‎4-8 Construction of the first four streets of Tehran on the ruins of the Golestan Palace walls in the Late Naseri era (base map adapted from Motamedi (2002); edits by the author) This royal palace; including the main royal building, garden and other dependent buildings within the former walls, has experienced major renovations and functional changes through time. Some buildings have been replaced with new ones and, as a result of all of these changes, the current boundaries of the palace are smaller compared to their former condition and are surrounded by governmental and office buildings (Figure 4-9).  102   Figure ‎4-9 Former and current boundaries of the Golestan Palace (diagram by the author)  Shahr (City) Park: Another important urban feature located in the study area is Shahr Park (Figure 4-10).  Figure ‎4-10 Shahr (City) Park (diagram by the author) This is the first and oldest urban park, and was built over one of the former residential neighborhoods, “Sangelaj”, in the first Pahlavi era. This park, originally a garden, was converted in 1953, with some major changes, into the current urban park, which possesses an area of 264880 square metres. This park formerly attracted a great number of families from all over the city; currently, however, despite its natural beauty and diversity, it has lost popularity with the 103  Tehran residents. The spatial isolation of the park from the remainder of the city and other UOS is one of the major reasons for the loss of safety in regions of the park and the consequent   reduction in visitors (Shakiba, 2004). This park itself is a good example for the need for a strong network of UOS, and the importance of a holistic view towards UOS as a network of interconnected spaces; it demonstrates also how an individual urban open space of high design quality might be rejected by local residents due to its lack of integration with the rest of the city and other UOS.  Panzdah-e-Khordad Street: Panzdah-e-Khordad Street, mentioned above as one of the first four streets to be built on the ruins of the Golestan Palace walls (Figure 4-8), is located on the south side of the palace. This street has been heavily trafficed for years. However, in on 2008 a small section of the street which is located on the north side of the Grand Bazaar of Tehran has been dedicated to pedestrians. (Figure 4-11) Pedestrianization of this street resulted in blocking the motorized vehicle access on both two ends of this section while there is only a single lane dedicated to emergency vehicles and recreational carriages on one side and a wide paved pedestrian walkway designed on the main part of the street.   Figure ‎4-11 Panzdah-e-Khordad Street (diagram by the author) Although the pedestrianization of the street plays an important role in strengthening the connection of the UOS on both sides of that route, since this pedestrian walkway is not connected to other, more distant UOS, as is the case with Shahr Park, only a very brief pedestrian experience is created for users. Put differently, despite the internal connectivity of all UOS on both sides of this street, Panzdah-e-Khordad St. is not connected to a larger network 104  of UOS on the larger scale and works as an active but still-isolated urban open space in the study area.   Figure ‎4-12 The pedestrian-dominated section of Panzdah-e-Khordad St. and the main locations on either sides to which it is connected (diagram by the author) This street and all of the other important sites to which it is connected on either side, including the Grand Bazaar, Imam Mosque, Melli bank, Sabze Meidan plaza, Ark Square and Golestan Palace (Figure 4-12), are very active and inviting during the day to the citizens of Tehran. However, despite the potential this street and palace hold for creating an active environment, these spaces are completely inactive during the evening and at night due to the surrounding governmental sites’ limited hours of operation.   The governmental complex: In the north-west section of the study area, there is a group of governmental buildings and open spaces which, despite some functional changes, are still actively functioning. The Ministry of Foreign Affairs building is one of the most important edifices of this complex (Figure 4-13). 105   Figure ‎4-13 Governmental Complex (diagram by the author)  Imam Mosque: Other important urban features in the study area include the historical Imam Mosque (Figure 4-14). This mosque and its grand central courtyard, located on the south side of Panzdah-e-KHordad Street and within the Grand Bazaar of Tehran, is not only functioning at the neighborhood scale, due to the numerous religious and political events taking place yearly in this centre, but it can also be considered an important religious centre on the city scale.   Figure ‎4-14 Imam Mosque (diagram by the author) This mosque, along with the Golestan Palace and the Grand Bazaar of Tehran, create the three important elements of the Islamic triangular urban structure (Figure 4-15). 106   Figure ‎4-15 Imam Mosque, one of the three elements that form the Islamic triangular urban structure (diagram by the author) With the construction of the vehicle-dominated streets on the ruins of Golestan Palace’s walls, and due to the heavy traffic of Panzdah-e-Khordad Street, pedestrian accessibility to this mosque from Golestan Palace became a seemingly unsolvable issue. However, once again in 2008, Panzdah-e-Khordad Street was converted into a pedestrian-dominated access area, and this change has made the connectivity of these urban elements -- the palace, bazaar and mosque -- stronger than that of the previous situation. 4.2.1.3 Population The overall residential population of Tehran has increased greatly from 1996 to 2006 at a population growth rate of 1.44. However, during the same period, the residential population growth rate in Region 12 was 0.76, which is about half of that of the city. The overall population growth rate in this region was -1.24 from 1980 to 2006, which shows a decrease in the overall residential population of this region (Bavand Consultants, 2006). The deprived conditions of the deteriorated residential fabric have resulted in an emigration of residents to other regions with higher living standards, thus causing a decrease in the residential population of Region 12.  On the contrary, the day-time population of this region increases every year. Considering the fact that a huge part of this region is under commercial and institutional land-use and that, moreover, an increasing number of residential areas convert yearly to commercial or office 107  land-uses, the day-time population of the region, including the number of employees and visitors, increases each year. Since the study area is located within Region 12 and partially covers Districts 1, 2 and 3, the population of the study area, including the residential population, day-time population and population density has been calculated as follows based on official reports and statistics:  The population and area statistics of each district within Region 12 have been adapted from official reports. Based on the population and area of each district, the population density has been calculated (Table 4-1). In order to calculate the population of the study area, it is first essential to measure the average population density of the study area. To do so, the sum of the populations of the three districts within the study area has been divided by the sum of the area of each district within the study area. P(1) : Population of District 1 Area(1) : Area of District 1 Area(T) : Total area of the study zone D(T) (Average population density of the study area) : ( (     (     (   (     (        (        (    P(T) (Total population of the study area) : D(T) x Area(T) TEHRAN-(RESIDENTIAL POPULATION) REGION DISTRICT RESIDENTIAL POPULATION DENSITY AREA RESIDENTIAL POPULATION people per hectare hectare people 12 1 111 A(1):361 P(1):40000 2 107 A(2):248.5 P(2):26500 3 139 A(3):258.5 P(3):36000 4 134 260.5 35000 5 303 229 69500 6 167 243 40500 TOTAL (Region 12) 156 1600 250000 Study Area Dt=( (     (     (   (     (        (        (    550 Dt*550 Table ‎4-1 Residential population and population density of each district within Region 12 of Tehran (table by the author; population densities adapted from (Region12 Tehran-Municipality Official Website, 2014)) 108  Area(T)= 550 Hectare D(T)= ( (     (     (   (    (       (       (   =                                 =118 P(T) (Total population of the study area) : D(T) x Area(T)=118 x 550 = 64900 In order to calculate the day-time population of the study area, the total day-time population of Region 12, which is around 1,400,000 people per day, has been divided by the total area of the region. The result is the day-time population density. For ease of calculation, the day-time population density of the study area has been considered equal to the day-time population density of the entire region. This number has been multiplied in the area of the case study. The result is the day-time population of the study area. TEHRAN-(DAY-TIME POPULATION) REGION DAY-TIME POPULATION DENSITY AREA DAY-TIME POPULATION people per hectare hectare people 12 875 1600 1400000 Study Area 875 550 481250 Table ‎4-2 Day-time population of Tehran’s study area (table by the author; Region 12 day-time population adapted from (Region12 Tehran-Municipality Official Website, 2014))  TEHRAN’S CASE STUDY AREA   POPULATION hectare total density Day-time Residential Day-time Residential people people/hectare 550 481250 64900 875 118 Table ‎4-3 Day-time and residential populations of Tehran’s case study (table by the author) 109  4.2.1.4 Land-use Another essential layer in the context analysis of the study area is that of land-use. The existing UOS within the study area are based on the fact that the dominant land-use of their locations attracts a variety of user groups. Additionally, the operation hours of UOS vary in terms of the dominant land-use of each part of the study area. Therefore, in order to have a better understanding of the network of UOS, the hours of activity of different parts of this network and the morphology of the network based on the dominant land-use, it is essential to prepare a base layer that includes the land-use data of the case study.  The following map (Figure 4-16) defines different land-uses, along with the area and density of each type, within the study area.  Based on the provided data (Table 4-4), the dominant land-uses in Tehran’s case study are commercial, residential, as green spaces, and institutional, respectively.  110   Figure ‎4-16 Land-use types and proportions in Tehran’s case study (map by the author; data adapted from Bayand Consultants (2006))      TEHRAN’S CASE STUDY LAND-USE Residential Commercial Institutional Greenspaces Industrial Religious Touristic area proportion area proportion area proportion area proportion area proportion area proportion area proportion hectare % hectare % hectare % hectare % hectare % hectare % hectare % 151.4 27.5 205.2 37.3 49.66 9.03 58.97 10.7 25.83 4.70 5.94 1.08 12.23 2.22 Table ‎4-4 Land-use types, areas and proportions in Tehran’s case study (table by the author)   111  4.2.1.5 Urban Structure and Street Network Another informative layer in the context analysis of the study area is the street pattern, including the primary and secondary access networks. This network has faced some major changes over time. Around 100 years ago, no vehicle-dominated streets existed; however, along with the appearance of the first group of imported motorized vehicles in Tehran, the urban fabric of the city gradually transformed to make room for the first four vehicle-dominated streets which were constructed on the ruins of the Golestan Palace in the historic core of the city (Figure 4-8). Moreover, the street pattern has changed from that of an organic pattern to a grid-based one. At the same time, new vehicle-oriented streets have been added to the existing street network and some streets have been widened (Sabri & Hamidi, 1998) (Figure 4-17).  Figure ‎4-17 The development of the street network pattern of the historic core of Tehran (figure adapted from Sabri & Hamidi (1998); edits by the author) These new developments have caused some problems in the historic urban fabric and resulted in fragmentation of this valuable historic core of the city into spatially isolated parts. “Spatially isolated” is a term referring to those fabrics with lack proper accessibility and spatial 112  integrity with the surrounding tissue. The connectivity of the pedestrian network at local scale has been affected by these fragmentations in spatially-isolated fabrics (Rismanchian & Bell, 2013). The grand Bazaar of Tehran is an example of the fragmented fabric that has been affected by street network development. This historic urban fabric, despite its inner pedestrian connectivity, lacks proper connectivity to the surrounding residential fabric and has become spatially isolated from the rest of the city (Figure 4-7). The following map (Figure 4-18) demonstrates the existing condition of the street network in Tehran’s case study.   Figure ‎4-18 The current conditions of the street network in Tehran’s case study (map by the author) In order to compare different street patterns in different parts of the study area, another map has been prepared which shows the street pattern superimposed upon the land-use layer (Figure 4-19). Based on this map, the entire primary street network has a grid-based pattern 113  with X- and T-junctions, while the density of this network is higher in the institutional areas of the case study.   Figure ‎4-19 The street network in different parts of Tehran’s case study (map by the author) The secondary street network in the same area is semi-grid-based with more T-junctions, fewer X-junctions and some cul-de-sacs. On the other hand, the secondary street network is denser in the commercial and residential areas. There are more T-junctions and cul-de-sacs in these areas of the case study which forms a semi-tributary street pattern. This network in the commercial zone is dedicated to pedestrians only. 114  4.2.2 Isfahan 4.2.2.1 Location Isfahan’s case study, which is approximately 567 hectares in size, partially covers four regions: 1, 3, 5 and 6 (Figure 4-20).   Figure ‎4-20 The selected case study in the city of Isfahan (diagram by the author; base map adapted from GoogleEarth ©2014 DigitalGlobe) 115  The study area is confined between the main streets and squares on all four sides (Figure 4-21): from the north, by Abdol-Razagh Street and Imam Ali Square (formerly Atigh Square); from the east, by Hatef Street and Charbagh Khajoo Road along with Feiz Street in the south side of the Zayandeh-Rood River; from the south, by Mir Street and from the west, by Shams Abadi and Tayeb Streets   Figure ‎4-21 The main streets and square around Isfahan’s case study (diagram by the author) This case study, just as with Tehran’s case study, includes the historic areas of the city, which have undergone numerous changes and developments through time. However, unlike Tehran, the city’s developments and modernizations have mostly adhered to Isfahan’s historic urban patterns. Even in the current versions of the city’s master plans, the attempt has always been made to adapt to the locality’s historical urban structure while considering of the current requirements of the residents (Karimi & Motamed, 2003) (Naghshe-Jahan-Pars Consultants, 1989).  Despite all of the developments, the urban fabric of this region of the city has generally maintained its integrity and, unlike the case of Tehran, the pedestrian experience is still strikingly alive at a greater scale in this area. Every year, the existing historical architectural and urban features of Isfahan attract millions of visitors, particularly to the selected study area, and 116  most such visitors prefer to travel on foot through the historic urban fabric of Isfahan (Isfahan Virtual Tourism, 2014). Despite the growing number of visitors and the increasing population of the city, the pedestrian access network still provides proper service to all concerned and interconnects the important urban features (Figure 4-22).  Figure ‎4-22 The continuous pedestrian access in the historic areas of Isfahan (diagram by the author) The primary (12th to 13th Centuries AD) core of the city dating to the earliest period is located in the northeast corner of the case study. The secondary historic core of the city, which was built during Isfahan’s Golden era of architecture and urban design (16th to 17th Centuries AD) is located in the central portion of the case study (Figure 4-23). 117   Figure ‎4-23 The primary and secondary historic cores of Isfahan in the study area (diagram by the author) 4.2.2.2 Important Urban Features  Isfahan’s Historic Bazaar: One of the most important urban features which is located in Isfahan’s case study is the historic Qeysarieh Bazaar. This linear bazaar, located between two historic cores of the city, acts as a spine connecting these two centres through its internal pedestrian network (Isfahan Virtual Tourism, 2014) (Figure 4-24). Possessing a reciprocal effect, the bazaar keeps these two centres active and connected and, as a result of this connection, the bazaar itself has remained active and alive over time between these two poles of attraction (Danesh Nama Journal, 2009). 118   Figure ‎4-24 Isfahan’s Historic Bazaar, between two historic cores of the city (diagram by the author) This bazaar, which is basically a trade centre, plays a very important role in the social communications among the residents and visitors of Isfahan (Isfahan Virtual Tourism, 2014). It possesses a powerful network of interconnected indoor public spaces, including central courtyards, passageways and main junctions (charsough) which provides an opportunity for citizens to communicate in the different forms and types of public spaces within this bazaar. Charsough is a multifunctional open space located in the main intersections of the bazaar’s passageways, which has been used for royal/governmental announcements, religious ceremonies and other purposes.  The passageways are linear branches of the bazaar which connect the neighborhood centres of the surrounding residential areas to the bazaar (Karimi K. , 1997). This bazaar is still connected to some neighborhood centres of the surrounding residential areas. Therefore, according to the historical bazaar pattern in Iran, this bazaar also plays an important role in maintaining the connectivity and integration of these neighborhood centres within the historic residential fabric of the city.  Historically, neighborhood centres were urban public spaces consisting of a mosque, water reservoir, public bath and flexible open space which was once used for religious ceremonies as well as daily social interactions (Soltanzadeh, 1998) (Figure 4-25). However, some of these 119  functions, including the public baths and water reservoirs, are no longer practical. As a result, some of these spaces have been replaced with other buildings, while others still extant in historic neighbourhoods are now used as museums and touristic attractions.  Figure ‎4-25 The linear pattern of historic bazaars and the elements of a neighborhood centre in Iran (diagram by the author)  Charhar-Bagh Boulevard (Four-Garden Boulevard): One of the most important urban features in Isfahan’s case study is the historical Chahar-Bagh Boulevard (Figure 4-26). This boulevard has been named Four Gardens or, in Farsi, Chahar-Bagh, after its four rows of trees. Unlike the Haussmann style street, this boulevard has been built outside of the historic fabric of the city and has changed the urban sprawl direction of Isfahan to the south side of the Zayandeh-Rood River (Karimi & Motamed, 2003). In the Safavid era (AD 1500-1600), a complex of garden-palaces graced both sides of this boulevard.  The innovation of the linear garden-street with its recreational and ceremonial purposes is also one of the features of the Isfahan style (Pirnia, 2003). This boulevard not only provides proper access on both sides to motorized vehicles but also provides a social space for citizens and visitors in its main part. The main portion of this boulevard is an ideal space for walking, sitting with friends and families and exercising. In keeping with its recreational function, a bike lane has recently been added to this boulevard. 120   Figure ‎4-26 Chahar-bagh Boulevard (diagram by the author)  Garden-Palaces: Other important urban features in Isfahan’s case study are garden-palaces such as Chehel-Sotoon and Hasht-Behesht. (Figure 4-27) These gardens, located on the eastern side of Chahar-Bagh Boulevard, are from the remains of numerous palaces and gardens which once stood on both sides of this boulevard.  These rectangular garden-palaces represent other features of the Isfahan Style.  Ali-Qapu is the name of a mansion which once formed the entrance to this group of gardens and palaces and is located on their eastern side.  An important property of these garden-palaces is the strong network of pedestrian walkways within and between these gardens. The cruciform pattern of the pedestrian walkways increases the number of desirable directions in which to stroll and provides convenient access to different areas of these gardens. The coherent connectivity of public open spaces is another characteristic of the Isfahan Style which is still effectively visible in the study area. 121    Figure ‎4-27 Garden-Palaces along Chahar-Bagh Boulevard (diagram by the author)  Naghsh-e-Jahan Square: Another important urban feature in Isfahan’s case study is the well-known Naghsh-e-Jahan Square (Figure 4-28). This multifunctional plaza, located in the heart of Isfahan, is the secondary core of the city which was built during the Safavid period (AD 1000-1006) (i.e. the Golden era of art and architecture in Iran). “The Grand Naghsh-e Jahan square can be named the heart of Esfahan. It was given the title of ‘World Heritage’ and registered in UNESCO due to its matchless beauty and grandeur.” (Isfahan Virtual Tourism, 2014)  122   Figure ‎4-28 Naghsh-e-Jahan Square (diagram by the author) As mentioned above, this secondary core of the city is connected to the old primary core via the Qeysarieh Bazaar (Figure 4-24). The innovation of the Naghsh-e-Jahan Square with its simple geometric form, and specific scale and measurements, is the result of the new urban design approach in the Isfahan Style (Karimi & Motamed, 2003). This rectangular square of about 8 hectares has been utilized for numerous purposes over time. Historically, it was the site for playing the Iranian version of polo matches (chogan matches) and for viewing royal marches. While some of these functions have lost their original application, newer functions and activities have been added to this square at a broader scale, including the Friday Prayers and religious ceremonies. Despite these changes in its functions, this plaza has always been a connector and an intermediate urban open space between the bazaar (commercial), Ali-Qapu Mansion (royal) and Imam (the former Shah’s) Mosque, which are the most important urban elements in historical Islamic city centres (Figure 4-29). 123   Figure ‎4-29 Naghsh-e-Jahan Square, enclosed by commercial (bazaar), religious (Imam Mosque) and royal (Ali-Qapu Mansion) functions (base map adapted from (Ardalan & Bakhtiar, 1973, pp. 98-99); edits by the author) Moreover, the extraordinary interconnection and integration of this square with the older fabric of the city is another outstanding property of this square (Browne, 1976).  Imam Mosque of Isfahan: Imam Mosque is another important urban element which is located on the southside of Naghsh-e-Jahan Square (Figure 4-30). This mosque, with its central courtyard (another type of UOS in Iran) and outstanding design, is not only an important 124  religious centre but also a touristic attraction for thousands of visitors from all around the world.  Figure ‎4-30 Imam Mosque on the south side of Naghsh-e-Jahan Square (diagram by the author)  Zayandeh-Rood River: Zayandeh-Rood River is a natural UOS located in the heart of the modern city of Isfahan (Figure 4-31). The intrinsic natural beauty of the river itself, along with recreational UOS on both sides of the river such as parks and green spaces transversed by paths and bridges, attracts thousands of citizens and tourist daily to this area.  Two of the most important historic bridges crossing this river are the Khaju (built in AD 1060) and AllahVerdi-Khan (Si-o-se Pol) (built in AD 1065). These two pedestrian bridges with their unique architecture and decorations are among the most beautiful historical bridges in the world. These two bridges, which connect the urban areas on the northern region of Zayandeh-Rood River to the lands in the southern area of that river, have played an important role in the urban expansion of Isfahan towards the south side of the river (Isfahan Virtual Tourism, 2014). 125   Figure ‎4-31 Zayandeh-Rood River, and the AllahVerdi Khan and Khaju Bridges (diagram by the author) 4.2.2.3 Population The method that has been applied to Tehran’s case study (see Section 4.2.1.3) has also been used to calculate the population of that of Isfahan’s.  Considering the population densities of 10 neighborhoods (Isfahan Municipality, 2014); the population of the portion located within the boundaries of the study area has been calculated (Figure 4-32) (Table 4-5). 126   Figure ‎4-32 The area, residential population and population density of the regions and neighborhoods within Isfahan’s case study (diagram by the author; population densities adapted from Isfahan Municipality Portal (2014) ) REGION NEIGHBOURHOOD RESIDENTIAL POPULATION DENSITY AREA POPULATION people per hectare hectare people 1 1-1 ABAS ABAD 66 4.9 323 1-2 SHAHZADEH EBRAHIM 82 11 902 1-3 KHALAJA 94 8.68 816 1-4 DARBE KOOSHK 88 21.5 1892 3 3-1 NAGHSH-E-JAHAN 37 231 8547 127  REGION NEIGHBOURHOOD RESIDENTIAL POPULATION DENSITY AREA POPULATION people per hectare hectare people 3-2 CHARKHAB 87 128 11136 5 5-1 BAGH-ZERESHK 110 13.2 1452 6 6-1 AEENEH-KHANEH BAGH-NEGAR 79 50.1 3958 6-2 FEYZ 107 6.25 669   STUDY AREA   52  567   29695   Table ‎4-5 The residential population and population density of each neighborhood within Isfahan’s case study (table by the author; population densities adapted from Isfahan Municipality Portal (2014)) Based on data presented in Table 4-5, Naghsh-e-Jahan Square has the lowest residential population density among all of Isfahan’s neighborhoods (37 people per hectare). However, considering the touristic conditions of this square and the fact that the latter is bound between a row of shops and two important religious centres, it is obvious that the day-time population of this area should be much greater than that of its residential population.  Isfahan has always been one of the most important touristic cities In Iran. Different touristic attractions within Isfahan’s case study attract diverse numbers of visitors during the high seasons and, as a result, the day-time population of the study area is not fixed throughout the year. In order to define the day-time population of Isfahan’s case study, numerous reports concerning the numbers of visitors to certain touristic attractions have been studied (Iranian Students News Agency, 2007) (Mehr News, 2013). Based on these reports, a day-time population of around 160,000 people has been assigned to Isfahan’s case study, which is close to the average number of visitors that has been reported for Naghsh-e-Jahan Square during the high seasons (Table 4-6). ISFAHAN-(DAY-TIME POPULATION) REGION DAY-TIME POPULATION DENSITY AREA DAY-TIME POPULATION people per hectare hectare people Study Area 282 567 160000 Table ‎4-6 The day-time population of Isfahan’s case study (table by the author) 128  Based on data from Tables 4-5 and 4-6, the total residential, and day-time population and population density of Isfahan’s case study have been presented in Table 4-7.  ISFAHAN’S CASE STUDY AREA   POPULATION hectare total density Day-time Residential Day-time Residential people people/hectare 567 160000 29695 282 52 Table ‎4-7 The day-time and residential populations of Isfahan’s case study (table by the author) 129  4.2.2.4 Land-use In Isfahan’s case study, based on the provided data (Table 4-8, Figure 4-33), the dominant land-use is residential (more than half of the case study), with a huge difference in its coverage area, and its commercial, institutional and green spaces, respectively.   Figure ‎4-33 Land-use types and proportions in Isfahan’s case study (map by the author; data adapted from (Bavand Consultants, 2003))      ISFAHAN’S CASE STUDY LAND-USE Residential Commercial Institutional Greenspaces Industrial Religious Touristic area proportion area proportion area proportion area proportion area proportion area proportion area proportion hectare % hectare % hectare % hectare % hectare % hectare % hectare % 313.6 55.3 51.63 9.11 37.22 6.57 30.80 5.43 7.17 1.27 10.56 1.86 14.32 2.53 Table ‎4-8 The land-use types, areas and proportions of Isfahan’s case study (table by the author) 130  4.2.2.5 Urban Structure and Street Network Although Isfahan has expanded significantly through time and the city structure has undergone major changes, these developments have been mostly in harmony with the historic urban structure and in consideration of the valuable fabric of the city (Karimi & Motamed, 2003) Figure ‎4-34 The transition of the urban structure and the street patterns of Isfahan (diagram by the author; data adapted from (Danesh Nama Journal, 2009) (Karimi & Motamed, 2003)) The urban structure of Isfahan in the Seljuk period (12-13th Centuries AD) consisted of one central core, namely, the Old Square (or Meydan-e-Kohne) and a linear bazaar axis beside this central core (Figure 4-34). The major changes in the urban structure of Isfahan took place during the Safavid period (16-17th Centuries AD) when the capital of Iran moved to Isfahan. Shah Abbas significantly expanded the boundaries of Isfahan and constructed an entire new set of buildings, palaces, mansions and gardens in the city. Overall, urban design and planning can be considered the first outcome of the Golden era of art and architecture in the Safavid period.  In this period, a new centre and linear axis have been added to the former urban structure of 131  Isfahan. Therefore, Safavid Isfahan included two main centres and two main axes. The structure of the Safavid city centre is similar to that of the Seljuk city centre. The old and new city centres each consist of a large open space surrounded by a variety of buildings, including mainly shops and commercial buildings (Danesh Nama Journal, 2009) (Karimi & Motamed, 2003).  While the old fabric of the city still possessed an organic pattern, the Safavid extension of the city surrounding the secondary core (Naghsh-e-Jahan Square) and the secondary axis (ChaharBagh Boulevard) was formed based on a grid-based pattern. New developments along ChaharBagh Boulevard including all of the royal gardens and their internal pedestrian network were entirely based on this grid-based pattern. One of the significant characteristic of the Safavid extension was its flawless interconnection with the old structures of the city (Karimi & Motamed, 2003). The secondary core is connected to the Old Square through a powerful chain of bazaars. In other words, the old bazaar encircling the old square has been extended to the point that it connects the two main cores of the city. In the Safavid period, another new axis was added to the urban structure of Isfahan. Extending the Charbagh Boulevard to the south side of Zayandeh-Rood River, this river has been added to the main structure of city as a new horizontal axis.  In other words, new developments in Safavid Isfahan took place along the north-south axis of ChaaharBagh Boulevard and the east-west axis along the Zayandeh-Rood River and the secondary core of the city, Naghsh-e-Jahan Square (Karimi & Motamed, 2003) (Danesh Nama Journal, 2009). During the Pahlavi Period (20th Century), along with all the new changes and developments that occurred in most of the cities of Iran, the pattern of the primary street network of Isfahan also changed significantly in some parts of the city, including in the old fabrics.  Considering the sensitivity of the historic fabric, a new, rectilinear, grid-based street pattern has been applied to some parts of this city. Although this new pattern has caused problems in some parts of the city, including its primary core, its general purpose was to minimize the harm conducted on the old fabric of the city while providing proper access for cars and pedestrians (Karimi & Motamed, 2003).  132  While the dominant street pattern in Isfahan since the Pahlavi period has been the rectilinear grid-based pattern, the Safavi core of the city and its two main axes have never lost their importance, and emphasis has remained, since the Safavi period, on the secondary core and these two axes. Figure 4-35 demonstrates the existing condition of the street patterns in Isfahan’s case study.  Figure ‎4-35 The current condition of the street network in Isfahan’s case study (map by the author) Today, the street network pattern of Isfahan’s case study consists of a semi-tributary pattern in the old residential fabric and also in the bazaar area, with a combination of T-junctions and cul-de-sacs (Figure 4-36). In the bazaar, the street network mostly consists of pedestrian accesses with minimal accessibility for motorized vehicles. In the central part of this case study, 133  including Naghsh-e-Jahan Square and the institutional areas and touristic gardens, the primary and secondary streets and accesses form a grid-based pattern with a combination of X- and T-junctions (Figure 4-36). This grid-based pattern is also the dominant pattern of the residential areas on the south side of the Zayandeh-Rood River.   Figure ‎4-36 The street network in different areas of Isfahan’s case study (map by the author) 4.2.3 Context Comparison  Population: In Tehran; the current capital of Iran, population growth has been considerably faster compared to that of other cities such as Isfahan.  134  Considering the case studies in Tehran and Isfahan, while the residential population density in Tehran’s case study is 118 persons per hectare, the number is only 52 in Isfahan. The day-time population density of Tehran’s case study is 875 people per hectare compared to 282 for Isfahan (Chart 4-1).  Chart ‎4-1 The residential and day-time population density in Tehran’s and Isfahan’s case studies (chart by the author)  Land-use: As has been mentioned above, the duration of activity of UOS is related to the dominant land-use of its context. For example, since institutional areas of the city are not active during the night, UOS within these areas have less chance of being used by residents. On the other hand, residential areas increase the chance of utilization for UOS which are related to this type of land-use, such as neighborhood parks and playgrounds. Considering the fact that more than half of Isfahan’s case study is under residential land-use, those UOS found in connection with residential areas have the potential to be utilized by residents over a longer period of time during the day. Tehran’s case study, on the other hand, comes more under commercial land-use. Although UOS found in connection with commercial areas attract thousands of people during the day, they are mostly inactive or inaccessible to public users at night (Chart 4-2). 875 118 282 52 01002003004005006007008009001000people per hectare Population Density TEHRANISFAHANResidential Population Density Day-time Population Density 135   Chart ‎4-2 The land-use proportion in Tehran’s and Isfahan’s case studies (chart by the author) But what are those types of UOS found related to each type of land-use? In order to answer this question, it is essential to study the distribution of UOS in relation to the different land-use types of each case study. Studying the UOS palette in each case study which will be presented in the next section of this chapter provides the opportunity to attain a better understanding of the UOS distribution pattern.   Street Network Pattern: As a result of rapid changes in the urban structures and street patterns of Tehran, some abnormalities appeared in the historic fabric of this city. The grid-based street network that has been imposed on this city in the modern era has fragmented its historic fabric. The grand bazaar of Tehran that was once connected with the residential sectors has lost its integration with the city and become an isolated island with minimal connectivity to the surrounding fabric. However, changes in the urban structures and street patterns of Isfahan have mostly been in harmony with its valuable historic urban fabric.  Generally, the primary street networks in these two cases have a grid-based pattern.  By comparing the street network patterns of the two case studies, it appears that in these two cases, the areas with the same sort of land-use present similar street network patterns:  0102030405060Land-use Proportion (%) Land-Use Proportion TEHRANISFAHANResidential Commercial Institutional Greenspaces Industrial Religious Tourostic 136  Commercial: In the bazaar and old residential fabrics of both cities, the secondary street network has a semi-tributary pattern, with emphasis on pedestrian accessibility. The primary street network pattern for the same land-use type is the semi-tributary one in Isfahan and the grid-based one in Tehran.  Institutional: In institutional areas including gardens and palaces and generally in the royal and governmental areas built from the Safavid period up until today, the street networks form rectilinear grid-based patterns in both cases. Residential: The secondary street network in the residential areas of Tehran’s case study shows a semi-grid-based pattern; however, the same network, in Isfahan’s case study, demonstrates a semi-tributary pattern. The primary street networks in the residential areas of both case studies demonstrate a grid-based pattern. 4.3 The UOS Palette The UOS palette presents an analysis of existing UOS in each case study.   Two different classification methods will be used in each UOS palette, namely, form/functional typology and morphological taxonomy of the existing UOS. These two classification methods have been elaborated on previously in Chapter 2 (See Table 2-7 and Section 2.1.2.3). In the form-functional classification, UOS that have been categorized into types and subtypes will be mapped and presented along with examples and brief introductions to all sub-types. For ease of access to this classification system, a brief version of the form-functional classification table is presented as follows (Table 4-9). The same colour codes which have been used in this table will be used to map all of the existing UOS within each case study (Figure 4-38). Complimentary data including the coverage area and fraction of each type and sub-type of UOS will be calculated for each case study. In order to analyze the morphological taxonomy of UOS, every different shape and size of UOS within each type will be organized and presented  in a fixed-size square (Figure 4-37), making it easier to compare the morphology of a certain type of UOS in both case studies. 137  Studying the morphological taxonomy of UOS is an essential step towards analyzing the distribution patterns of UOS within each case study. Type Sub-Type 1-      Parks and Gardens Urban parks Neighborhood parks Pocket parks Gardens Community gardens 2-      Natural/ Semi-natural Features Agricultural fields Water surfaces Habitats Woods Wetlands Meadows ... 3-      Water edges Beaches Waterfronts Harbours Piers River fronts 4-      Sports fields Outdoor sports fields Playgrounds 5-      Civic Spaces Squares Plazas Institutional open spaces Forecourts Courtyards Entrances 6-      Streets and Corridors Pedestrian alleys Pedestrian sidewalks Pedestrian streets Traffic restricted streets Pedestrian linear corridors Trails 7-      Incidental Spaces Vacant open spaces Parking Derelict open spaces Storages Table ‎4-9 Form-functional types and sub-types of UOS (table by the author) 138   Figure ‎4-37 Examples of the morphological taxonomy of three different UOS types (diagram by the author) 4.3.1 Tehran’s UOS Palette  Figure ‎4-38 The existing types of UOS in Tehran’s case study (map by the author) 139  TEHRAN UOS TYPE TOTAL AREA FRACTION AREA PER CAPITA Hectare % square meter per person 1- Parks and Gardens 57.20 10.40 1.20 2- Natural/Semi-natural Features 0.94 0.17 0.00 3- Water Edges 0.00 0.00 0.00 4- Sports Fields 0.17 0.03 0.00 5- Civic Spaces 7.54 1.37 0.20 6- Streets and Corridors 37.40 6.80 0.80 7- Incidental Spaces 11.00 2.00 0.20 TOTAL UOS 107.31 19.51 2.23 Table ‎4-10 The existing types of UOS in Tehran’s case study (table by the author) Currently, 19.5% of Tehran’s case study is covered with 6 different types of UOS (Figure 4-38). Approximately 10% of this study area falls under the “Parks and Gardens” type, which has the highest fraction, and 6.8% under “Streets and Corridors”, with the second highest fraction of all types. With a significant difference in their fraction, the “Incidental Spaces”, “Civic Spaces”, “Natural/Semi-natural Features” and “Sports Fields” are the other existing types of UOS in this case study, in descending order. Considering the day-time population of the study area (Table 4-3), the per capita of each type of UOS has been calculated. Except for “Parks and Gardens” (1.2 square metres per capita) and “Streets and Corridors” (0.8 square metres per capita), the results show that for the other types there are very close to zero available UOS per capita  (Table4-10). Based on the official statistics, green space2 in the entirety of Tehran’s Region 12 is about 4.9 square metres per capita, and this number increases to 61 square metres per capita in Region 19 of the city (The Municipality of Tehran, 2014). As a result, the presented results in Table 4-10 demonstrate a significant imbalance between the day-time population of Tehran’s case study and the available UOS in the historic core of this city.                                                      2 In official reports by the municipality of Tehran, Green Space includes: parks, urban forests, green squares, boulevards, street-side trees, and other planted fields (The Municipality of Tehran, 2014). 140  4.3.1.1 Parks and Gardens  Figure ‎4-39 The map and morphological taxonomy of the Parks and Gardens type of UOS in Tehran’s case study (map by the author) TEHRAN Type Sub-type Figure # % of the same type % of the total existing UOS Parks and Gardens POCKET PARKS  4-40 18.1 9.4 GREEN SQUARE  4-41 4.6 2.4 GREEN PUBLIC COURTYARDS  4-42 32.9 17.1 GARDENS  4-43 2.4 1.2 URBAN PARKS  4-44 42.1 21.9 Table ‎4-11 The fraction of the Parks and Gardens type and sub-types in Tehran’s case study (table by the author)    141   Figure ‎4-40 Pocket parks in Tehran’s case study (map by the author)  Figure ‎4-41 Green squares in Tehran’s case study (map by the author) 142   Figure ‎4-42 Green public courtyards in Tehran’s case study (map by the author)  Figure ‎4-43 Gardens in Tehran’s case study (map by the author) 143   Figure ‎4-44 Urban parks in Tehran’s case study (map by the author) 144  4.3.1.2 Natural/Semi-natural Features  Figure ‎4-45 The map and morphological taxonomy of the Natural/Semi-natural type of UOS in Tehran’s case study (map by the author) TEHRAN Type Sub-type Figure # % of the same type % of the total existing UOS Natural/ Semi Natural Features LAKES  4-46 51.7 0.4 POOLS  4-47 48.3 0.4 Table ‎4-12 Fraction of the Natural/Semi-natural type and sub-types in Tehran’s case study (table by the author)   145   Figure ‎4-46 A lake in Tehran’s case study (map by the author)  Figure ‎4-47 A pool in Tehran’s case study (map by the author) 146  4.3.1.3 Sports Fields  Figure ‎4-48 The map and the morphological taxonomy of the Sports Fields type of UOS in Tehran’s case study (map by the author) TEHRAN Type Sub-type Figure # % of the same type % of the total existing UOS Sports Fields OUTDOOR SPORT FIELD 4-49  100.0 0.2 Table ‎4-13 A fraction of the Sports Fields type and sub-types in Tehran’s case study (table by the author) 147   Figure ‎4-49 Outdoor Sports Fields in Tehran’s case study (map by the author) 148  4.3.1.4 Civic Spaces  Figure ‎4-50 Map and morphological taxonomy of the Civic Spaces type of UOS in Tehran’s case study (map by the author) TEHRAN Type Sub-type Figure # % of the same type % of the total existing UOS Civic Spaces PLAZA 4-51 4.4 0.3 ROUNDABOUT 4-52 15.0 1.0 SQUARE 4-53 15.0 1.0 CENTRAL COURTYARDS 4-54 65.7 4.5 Table ‎4-14 A fraction of the Civic Spaces type and sub-types in Tehran’s case study (table by the author)   149   Figure ‎4-51 A Plaza in Tehran’s case study (map by the author)  Figure ‎4-52 Roundabouts in Tehran’s case study (map by the author) 150   Figure ‎4-53 Public squares in Tehran’s case study (map by the author)  Figure ‎4-54 Central courtyards in Tehran’s case study (map by the author) 151  4.3.1.5 Streets and Corridors  Figure ‎4-55 Map and morphological taxonomy of the Streets and Corridors type of UOS in Tehran’s case study (map by the author) TEHRAN Type Sub-type Figure # % of the same type % of the total existing UOS Streets and Corridors PEDESTRIAN STREETS  4-56 3.0 1.1 SIDEWALKS  4-57 83.7 28.9 PEDESTRIAN CORRIDORS  4-58 13.2 4.6 Table ‎4-15 A fraction of the Streets and Corridors type and sub-types in Tehran’s case study (table by the author) 152   Figure ‎4-56 Pedestrian streets in Tehran’s case study (map by the author)  Figure ‎4-57 Sidewalks in Tehran’s case study (map by the author) 153    Figure ‎4-58 Pedestrian corridors in Tehran’s case study (map by the author) 154  4.3.1.6 Incidental Spaces  Figure ‎4-59 Map and morphological taxonomy of the Incidental Spaces type of UOS in Tehran’s case study (map by the author) TEHRAN Type Sub-type Figure # % of the same type % of the total existing UOS Incidental Spaces VACANT OPEN SPACES  4-60 59.5 6.0 PARKING  4-61 32.7 3.3 STORAGE  4-62 7.8 0.8 Table ‎4-16 A fraction of the Incidental type and sub-types in Tehran’s case study (table by the author) 155   Figure ‎4-60 Vacant open spaces in Tehran’s case study (map by the author)  Figure ‎4-61 Parking in Tehran’s case study (map by the author) 156   Figure ‎4-62: Storage spaces in Tehran’s case study (map by the author) 157  4.3.2 Isfahan’s UOS Palette  Figure ‎4-63 The existing types of UOS in Isfahan’s case study (map by the author) ISFAHAN UOS TYPE TOTAL AREA FRACTION AREA PER CAPITA Hectare % square meter per person 1- Parks and Gardens 25.52 4.50 1.60 2- Natural/Semi-natural Features 34.76 6.13 2.20 3- Water Edges 2.38 0.42 0.15 4- Sports Fields 2.04 0.36 0.13 5- Civic Spaces 16.44 2.90 1.00 6- Streets and Corridors 32.55 5.74 2.00 7- Incidental Spaces 5.05 0.89 0.30 TOTAL UOS 118.73 20.94 7.42 Table ‎4-17 Existing types of UOS in Isfahan’s case study (table by the author)    158  Currently, 20.94% of Isfahan’s case study is covered by 7 different types of UOS (Figure 4-63). Approximately 6.13% of this study area is classified under the “Natural/Semi-natural features” type, which has the highest fraction. With a slight difference, “Streets and Corridors”, “Parks and Gardens” and “Civic Spaces”, respectively, are other types of UOS with the highest densities.  With a significant difference, “Incidental Spaces”, “Water Edges” and “Sports Fields”, in descending order, are the other existing types of UOS in this case study. Considering the day-time population of the study area (Table 4-7), the per capita of each type of UOS has been calculated. The results show that only in the three types of UOS with the lowest, near-zero, fraction of UOS per capita are found the “Incidental Spaces”, including the vacant open spaces, parking lots and storage spaces (Table4-17). 159  4.3.2.1 Parks and Gardens  Figure ‎4-64 The map and the morphological taxonomy of the Parks and Gardens type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Parks and Gardens GREEN PUBLIC COURTYARDS  4-65 25.1 5.6 GARDENS  4-66 42.9 9.5 URBAN PARKS  4-67 32.0 7.1 Table ‎4-18 Fraction of the Parks and Gardens type and sub-types in Isfahan’s case study (table by the author)   160   Figure ‎4-65 Green public courtyards in Isfahan’s case study (map by the author)  Figure ‎4-66 Gardens in Isfahan’s case study (map by the author) 161   Figure ‎4-67 Urban parks in Isfahan’s case study (map by the author) 162  4.3.2.2 Natural/Semi-natural Features  Figure ‎4-68 Map and morphological taxonomy of the Natural/Semi-natural type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Natural/ Semi-natural Features RIVER 4-69 85.0 23.5 STREAMS (MAADI) 4-70 13.2 3.7 POOL 4-71 1.8 0.5 Table ‎4-19 Fraction of the Natural/Semi-natural type and sub-types in Isfahan’s case study (table by the author)   163   Figure ‎4-69 A river in Isfahan’s case study (map by the author)  Figure ‎4-70 Streams in Isfahan’s case study (map by the author) 164   Figure ‎4-71 Pools in Isfahan’s case study (map by the author) 165  4.3.2.3 Water Edges  Figure ‎4-72 Map and morphological taxonomy of the Water-Edges type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Water Edges RIVER FRONTS 4-73 67.0 1.8 STREAM SIDE ALLEYS 4-74 33.0 0.9 Table ‎4-20 Fraction of the Water-Edges type and sub-types in Isfahan’s case study (table by the author) 166   Figure ‎4-73 River fronts in Isfahan’s case study (map by the author)  Figure ‎4-74 Stream side alleys in Isfahan’s case study (map by the author) 167  4.3.2.4 Sports Fields  Figure ‎4-75 Map and morphological taxonomy of the Sports Fields type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Sports Fields OUTDOOR SPORT FIELD 4-76 89.2 1.5 PLAYGROUNDS 4-77 10.8 0.2 Table ‎4-21 Fraction of the Sports Fields type and sub-types in Isfahan’s case study (table by the author) 168   Figure ‎4-76 Outdoor sports fields in Isfahan’s case study (map by the author)  Figure ‎4-77 Playgrounds in Isfahan’s case study (map by the author) 169  4.3.2.5 Civic Space  Figure ‎4-78 Map and morphological taxonomy of the Civic Spaces type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Civic Spaces PLAZA  4-79 48.3 6.7 ROUNDABOUT  4-80 9.0 1.2 FORECOURT  4-81 6.9 1.0 CENTRAL COURTYARD  4-82 35.7 4.9 Table ‎4-22 Fraction of the Civic Spaces type and sub-types in Isfahan’s case study (table by the author) 170   Figure ‎4-79 Plazas in Isfahan’s case study (map by the author)  Figure ‎4-80 Roundabouts in Isfahan’s case study (map by the author) 171   Figure ‎4-81 Forecourts in Isfahan’s case study (map by the author)  Figure ‎4-82 Central courtyards in Isfahan’s case study (map by the author) 172  4.3.2.6 Streets and Corridors  Figure ‎4-83 Map and morphological taxonomy of the Streets and Corridors type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Streets and Corridors BRIDGES  4-84 4.6 1.3 SIDEWALKS  4-85 73.6 21.3 GREEN PEDESTRIAN STREETS  4-86 21.7 6.3 Table ‎4-23 Fraction of the Streets and Corridors type and sub-types in Isfahan’s case study (table by the author) 173   Figure ‎4-84 Bridges in Isfahan’s case study (map by the author)  Figure ‎4-85 Sidewalks in Isfahan’s case study (map by the author) 174   Figure ‎4-86 Green pedestrian streets in Isfahan’s case study (map by the author) 175  4.3.2.7 Incidental Spaces  Figure ‎4-87 Map and the morphological taxonomy of the Incidental Spaces type of UOS in Isfahan’s case study (map by the author) ISFAHAN Type Sub-type Figure # % of the same type % of the total existing UOS Incidental Spaces VACANT OPEN SPACES  4-88 57.6 2.4 PARKING  4-89 42.4 1.8 Table ‎4-24 Fraction of the Incidental type and sub-types in Isfahan’s case study (table by the author) 176   Figure ‎4-88 Vacant Open Spaces in Isfahan’s case study (map by the author)  Figure ‎4-89 Parkings in Isfahan’s case study (map by the author) 177  4.3.3 UOS Palette Comparison 4.3.3.1 UOS Fraction Comparison The total UOS fraction in Isfahan is only slightly higher than that of Tehran’s case (Chart 4-3). Considering the generic assumption that, in Isfahan, the UOS network performs significantly better compared to that of Tehran’s case, the equal UOS fraction is clearly not the only factor affecting the spatial qualities of the UOS network in these two cases. Therefore, adding random UOS to the network, or densification, while ignoring the effects of the other factors on the performance and spatial qualities of the UOS network, is not a very thoughtful enhancement strategy.   Chart ‎4-3 Comparison of the UOS fractions in Tehran and Isfahan (chart by the author, based on Tables 4-10 and 4-17) 4.3.3.2 UOS Diversity Comparison Along with considering the fraction of UOS, the diversity of existing types and subtypes of UOS is another key factor that should be considered while studying the distribution patterns of 0%10%20%30%1- Parks and Gardens2- Natural/Semi-NaturalFeatures3- Water Edges4- Sport Fields5- Civic Spaces6- Streets and Corridors7- Incidental SpacesTOTAL UOS0% 0% 0% 20% 0% 0% 21% Fraction UOS Fraction Tehran Isfahan178  UOS in the network. Overall, Isfahan’s case demonstrates more diversity in form-functional types compared to Tehran’s case (Chart 4-3). Parks and Gardens Type and Sub-Types: Tehran only shows a good diversity of sub-types in the ‘Parks and Gardens’ type of UOS. Pocket parks and green squares are two subtypes that do not exist in Isfahan’s case study (compare Tables 4-11 and 4-18). Natural and Semi-natural Type and Sub-Types: Zayandeh-Rood River in Isfahan’s case study not only significantly increases the fraction of the “Natural” type, but also improves the diversity of UOS in the network by adding a “River” sub-type. There are also some water streams (which the locals call Maadi) in Isfahan’s case study, while again in Tehran this subtype does not exist. In Tehran, there is an artificial lake in Shahr Park (Figure 4-47), while this subtype does not exist in Isfahan (compare Tables 4-12 and 4-19) Water Edges Type and Sub-Types: Riverfronts and stream side valleys in Isfahan’s case, although low in coverage area and almost zero in fraction,  are other subtypes of the “Water Edges” type that increase the diversity of UOS in the network. The Water Edges type does not exist in Tehran’s case study (see Table 4-20). Sports Fields Type and Sub-Types: The Playground subtype in Isfahan’s case study does not exist in Tehran’s case. And this again increases the diversity of subtypes in Isfahan’s case study (compare Table 4-13 and Table 4-21). Civic Spaces Type and Sub-Types: In both cases, there are four subtypes of Civic Spaces which do not affect the comparative diversity of UOS in these cases.   Streets and Corridors Type and Sub-Types: in both cases, there are three subtypes in this category. However, it is worth mentioning that while in Tehran there are some pedestrian streets and corridors, In Isfahan there are green pedestrian streets which significantly increases the quality of this sub-type. Moreover, historic “Bridges” as another subtype of Streets and Corridors in Isfahan’s case are one of the tourist attractions of this city, attracting hundreds of visitors daily (compare Tables 4-15 and 4-23) While in Tehran most historic streets have lost their recreational aspect, Chahar-Bagh Street in Isfahan is still one of the most powerful recreational axes within the city. Recently, Panzdah-179  e-Khordad Street in Tehran has become once again a pedestrian dominated access, and some recreational activities have been embedded in this street. Incidental Type and Sub-Types: This type lacks the essential qualities needed for the social life of citizens and should be considered service spaces. These spaces have the potential to turn into future UOS which might enhance citizens’ social lives. Parking and vacant open spaces exist in both Tehran’s and Isfahan’s case studies. There are also some open spaces used as storage areas in Tehran’s case study, particularly in the bazaar area, which were formerly transformed from central courtyards (compare Tables 4-16 and 4-24) 4.3.3.3 UOS Per Capita Comparison Considering the fact that the day-time population in Tehran’s case study is three times higher than that of Isfahan’s, the per capita UOS in Isfahan is approximately three times higher than Tehran’s (Chart 4-4).  Chart ‎4-4 A comparison of UOS per capita in Tehran and Isfahan (chart by the author) 0.002.004.006.008.001- Parks and Gardens2- Natural/Semi-Natural Features3- Water Edges4- Sport Fields5- Civic Spaces6- Streets andCorridors7- Incidental SpacesTOTAL UOS0.00 0.00 0.00 2.23 7.42 m2 per capita UOS Per Capita Tehran Isfahan180  4.3.3.4 Morphological Taxonomic Comparison In order to compare the morphologies of existing UOS within chosen case studies, some factors need to be considered, such as the “Grain Size” of UOS (Metric B1-2, Fine/medium/coarse), the “Diversity” of shapes and sizes (Metric B1-3: Low/moderate/high) within an identical type, and the “Distribution Balance” (Metric B1-4, Low/moderate/high) within the case study and accumulation areas (Tables 4-25 to 4-31). Parks and Gardens: (Table 4-25, Figure 4-39, and Figure 4-64) UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Parks and Gardens Tehran  -Fine -medium -Coarse high  high Moderate (Figure  4-39)  Institutional, Residential and Touristic areas (Figure  4-16)  Isfahan  -fine -medium  high   moderate Moderate (Figure  4-64)  Institutional, Touristic and River-side Residential Areas (Figure  4-33)  Table ‎4-25 A morphological comparison of the “Parks and Gardens” type in Tehran’s and Isfahan’s case studies (table by the author)  A comparison of the morphological taxonomies of the Parks and Gardens type in Tehran and Isfahan reveals that most UOS of this type are accumulated in institutional areas (Figures 4-39 and 4-64). In both case studies, the diversity of shapes is high; however, Tehran’s case presents a more diverse range of sizes of this type. 181  Natural/Semi-natural Features: (Table 4-26, Figure 4-45, and Figure 4-68)  UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Natural/Semi-Natural Features Tehran  medium low  low Low (Figure  4-45)  Urban park (Figure  4-16)   Isfahan  -medium -coarse  moderate  moderate  Moderate (Figure  4-68)  Residential areas (Figure  4-33)  Table ‎4-26 A morphological comparison of the “Natural/Semi-natural Features” type in Tehran’s and Isfahan’s case studies (table by the author) Comparing the morphological taxonomies of the Parks and Gardens type in Tehran and Isfahan shows that most UOS of this type are accumulated in institutional areas (Figures 4-39 and 4-64). In both case studies, the diversity of shapes is high; however, Tehran’s case presents a more diverse range of sizes of this type. Water Edges (Table 4-27 and Figure 4-72): Since the Water Edges type does not exist in Tehran’s case study, the overall diversity of shapes and types of UOS will be relatively higher in Isfahan’s UOS network. These linear UOS, with a moderate distribution balance, are generally accumulated in the residential areas of Isfahan’s case study (Figure 4-72)   182  UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Water Edges Tehran This type does not exist in Tehran’s case study - - - -  -  Isfahan  medium   low  low Moderate (Figure  4-72)  Residential areas (Figure  4-33)  Table ‎4-27 A morphological comparison of the “Water Edges” type in Tehran’s and Isfahan’s case studies (table by the author) Sports Fields (Table 4-28, and Figures 4-48 and 4-75): The medium grained sport field in Isfahan’s case study slightly improves the diversity of sizes of this type in this study area in comparison to that of Tehran’s case study. In both cases, sports fields are located in residential areas. The diversity of shapes in both cases is low.  183  UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Sport Fields Tehran  fine low low  Moderate (Figure  4-48) Residential areas  (Figure  4-16)  Isfahan  -fine -medium low   medium Moderate (Figure  4-75) Residential Areas (Figure  4-33)   Table ‎4-28 A morphological comparison of the “Sports Fields” type in Tehran’s and Isfahan’s case studies (table by the author) Civic Spaces (Table 4-29, and Figures 4-50 and 4-78): UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Civic Spaces Tehran  -fine -medium moderate moderate  low  (Figure  4-50) Bazaar  and  institutional areas (Figure  4-16)  184  UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Isfahan  -fine -medium -coarse   moderate high  Moderate (Figure  4-78)  Bazaar and Institutional areas (Figure  4-33)  Table ‎4-29 A morphological comparison of the “Civic Spaces” type in Tehran’s and Isfahan’s case studies (table by the author) A comparison of the morphological taxonomies of the Civic Spaces type in Tehran and Isfahan demonstrates that most UOS of this type are accumulated in the historic bazaars and institutional areas of both cases (Figures 4-50 and 4-78). In both case studies, the diversity of shapes is moderate; however, Tehran’s case presents a more diverse range of sizes of this type. On the other hand, UOS of this type have greater spatial dispersion in Isfahan’s case study.  Streets and Corridors (Table 4-30, and Figures 4-55 and 4-83): UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Streets and Corridors Tehran  -Narrow sidewalks and  -a pedestrian corridor -a pedestrian street moderate moderate  high  (Figure  4-55)  All over the case study (Figure  4-16)  185  UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Isfahan  -Narrow sidewalk  -many greet pedestrian streets  moderate moderate  high  (Figure  4-83) All over the case study  (Figure  4-33)  Table ‎4-30 A morphological comparison of the “Streets and Corridors” type in Tehran’s and Isfahan’s case studies (table by the author) The Streets and Corridors type in both cases is relatively well-distributed throughout both case studies. The diversity of shapes and sizes of this type is moderate in both case studies. However, there are a greater number of wide, pedestrian dominated streets in Isfahan than Tehran. Incidental Spaces (Table 4-31, Figures 4-59 and 4-87): The morphological taxonomy of the Incidental Spaces type in Tehran reveals that UOS of this type are distributed throughout the study area. While low quality incidental spaces in Tehran’s case study mostly include vacant lots and storage areas, this type includes parking lots in Isfahan’s case, with these parking lots mostly being located in Institutional areas. The diversity of shapes is higher in Tehran’s case study as there are many vacant lots and abandoned spaces of different shapes and sizes throughout Tehran’s case study.   186  UOS Type Case  Morphological Taxonomy Grain Size Diversity Distribution shapes sizes balance accumulation area Incidental Spaces Tehran  -fine -medium high moderate   High (Figure  4-59) All over the site except Institutional and Touristic areas (Figure  4-16)  Isfahan   -medium  low  low Moderate (Figure  4-87)  Institutional, Industrial and Commercial areas (Figure  4-33)  Table ‎4-31 A morphological comparison of the “Incidental Spaces” type in Tehran’s and Isfahan’s case studies (table by the author) 4.3.3.5 UOS Palette Analysis: Conclusions Based on the available and accessible data concerning UOS enhancement policies in Tehran, the main policy in enhancing UOS there concerns green space densification. Increasing the amount of green space per capita includes adding more parks, urban forests and other types of green spaces of random size and shape to the city. Turning abandoned fields and vacant lots into green space is representative of implementing this policy in Tehran. While this policy is beneficial for the overpopulated city of Tehran, it might not be the main priority in all districts. Any decision regarding UOS enhancement should be made with consideration to the main problems in the existing conditions of UOS in those areas. This comparison clearly defines some of the main issues regarding the spatial aspects of UOS in both cases. While random modifications might be a waste of time, funds and energy, diagnosing the problems before applying enhancement strategies will undoubtedly lead to better results. In order to enhance 187  the spatial qualities of UOS before intervening in the latter existing condition, the spatial distribution of the UOS in the network needs to be studied. To be able to critique and improve the distribution quality of the UOS within networks, certain key factors need to be taken into account. These include the area of UOS per capita (with consideration given to their day-time population), the diversity of their types and sub-types, the fraction of UOS within the study area and also the morphology of the UOS in the network. A comparison of the UOS palettes of Tehran and Isfahan demonstrates a greater diversity of their shapes, sizes, types and sub-types in Isfahan’s case study. Also, there are more UOS of each type per person in Isfahan’s case. Distribution of UOS over the study areas is more balanced in Isfahan. Incidental spaces that are distributed throughout Tehran’s case study, especially in the commercial areas, offer the opportunity for future enhancements in this case study. These spaces have the potential to turn into other high quality types of UOS and thus improve the overall spatial aspect of the UOS network in this case study.  To formulate a better decision in relation to enhancing the spatial qualities of UOS, there is the need to develop a holistic view towards these spaces. Therefore, rather than focusing on individual UOS and applying random enhancement strategies to them, these spaces need to be studied and analyzed as a network of connected, interacting and interdependent elements. In this regard, the following section is dedicated towards analyzing the spatial attributes of UOS networks. 4.4 UOS Networks Based on the main statement of this thesis, In order to study the spatial organization of UOS, one should view these spaces as interconnected, interacting and interdependent elements in the urban context and as a complex system or “Network”. As indicated in the proposed SAF (Figure 3-3), analyzing the spatial organization of UOS includes studying; 1. The “distribution” of UOS and 2. The spatial “integration” of UOS into a network (see also 3.1.2.1 and 3.1.2.2). Through studying the contexts and UOS palettes in the first two sections of this chapter, the “distribution” of UOS within a network has been analyzed. 188  However, In order to study the spatial “integration” of UOS networks, it is essential to analyze the spatial attributes of these networks. Accordingly, the spatial attributes of the UOS network include “Proximity” to different types of UOS, as well as its “Network Pattern”, “Privacy Gradient”, and “Network Connectivity and Complexity”, which will be studied comprehensively below.  4.4.1 Proximity Proximity analysis includes studying the nearness to UOS within a 5-minute walking distance or 400 to 450 metres, as represented by each circle on the proximity map (Figure 4-90). The colour coding system as employed in the UOS palettes will be used in the proximity maps. Each colour represents a specific form-functional UOS type (Figures 4-91 to 4-94).   Figure ‎4-90 An example of a circle with a radius of 400 m, representative of areas with proximity to a certain UOS within a less than 5-minute walking distance from the UOS (diagram by the author)  By employing a proximity indicator and the related metrics detailed in Chapter 3 (see Section 3.4, Indicator C4, and Metrics C4-1 to C4-10), the following will be calculated for both case studies (Tables 4-32 and 4-33):  The “Effective Domain” of a certain type of or all UOS  The ”Percentage of the Study Area  Effective Proximity” to a certain or any type of UOS  The ”Total Area without Effective Proximity” to a certain or any type of UOS  The ”Percentage of the Study Area without Effective Proximity” to a certain or any type of UOS 189  Since incidental spaces are low quality open spaces (storage areas, parking lots, vacant lots, etc.), proximity to this type of UOS is not beneficial in the social life of residents or visitors. However, these spaces can potentially become valuable UOS in the future. Therefore, the proximity to all UOS will be calculated twice: with and without considering the “Incidental Spaces” type. 4.4.1.1 Proximity: The Tehran Case Study  Figure ‎4-91 The proximity to all types of UOS in the Tehran case study (map by the author) TEHRAN UOS Type Proximity covered not covered Effective domain % of the study area with effective proximity to UOS total area without effective proximity % of the study area without effective proximity to UOS hectare % hectare % 1- Parks and Gardens 332.8 60.5 217.2 39.5 2- Natural/Semi-Natural Features 89.9 16.3 460.1 83.7 3- Sport Fields 73.4 13.3 476.6 86.7 4- Civic Spaces 186.5 33.9 363.5 66.1 5- Streets and Corridors 161.1 29.3 388.9 70.7 6- Incidental Spaces 399.0 72.6 150.9 27.3 TOTAL UOS including Incidental Spaces 517.4 94.1 32.6 5.9 TOTAL UOS excluding Incidental Spaces 448.5 81.6 118.5 18.4 Table ‎4-32 The proximity to all types of UOS in the Tehran case study (table by the author) 190   Figure ‎4-92 The proximity to each type of UOS in the Tehran case study (map by the author) 191  4.4.1.2 Proximity: The Isfahan Case Study  Figure ‎4-93 The proximity to all types of UOS in the Isfahan case study (map by the author) ISFAHAN UOS Type proximity covered not covered effective domain % of the study area with effective proximity to UOS total area without effective proximity % of the study area without effective proximity to UOS hectare % hectare % 1- Parks and Gardens 305.8 53.9 261.2 46.1 2- Natural/Semi-Natural Features 349.4 61.6 217.6 38.4 3- Water Edges 244.9 43.2 322.1 56.8 4- Sport Fields 60.6 10.7 506.4 89.3 5- Civic Spaces 335.4 59.2 231.6 40.8 6- Streets and Corridors 301.2 53.1 265.8 46.9 7- Incidental Spaces 286.6 50.5 280.4 49.5 TOTAL UOS including Incidental Spaces 554.6 97.8 12.4 2.2 TOTAL UOS excluding Incidental Spaces 521.7 92.0 45.3 8 Table ‎4-33 The proximity to all types of UOS in the Isfahan case study (table by the author) 192   Figure ‎4-94 The proximity to each type of UOS in the Isfahan case study (map by the author) 193  4.4.1.3 Proximity Comparison In Tehran’s case study, the effective domain of the Incidental Spaces type covers 73% of the study area, while in Isfahan’s case study the Natural/Semi-natural Spaces type covers 62% of the study area (Chart 4-5, and Figures 4-92 and 4-94).   Chart ‎4-5 A comparison of proximities to each type of UOS in Tehran’s and Isfahan’s case studies (chart by the author) As has been mentioned above, the Incidental Spaces are low quality open spaces (storage areas, parking lots, vacant lots, etc.), which are neither effective nor beneficial in their current condition to the social lives of the residents. Therefore, proximity to this type might increase the effective domain of all UOS types in numbers without actually improving the existing conditions of UOS networks. Analyzing the effective domain of all UOS, including and excluding the “Incidental Spaces” type (Figures 4-91 and 4-93) clearly shows that while including “Incidental Spaces” type the percentage of the study area with effective proximity to any UOS will increase by around 17% in Tehran’s case study, while including the “Incidental Spaces” type only increases this percentage by about 6% in Isfahan’s. As a result of this analysis, it is clear that the incidental type has 0%50%100%1- Parks and Gardens2- Natural/Semi-NaturalFeatures3- Water Edges4- Sport Fields5- Civic Spaces6- Streets and Corridors7- Incidental SpacesTOTAL UOS including IncidentalTOTAL UOS excludingIncidental73% 99% 82% 62% 98% 92% % of the study area % of the Study Area with Effective Proximity to Each UOS Type Tehran Isfahan194  unwittingly increased the effective domains of all UOS in both cases, although this increase is more apparent in Tehran’s case study (Tables 4-32 and 4-33). 60% of Tehran’s case study possessses proximity to the Parks and Gardens type within a 5-minute walking distance. This number decreases dramatically in Tehran’s case study to 33.9% for Civic Spaces, 29.3% for Streets and Corridors, 16.3% for Natural/Semi-natural Spaces, and 13.3% for Sports Fields. However, in Isfahan’s case study, the effective domains of each of the five UOS types cover more than 50% of the study area. These types include Natural/Semi-natural Features, Civic Spaces, Parks and Gardens, Streets and Corridors, and Incidental Spaces, respectively (Chart 4-6).  Chart ‎4-6 The percentage of the study area with an effective proximity to a certain UOS type in Tehran’s and Isfahan’s case studies, downward trend (chart by the author) 0%10%20%30%40%50%60%70%80%7- IncidentalSpaces1- Parks andGardens5- Civic Spaces6- Streets andCorridors2- Natural/Semi-Natural Features4- Sport Fields3- Water Edges% of the study area % of the Study Area with Effective Proximity to a Certain UOS Type in Tehran's Case Study 0%10%20%30%40%50%60%70%2- Natural/Semi-Natural Features5- Civic Spaces1- Parks andGardens6- Streets andCorridors7- IncidentalSpaces3- Water Edges4- Sport Fields% of the study area % of the Study Area with Effective Proximity to a certain UOS Type in Isfahan's Case Study Higher than 50% Lower than 20% 195  While circles that are representative of the effective domains of individual UOS are transparent on the map, aggregations and overlaps of these circles in one spot decreases the location’s transparency and makes the colour more intense in that spot. When considering this fact and comparing Tehran’s and Isfahan’s case studies, it is obvious that Isfahan’s proximity map is less transparent and the colour more intense in this case. This shows that the effective domain of UOS have more overlaps in Isfahan’s case study. This is due to the fact that the effective domains of five different types of UOS cover more than 50% of Isfahan’s case study. Therefore, overlaps of these effective domains are inevitable (Figures 4-91 and 4-93). Based on Figures 4-91 and 4-93, while including the Incidental Spaces, 2.2% of Isfahan’s case study does not have access to any type of UOS within a 5-minute walking distance, while this number is only about 1% in Tehran’s case study. However, excluding the incidental spaces, 18.2% of Tehran’s case study is not close to any type of UOS within a less than 5-minute walking distance, while this number is only 8% in Isfahan’s case study. As a result, excluding the Incidental Spaces causes a huge difference between Tehran’s and Isfahan’s case studies (Tables 4-32 and 4-33). Therefore, considering these facts, Isfahan’s case study demonstrates a more balanced distribution with the highest possibility of proximity to various types of high quality UOS within less than a 5-minute walking distance when compared with Tehran’s case study. Areas without access to UOS within a 5-minute walking distance are mostly located on the southern and eastern portions of Tehran’s case study. This dense and old urban fabric is dedicated to the Grand Bazaar of Tehran. Most of the existing UOS in this bazaar are former central courtyards which have been transformed into storage spaces, abandoned spaces, and in general Incidental Spaces. While these spaces are low quality UOS, they have the potential to turn into high quality future UOS with a few modifications. As a result of these changes, their proximity to high quality UOS will increase and there would be fewer spaces without effective proximity to any type of UOS in southern and eastern parts of Tehran’s case study. Increasing the proximity to UOS and providing the opportunity for all residents and visitors in this area to have proper access to high quality UOS strongly demonstrates the importance of enhancing the 196  incidental spaces in the Grand Bazaar of Tehran. This is another indication of the importance of setting priorities before performing any random interventions aiming to enhance the existing conditions of UOS.  4.4.2 Network Pattern As indicated in the SAF (see Section 3.3, Figure 3-3), another important step in the spatial analysis of the UOS network is to perform a study of the network pattern and its attributes. Accordingly, one method to study, understand and analyze the complex UOS network system, is to break down the complex network into simplified patterns. Therefore, the dominant patterns of the UOS networks of both case studies have been extracted for further spatial analysis.  To be able to compare the different network patterns within each case study spatially, attempt was made to discover the similarities and differences between the dominant patterns of each case and eventually to formulize the relationships between these patterns and their contexts. Finding 1: After conducting experiments on both cases, it was ascertained that the network patterns within the different land-use types are significantly different in their shapes, sizes, fractions and connectivity patterns.  This means that, for instance, the network pattern in the commercial areas of Tehran’s case study is different from that in the Institutional areas of the same case. For further investigation, each case has been divided into four major sections with different land-use types: Commercial, Residential, Institutional and Mixed-use of Commercial and Residential. Moreover the dominant pattern in each area has been extracted and presented.  In both cases, while the UOS network pattern has a more organic and tributary arrangement in the commercial areas (the bazaar), the pattern in the institutional areas is grid-based. The UOS network pattern in the residential areas falls somewhere between the above mentioned patterns (Figures 4-95 and 4-96). Finding 2: Not only are the network patterns in areas with dissimilar land-use completely different, but also the network patterns in areas with the same land-use in the unlike case studies have some noticeable similarities and attributes.   197  For instance, while the network pattern in the commercial areas of Tehran’s case study is different from the Institutional areas of the same case, it turns out that the network pattern in the commercial areas of Tehran’s case study is very similar to that in commercial areas of Isfahan’s (Figures 4-95 and 4-96). 198  4.4.2.1 Network Pattern: The Tehran Case Study  Figure ‎4-95 The Dominant UOS network pattern in the Residential, Commercial and Institutional areas of Tehran’s case study (diagram by the author) 199  4.4.2.2 Network Pattern: The Isfahan Case Study  Figure ‎4-96 The Dominant UOS network pattern in the Residential, Commercial and Institutional areas of the Isfahan case study (diagram by the author) 200  4.4.2.3 Network Pattern Comparison For further studies and comparisons of network patterns in both cases, three tables have been prepared and presented below. Each table includes a diagram of the dominant UOS network pattern in one of the three major land-use sectors, including Commercial (Table 4-34), Residential (Table 4-35) and Institutional (Table 4-36). Moreover, network attributes, including the UOS fraction, the duration of activity in that area, chance of proximity to UOS, the diversity of shape and size in the pattern and also the network connectivity pattern type have been studied for both cases, and the results presented in the same table. Commercial (Table 4-34): The commercial zone of Tehran’s case study mainly includes the Grand Bazaar of Tehran, which has been mentioned above as one of the “Important Urban Features” of Tehran’s case study (see Section 4.2.1.2). The commercial sector of Isfahan’s case study also includes Isfahan’s historic bazaar, or the Qeysarieh Bazaar, which has been referred to above as one of the Isfahan case study’s “Important Urban Features” (see Section 4.2.2.2).  Dominant network connectivity patterns in the commercial areas of both cases demonstrate a tributary pattern along a main arterial corridor. While both cases demonstrate the same network pattern, the UOS fraction in Isfahan’s case is 5 times higher than that of Tehran’s. Moreover, although the main retail stores in both bazaars close at around 5 to 6 pm, Isfahan’s historic bazaar’s including some tourist attractions means that the bazaar’s duration of activity is not limited to office hours; this particularly applies to the region adjacent to Naghsh-e-Jahan Square, which is the large UOS patch in Isfahan’s pattern diagram (Table 4-34). Since UOS have been distributed in both cases all over the commercial zones, considering the higher fraction of UOS in Isfahan’s commercial area, the opportunity for proximity to UOS is moderate in Tehran’s commercial area and higher in Isfahan.  Moreover, as mentioned above in the proximity analysis, most existing UOS in Tehran’s Grand Bazaar are former central courtyards that have been transformed into storage areas and other low quality UOS.  The diversity of shapes in both cases is very low as all existing UOS are rectangular in both Tehran’s and Isfahan’s bazaars. Moreover, the UOS in both cases are mostly small- to medium-201  sized patches. However, the large, rectangular patch in Isfahan’s case study increases the diversity of size in this case as compared to that of Tehran’s bazaar.   Overall, although very similar, the UOS network pattern in Isfahan’s bazaar reveals a more balanced spatial distribution than Tehran’s. land-use case Dominant UOS Network Pattern UOS Fraction Duration of Activity Proximity to UOS Diversity Network Connectivity Pattern Type shapes sizes Tributary,  Semi-Tributary, Semi-Grid-based, Grid-based % hours a day low-moderate-high low-moderate-high Commercial Tehran  6 8 moderate low moderate tributary Isfahan   33 12 high low high tributary Table ‎4-34 A comparison of the UOS network patterns in the Commercial areas of the Tehran and Isfahan case study (table by the author) Residential (Table 4-35): The dominant network connectivity patterns in the residential areas of both cases reveal a semi-tributary pattern, with relatively higher connectivity when compared with those of the commercial areas.   While both cases are active during the day, the fraction and proximity to UOS of the residential areas of Isfahan’s case study are higher than that of Tehran. Since the UOS network has been formed from small UOS patches, the diversity in size is low in both cases. However, the UOS network in Tehran’s residential areas includes a greater diversity in the shape of its UOS than that of Isfahan’s residential areas.  202  Again, although very similar in form, the UOS network pattern in the residential areas of Isfahan’s case study are in better spatial condition than those of Tehran. land-use case Dominant UOS Network Pattern UOS Fraction Duration of Activity Proximity to UOS Diversity Network Connectivity Pattern Type shapes sizes Tributary,  Semi-Tributary,  Semi-Grid-based, Grid-based % hours a day low-moderate-high low-moderate-high Residential Tehran  3 24 low moderate low semi-tributary Isfahan   14 24 moderate low low semi-tributary Table ‎4-35 A comparison of the UOS network patterns in the Residential areas of the Tehran and Isfahan case studies (table by the author) Institutional (Table 4-36): The dominant network connectivity patterns in the institutional areas of both cases exhibit a grid-based pattern with the highest level of connectivity when compared with those of the commercial and residential areas. The UOS fraction and opportunity for proximity to UOS in the institutional areas, while strikingly higher than that of the commercial and residential areas, is equal in both cases.  The institutional areas of Isfahan’s case study include some tourist attractions such as gardens and palaces which are open to visitors even after office hours. Therefore, the duration of activity in this case is more than that of the institutional areas in Tehran’s case study.  Although the UOS network pattern in the institutional areas of Tehran’s case study is in better spatial condition than the residential areas of the same case, while the duration of 203  activity is less than that of the residential areas, residents and visitors have less overall opportunity for utilizing the UOS. land-use case Dominant UOS Network Pattern UOS Fraction Duration of Activity Proximity to UOS Diversity Network Connectivity Pattern Type shapes sizes Tributary,  Semi-Tributary, Semi-Grid-based, Grid-based % hours a day low-moderate-high low-moderate-high Institutional Tehran  34 8 high low low grid-based Isfahan   36 12 high low low grid-based Table ‎4-36 Comparison of UOS network patterns in Institutional areas of Tehran’s and Isfahan’s case studies (table by the author) Since the network pattern only includes moderate sizes of rectangular UOS, the diversity in the shape and size of UOS in the institutional areas of both cases are low. 4.4.3 Neighbourhood Scale Case Studies After analyzing the dominant UOS network pattern within each case study, based on the proposed SAF (Figure 3-3), two other spatial attributes of UOS networks which need to be analyzed include “Network Connectivity” and the “Privacy Pattern”.   Many factors are involved in studying and analyzing these two network attributes. These UOS network attributes need to be studied at a smaller scale and with greater detail. Therefore, in order to analyze and compare these spatial attributes of UOS networks with more accuracy, it is essential to define smaller scale case studies. 204  On the other hand, as mentioned above, one of the interesting findings from prior analysis of the UOS network pattern has indicated that, in both case studies, areas with the same land-use reveal noticeable similarities in their UOS network patterns. Considering these similarities, it seems that the most accurate method that can be used for studying the network connectivity and privacy pattern is to select smaller scale (neighbourhood scale) cases within each larger case study. Accordingly, three neighbourhood scale case studies have been carefully selected as representative of one of the commercial, residential and institutional land-use sectors (Figure 4-97). Studying neighbourhood scale cases offers the chance to compare the spatial attributes of UOS networks in Tehran’s and Isfahan’s case studies at greater detail and with more accuracy.  Each of these cases is confined within a circle in an area of approximately 45 hectares:  Case #1: Commercial Case #2: Residential Case #3: Institutional In order to afford a closer look at these smaller scale cases, two additional figures have been presented as follows. Figure 4-98 represents aerial views of each of these neighbourhood-scale case studies of Tehran and Isfahan confined in a circular boundary. Also, Figure 4-99 represents the existing form-functional UOS within each case study. 205   Figure ‎4-97 Three neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) 206   Figure ‎4-98 An aerial view of the three neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (base map adapted from GoogleEarth 2013; edits by the author) 207   Figure ‎4-99 The existing UOS in neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (base map adapted from GoogleEarth 2013; edits by the author) 208  4.4.4 The Privacy Gradient As mentioned above in the second chapter of this thesis, when referring to public spaces, we are actually delineating a group of spaces with varying degrees of “publicness”. Not all UOS share the same degree of privacy.  The focus of the privacy gradient analysis is on studying the existing UOS from varied case studies with their diverse degrees of privacy and extracting the patterns by which they are connected within complex networks. How UOS with dissimilar degrees of publicness are connected might be one of the defining features of complex UOS networks. Therefore, hierarchy is a visible characteristic of complex networks. Since the degree of UOS publicness is highly dependent on such variables as the duration of activity, the size and scale of these spaces, defined user groups, physical and visual accessibility, and cultural perceptions of privacy, the effort was made to prepare a list of the existing UOS within each case (i.e. Commercial, Residential, and Institutional cases) as well as define the degree of publicness of each UOS type in the list. This list indicates the degree of publicness or privacy of each UOS from “public” through “semi-public”, “in-between”, and “semi-private” spaces in the Commercial, Residential and Institutional cases of Tehran and Isfahan (Figures 4-100, 4-101 and 4-102)  Figure ‎4-100 The privacy gradient of the existing UOS in neighbourhood-scale Commercial case studies of Tehran and Isfahan (diagram by the author) 209   Figure ‎4-101 The Privacy gradient of the existing UOS in neighbourhood-scale Residential case studies of Tehran and Isfahan (diagram by the author)   210   Figure ‎4-102 The privacy gradient of the existing UOS in neighbourhood-scale Institutional case studies of Tehran and Isfahan (diagram by the author) Defining the privacy degree of each individual UOS in neighbourhood-scale case studies of Tehran and Isfahan, and based on the provided lists above,  six new drawings have been produced which demonstrate the privacy gradient in each individual neighbourhood scale case study. These plan-view drawings clearly demonstrate how UOS with different degrees of privacy connect as a network in each case study. In other words, these maps demonstrate how one can move from the public main street to semi-private parts of the neighbourhood while passing through semi-public and in-between UOS (Figure 4-103) However, the privacy gradient pattern is not always identical for all cases. While in some cases such as the Residential case in Isfahan a complete hierarchy of UOS with different degrees of privacy can be observed, in other cases, such as the Residential case of Tehran, the hierarchy 211  has been corrupted in some areas. To attain a clear understanding of the diverse privacy gradients in each case, the most dominant patterns of privacy need to be extracted and presented for each of these six case studies. Moreover, while in some cases, such as the Commercial neighbourhood scale case study of Tehran, there might only be a single privacy gradient pattern, in other cases, such as the Residential neighbourhood scale case study of Tehran, there might be more than one dominant pattern. 212   Figure ‎4-103 The privacy Gradient of the neighbourhood scale cases of Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) 213  4.4.4.1 Privacy Gradient Comparison In order to compare the privacy gradients of UOS networks in the Tehran and Isfahan cases based on the provided privacy gradient maps, dominant privacy patterns have been extracted and articulated as follows. These simplified diagrams demonstrate how UOS with varying degrees of privacy are connected within networks. The most dominant pattern has been detailed first on the list, followed by other existing privacy gradient patterns of the same case study (Figure 4-1-4).  Figure ‎4-104 The dominant privacy gradient patterns from neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (diagram by the author) 214  While the privacy gradient patterns in the Commercial and Institutional case studies of Tehran and Isfahan are similar, most differences in the privacy gradient pattern can be observed by comparing the Residential neighbourhood-scale case studies. In order to have a clear idea of the similarities and differences in the privacy gradient patterns, similar cases in both cities have been compared and the results articulated as follows: The Commercial neighbourhood scale case studies of Tehran and Isfahan:  The Commercial case studies of Tehran and Isfahan have been formed based on their similar privacy gradient pattern. However, In Isfahan’s case study there are a greater number of “in-between” UOS compared with Tehran’s case (Figure 4-103). Considering the fact that UOS in commercial areas are mostly accessible to all public users, no semi-private UOS only accessible by certain users groups are extant in these two cases. Therefore, in Figure 4-103 there is no semi-private (pink) UOS in the Commercial cases of Tehran and Isfahan, while this is also clear in the privacy gradient pattern of these cases, presented as a dotted circle in Figure 4-104.  Since Tehran’s and Isfahan’s bazaars were both formed based on the Iranian traditional bazaar pattern, this similarity in the privacy gradient patterns of both Commercial cases is reasonable and explainable.  The Residential neighbourhood scale case studies of Tehran and Isfahan:  The most noticeable differences in the privacy gradient patterns can be observed while comparing the Residential case study of Tehran to that of Isfahan. While the most dominant privacy gradient patterns in both cases are similar (Public, Semi-public, and “In-between”), there are apparent differences in the second group of dominant privacy patterns of these cases. The second dominant privacy gradient pattern of the Residential case of Isfahan demonstrates a complete hierarchy of connected UOS from public to semi-private. However, the hierarchy of UOS from semi-public to semi-private spaces has been corrupted in the second 215  dominant pattern of the Tehran’s Residential case.  In this pattern, the in-between space which should connect the semi-public to semi-private spaces is missing (Figure 4-104). Therefore, the semi-private spaces, including neighbourhood centres, are directly connected to semi-public local streets (Figures 4-101 and 4-103) Overlooking the importance of space hierarchy in residential areas, with neighbourhood centres being directly connected to local streets while no in-between UOS connects these spaces, creates a corrupted privacy gradient pattern which will cause ambiguity in defining the degree of privacy of the neighbourhood centres. The corrupted privacy gradient pattern might also result in a noticeable decrease in the sense of community and eventually in the destruction of the identity of the old residential neighbourhoods of Tehran.  Another difference which is noticeable in the privacy gradient map of Tehran’s and Isfahan’s Residential cases is the more diminished number of semi-private (pink) spaces in Tehran’s case.  The semi-private type which according to the list provided in Figure 4-101 includes enclosed and semi-enclosed neighbourhood centres is highly effective in the sense of community it imparts to residential neighbourhoods.  In order to increase the sense of community and reclaim identity in the Residential neighbourhood scale case of Tehran, besides considering the importance of the spatial hierarchy from its public to semi–private sectors, more semi-private UOS need to be added to the existing residential fabric. This will eventually increase the spatial integration of UOS to this fabric.  Institutional neighbourhood scale case studies of Tehran and Isfahan:  The dominate privacy gradient patterns in the Institutional case studies of both Tehran and Isfahan indicate a similar hierarchy in the UOS from the public through the semi-public and in-between spaces (Figure 4-104). Just as in the Commercial cases, since Tehran’s and Isfahan’s Institutional cases have both been formed based upon similar historical urban patterns and with similar urban features such as garden-palaces, plazas, large-scale historical buildings, and grid-based internal corridors,  the 216  similarity in the privacy gradient patterns of both Institutional cases is reasonable and explainable (Figure 4-103). Although similar in the dominant privacy gradient pattern, there is only a limited number of semi-private (pink) UOS with limited accessibility to public users in Tehran’s Institutional case. These central courtyards of institutional buildings (Figure 4-102) are only accessible to certain users and therefore should be counted under the semi-private spaces. Therefore, there is another privacy gradient pattern in the Institutional case of Tehran which constitutes a hierarchy of UOS from public through semi-public, in-between and finally semi-private spaces. 4.4.5 Network Connectivity and Complexity In order to analyze and enhance the integration of UOS with the urban fabric, two other effective factors require studying: the Connectivity and Complexity of the network. Connectivity is the vital factor that links the components of a complex system into a unified whole. However, a high level of connectivity alone does not create a powerful network. Therefore, since the UOS network is a complex system, it should demonstrate some level of complexity along with its connectivity. In other words, the best UOS networks are those which attain a certain degree of complexity along with their proper connectivity.  Six maps have been presented below which demonstrate the pedestrian and street network connectivity extant in all neighbourhood scale case studies of Tehran and Isfahan. In all six maps, pedestrian paths are assumed to pass through open spaces (Figure 4-105). Moreover, to study the complexity of these networks, all existing crossroads, three-way intersections, loops, and cul-de-sacs have been marked and presented in Figure 4-106.  Once again, effort is being made to compare cases from Tehran and Isfahan with similar land-use practices (i.e. Commercial, Residential and Institutional cases). 217   Figure ‎4-105 Network Connectivity in neighbourhood-scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) 218   Figure ‎4-106 Intersections in neighbourhood scale cases from Tehran and Isfahan in the commercial, residential and institutional sectors (map by the author) 219  4.4.5.1 A Comparison of Network Connectivity and Complexity  In order to be able to compare the complexity and connectivity of UOS networks in similar case studies, along with the presented simplified network and intersection patterns, it was necessary to measure the number of links (metric C2-7) and each type of intersection, pedestrian path and street length, the fraction of existing UOS within the network and the number of UOS types.   Based on the measurements provided in the following tables, the connectivity and complexity of the network have been defined for each of the six neighbourhood scale case studies (Tables 4-37, 4-38 and 4-39). Commercial Case Studies (Table 4-37):   case AREA (hectare) DIAGRAM OF UOS NETWORK PATTERN .INTERSECTION PATTERN Pedestrian path length (m) Street length (m) UOS fraction % #UOS TYPES # LINKS (Metric C2-7) # OF JUNCTIONS Connectivity Index  (Metric C2-8) Complexity (Metric C3-1) X T CUL-DE-SACS & LOOP Commercial Tehran 45   9838 613 9% 3 194 13 85 57 1.25 low Isfahan 45   7992 5194 18 % 3 230 27 78 52 1.46 moderate Table ‎4-37 A comparison of the Connectivity and Complexity from the Commercial neighbourhood scale case studies of Tehran and Isfahan (table by the author) Network Connectivity: The connectivity of the vehicle dominated access network to the old and historic fabric of Tehran’s bazaar is very limited compared to that of Isfahan. A lack of proper access to the inner sectors of Tehran’s bazaar has caused some abnormalities in this Tributary Semi-tributary 220  region of the city. The conditions of this historic commercial fabric have gradually deteriorated, while the site has simultaneously become isolated from the rest of the city and lacks true internal connectivity.  The Network Connectivity Pattern: While there are more X- and T-junctions available in the Commercial case study of Isfahan, and the total length of the pedestrian and vehicle dominated accesses is more than that of Tehran’s Commercial case study, it can be concluded overall that the UOS network has a relatively higher degree of connectivity within Isfahan’s bazaar as compared with that of Tehran’s.  The connectivity index scores reveal that the UOS network in the Commercial case study of Isfahan is more walkable than that of Tehran’s Commercial case study. “A score of 1.4 is the minimum needed for a walkable community (02 Planning + Design & Calgary Regional Partnership, 2013, p. 64).” Also, since the UOS fraction in Isfahan’s case is two times greater than Tehran’s, Isfahan’s pedestrian paths and streets provide greater accessibility to more UOS as compared with Tehran’s case study. Moreover, as mentioned earlier in this chapter, the existing UOS in Tehran’s bazaar are mostly Incidental Spaces, being low quality open spaces that include storage areas and parking lots.  This fact accentuates the need to modify the existing conditions of UOS networks by transforming these spaces into other types of high quality UOS. Network Complexity: The UOS network in Tehran’s Commercial case study includes mostly tributary and some semi-tributary network patterns. Comparatively, the UOS network in Isfahan’s Commercial case study includes simultaneously tributary, semi-tributary and also some semi-grid-based patterns. Therefore, network complexity is higher here as compared with that of Tehran. Residential Case Studies (Table 4-38): Network Connectivity: Considering the total street length and number of intersections, and based on the network pattern presented in Figure 4-105, it is apparent that, in the Residential case studies of Tehran and Isfahan, the connectivity of vehicle dominated accesses is higher in Tehran’s case as compared to Isfahan’s. However, while examining the connectivity and total 221  length of the pedestrian paths, Isfahan’s Residential case study has a comparatively better situation. The connectivity index scores reveal that, in the Residential case of Isfahan, it is slightly easier to attain access on foot to the internal portions of the neighbourhood and is thus more pedestrian friendly, while vehicle access is limited. In the Residential case of Tehran, the semi-tributary street network accesses the inner sectors of the neighbourhoods, while pedestrian access is limited to narrow sidewalks and some semi-private driveways.    case AREA (hectare) DIAGRAM OF UOS NETWORK PATTERN INTERSECTION PATTERN Pedestrian path length (m) Street length (m) UOS fraction % #UOS TYPES # LINKS (Metric C2-7) # OF JUNCTIONS Connectivity Index  (Metric C2-8) Complexity (Metric C3-1) X T CUL-DE-SACS & LOOP Residential Tehran 45   1578 12831 5% 5 409 31 170 128 1.24 high Isfahan 45   7687 9081 10% 6 328 14 159 77 1.31 high Table ‎4-38 A comparison of the Connectivity and Complexity of the Residential neighbourhood scale case studies of Tehran and Isfahan (table by the author) Network Connectivity Pattern: While both case studies demonstrate a semi-tributary network pattern, there are more cul-de-sacs in Tehran’s case study which block movement flow in some parts of this network. Moreover, the total length of the pedestrian paths and streets is greater in Isfahan’s case. Therefore, the UOS network connectivity in Isfahan’s Residential case study is higher overall than Tehran’s.  Semi-tributary Semi-tributary 222  The UOS diversity of types and fractions is greater in the Isfahan case study. Therefore, the access network in the Residential case study of Isfahan connects a greater number of UOS, creating a higher degree of diversity among its accumulated of types. Network Complexity: The UOS networks in Isfahan’s and Tehran’s Residential case studies have been formed simultaneously of tributary, semi-tributary and semi-grid-based patterns. Therefore, network complexity is high in both case studies. Institutional Case Studies (Table 4-39):   case AREA (hectare) DIAGRAM OF UOS NETWORK PATTERN INTERSECTION PATTERN Pedestrian path length (m) Street length (m) UOS fraction % #UOS TYPES # LINKS  (Metric C2-7) # OF JUNCTIONS Connectivity Index  (Metric C2-7) Complexity (Metric C3-1) X T CUL-DE-SACS & LOOP Institutional Tehran 45   7930 4060 43% 5 61 3 39 11 1.15 low Isfahan 45   8686 6335 37% 4 384 42 146 23 1.82 moderate Table ‎4-39 A comparison of the Connectivity and Complexity of the Institutional neighbourhood scale case studies of Tehran and Isfahan (table by the author) Network Connectivity: Comparing the connectivity index scores, and based on the network patterns presented in Figure 4-105, it is apparent that network connectivity is higher in Isfahan’s Institutional case than it is for Tehran. Moreover, large scale buildings in Tehran’s case study have occupied a large portion of this site while leaving less space for access networks. Grid-based Grid-based 223   Figure ‎4-107 Connectivity of the UOS networks in the Institutional case studies of (a) Tehran and (b) Isfahan (diagram by the author)  Network Connectivity Pattern: Although both cases share a similar grid-based network pattern, since the total number of X- and T-junctions is higher in Isfahan’s case study, the network connectivity is in better condition there. The UOS network in Tehran’s case study has a slightly higher fraction and diversity of its UOS types when compared to Isfahan’s case.   Network Complexity: The UOS network in Tehran’s Institutional case study has been formed based on simple grid-based network patterns with low complexity. In comparison, the UOS network in Isfahan’s Institutional case study includes simultaneously grid-based and semi-grid-based patterns with a higher number of cul-de-sacs and T and X junctions. Therefore, network complexity is slightly higher in the Institutional case study of Isfahan as compared to that of Tehran. 4.5 Proposed Enhancement Strategies In summary, SAF has been tested on case studies of Tehran and Isfahan as a diagnostic tool to detect any problems within the spatial organization of the UOS network. Accordingly, via the SAF, the urban contexts, and UOS palettes and UOS networks of both cases have been analyzed. In this context, analysis factors such as population, land-use and street networking have been studied. In the UOS palette, the diversity of UOS types, shapes, and sizes along with the fraction of each type in the study area have been analyzed. And finally, in the UOS network analysis, network attributes, including proximity to UOS, network patterns, privacy gradients, network connectivity, and complexity have been studied. Testing the SAF on Tehran’s and Isfahan’s case studies demonstrates that, despite all of the similarities in the historic urban structures of both cases, the UOS network has better spatial integration in all three neighbourhood scale cases of Isfahan (i.e. the Commercial, Residential and Institutional cases). In Tehran’s case study, the fragmentation of the historic fabric has 224  caused many problems in the spatial integration of the UOS network. In comparison to Isfahan, the UOS network in Tehran’s case study suffers from: - Insufficient UOS area per capita, - A low diversity of types, shapes, and forms, - Short activity duration and limited access to most UOS located in the commercial and institutional areas, - A lack of proper proximity to high quality UOS especially in commercial areas, - Corrupted UOS hierarchies, with different publicness degrees, particularly in the residential areas, - A lack of proper connectivity between different UOS, particularly in the commercial and residential areas, and - Low network complexity in commercial areas. In addition to diagnosing such problems, might the SAF be useful as a mean to propose proper enhancement strategies deemed suitable and proportionate to the diagnosed problems?  Any intervention in UOS networks should be based on a comprehensive analysis, while general and piecemeal solutions are not suitable approaches to UOS networks. For example, the green space densification policy, currently very common in Tehran, adds random UOS to the UOS network, regardless of any problems in this network, and is not a very thoughtful enhancement strategy. More effective solutions are site-specific, but formulated with awareness of and sensitivity to the integrity of the network. A suitable enhancement policy should be developed with consideration to all restrictions and sensitivities within the context of the study area. Three focal enhancement policy options have been defined to test this question, namely: (1) Adaptation (minimal intervention), (2) Modification of the existing conditions and (3) Infill (adding more UOS where necessary). To implement any enhancement policies, a variety of case-specific enhancement strategies may be required.  225  To test the effectiveness of the proposed SAF on defining suitable enhancement options and strategies, the Commercial case study of Tehran has been selected to propose case-specific enhancement options and strategies considered suitable for the diagnosed problems. 4.5.1 Proposed Enhancement Strategies for the Commercial Case Study of Tehran To begin with, the following report card has been provided for the Commercial case study of Tehran (Table 4-40). This report card indicates any sensitivity within the context of the case study which may limit the ability of intervention in the UOS network. Also, this report card underlines specific needs for improvement in the existing conditions of the UOS palette and network, and reveals numerous possibilities for enhancing the existing conditions of that network within the Commercial case study of Tehran. TEHRAN Commercial Neighbourhood-scale Case Study UOS Types 1-Parks and Gardens 2-Natural/Semi-Natural Features 3-Water Edges 4-Sport Fields 5-Civic Spaces 6-Streets and Corridors 7-Incidental Spaces Total UOS  Context Sensitivity Historic-cultural  Ecological - Structural  UOS Palette Fraction Per capita         Diversity Form-Functional Types         Shapes         Sizes         Distribution Balance    UOS Network Proximity         Duration of Activity   Privacy Gradient  - Network Connectivity   Network Complexity  - Proposed Enhancement Policy Modification of the existing condition, and Adaptation to the context’s sensitivities Symbols Increase   Decrease  Unchanged - Table ‎4-40 The report card of the Commercial neighbourhood scale case study of Tehran (table by the author) 226  In general, considering the historic-cultural and structural sensitivities of the fine-grain fabric of the bazaar, “modification” of the existing UOS and “adaptation” to the context’s sensitivities are the two most suitable enhancements policies that can be applied in this area. These policies can be broken down to the three following improvement strategies: 1) The first case-specific enhancement strategy amends the many low-quality incidental spaces that are distributed throughout the study area into other high quality types of UOS. This modification will effectively improve the spatial aspects of the UOS network in Tehran’s case study. Based on spatial analysis, there are many “Incidental Spaces” in the form of storage spaces and vacant lots in the commercial areas of Tehran’s case study. These low-quality UOS might potentially be modified and developed into other types of UOS. Based on the spatial analysis (see Section 4.4.1.3), suitable UOS types for the Commercial case study of Tehran include civic spaces, green central courtyards, and small pocket parks (Figure 4-108). Such an alteration of types would increase the diversity of the UOS subtypes, fractions of high quality UOS within the study area, and also the effective proximity to high quality UOS in the Commercial case study of Tehran. This alteration in UOS types would improve the Commercial neighbourhood scale case study of Tehran as well as the larger case study area. Such a change would also increase the effective domain (Metric C4-7) of the “Parks and Gardens” and “Civic Spaces” types at both scales (Figure 4-109, Table 4-41, and Chart 4-7).  227   Figure ‎4-108 A modification of the existing “Incidental Spaces” in the commercial area of Tehran’s case study to the “Parks and Gardens” and “Civic Spaces” types. (a) The Commercial neighbourhood scale case study of Tehran, (b) Existing “Incidental Spaces” within the commercial area, (c) Modifications of the “Incidental Spaces” to “Civic Spaces” and green central courtyards, (d) Proximity to low quality “Incidental Spaces” before the modification, and (e) Proximity to high quality UOS following modification (diagram by the author) 228   Figure ‎4-109  (a) The effective domain of the “Parks and Gardens” type before modification, (b) The effective domain of the “Parks and Gardens” type following modification, (c) The effective domain of the “Civic Spaces” type before modification, (d) The effective domain of the ”Civic Spaces” type following modification, (e) The effective domain of the “Incidental Spaces” type prior to modification, (f) The effective domain of the ”Incidental Spaces” type following modification (diagram by the author) 229   TEHRAN UOS Type status proximity covered not covered Effective domain % of the study area with effective proximity to UOS total area without effective proximity % of the study area without effective proximity to UOS hectare % hectare % Parks and Gardens before 332.8 60.5 217.2 39.5 after 462.0 84.0 88.0 16.0 Civic Spaces before 186.5 33.9 363.5 66.1 after 231.0 42.0 319.0 58.0 Incidental Spaces before 399.0 72.6 150.9 27.4 after 352.0 64.0 198.0 36.0 TOTAL UOS including Incidental before 544.5 99.0 5.5 1.0 after 544.5 99.0 5.5 1.0 TOTAL UOS excluding Incidental before 448.5 81.6 118.5 18.4 after 503.3 91.5 46.7 8.5 Table ‎4-41 The effects of applying the first enhancement strategy  (i.e. the modification of UOS types from the “Incidental Spaces” type to “Parks and Gardens” and “Civic Spaces” types) on the effective domain of the UOS in the case study of Tehran (table by the author) Currently, before applying an enhancement strategy to the case study of Tehran, 60.5% of the study area has an effective proximity to the “Parks and Gardens” type. However, after applying the enhancement strategy, this number will increase to 84%. In other words, the probability of being within a 5-minute walking distance of the “Parks and Gardens” type will increase by about 24% in the case study of Tehran (Table 4-35 and Chart 4-7). Also, before applying the enhancement strategy, 34% of the study area has an effective proximity to the “Civic Spaces” type. After applying the enhancement strategy, this number will also increase, to 42%. In other words, the probability of being within a 5-minute walking radius of the ‘Civic Spaces’ type will increase by about 8% in the case study of Tehran. This enhancement policy only suggests the modification of existing types without adding any new UOS to the dense historic fabric of the bazaar. Since no new UOS have been added to the 230  network, the effective domain of all UOS, including the “Incidental Spaces”, will remain unchanged. In other words, before and after modification, 99% of the study area has an effective proximity to at least one UOS type including the “Incidental Spaces” type. However, excluding the “Incidental Spaces” from the calculations, the modification of the UOS types will increase the probability of nearness to at least one type of UOS in the study area by around 10% (Table 4-41, Chart 4-7, and Figure 4-110).   Chart ‎4-7 Percent of the study area with effective proximity to certain UOS types and total UOS, before and after applying the enhancement strategy (chart by the author) As mentioned in Section 4.4.1.3, areas without access to UOS within a 5-minute walking distance are mostly located on the southeast corner of Tehran’s case study area. Accordingly, the modification of exiting UOS in the commercial area will eventually solve the existing problem of insufficient proximity to high quality UOS in the southeastern portion of the case study of Tehran. Altering the “Incidental Spaces” extant in the Bazaar of Tehran into green central courtyards, pocket parks, and civic spaces, will increase the effective domain of all high quality UOS (Figure 4-110). 0%20%40%60%80%100%% of the study area with effective proximity to a certain type of UOS  UOS Type Before Applying theImprovementsAfter Applying theImprovementsParks  and Gardens Civic Spaces Incidental Spaces All UOS Including Incidental Spaces All UOS Excluding Incidental Spaces +10 +8 +24 231   Figure ‎4-110 Proximity to all UOS including the “Incidental Spaces” (a) before and (b) after modification, and the increase in the effective domain of all UOS excluding the “Incidental Spaces” (c) before and (d) after modification (diagram by the author) 2) The report card (Table 4-40) also reveals issues concerning the short duration of activity of some high quality UOS within the network. These UOS are mostly located in areas holding monotonic land-use.  This problem can be solved by altering the land-use of these areas and applying a mixed-use development plan. UOS located in mixed-use areas have a greater opportunity of becoming more active during the day and night (Table 4-40). 232  3) Although the report card reveals the need to increase the connectivity in the inner part of the Grand Bazaar of Tehran, the sensitivities of the fine-grained historic fabric of the bazaar limits the possibility of increasing the connectivity in the access network of this area.  However, the possibility still exists of modifying the existing access network to create high quality pedestrian corridors on the existing pathways. This way, without rendering the fine-grained fabric of the historic bazaar apart, new, high quality sub-types of “Streets and Corridors” will be added to the network. This modification can increase the diversity of the UOS subtypes while enhancing the quality of these corridors. In summary, testing the proposed SAF on the two case studies of Tehran and Isfahan has confirmed the SAF as an effective tool in diagnosing the problems within the spatial aspects of UOS networks. Also, testing the SAF on the Commercial case study of Tehran indicates that the proposed SAF can be also used as an effective tool for identifying places and opportunities for future enhancements and predicting the results of any future interventions and alterations in the UOS networks and also for proposing suitable and case-specific enhancement strategies.  While the main focus of this research has been the diagnostic aspects of the proposed SAF, evaluating and testing the prognostic aspects of this tool demonstrated potentials, which might be further explored with greater details in future studies.   233  Chapter 5: Discussion 5.1 Thesis Contribution and Significance UOS, as with any other sustainable system, is more than just the sum of its parts. There are also many factors that affect the overall network performance, and any change in the network might produce disproportional effects. This research elevates the needs and the opportunities for studying UOS as a complex system or network of interconnected, interacting and interdependent elements. The many factors and analytical layers involved in studying UOS networks amplify the complexity and importance of conducting a comprehensive UOS network analysis. This thesis not only contributes towards understanding the different factors that affect UOS networks but also demonstrates the potential to predict the effects of future interventions. The principal contributions of this work have addressed the following four main problems (Articulated in Section 1.2): 1) Lack of Proper Studies on UOS Networks: To date, most studies concerning the UOS have mostly focused on individual UOS. A holistic look at these spaces as a network of interconnected elements is found to have been missing from most of these studies. To fill in this gap, this thesis focuses holistically, utilizing broader scales, on UOS networks. To study how UOS networks perform, it was first deemed essential to understand the concept of the UOS network. A perusal of numerous literature sources has highlighted the absence of a comprehensive definition of UOS and UOS networks. To solve this problem, this thesis successfully presents new and comprehensive definitions of UOS and UOS networks which simultaneously suit the specific requirements of this research. In order to redefine UOS and UOS networks, numerous definitions discovered in connection with public spaces, open spaces, UOS, complex systems, and networks have been studied and critiqued from many different aspects (see Sections 2.1.1 and 2.2.1). Consequently, the first achievement of this research is the creation of a number of new, comprehensive definitions, unique to this thesis, for UOS and UOS networks.  234  Additionally, to study how UOS networks work, a classification system of these spaces has been developed. Four typologies of UOS which specifically suit the spatial analysis of UOS networks have been redefined and presented as the second product of this research. These typologies are based on 1-functional forms, 2-morphology, 3-scale and the domain of influence, and 4-the degree of publicness openness to the public of the UOS.  These four typologies have proven to be very useful for analyzing the spatial organization of UOS networks (see Section 2.1.2). 2) Lack of Practical Strategies for Diagnosing Spatial Problems in UOS Networks: While many cities, including Tehran, possess severe problems within their UOS networks, no practical or effective method existed to diagnose these problems. The first step in improving the existing conditions of any UOS network is to diagnose the problems extant within the system. Applying random enhancement strategies while omitting the diagnosis process, will most probably result in a loss of time, money and energy, and might also threaten valuable aspects of the network. While an urge had existed to diagnose the problems within the UOS networks, many diverse factors affecting these systems complicated the diagnostic process. All of these factors underline the importance of designing a comprehensive diagnostic tool that can be implemented on UOS networks. In response to the need for such a diagnostic tool, this research contributes towards designing a new, step-by-step framework for analyzing the spatial organization of UOS networks. The proposed Spatial Analysis Framework (SAF), which has been presented in Chapter 3, is an effective and strategic tool that can be used at different scales for diagnosing the spatial problems of the UOS networks (see Section 3.3). Diagnosing the spatial problems of UOS networks requires evaluating and calculating different variables which affect the latter’s spatial qualities. Accordingly, this thesis contributes towards the preparation of a comprehensive list of indicators and related metrics as a supplement to the proposed SAF. The proposed SAF, indicators, and related metrics have been tested on two case studies for diagnosing the spatial problems which exist in the UOS networks of the selected cases. These 235  case studies have been carefully selected from the historic core of Tehran, the current capital of Iran; and Isfahan, Iran’s former capital. The promising results of these analyses prove the effectiveness and applicability of the proposed SAF as a diagnostic tool.  To improve the usefulness of the proposed SAF, the framework has been applied to the selected case studies multiple times and the results of these tests have been used for refining and editing the early drafts of the SAF and its associated indicators. Also throughout the tests on the selected case studies, new metrics were defined and added to the SAF, where necessary. For example, the network complexity had not been defined as one of the analytical layers of the early versions of the SAF. However, while testing the SAF on the selected case studies, it turns out that in order to analyze the spatial integration of UOS networks, it is essential to study the network complexity alongside the network connectivity. Accordingly network complexity indicator and its associated metrics were added to the final version of the proposed SAF which has been presented in Chapter 3 (See Figure 3-3). The proposed Spatial Analysis Framework (SAF), along with a list of rigorously selected indicators and related metrics, forms a strategic tool for diagnosing and analyzing the spatial aspects of the UOS network.  To analyze and compare case studies and to demonstrate the results of these comparisons, it seemed inevitable that a proper mapping system be defined to meet the specific requirements of this research.  Accordingly, a colour-coding system was defined specifically for this research. This system, which has been used continuously throughout the entire analysis, in all of its 2-D maps and tables, has provided the essential basis for comparing both case studies in the clearest and most logical way. Also, employing 3-D modeling skills combined with 2-D analytical maps, tables, and charts provided a suitable platform for demonstrating the results of these analyses.  In addition to the proposed SAF and indicators, the case studies chosen for this research add to the significance of this study. The proposed SAF has been specifically designed for evaluating the spatial organization of UOS networks in the historic neighbourhoods of Iranian cities. The cultural and historical sensitivities which exist in the contexts of such cases restrict the ability to 236  intervene freely in these spaces and therefore amplify the complexity and importance of utilizing comprehensive UOS network analysis in these neighbourhoods. Accordingly, the proposed SAF has been tested on two case studies from Tehran and Isfahan.  As expected, applying the SAF to both cases and comparing the results reveals some defects and problems in Tehran’s case study as compared to Isfahan. While some of the diagnosed problems are related to the spatial organization of the UOS network solely, some others are associated with the context of the study area. For example, the SAF indicates that the UOS network in Tehran, although consisting of many high quality individual UOS, suffers from fragmentation and a lack of connectivity and interdependence among its elements. Lack of proper connectivity and proximity to high quality UOS in some parts of the study area is mainly related to the spatial organization of UOS network. Additionally, the SAF indicates that there are many incidental spaces distributed in Tehran’s case study. While these low quality UOS affect the overall performance of the UOS network, this problem is mainly associated with the context of the study area and not the spatial aspects of the UOS network. This problem is probably an indication of a derelict and deteriorating neighbourhood rather than a defective UOS network. Not all incidental spaces could, or should, be repurposed to improve the effective proximity to UOS in that area. While most of the diagnosed problems within UOS networks and contexts of both case studies were expected, some other results are not close to presumptions about Tehran and Isfahan. For example, although the general assumption about Isfahan indicates that this city is more walkable compared to Tehran, studying the network connectivity in Residential neighbourhood-scale case studies of Tehran and Isfahan reveals that, both cases suffer from insufficient connectivity in residential areas.  The results of these analyses prove the effectiveness of the SAF in analyzing the spatial organization of UOS networks (see Chapter 4). 3) Lack of Practical Strategies for Enhancing the Spatial Qualities of UOS Networks: Devising an effective tool for diagnosing the spatial problems of the UOS networks-in-hand is not sufficient. This diagnostic tool should be supported by effective improvement policies and 237  strategies deemed proportionate to the diagnosed problems. Testing the proposed SAF on case studies has proven that the SAF is not only a diagnostic tool but can also be employed to identify places and opportunities for future enhancements, and estimate the results of any future interventions in the UOS networks and also to propose suitable, case-specific enhancement strategies. However, the latter would require further investigation with greater details. Three main enhancement policies that have been defined in this research include: (1) adaptation (minimum intervention), (2) modification of the existing conditions and (3) infill (adding more UOS where necessary). A suitable and case-specific enhancement strategy should be selected with consideration of all restrictions, sensitivities, and special conditions within the context of the study area. Accordingly, to test the effectiveness of the SAF in proposing suitable enhancement strategies, an example of how the SAF can be used in enhancing the existing conditions of UOS networks has been provided in Chapter 4 (see Section 4.5). However, in this research, the main focus has been on the diagnostic aspects of the proposed SAF. Evaluating and testing the prognostic aspects of this tool should be further explored in future studies. 4) Lack of Priorities and Proper Phasing in Enhancements: After diagnosing the problems and selecting appropriate enhancement strategies, appropriate phasing in applying the enhancements is an essential step to improving the spatial aspects of UOS networks. Applying enhancements in random order can cause a loss of time, energy and money. Applying the SAF to the commercial case study of Tehran has proved that there are better and more rapid ways to enhance the existing conditions of UOS networks in this area. While the general policy of adding more green spaces to the UOS network is usually beneficial, this is not always the priority or the solution to all of the issues extant within the UOS networks. Prioritizing the appropriate enhancement strategies which are proportionate to the diagnosed problems will guarantee the best results in the shortest possible time. Based on a test conducted in the commercial case study in Tehran, the proposed SAF has successfully proven to be highly beneficial in defining the priorities in enhancement strategies.  238  5.2 The Potential Replicability of Findings 1) Considering the critical condition of UOS networks in Iranian cities, the proposed SAF is a suitable addition to the urban development plans of any cities in Iran with similar historical sensitivities. Applying the proposed SAF to other Iranian cities and in other parts of Tehran and Isfahan may very likely result in a better understanding of how UOS networks work and a more accurate diagnostic of the spatial problems within UOS networks.  2) Although the proposed SAF has been designed for application to such historic cities as Isfahan, with some modification this framework could be a useful guide for analyzing the spatial organization of UOS networks in other cities with different contexts. Modifications might be needed in the following sections of the proposed SAF (Figure3-3). The “history” layer in the context analysis specifically studies the transitional process of UOS through time. However, considering the special conditions within the context of future case studies, the context analysis portions might require some alterations or additional analytical layers. For example, where ecological sensitivities might exist in future cases, the context would first call for an analysis with consideration to all of the limitations that these sensitivities might cause when applying the enhancement strategies.  Further, future case studies might need some modifications in the UOS palette analysis. Although all UOS will eventually fit into one of the seven form-functional types that have been defined in this thesis (see Section 4.3 and Table 4-9), the subtypes defined under each main UOS type might alter when applied to diverse case studies.  Another likely change in the UOS palette might be in the UOS classification system based on the degree of “publicness”. As mentioned before, culture is highly dependent on the way in which people might define their desired degree of privacy in different urban spaces (Madanipour, 2003). This fact directly affects the degree of publicness of a specific UOS in different cultural contexts. In other words, a certain UOS might obtain different degrees of publicness in different cultural contexts. Accordingly, for future case studies, before going through the UOS palette analysis, it is essential to define the degree of publicness degree of all of the existing UOS within that case study. 239  Moreover, some particular indicators might be in greater or lesser priority in different contexts. Accordingly it is essential to change the order of some analytical layers in the proposed SAF. For example while proximity analysis might be a priority in one case study, it might need less emphasis in other cases. And finally, in different contexts, the benchmarks and norms used in the performance analysis of UOS networks might need recalibration and adaptation. For example, in general, an effective proximity to an UOS is within a 5 minute walking distance. While generally a 5 minute walking distance equals to approximately 450 metres, this number might decrease in cases with steep slopes. Therefore the circle (radius: 450 metres) which is indicator of the effective domain of UOS need to be resized accordingly. Applying the above mentioned modifications, the proposed SAF can be used as an effective tool to diagnose problems within the spatial organization of UOS networks in a variety of case studies from different contexts.   5.3 Limitations 1) One of the limitations of this research was the lack of sufficient literature concerning the UOS network and its attributes. Although there are numerous studies on UOS, when it comes to UOS as a network, only a limited number of valuable studies exist related to this topic. Therefore, in order to define the UOS network and its qualities, proper information and data have been borrowed from studies indirectly related to this topic. Some examples of these studies include street patterns and street networks (Marshall, 2005) (Girling & Kellett, 2005), and defining factors of complex systems (Erickson, 2006). In other words, the UOS network and its attributes have been defined in comparison to similar systems such as street networks and complex systems.  2) Choosing the most informative and comparable case studies presents limitations to this research. While testing the SAF on a single case study may not offer results or informative feedback sufficient for refining the framework, an excessive number of case studies from diverse contexts amplify the complexity of the analysis and the comparison of different 240  variables. However, after a consideration of several possible cases, two have been selected from the historic cores of Tehran and Isfahan. The two selected case studies demonstrate many similarities. Cases with similar contexts enable the researcher to focus with greater detail and accuracy on limited variables. However, this practice may also limit the replicability of the approach. 3) In order to analyze the spatial organization of UOS, a comprehensive list of indicators has been developed as a supplement to the proposed SAF. The main issue in working with a variety of indicators and metrics was that each of them only functioned on a certain scale, from the large or city scale through the neighbourhood scale and to the block scale. Furthermore, some aspects of UOS can be studied more readily at certain scales than at others.    For example, while a population density can be analyzed at the city scale, other indicators such as network connectivity or network complexity are only meaningfully applicable to smaller-scale study areas. Therefore, to solve this issue, the research approach combines analyses at two scales. As Gehl explains in his book Cities for People, “… urban design and city planning can be described as work involving several very different levels of scale (2010, p. 195).” Accordingly, for analyzing other attributes of UOS networks which require working on a smaller scale, three neighbourhood scale case studies have been defined within the case studies of Tehran and Isfahan. 4) The proposed SAF specifically focuses on the spatial attributes of UOS networks. However, there might be equally important qualitative and experiential factors that affect the performance of the UOS network. For example, a well-designed multi-functional UOS might set a larger effective domain and therefore attract visitors from a farther distances compared to other types of UOS. In other words, the program and design of an UOS might affect performance of the UOS network and eventually affect the result of the analysis. However, considering the main scope of this research which is on the spatial aspects of UOS networks, other qualitative, experiential and contextual factors associated with individual UOS have not been a part of the analysis which is one of the limitations of the proposed analytical method.  241  5) Another limitation in this research was the lack of proper access to updated documents and accurate data for each case study. Discrepancies existed between the numbers and statistics recorded by publicly available data found in unlike sources. Such accumulated differences eventually decreased the accuracy of the analysis. Also, since the boundaries of each case were defined specifically for this research, some metrics such as population density and day-time population required recalculating for the specific study area. Differences in available data, on one hand, and changing numbers of day-time populations, on the other, complicated and thus made the recalculations less accurate as compared to the actual statistics. Nevertheless, effort was made to calculate these metrics as accurately as possible. To do so, after comparing the available sources, the most realistic reports were selected as input data. Also, a spreadsheet-based calculator has been used to recalculate the metrics for each case study using selected input data.  5.4 Possible Future Research Directions Focusing on the spatial aspects of UOS networks, specifically in cases with historical sensitivities, opens the door to a variety of possible future studies and opportunities. Some of the future research opportunities are as follows: 1) The last evaluation question that has been posed in this thesis (see Section 3.1) has the potential of becoming an original topic of future research. This question specifically looks at the suitability of any proposed changes in the UOS system or network. After diagnosing the problems within the UOS network, proper enhancement strategies can be defined for different conditions. However, prior to applying any enhancement strategies to these issues, the following question first needs to be resolved: Is modifying the UOS deemed suitable? This question aims to estimate the opportunities and constraints extant in intervening, modifying and enhancing the existing conditions of UOS. Changes and decisions should always be formulated in consideration of the special conditions and context of the site and UOS. In order to prevent further damage, suitability studies should be conducted before implementing any decisions or modifications. 242  2) The proposed SAF has the potential of being translated into an analytical computer software program. All indicators and metrics that have been defined in this research to support the proposed SAF can be used as modifiable and measurable variables. This software program can be used as a convenient, fast and effective tool in analyzing the spatial organization of many more UOS networks in varied cities worldwide. The fast application of the analytical computer software program on many case studies might result in many data points and benchmarks that would fill some of the research gaps extant in the norms and standards associated with UOS network attributes.  A base map, data related to population and land-use, and existing UOS of different types and subtypes will form portions of the Input data for this software. The output data will include: population density, land-use proportions, UOS-type fractions evaluations of the UOS diversity and morphological taxonomy, proximity to UOS, and evaluations of the networks’ connectivity, complexity, and permeability. 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