A perceptual evaluation of urban space using GIS based 3d volumetric visibility analysis

3D indices show significant differences between a and b ...128 Table 6 Descriptive statistics of spatial and temporal perceptions from six locations ...157 Table 7 Descriptive statistics

Dr Phillip Bay for his inputs on human perception-cognition theories and research methodology And to other academic, administrative, and technical staffs of NUS Department

of Architecture, and also my CASA colleagues who supported my research in many ways To Prof Michael Batty from University College London for his inputs on GIS and visibility analysis To my spiritual family in City Harvest Church, for their love, prayer, and supports

To my parents and my brothers for loving and supporting me every step of the way To my dearest wife Lusi for her love, support, patience, understanding, and sacrifices And my final and utmost gratitude and dedication, to my Lord and my God Jesus Christ, for His love, grace, guidance, blessings, provisions, and to whom all glory is due

TABLE OF CONTENTS

ACKNOWLEDGEMENTS 1

TABLE OF CONTENTS 2

SUMMARY 5

LIST OF TABLES 7

LIST OF FIGURES 9

LIST OF SYMBOLS 13

LIST OF RELATED PUBLICATIONS 17

CHAPTER 1 INTRODUCTION 18

1.1 BACKGROUNDANDMOTIVATION 18

1.1.1 CHALLENGES OF URBAN DESIGN PROCESS 18

1.1.2 VISIBILITY ANALYSIS 19

1.1.3 INFORMATION SYSTEMS FOR URBAN DESIGN 20

1.1.4 GEOGRAPHIC INFORMATION SYSTEMS 21

1.1.5 3D GIS AND 3D URBAN MODELS 25

1.2 IDENTIFICATIONOFKNOWLEDGEGAP 27

1.3 OBJECTIVESANDPRIORITIES 28

1.4 RESEARCHQUESTIONSANDHYPOTHESES 29

1.5 STRUCTUREOFDISSERTATION 32

1.6 DEFINITIONSREFLECTINGMETHODOLOGY 33

1.7 SCOPEANDLIMITATIONS 37

1.8 IMPORTANCEANDPOTENTIALCONTRIBUTION 38

CHAPTER 2 VISIBILITY ANALYSIS AND THREE-DIMENSIONAL PERCEPTIONS OF URBAN SPACE 41 2.1 INTRODUCTIONTOREVIEWSOFVISIBILITYANALYSESAND3DPERCEPTIONS OFURBANSPACE 41

2.2 HUMANTHREE-DIMENSIONALPERCEPTIONSOFURBANSPACE 42

2.2.1 DISCUSSION ON SPATIAL PERCEPTIONS OF URBAN SPACE 43

2.2.2 DISCUSSIONS OF TEMPORAL PERCEPTIONS OF URBAN SPACE 48

2.3 ‘VISIBILITY’ANDVISIBILITYANALYSIS 50

2.3.1 MEANINGS OF ‘VISIBILITY’ 50

2.3.2 VISIBILITY ANALYSIS AND HUMAN PERCEPTION AND COGNITION 52

2.4 REVIEWANDCRITIQUEOFPRECEDING2DVISIBILITYANALYSES 53

2.4.1 NON-COMPUTATIONAL VISIBILITY ANALYSIS 53

2.4.2 PLANAR VISIBILITY ANALYSIS 54

2.4.3 PLANAR VISIBILITY ANALYSIS AND SEQUENTIAL-TEMPORAL PERCEPTION 66 2.4.4 GIS AS PLATFORM FOR PLANAR VISIBILITY ANALYSIS 67

2.4.5 LIMITATION OF PLANAR VISIBILITY ANALYSIS IN PREDICTING SPATIAL AND TEMPORAL PERCEPTION 69

2.4.6 LIMITATION OF PLANAR VISIBILITY ANALYSIS IN COMPUTING AMBIENT OPTIC ARRAY 71

2.5 SPATIALANDTEMPORALINDICATORSOFURBANSPACE 71

2.5.1 DISCUSSIONS OF SPATIAL INDICATORS OF URBAN SPACE 71

2.5.2 DISCUSSIONS OF TEMPORAL INDICATORS OF URBAN SPACE 76

2.5.3 PRECEDING SHAPE INDICATORS 78

2.6 DISCUSSIONSOFPRECEDINGSPHERICALVISIBILITYANALYSES 81

2.6.1 SKY-ORIENTED, SURFACE-BASED 3D ANALYSES 82

2.6.2 SPACE-ORIENTED, VOLUME-BASED 3D ANALYSES 83

2.7 CONCLUSIONTOREVIEWSOFVISIBILITYANALYSESANDSPATIALAND TEMPORALPERCEPTIONS 85

CHAPTER 3 DEVELOPMENT OF GIS-BASED VIEWSPHERE ANALYST AND

VIEWSPHERE INDICES 88

3.1 INTRODUCTIONTOVIEWSPHEREDEVELOPMENT 88

3.2 CONCEPTUAL 89

3.3 VIEWSPHEREDEFINITION 91

3.4 MATHEMATICALDEVELOPMENT 92

3.4.1 INITIAL INPUTS TO CONSTRUCT LINE OF SIGHT 92

3.4.2 CONSTRUCTION OF LINE OF SIGHT 92

3.4.3 TRANSFORMATION THE LINE OF SIGHT TO VIEWSPHERE ARRAY 95

3.4.4 VOLUME OF SIGHT AS INDICATOR OF VISIBILITY 96

3.4.5 VIEWSPHERE OPERATION FOR 2D URBAN FORM INDICES 98

3.4.6 VIEWSPHERE INDEX (VSI) 99

3.4.7 VIEWSPHERE INDEX (VSI) AND SKY VIEW FACTOR (SVF) 104

3.5 COMPUTATIONALDEVELOPMENT 105

3.6 VIEWSPHEREANDOTHERSPHERICALANALYSES 108

3.7 METHODOLOGYFORVIEWSPHEREAPPLICATION 112

3.7.1 NON-SEQUENTIAL AND SEQUENTIAL-TEMPORAL VIEWSPHERE ANALYSIS 112 3.7.2 VIEWSPHERE’S GIS APPLICATION AND LIMITATIONS 114

3.7.3 VIEWSPHERE AND TRADITIONAL URBAN SPACE ANALYSIS TOOLS 117

3.8 TESTCASE:SINGAPOREMANAGEMENTUNIVERSITY 122

3.8.1 URBAN CONTEXT OF THE SINGAPORE TEST CASE 122

3.8.2 RESEARCH DESIGN AND GIS DATA REQUIREMENT 122

3.8.3 APPLYING 2D AND 3D INDICES 125

3.8.4 THE EFFECTIVENESS OF 2D AND 3D INDICES 126

3.8.5 PLANAR (A) AND VOLUMETRIC (VOS & VSI) ANALYSES ON SMU DESIGN PROPOSALS 128

3.9 CONCLUSIONTOVIEWSPHEREDEVELOPMENTANDTESTS 134

CHAPTER 4 VIEWSPHERE INDICES AS INDICATORS OF PERCEPTIONS OF URBAN SPACE 138 4.1 INTRODUCTIONTOVIEWSPHERE’SMEANINGSINVESTIGATION 138

4.2 HYPOTHETICALMEANINGSOFVIEWSPHEREINDICES 138

4.2.1 VIEWSPHERE INDICES AND SPATIAL PERCEPTIONS 138

4.2.2 VIEWSPHERE INDICES AND TEMPORAL PERCEPTION 142

4.3 RESEARCHMETHODOLOGY 145

4.3.1 PREDICTION PROCESS: FROM URBAN GEOMETRY TO PERCEPTION 145

4.3.2 RANDOM & GROUP SAMPLE SELECTION FOR EXPERIMENT 1 AND 2 146

4.3.3 QUESTIONNAIRE DESIGN 148

4.3.4 VIEWSPHERE ANALYSIS ON 3D MODELS 149

4.4 QUANTITATIVEVALIDATIONOFMEANINGS 150

4.4.1 BACKGROUND OF CASE STUDY: SINGAPORE’S DISTRICTS 150

4.4.2 STATISTICAL ANALYSIS OF LOCATIONAL FACTOR 156

4.4.3 STATISTICAL ANALYSIS OF SPATIAL PERCEPTION SURVEY 161

4.4.4 STATISTICAL ANALYSIS OF TEMPORAL PERCEPTION SURVEY 176

4.5 FROMSPATIALINDICATORSTOPERCEPTUALINDICES:REGRESSIONMODELS ANDCLASSIFICATIONS 184

4.5.1 DEVELOPMENT OF PERCEPTUAL INDICES FOR SPATIAL PERCEPTIONS 184

4.5.2 DEVELOPMENT OF 3D PERCEPTUAL INDICES FOR TEMPORAL PERCEPTIONS 192

4.6 CONCLUSIONSABOUTVIEWSPHERE’SMEANINGS 194

CHAPTER 5 APPLICATIONS OF VIEWSPHERE ANALYSIS AND INDICES IN URBAN DESIGN CASE STUDIES 199

5.1 INTRODUCTIONTOVIEWSPHERE’SAPPLICATIONSFORURBANDESIGN 199

5.2 APPLICATIONTOANALYSESPATIALPERCEPTIONSOFSINGAPOREDISTRICTS’ URBANSPACESANDSTREETS 200

5.2.1 CONTOUR PATTERN ANALYSIS BETWEEN DISTRICTS BASED ON VIEWSPHERE’S PERCEPTUAL CLASSIFICATION 201

5.2.2 SAMPLES OF URBAN SPACES AND STREETS AND THEIR PREDICTED SPATIAL PERCEPTIONS 209

5.2.3 URBAN SPACE AND STREET TYPOLOGY AND SPATIAL PERCEPTIONS 211

5.3 APPLICATIONTOANALYSEPERCEPTUALIMPACTSOFURBANDESIGN PROPOSALS 219

5.3.1 CONTOUR PATTERN ANALYSIS OF DESIGN PROPOSAL’S IMPACT ON EXISTING SITE 220

5.3.2 SEQUENTIAL-TEMPORAL PATTERN EVALUATION 226

5.4 APPLICATIONTOANALYSEPERCEPTUALIMPACTSOFPLANNINGSTRATEGIES 229 5.4.1 PERCEIVED DENSITY, VISIBILITY, AND SPATIAL OPENNESS METRICSs 231

5.4.2 BACKGROUND OF CASE STUDY: ARCHETYPES 232

5.4.3 GIS MODELLING OF ARCHETYPES 234

5.4.4 CONTOUR PATTERN ANALYSIS OF VIEWSPHERE INDICES 237

5.4.5 ANALYSIS ON PLANNED DENSITY (GROSS PLOT RATIO) AND PERCEIVED DENSITY (VSI MIN ) 237

5.4.6 ANALYSIS ON DENSITY, TYPOLOGY AND DAYLIGHT EXPOSURE (SVF) 241

5.4.7 ANALYSIS ON DENSITY, TYPOLOGY, AND VISIBILITY 242

5.5 APPLICATIONTOANALYSEPERCEPTUALIMPACTOFDENSITYAND TYPOLOGYVARIATIONS 245

5.5.1 BACKGROUND OF CASE STUDY: NEW DOWNTOWN AT MARINA BAY 245

5.5.2 GIS MODELLING OF A SINGLE BUILDING MORPHOLOGICAL VARIATIONS 247 5.5.3 STATISTICAL analysis of IMPACTS OF MORPHOLOGICAL VARIATIONS 249

5.5.4 CONTOUR PATTERN ANALYSIS OF IMPACTS OF MORPHOLOGICAL VARIATIONS ON VISIBILITY 251

5.5.5 CONTOUR PATTERN ANALYSIS OF IMPACTS OF MORPHOLOGICAL VARIATIONS on PERCEIVED DENSITY AND DAYLIGHT EXPOSURE 254

5.5.6 DISCUSSIONS ON THE RESULTS OF VIEWSPHERE ANALYSIS OF VISIBILITY, PERCEIVED DENSITY, AND DAYLIGHT EXPOSURE 256

5.6 CONCLUSIONSABOUTVIEWSPHERE’SAPPLICATIONSFORURBANDESIGN 259 5.6.1 ON APPLICATION FOR ANALYSING SPATIAL PERCEPTIONS OF SINGAPORE DISTRICTS’ URBAN SPACES AND STREETS 261

5.6.2 ON APPLICATION FOR ANALYSING PERCEPTUAL IMPACTS OF URBAN DESIGN PROPOSALS 262

5.6.3 ON BOTH APPLICATIONS FOR ANALYSING PERCEPTUAL IMPACTS OF DIFFERENT DENSITIES, TYPOLOGIES, AND MORPHOLOGICAL VARIATIONS 263

CHAPTER 6 CONCLUDING REMARKS AND LIMITATIONS 265

6.1 CONCLUDINGREMARKS 265

6.1.1 CONCLUDING REMARKS ON HYPOTHESES AND FINDINGS 265

6.1.2 CONCLUDING REMARKS ON URBAN SPACE EVALUATION AND TYPOLOGY 268 6.1.3 CONCLUDING REMARKS ON TECHNOLOGICAL CONTRIBUTION 271

6.2 LIMITATIONS 272

6.2.1 OF GIS-BASED 3D DATA STRUCTURE AND DIGITAL URBAN MODEL 272

6.2.2 OF VIEWSPHERE ANALYST 272

6.2.3 OF VIEWSPHERE INDICES 273

6.2.4 OF URBAN SPACE USERS’ SURVEY ON THEIR SPATIAL AND TEMPORAL PERCEPTIONS 274

6.3 POTENTIALFUTUREWORKS 275

BIBLIOGRAPHY 277

APPENDIX A 286

APPENDIX B 302

SUMMARY

A PERCEPTUAL EVALUATION OF URBAN SPACE USING GIS-BASED 3D

VOLUMETRIC VISIBILITY ANALYSIS

Simon Yanuar PUTRA Department of Architecture, National University of Singapore

Traditional GIS (Geographic Information System)-based visibility analysis has been conducted mainly two-dimensionally based on the concept isovist in architectural and urban space or the concept viewshed in terrain and landscape analysis Recent developments of non-GIS spherical analysis such as SOI, SVF, and Sky Opening, have inspired development of a GIS-based volumetric visibility analysis referred as the Viewsphere It was proposed in this project for measuring the quality of urban open space, by volumetric computation of ambient optic array, a concept originated from Gibson’s ecological perception theory As its predecessors the isovist and the viewshed analysis, the concept of viewsphere identifies visible and invisible parts of geometrical surfaces from vantage points Through its development, we can operate volumetric computation of visible urban space

Test cases were conducted in Singapore’s urban spaces, such as Singapore Management University site at Museum District, Orchard Road, CBD areas of Raffles Place and Tanjong Pagar, Rochor district, and Chinatown Viewsphere analyse the existing and proposed urban form in these cases with rapid geometric modelling and visibility computation, and the results are evaluated for understanding the potential impact of open space quality induced by urban geometry The visual volumes of pedestrian viewers along urban streets and public spaces are monitored and measured by urban space indicators quantitatively The results are then compared with the traditional isovist-based visual analysis Surveys of pedestrians’ spatial and temporal perceptions were conducted at above locations, and the results were then compared statistically with new volumetric urban space indicators Statistical relationships were established by correlation and regression analyses, and significant quadratic relationships were discovered between human perceptions and volumetric measurement of visible space The regression analyses then established predictive models to evaluate pedestrian spatial perceptions of any given 3D model of urban spaces With the consideration

of the third dimension of urban space, a volumetric visibility impact assessment for scale urban design and development will provide a more relevant result than planar

large-measurement and will provide better information about how urban quality can be achieved by the alternative urban forms and interventions

This research contributes in establishing new volumetric visibility analysis of urban geometrical space, in particular by the computational development of Viewsphere Analyst The theoretical contribution will bring a fundamental contribution in the realm of visibility analysis studies, from predominantly planar to volumetric methodology A theoretical stand is taken that urban space is not only an abstract, residual perception from its surfaces, but a volumetric entity by itself represented by its Viewsphere array This understanding is parallel with Gibson’s ambient optic array theory, as viewsphere array can be the spatial representation of collective ambient optic array in a specific setting (Gibson, 1986) The result

of this computational analysis, known as Volume of Sight “VoS” and ViewSphere Indices

(VSI), will contribute a set of quantitative perceptual indices derived from characteristics of urban geometrical space These volumetric indices provide three-dimensional alternatives for the existing planar indicators Unlike previous perceptual indices, Viewsphere is capable of analysing geometrically-irregular urban space, and is not limited to archetypal or ideal space, such as ‘infinite straight urban canyon’ The application of these indicators will contribute significantly to urban design and decision-making process, in the area of urban space design, urban climatic and thermal comfort, and urban planning Organizations and authorities which contribute to the shaping of urban environment will benefit from this methodological and technological contribution

LIST OF TABLES

Table 1 Perception of enclosure comparison with vertical angle ( ), D/H ratio, and VSI 74

Table 2 Statistical correlations (Linear R square and Pearson’s) between GPR ( p) and VSI 103

Table 3 Comparative analysis of 4 spherical-based analyses (Yang & Putra, 2003) 109

Table 4 Evaluation using 2D indices The results are identical between (a) and (b) 127

Table 5 Evaluation using 3D VSI 3D indices show significant differences between (a) and (b) .128

Table 6 Descriptive statistics of spatial and temporal perceptions from six locations 157

Table 7 Descriptive statistics (average) of selected visibility parameters from six locations 157

Table 8 Descriptive statistics (coefficient of variance) of selected visibility parameters from six locations 157

Table 9 Test of Homogeneity of Variances among Spatial Indices 159

Table 10 Parameters which may classify locations into distinctive groups 160

Table 11 Test of Homogeneity of Variances of spatial perceptions survey 162

Table 12 One-Way ANOVA of spatial perceptions survey and their location factor 162

Table 13 Correlations between six surveyed terms of spatial perceptions 164

Table 14 Correlations between visibility (in terms of quantity and distance) and indices 166

Table 15 Regression results investigating 2D and 3D indices and visibility (quantity and distance) 166

Table 16 Correlations and regressions between perceptions of enclosure (represented by openness) and indices 170

Table 17 Correlations and regressions between perceptions of enclosure (represented by spatial definition) and indices 172

Table 18 Correlations and regressions between perceptions of scale and indices 174

Table 19 Descriptives of temporal perception survey (combined experiment) 177

Table 20 Test of Homogeneity of Variances of temporal perception survey (combined) 177

Table 21 ANOVA of temporal perception survey (combined) 178

Table 22 Homogeneous Subset - Scheffe 178

Table 23 Correlations results investigating spatial perceptions (static indices) 179

Table 24 Regression results investigating static indices and sense of time 180

Table 25 Sense of Time, Coefficient of Variances of indices & their Pearson’s correlation 181 Table 26 Summary of correlation and regression between VSI and spatial perceptions 186

Table 27 Curved regression result between VoS and visibility (quantity and distance) 187

Table 28 Classification based on ranges of surveyed visibility quantity and predicted VoS (rounded to thousands m3) 188

Table 29 Classification based on ranges of surveyed visibility distance and predicted VoS (rounded to thousands m3) 188

Table 30 Curved regression results between VoS and variances of enclosure 189

Table 31 Classification based on ranges of surveyed openness-enclosure and predicted VoS (rounded to thousands m3) 190

Table 32 Classification based on ranges of surveyed spatially defined-undefined and predicted VoS (rounded to thousands m3) 190

Table 33 Curved regression results between VoS and perception of scale 191

Table 34 Classification based on ranges of surveyed perceived scale and predicted VoS (rounded to thousands m3) 192

Table 35 Logarithmic regression for predicting sense of time experienced by changes of VSI min 193

Table 36 Urban space samples and their predicted spatial perceptions 209

Table 37 Classification of urban space and street typologies based on VoS values 212

Table 38 Approximated ‘sense of time’ L based on coefficient of variances of VSI min 229

Table 39 Building coverage ( b) and plot ratio ( p) for Type A, B and C 235

Table 40 Statistical correlations (Linear R square and Pearson’s) between GPR ( p) and VSI 239

Table 41 Morphological variations and their impact to density, visibility & daylight 250

Table 42 Survey results (Experiment 1) from Orchard path 1 Code descriptions are in questionnaire form (Figure 103) 303

Table 43 Survey results (Experiment 1) from Orchard path 2 Code descriptions are in questionnaire form (Figure 103) 304

Table 44 Survey results (Experiment 1) from Rochor path Code descriptions are in questionnaire form (Figure 103) 305

Table 45 Survey results (Experiment 1) from CBD path Code descriptions are in questionnaire form (Figure 103) 306

Table 46 Survey results (Experiment 1) from Chinatown path Code descriptions are in questionnaire form (Figure 103) 306

Table 47 Survey results (Experiment 2) from Tanjong Pagar path 307

Table 48 Survey results (Experiment 2) from CBD path 308

Table 49 Survey results (Experiment 2) from Chinatown path 309

Table 50 Survey results (Experiment 2) from Orchard path 311

Table 51 Survey results (Experiment 2) from Rochor path 313

LIST OF FIGURES

Figure 1 Ambient optic array from a person's visual system (Gibson, 1986) 46

Figure 2 Line of Sight (LoS) studies on visibility and archaeological significance (Fisher, 1995) 56

Figure 3 Isovist application for deconstructing architectural space (Hanson, 1994) 57

Figure 4 Isovist application for Virtual Tate Gallery, London (Batty et al., 1998) 58

Figure 5 ‘Isovist graph’ or ‘visibility graph’ (Turner et al., 2001) 59

Figure 6 Isovist application for public safety in urban space (Desyllas, Connoly & Hebbert, 2003) 60

Figure 7 Total viewshed analysis on DEM for determining different preferred paths (Lee & Stucky, 1998) 61

Figure 8 Visual quality mapping of skylines by overlaying viewshed analysis and human visual ergonomics and psychology parameter analysis (He &Tsou, 2002) 62

Figure 9 Analysis of visual magnitude and visual change using viewshed (Llobera, 2003) 64

Figure 10 (a.) Sequential temporal analysis through series of vantage points simulating a pedestrian path and (b.) non-sequential temporal analysis from a single vantage point 67

Figure 11 Proportional indicator applicable only for ideal situation of ‘infinite straight urban canyon’ (Oke, 1987) 73

Figure 12 Thiel’s Anatomy of Space (1996) 76

Figure 13 Sky Opening analysis’ double projection (Teller, 2003) and Spatial Openness Index (SOI) (Fisher-Gewirtzman, 2003) 83

Figure 14 Viewsphere analysis in operation; the radiating ‘rays’ are the viewsphere array 91

Figure 15 Line of Sight LoS ij components: O i , T ij , and Q ij 94

Figure 16 Invisible and Visible parts of line of sight LoS ij 94

Figure 17 3D transformation from Line of Sight LoS ij to Viewsphere array VS ij 95

Figure 18 Distribution of ambient optic array and invisible parts in Viewsphere analysis 100

Figure 19 ViewSphere Indices (VSI): VSI, VSImax, VSIave, and VSImin from hemispheres .101

Figure 20 Viewsphere array (a.) captured from urban structure and (b.) representation only .105

Figure 21 Viewsphere Analyst’s Graphic User Interface (GUI) in ArcGISTM ArcMapTM 2D view 106

Figure 22 Viewsphere Analyst GUI (lower-left) in ArcGISTM ArcSceneTM Test case using large GIS-based extruded TIN model of Singapore CBD 106

Figure 23 Viewsphere Analyst in operation, projecting Viewsphere arrays towards 2 radial directions 107

Figure 24 Viewsphere Analyst results shown in message box 107

Figure 25 SOI visibility computation (Fisher-Gewirtzman et al., 2005), which includes component A and B, unlike VoS which only refers to component B 110

Figure 26 SOI computational tool and results on 3D rectilinear raster model of Trieste (Fisher-Gewirtzman et al., 2005) 111

Figure 27 (a) Grid vantage sample points for raster mapping and (b) sequential vantage sample points (1-10) along pedestrian route 113

Figure 28 Experiment of Viewsphere’s accuracy on Raffles Place urban space: (a) Grid vantage sample points for raster mapping, (b) VoS with r n = 500, N 0 = 120, and E i = 98.91%, (c) VoS with r n = 1000 and N 0 = 120, and E i = 98.96%, (d) VoS with r n = 1000 and N 0 = 180, and E i = 98.96% 116

Figure 29 To which direction should Distance-Height angle and proportion be applied from this position in this urban case? 118

Figure 30 Viewsphere overcomes the limitation with a single VSI unit computation that covers all possible directions irrespective of irregularity of surrounding geometry 118

Figure 31 (a) Urban reality and (b) its figure-ground abstraction, compared with mapping of

(c) VoS (implying visibility) and (d) VSI (implying perceived density) 120

Figure 32 “No Action” existing site (top) and two proposals (A, B, C, D) of SMU New Campus 123

Figure 33 “No Action” existing site (top) and two proposals A and B of SMU New Campus; all with 10 observation points along Bras Basah Road 124

Figure 34 3D models of Proposal NA with (a) H1: original building height (1x); and (b) H2: double building height (2x) 127

Figure 35 Comparison of (a) VoS_H1 and VoS_H2 and (b) VSI_H1 and VSI_H2 127

Figure 36 Comparisons of the visible area A and the volume of sight VoS of (a) Proposal NA, (b) Proposal A, and (c) Proposal B, with legend of A and VoS value ranges (d) 130

Figure 37 Contour of Viewsphere Index Minimum VSI min for the proposals NA (top), A (left) and B (right) 132

Figure 38 Charts of 3D evaluations of 3 proposals for SMU campus: (a) Volume of Sight VoS and (b) Viewsphere Index VSI 133

Figure 39 Degree of explicitness and specific volume zones relationship with degree of enclosure (Thiel, 1996) .141

Figure 40 Research Methodology from urban geometrical form to Viewsphere Indices, then to prediction of spatial and temporal perceptions 146

Figure 41 Plan view of Orchard Path 1 & 2 152

Figure 42 Photos of Orchard Path 1 (a) & 2 (b) 152

Figure 43 Plan view of Rochor Path 3 153

Figure 44 Photos along Rochor Path 3: (a) inside Bugis village, and (b) in front of Burlington Square 153

Figure 45 Plan view of CBD Path 4 and Tanjong Pagar Path 6 154

Figure 46 Photos of CBD’s Path 4 (a) & Tanjong Pagar’s Path 6 (b) 154

Figure 47 Plan view of Chinatown Path 5 155

Figure 48 Photos of Chinatown: (a) Path 5 starting point & (6) aerial view of Chinatown district 155

Figure 49 3D geometric models of surveyed locations with their assigned paths, each modelled by 22 sequential observation points Paths of (a) Orchard Path 1 and 2, (b) Rochor, (c) CBD, and (d) Chinatown 156

Figure 50 Curve estimations from regression analysis of VoS (m3) in relation with perception of visibility (quantity and distance) 168

Figure 51 Curve estimations from regression analysis of VoS in relation with perception of openness-enclosure 171

Figure 52 Curve estimations from regression analysis of VoS and VSI max in relation with perception of enclosure (spatial definition) 173

Figure 53 Curve estimations from regression analysis of VoS in relation with perception of scale 175

Figure 54 Quadratic and logarithmic trend-lines of relationships between sense of time and changes of VSI min and ave 184

Figure 55 Visibility (quantity) contour pattern maps of districts (a) Orchard, (b) Rochor, (c) CBD, and (d) Chinatown 203

Figure 56 Visibility (distance) contour pattern maps of districts (a) Orchard, (b) Rochor, (c) CBD, and (d) Chinatown 204

Figure 57 Enclosure (openness) contour pattern maps of districts (a) Orchard, (b) Rochor, (c) CBD, and (d) Chinatown 205

Figure 58 Enclosure (spatial definition) contour pattern maps of districts (a) Orchard, (b) Rochor, (c) CBD, and (d) Chinatown 207

Figure 59 Scale contour pattern maps of districts (a) Orchard, (b) Rochor, (c) CBD, and (d) Chinatown 208

Figure 60 Samples of urban space of each districts and their locations: (a) Sample A Orchard) (b) Sample B CBD, (c) Sample C Rochor, and (d) Sample D Chinatown 210

Figure 62 VoS range (m3) and urban space and street typologies with colour representation216

Figure 63 Interpretation of urban space and street typologies in Orchard based on VoS values

216

Figure 64 Interpretation of urban space and street typologies in CBD based on VoS values 217

Figure 65 Interpretation of urban space and street typologies in case studies based on extreme

low and high classes of VoS values, (a) Orchard, (b) Rochor, (c) CBD, and (d)

Chinatown 218 Figure 66 Contour pattern maps of visibility (quantity) for Proposal NA (top), Proposal A (left) and Proposal B (right) 222 Figure 67 Contour pattern maps of visibility (distance) for Proposal NA (top), Proposal A (left) and Proposal B (right) 222 Figure 68 Contour pattern maps of enclosure (openness) for Proposal NA (top), Proposal A (left) and Proposal B (right) 223 Figure 69 Contour pattern maps of enclosure (spatial definition) for Proposal NA (top), Proposal A (left) and Proposal B (right) 224 Figure 70 Contour pattern maps of scale perception for Proposal NA (top), Proposal A (left) and Proposal B (right) 225 Figure 71 Contour pattern maps of space typologies for Proposal NA (top), Proposal A (left) and Proposal B (right) 226

Figure 72 Charts of Volume of Sight VoS sequence of 3 proposals for SMU campus: (EX =

proposal NA; KN = proposal A; AK = proposal B) 227

Figure 73 Charts of VSI min sequence of 3 proposals for SMU campus: (EX = proposal NA;

KN = proposal A; AK = proposal B) 228 Figure 74 Three archetypes of urban form in unit cells, based on Martin and March (1972) (black colour represents built area) 233 Figure 75 Numerical form of three archetypes 234 Figure 76 Raster surface model of derivative models of Type A 236 Figure 77 GPR (or p) distribution in variation matrix, which is identical for all typologies (A,

B, C) 238 Figure 78 Chart of Linear correlations between GPR ( p ) & VSImin 239 Figure 79 Perceived Density distribution (VSI min) for Typology A, B, and C; Y axis =

Building Height, X axis = Building Coverage 240

Figure 80 Daylight distribution (SVF) for Typology A, B, and C; Y axis = Building Height, X

axis = Building Coverage 242

Figure 81 Visibility distribution in quantity (VoS) for Typology A, B, and C; Y axis =

Building Height, X axis = Building Coverage 243

Figure 82 Visibility distribution in distance (VoS) for Typology A, B, and C; Y axis =

Building Height, X axis = Building Coverage 243 Figure 83 Gross Plot Ratio (GPR) distribution of New Downtown @ Marina Bay (min 8, max 25) 246 Figure 84 (a) Experimental building (Bldg A) and (b) artist’s impression of New Downtown 247 Figure 85 Building coverage variations inspired by Fresnel diagram, and resulting height variations 248 Figure 86 Analysis on (a) existing site using (b) Viewsphere analysis from a sample point 249

Figure 87 Impact of morphological variations on visibility (a) VoS and (b) VSI 251 Figure 88 Contour pattern map of visibility in quantity (VoS) with 100% building coverage

morphological variation 252

Figure 89 Contour pattern map of visibility in quantity (VoS) with 10% building coverage

morphological variation 252

Figure 90 Impact or sensitivity map of visibility in quantity (VoS) with between 100% and

10% building coverage morphological variations 253 Figure 91 Impact of morphological variations on (a) perceived density and (b) daylight exposure 254

Figure 92 Impact (or sensitivity) mapping of perceived density (VSImin) 255

Figure 93 Impact (or sensitivity) mapping of daylight exposure (SVF) 256

Figure 94 Graphic User Interface (GUI) for Viewsphere Analyst 288

Figure 95 Computational objects of Line of Sight (LoS) 291

Figure 96 Visible and invisible segments (volumes) 294

Figure 97 Computational objects of Line of Sight (LoS)’s visible and invisible segments 294

Figure 98 Survey Questionnaire Form for Experiment 1 (June 2004) 302

Figure 99 Survey Questionnaire Form for Experiment 2 (August 2004) 302

LIST OF SYMBOLS

A b Area of sample building (m2)

a i area of visible 2D shape observable from point O i, either generated from

isovist/viewshed or viewsphere array VS ij (square metre)

A ij or a ij two-dimensional (area) component of ‘viewsphere array’ VS ij ; A ij represents

the area object, and a ij represents the area index (square metre)

A s Area of sample site (parcel) (m2)

coef(VSI min ) coefficient of variance of VSI min, which is equals to standard deviation of

VSI min divided by mean of VSI min

D i fractal dimension of visible 2D shape observable from point O i

(dimensionless)

E i ‘visual edge’, index proportion between number of Q ij occurred and number

of T ij inside the 2 rotation with O i as the centre (dimensionless or percentage)

El i ellipticity of visible 2D shape observable from point O i (dimensionless) GSI i convolution (grain shape index) of visible 2D shape observable from point O i

(dimensionless)

H building height (m)

h height of storeys (m)

HS iave volume of virtual 3D hemisphere (or ‘half-sphere’) generated from O i with

radius r ave (cubic metre)

HS imax volume of virtual 3D hemisphere (or ‘half-sphere’) generated from O i with

radius r max (cubic metre)

HS imin volume of virtual 3D hemisphere (or ‘half-sphere’) generated from O i with

radius r min (cubic metre)

HS in volume of virtual 3D hemisphere (or ‘half-sphere’) generated from O i with

radius r n (cubic metre)

j q qth azimuthal angle between O i and T ij (radial or degree)

K i compactness of visible 2D shape observable from point O i (dimensionless)

l Length of sample building (m)

L the length of the journey (metres)

LoS ij ‘line of sights’ from O i to all T ij , indicating visibility status of T ij and the

location of obstruction point Q ij (computational object, not an index)

n number of storeys (dimensionless)

N 0 user-assigned number of T ij and thus LoS ij inside the 2 rotation with O i as

the centre (integer, dimensionless)

N obst number of Q ij occurred inside the 2 rotation with O i as the centre (integer,

p i perimeter of visible 2D shape observable from point O i, either generated from

isovist/viewshed or viewsphere array VS ij (metre)

Q ij obstruction point, where visible segment from O i ends and invisible segment

starts, if T ij is not visible, otherwise Q ij is null (x, y, z coordinate)

rave average visible distance, or the mean (average) value of r ij (metre)

r ij two-dimensional (line) component of ‘viewsphere array’ VS ij ; R ij represents

the line object, and r ij represents the radius index (metre)

rmax farthest visible distance, or the highest value of r ij (metre)

rmin nearest visible distance, or the lowest value of r ij (metre)

r n user-assigned radial distance from O i to all T ij (radial or degree)

Scale quantified index of respondent’s perceived scale (1 to 7, dimensionless) Spacedef quantified index of respondent’s perceived spatial definition (1 to 7,

dimensionless)

Spacious quantified index of respondent’s perceived spaciousness (1 to 7,

dimensionless)

T real ‘clock time’ (in this dissertation with average walking speed of 0.0179

minutes or 1.079 second per metre, or approximately 3.34 km/hour)

T ij target points with certain distance r n from O i towards all possible directions

of azimuthal angle j (x, y, z coordinate)

T L real ‘clock time’ spent after walking L metre of the journey

VisiDist quantified index of respondent’s perceived distance of visibility (1 to 7,

dimensionless)

VisiQuan quantified index of respondent’s perceived quantity of visibility (1 to 7,

dimensionless)

VoS ave sum of all ‘volume of sight’ VoS ij visible from O i but not including volumes

farther than rave (cubic metre)

VoS ij ‘volume of sight’ or visible volume of space from O i to each Q ij, a

three-dimensional (volume) expression of ‘viewsphere array’ VS ij, and a volumetric index (cubic metre)

VoS max sum of all ‘volume of sight’ VoS ij visible from O i but not including volumes

farther than rmax (cubic metre)

VoS min sum of all ‘volume of sight’ VoS ij visible from O i but not including volumes

farther than rmin (cubic metre)

VSI ‘viewsphere index’ or index proportion of VoS i and HS i(n) (0 to 1,

dimensionless)

VSI ave ‘viewsphere index average’ or index proportion of VoS ave and HS i(ave) (0 to 1,

dimensionless)

VS ij ‘viewsphere array’ from O i to all Q ij, representing the 3D object, form, and

graphic of ‘ambient optic array’; measure-able as 2D (r ij , a ij, ij ) and 3D (VoS ij) (computational object, not an index)

VSI max ‘viewsphere index maximum’ or index proportion of VoS max and HS i(max) (0 to

1, dimensionless)

VSI min ‘viewsphere index minimum’ or index proportion of VoS min and HS i(min) (0 to

1, dimensionless)

Z i exhaustive spatial reference of raster dataset collected from the 3D

geometrical model of urban space (x, y, z coordinate)

n user-assigned azimuthal angle which is equal to 2 divided by N user (radial or

degree)

ave average angle of elevation, or the mean (average) value of ij (radial or

degree)

ij two-dimensional (vertical angle) component of ‘viewsphere array’ VS ij;

between O i to ‘each’ Q ij (radial or degree)

max maximum angle of elevation, or the highest value of ij (radial or degree)

min minimum angle of elevation, or the lowest value of ij (radial or degree)

n user-assigned maximum angle of elevation (between O i and Q ij) permitted in

the calculation (radial or degree) the difference between ‘sense of time’ and real ‘clock time’ for every metre

of the journey (minutes or seconds)

L the difference between ‘sense of time’ and real ‘clock time’ after L metre of

the journey (minutes or seconds)

‘sense of time’ or remembered duration of passed time for every metre of the journey (minutes or seconds)

L ‘sense of time’ or remembered duration of passed time after L metre of the

journey (minutes or seconds)

b building coverage ratio (dimensionless)

p plot ratio (dimensionless)

SVF (0 to 1, dimensionless)

ave mean SVF of open space in study area (0 to 1, dimensionless)

LIST OF RELATED PUBLICATIONS

The following are my publications related to the development of this dissertation:

Yang, P Putra, S.Y., Li, W., 2006 Viewsphere: a GIS-based 3D visibility analysis for urban design evaluation Environment and Planning B: Planning and Design Accepted

for publication

Putra, S.Y., Peresthu, A., Yang, P., 2006 Design Support System for Sustainable Urban Development: Integrated framework of urban modeling, spatial-environmental analysis and decision-making In Proceedings of the 12th Annual International Sustainable

Development Research Conference 2006 in Hong Kong

Putra, S.Y., Yang, P., 2006 Volumetric Indices of 3D-GIS Urban Model and Spatial Perceptions In Proceedings of the Asia GIS 2006 International Conference in Malaysia Putra, S.Y., Yang, P., 2005 Analysing mental geography of residential environment in Singapore using GIS-based 3D visibility analysis In Proceedings of the International

Conference: Doing, thinking, feeling home: the mental geography of residential environments in Delft, The Netherlands

Putra, S.Y., Yang, P., Li, W.,2005 GIS-based 3D visibility analysis for high-density urban living environment In Proceedings of 5th China Urban Housing Conference in Hong Kong

Yang, P., Putra, S.Y., Li, W., 2005 Impacts of density and typology on design strategies and perceptual quality of urban space In Proceedings of Map Asia 2005 Conference in

Jakarta

Yang, P., Putra, S.Y., Heng, C.K 2004 Computing the “Sense of Time” in Singapore urban streets In Proceedings of the 3rd Great Asian Streets Symposium in Singapore

Li, W., Putra, S.Y., Li, Z., Yang, P 2004 Climatic Performance of 3D Urban Geometry:

A GIS-based analysis tool for climatic evaluation of Singapore downtown space In

Proceedings of the 6th Biennial Conference of the International Urban Planning and Environmental Association in Louisville, KY, USA, 2004

Li, W., Putra, S.Y., Li, Z., Yang, P 2004 GIS Analysis for the Climatic Evaluation of 3D Urban Geometry – The development of GIS analysis tools for Sky View Factor In

Proceedings of GIS in Developing Countries (GISDECO) 2004 Conference in Johor Bahru

Yang, P., Putra, S.Y., 2003 GIS Analysis for Urban Design and Redevelopment, Two cases in Singapore, In Proceedings of 8th Conference on Computers in Urban Planning and Urban Management (CUPUM 2002) in Sendai

CHAPTER 1 INTRODUCTION

1.1 BACKGROUND AND MOTIVATION

1.1.1 CHALLENGES OF URBAN DESIGN PROCESS

Urban designers are obliged to give more emphasis on the betterment of physical and visual expression of the society Desirable visual expression can be achieved from different visual aspects of urban form, such as its architectural and landscape designs To achieve these visual expressions, urban designers utilize planning and design instruments appropriate for city-wide scale, such as density and typology, so that they are able to balance different needs of society while maintaining visual quality of public open spaces

Architects and urban designers have been known to make their design decisions subjectively, based on intuition and limited understanding of up-to-date user perception of the complex urban environment Large-scale urban design and development processes usually lack systematic analysis of the urban form and its implications to visual, climatic, traffic and other environmental aspects of the urban physical configurations The lack of objective understanding and analysis of urban form and urban space may result in wrong-scale urban project, unpredictable environmental impact, or simply undesirable design of public space The origin of these problems may lie on the incoherent understanding between design needs and solutions, between user-perceivers and designers of urban public space What perceived as ‘dense’ or ‘open’ by designers may not be perceived similarly or at the same degree by the public users This difference must be bridged by using a ‘common language’ or

‘common standard’, by ‘standardizing’ both perceptions based on a reliable quantitative indicator of human perceptions of urban space With the quantitative indicator as the language

of mediation and negotiation, designers can understand user’s spatial and visual preference,

‘density’ or ‘openness’ Designers can further communicate their design ‘answers’ back to the users to better satisfy their perceptual preferences

1.1.2 VISIBILITY ANALYSIS

A reliable analytical tool that can objectively assess urban spaces three-dimensionally based

on explicitly-understandable indicators is critically required, which brought me to the idea of

a computational 3D volumetric visibility analysis The tool should be able to generate the desired quantitative indicators (indices), and thus contribute to the greater aim of developing computational urban design support system One of my objectives is to achieve more user-oriented and participatory urban design process for stakeholders involved in the design process, and to promote user-friendlier urban environment for the urban masses

The perception of term “design” in urban design itself generally relates to visual aesthetics of shaping urban space and urban form Therefore, in urban design it is fundamental and natural to emphasis on visual aspects, such as visibility of urban space Vast studies of visual aspects in urban design have been accomplished in the past century without any support from digital information technology, until recently Considering the need for advancing urban design forward in digital realm, this dissertation will bring forth the implementation of new ideas, theories and methodology of visibility analysis into current urban design practice

Analyzing visibility aspect of three-dimensional urban form is the focus of this thesis From environmental psychology point-of-view, visual quality is a very strong indicator of order and quality of life in urban life The presence of order, whether it is social, economical,

or political order manifested in physical order, can be perceived as better quality of life in the society The quality of life may include safer, healthier, cleaner living and working environment, and even better economic situation, better infrastructure, and better quality of life All these are the aspirations of every individual, society, city, and nation, which have already brought us naturally to competitions at all these levels, i.e global city competitiveness

1.1.3 INFORMATION SYSTEMS FOR URBAN DESIGN

The research started from some fundamental questions raised in urban design practices and studio teaching: How can we make the visual and other environmental effects of urban form more predictable? What kind of emerging digital tools and technologies could help urban designers make better design decisions through spatial analysis?

These two questions can be understood in the context of “computable city”, a new concept proposed by Michael Batty One of the significant aspects of the “computable city” is that computers and information technologies have changed the ways and methods we understand and analyze the cities and have generated enormous opportunities for planning the cities (Batty, 1997) The new digital ways of thinking about non-digital city are influenced by the reception of urban data in real time, precise and fine-grain spatial analysis and more appropriate technologies for visualization, spatial representations and urban simulation The capability for urban designers to analyze urban space digitally has created tremendous opportunities to rethink the design processes and procedures It causes the need to develop a new urban design approach with grounded spatial analysis, digital visualization and computation, which may be referred as knowledge-based urban design approach

Urban design process today requires knowledge of people and the environment, and pure imagination (Lang 1994, 132) Knowledge itself, as defined by Robert Laurini, is

“derived information about information, data items organized and processed to convey understanding, experience, accumulated learning and expertise” (Laurini, 2001) Urban design process requires vast information of present and past indicators to generate design-related knowledge sufficient for decision-making for the future Thus urban design process must adopt supporting tools, such as computer-based information systems, to manage different types of urban design related information

This process of adoption started in the mid 1950s in Canada and US, once the digital computer was developed half century ago, applications in urban planning and management

started with population and transportation data processing By the late 1960s in US, urban data management systems were being widely implemented by public agencies for strategic urban planning and design functions Since the development of personal computers (PC) in 1970s and 1980s, a much more personal computing style in which graphical display of urban data now provides the focus of design computing studies With the latest rapid advances in computer aided design (CAD) in 1990s, especially in the realm of three-dimensional visualization, designing urban form using computer-based technology is now fast, easy and reliable

These advances have encouraged a ‘trend’ for building computer-generated 3D models of cities using various applications However, these CAD-generated ‘3D cities’ are limited in supporting design process since they are mostly just 3D graphical model of the city, without any link or storage of its strategic, demographic, socio-economy, and infrastructure information, and therefore they are less useful for analysing the city’s spatial quality This inherent limitation of CAD-generated 3D urban models is caused by lack of information system functionality of CAD technology Researches should be conducted to overcome this limitation, by integrating urban information systems, such as Geographic Information Systems (GIS), with 3D model of cities

1.1.4 GEOGRAPHIC INFORMATION SYSTEMS

In the realm of spatial information system, GIS is currently used extensively in planning and geography, but not as much for knowledge-based urban design (Brail & Klosterman 2001, Han & Kim 1990, Laurini 2001) The scope of GIS was apparently halted

at the spatial scale of urban planning at least until recent years, for it is obvious that GIS has a drawback in representing complex geometry objects Twenty years ago, the early beginnings

of GIS were as an adjunct to strategic planning, particularly in landscape and resources management In the last 10 years, the emphasis has shifted to graphic display, the representation of spatial data, and its manipulation in quite straightforward ways In terms of

urban design processes, to date there has been very little emphasis on formal visibility analysis, simulation and modelling (Batty, 2001), but emphasizing more on 3D GIS, visualization, and spatial information and modelling theories

When originally invented, GIS was never intended for design-oriented purpose, and thus it’s understandable that urban design practice has not been able to utilize GIS intensively, not until recent development on design and editing tools Thus, one of the current hindrances

to the slow development of GIS-based urban design information system is because it needs to deal with human visual perception of space, which is extremely difficult to model in spatial database As the result, GIS analytical tools have not been designed appropriately to the requirements of urban design applications The more attractive aspect of visualization, instead

of visual analysis, was always the emphasis of computational design applications Visualization deals with representation of spatial form in graphics, while visual analysis put more emphasis on determining the visual properties of spatial form of the ‘real world’ This may be influenced by the drive to support creative thinking with better visualization, instead

of supporting analytical thinking in design process

The experience of GIS analysis for urban design evaluation in Singapore reveals the importance of combining large-scale design, spatial analysis and emerging technologies It implies the potential applicability of GIS to the area of urban design analysis On one hand, GIS is regarded as a new design tool of managing urban change, although not reducing its complexity For large-scale urban design, it is always too complicated to be manipulated by simple visual plans or static physical models because of the extensive spans of space and time, indeterminate programs, multiple ownership and users, which make the decision-making very difficult Solutions of large-scale urban transformation rely upon the dynamic visualization of architecture and urban form as well as the urban database and spatial analysis behind it GIS introduces more systematic, informative design support system for urban design process, capable of processing of quantitative and limited qualitative information of urban built-environment and related issues On the other hand, GIS analysis can help predict

supporting analysis to help the designer smartly design spatial form with respect to the measurable principles and processes This prediction, or rather evaluation process, may provide the foundation for iterative, cyclical and experimental design process

The question to be answered is whether this GIS-based analytical methodology can be introduced effectively in urban design process The strength of GIS technology has always been in ‘numbers’, enabling quantitative analyses and ‘limited’ qualitative analyses of spatial databases The spatial database technology enables storage of nearly limitless amount of data and information, which is crucial as information system support for urban design practice In conclusion, to achieve integration of GIS to urban design field, several aspects of design process must be accommodated:

Trial-error and experimental process In current GIS stage, trial process through

implementing design proposal inside the GIS database fall short of the required

‘error’ process through analysis of implemented design proposal

Creative process: design interventions in terms of forms, functions, programs,

structures, and aesthetic values, should be easily implemented inside the system through three-dimensional design tool

Iterative or cyclical process: the creative and trial-error process should be

conveniently and speedily repeatable in the GIS system

Technological development is one aspect that can be pursued; however it doesn’t guarantee smooth adoption and implementation of this new technology to urban design process The challenges lie on both sides of the gap, the urban designers and the GIS developers Urban designers are required to set aside the scepticism and have willingness to experiment with new technologies albeit the outcome, in order to better understand their own requirements compared to technological development of design tools offered by vendors Another issue is how urban design process can adopt and incorporate new quantitative indices that arrive alongside with new methods and techniques, and in the same time maintaining sensitivity of greater qualitative qualities of the target space, to the point of blending both aspects of urban spatial understanding needed for successful design process On the GIS side,

developers must broaden their perspective beyond geographic, cartographic, and planning disciplines, and adopt design approach and creative process in future GIS development Visualization of 3D urban form is currently not a problem in GIS, which definitely will foster interests from both sides to approach GIS use urban design in more creative and aesthetical way, covering both analytical and synthetical design process

The use of systematic, publicly more informative and objective GIS-based analysis and indicators allows a universality of design negotiation and assessment process, establishing common language of mediation, leading to implementation of design guidelines and standards This supports the function of urban design as an accountable and responsible process of

‘negotiation and mediation’ of public interests, and not just avant-garde designer’s expression on public property This design approach requires common language for communicating design concepts as platform of negotiable design process, a language for reaching agreement between stakeholders of what exactly is acceptable and unacceptable, and for supporting conflict resolution in urban design process Thus, we can propose a more

self-‘accountable’ urban design practice, whereby public stakeholders can use the language to understand and assess urban space design beyond colourful graphic presentations and design marketing jargons, and whereby urban designers are encouraged to have explicit public responsibility of impacts of their designs and effectiveness of public investment on public built-environment

This design control mechanism has not existed so far, not beyond setting up ‘public committee’ consisting of fellow designers and decision-makers, or public design exhibition and roadshows These elitist public participation approaches are exactly the ones to be avoided, because they never reach deeper understanding of grass-root perception of space This dissertation introduces an analytical methodology that involves survey of public place users’ perceptions, as a form of direct public participation This methodology allows more publicly acceptable and justifiable design for public places

1.1.5 3D GIS AND 3D URBAN MODELS

The new 3D GIS development is much more applicable to urban design field, since visual quality of urban form must be expressed adequately for designers to utilize GIS The recent development of 3D GIS hopefully will help establish a platform for developing solutions for those information-system-related and visual-perception-related limitations In technical realm, however, 3D GIS still have limitations in supporting and modelling “full” 3D objects Current commercial GIS only support 2.5D topology and face problems supplying 3D spatial analysis,

in the same graphic quality as in the CAD domain This is the limitation related to technical modelling of 3D GIS

Moreover, these ‘3D cities’ are actually a visual representation from planner and designer’s ‘bird-eye’ view or isometric view design perspective, which provides designers with very different perception than that perceived by pedestrians from its ecological egocentric perception of the city This perceptual difference between ‘plan view’ and

‘pedestrian view’ has caused misconception and misrepresentation between planner and designer ideas and ‘real-world’ user’s or pedestrian’s needs, a dilemma criticized previously

by Jane Jacobs (Jacobs, 1961) This is the limitation related to visual perception Researches

in the realm of urban design and pedestrian’s perception must be initiated to utilize 3D graphical model of cities for analyzing pedestrian’s perception of urban space visual quality

To optimize the use of 3D model of cities, we must integrate the 3D model with visibility analysis and spatial information systems, in this case GIS Visibility analysis is more difficult to model in current spatial information systems than in CAD systems, since there is only little widely accepted spatial algorithm for defining abstract and fuzzy designer’s space

2D and 2.5D (two-dimensional with extrusion) visibility analyses of urban form are currently provided by urban information system, particularly by GIS This is the main obstacle for 3D urban design process to utilize information systems, particularly for processing spatial and visual perception of urban environment as a consideration in designing urban spaces Traditional perception-cognition theories verified that design process using

two-dimensional models, such 2D maps, is very different with that using three-dimensional models, and doesn’t lead to comprehensive perception of three-dimensional built-environment Two-dimensional visibility analysis is certainly different than its three-dimensional counterpart, in a way that a 3D realistic model of the environment will allow analysis and operations normally not attainable by traditional 2D tools (Slocum et al, 2001) The traditional exploitation of 3D urban digital model did not enable digital design tools

in predicting the impact of newly drafted or existing 3D forms, as model of real urban objects,

on existing urban site, except only through visualization and direct observation This is probably because of the problem existed in obtaining 3D dataset of urban areas, although this may be overcomed with the recent laser scanning and LIDAR data collection technology (Ratti & Richens, 2004) Realistic visualization of 3D urban models, however visually entertaining, has limited capacity to display more implicit impacts of design proposals to the urban environment Ever-developing 3D visualization techniques, pushed by the market demand of design industry, have imposed biases that may influence the outcome of any objective research exercise (O’Connell & Keller, 2002) Celebrated projects of developing

‘digital cities’, which most oftenly implies of merely drafting and databasing a part or the whole city to 3D models, most probably will end up in presentation slides solely for visualization purpose, though there are wider analytical utilizations such as for simple animated flooding analysis In the name of public participation, visualization of 3D urban models was proposed as a new technology for participation, but in most cases the models are not user-friendly enough and the outcome may not be far from one-way persuasive communication (Carvel et al, 2001) It’s blatantly obvious that the relationship between ‘ideal situation’ depicted by ‘eye-catching’ visualization and the ‘real spatial and environmental impact’ to be understood in urban design process is lacking, and often misleading

This thesis aspires to promote utilization of urban information system in urban design process, by providing a three-dimensional visibility analysis as the basis of processing information of spatial and visual perception in the design process Hopefully, this thesis will

1.2 IDENTIFICATION OF KNOWLEDGE GAP

Developments of visibility analyses of urban geometrical space have started from dimensional planar analyses, and have arrived at the stage of ‘near 3D’ spherical analysis These spherical analyses are still not capable of analysing large and complex 3D urban model, since they do not capitalize established urban information system technology such as GIS, which may enable storing, sketching, editing, and 3D visualization of analysed urban model Therefore, a GIS-based three-dimensional volumetric visibility analysis is needed The literature review will reveal that currently there is a lack of ‘established’ and dependable

two-three-dimensional performance criteria (or perceptual indices) of geometrical characteristics

of urban space Thiel expressed this limitation by addressing the lack of computational 3D analytical tools for “360 degrees metrical analysis of geometric environments” (Thiel, 1996) Because of this limitation, design theories related to visibility and spatial perceptions remained two-dimensional (i.e D/H ratio for indicating degree of enclosure discussed by Ashihara, 1983; Lynch, 1962; Spreiregen, 1965) These ‘rule-of-thumb’ design theories are highly-approximated and generalized classification of spatial characteristics They are usually dedicated only to certain typology or constrained by certain shape that needs to be pre-assumed, since they will face regularity and continuity problems if otherwise

Efforts were made in providing solutions to fill the limitation mentioned above, mostly in the forms of new spherical-based three-dimensional analyses and metrics of urban space Sky View Factor (SVF) discussed by Markus & Morris (1980), Bosselman (1998), and Ratti (2002), Spatial Openness Index (SOI) proposed by Fisher-Gewirtzman (2003), and Sky Opening indicator proposed by Teller (2003) are most prominent works concerning the new genre of spherical-based spatial analyses However, constraints have been discovered in these analyses and their applications when applying them for analysis of human spatial perception

on real and complex 3D urban model

To answer these limitations, Viewsphere Analyst will be developed to analyse urban spatial characteristics and perceptions based on visibility of surrounding urban geometry Viewsphere analysis provides quantitative indices (or metrics) referred as ViewSphere Indices (VSI), which meanings and relationships to perceptions of urban space are still unknown Quantitative metrics, according to Kevin Lynch, will only be useful if it gives a set

of value judgements, acting further as performance criteria (or perceptual indices), which can

be congenial and useful to design decision-making process (Goodman et al eds., 1968:261) Therefore, the secondary objective of this dissertation is to provide meanings to the Viewsphere Indices (VSI), derived from their relationships with surveyed users’ spatial and temporal perceptions of urban space

The perception and understanding of urban space as volume will draw visibility analysis

closer to human three-dimensional spatial and temporal perception better than as area or as perimeter Thus volumetric Viewsphere indices can represent spatial and temporal perceptions of urban space better than any planar indices, since only volumetric indices are able to consider changes of surrounding surfaces’ vertical dimension In the end, better understanding and representation of user’s spatial and temporal perception from volumetric indices will be essential in improving the quality, desirability, and ‘user-friendliness’ of urban environment, and in facilitating user-oriented urban design process

1.3 OBJECTIVES AND PRIORITIES

Consequently, the objectives of this research are listed below, in the order of my research priority:

geometry of urban space and urban form with perceptions of urban space The knowledge

of these relationships will make the impacts of urban space geometry more predictable

Gibson’s concept of ambient optic array to generate the index Volume of Sight VoS and its inferences Viewsphere Indices (VSI)

3. To understand Viewsphere’s contributions to the fields of spatial analysis, visibility

analysis, and urban design analysis, in comparison with various traditional planar and spherical visibility analyses of urban geometrical space

and temporal perceptions of urban space, and thus establishing VoS and VSI as objective performance criteria or more accurately perceptual indices of urban space

5. To apply Viewsphere analysis and its indices on urban design case studies, particularly in

Singapore, and to understand spatial perceptions of existing urban environment, and the mental-perceptual impact of design proposals and new urban developments Successful application will imply that objectives of this dissertation are achieved, and will initiate the formulation of urban design support system, which is the design-related knowledge generation system, particularly in relation with human perception of urban environment

1.4 RESEARCH QUESTIONS AND HYPOTHESES

and its perceptions? If there is, can these effects or impacts be more predictable? Are available analyses or measurements valid for evaluating these impacts? To answer research questions 1, hypothesis 1 is offered: Geometrical form of urban space has been identified as influential factor to human spatial and temporal perceptions, and these influences will be more predictable if quantitatively measured Computations of spatial dimension of ambient optic array, which generate measures of visible urban space, have been proposed recently by various spherical visibility analyses However, these analyses have limitations in evaluating human perceptions

2 Can a valid and operational visibility analysis to measure volume of ambient optic arrays

be developed? Can volume of ambient optic array be applied as visible volume of

geometrically irregular urban space? Until what extent the newly developed volumetric visibility indices can contribute to existing planar indices? To answer research question 2, hypothesis 2 is offered: A Geographic Information Systems (GIS)-based volumetric visibility analysis can be developed by modelling and measuring spatial dimension of ambient optic array from geometrically irregular urban forms in GIS ‘Viewsphere array’

is Viewsphere Analyst’s computation model of ambient optic array as a dimensionally measurable spatial entity Its volume is identical with the visible volume of

three-geometrically irregular urban space, namely Volume of Sight VoS Perceptual indices Viewsphere Indices VSI can be derived further statistically from VoS These indices

represent visibility of urban space and urban form better than 2D visibility indices

If there is, what kinds of perception are related to VoS? What is the nature and degree of

their relationships? To answer research questions 3, hypothesis 3 is offered: There are

significant relationships between visible volumes of urban space VoS with spatial and

temporal perceptions of urban space, which include spatial perceptions of visibility, enclosure, and scale, and temporal perception of ‘sense of time’ (or ‘remembered duration

of passed time’) The nature and degree (of significance) of these relationships are

determined statistically by ANOVA’s significance level (if significance p is below 0.05), Peason’s correlation (if higher than 0.5 of 1), and linear or curved regressions (if R is

higher than 0.5 of 1)

4 Until what extent the Viewsphere analysis and its volumetric indices can contribute in

urban design process? To answer research question 4, hypothesis 4 is offered: The

Viewsphere Analyst and Viewsphere Indices, including VoS, can contribute to analysis of

perceptual impacts from existing, intervening or proposed urban space geometric configuration, such as from different building designs, from different urban design proposals, and from different planning strategies relating to density and typology

To validate hypothesis 3, the subhypotheses below are to be validated:

5. Hypothesis 5 states that “higher Volume of Sight “VoS” indicates larger quantity of

volume of space (in m3) thus implying observer’s higher visibility quantity and distance to all possible directions.” Hence, Viewsphere Analyst from a vantage point inside a large open space with many and farther objects visible (i.e a huge city square) will likely

generate higher VoS

6 Hypothesis 6 states that “higher Volume of Sight “VoS” indicates larger quantity of

volume of space (in m3) thus implying observer’s higher perceived openness and spatial definition, hence lower perceived enclosure and less spatially undefined.” Hence, Viewsphere analysis from a vantage point inside a large, open, and easily defined space (i.e a large and open city square) will likely generate higher VoS

of space (in m3) thus implying that observers perceive larger ‘sense of scale’ to all possible directions.” Therefore, larger scale will be perceived for urban spaces with higher

VoS

certain path when the path’s VoS is higher, hence implying higher visible distance and

volume, larger scale, and lower enclosure.”

9. Hypothesis 9 states that “changes in urban space, represented by changes in VoS, may

affect significantly the retrospective reflection of duration.” Thus samples will perceive longer duration of passed time along certain path when the path’s variance (in terms of

standard of deviation and coefficient of variance) of VoS is lower, hence implying fewer

changes of spatial characteristics along the walking path

Chapter 2 “Visibility analysis and three-dimensional perceptions of urban space” aims to answer research question 1 and validate hypothesis 1 Literature reviews on various aspects of urban design, urban spaces, spatial and temporal perceptions, GIS-based spatial and visibility analysis, will be conducted for this purpose

Chapter 3 “Development of GIS-based Viewsphere Analyst and Viewsphere Indices” aims to answer research question 2 and to validate hypothesis 2 As expressed in the title, Viewsphere Analyst and its indices will be developed in series of development stages: conceptual and theoretical, mathematical, and finally computational Methodologies of operating and implementing Viewsphere on researches using GIS-based urban models will be elaborated A few tests will be conducted to verify that the prototype developed is operational, by comparing results from existing 2D analysis with those from Viewsphere Analyst

Chapter 4 “Viewsphere Indices as indicators of perceptions of urban space” aims to answer research questions 3 and to validate hypothesis 3 Hypothetical meanings of Viewsphere

Indices (VSI), including Volume of Sight VoS, will be presented These meanings are VSI

possible representations of certain spatial and temporal perceptions of urban space Pattern analysis of urban spaces will be conducted to validate these meanings A user survey was conducted to collect respondents’ spatial and temporal perceptions of certain districts in Singapore Statistical investigations, such as ANOVA, Pearson’s correlations, linear and curved regressions, will be conducted between results from user survey and from VSI, in

adequately correlated meanings are deemed eligible as quantitative perceptual indices of urban space, and can be developed further to establish perceptual classification based on regressions’ predictive models The perceptual classification will be useful to predict the meanings of urban space and urban form, displayed on VSI contour pattern maps

Chapter 5 “Applications of Viewsphere Analysis and Indices in urban design case studies” aims to answer research question 4 and to validate hypothesis 4 As discussed in hypothesis 4, four types of Viewsphere application on urban design process will be investigated in this chapter The first application is for analysing perceptions of existing urban spaces, the second application is for analysing impacts of building form changes, the third application is for analysing impacts of different design proposals, and the fourth application is for analysing impacts of different planning and design strategies related to density and typology

In terms of computational support, this research may utilise and even customize these software in local level and client-server network level, such as:

Geographic Information System: ArcGIS 8.3 and its extensions

CAD: AutoCAD

Programming: ArcObjects components and Visual Basic for Applications (VBA) Statistical analysis: SPSS and Microsoft Excel

1.6 DEFINITIONS REFLECTING METHODOLOGY

These definitions are provided to clarify the scope of new terms introduced in this dissertation Not all definitions are presented in this subchapter Definitions from other literatures are discussed in the next chapter, and more definitions are also available in later parts of dissertation’s body

Urban geometrical form is the physical and visible urban form which in this case is

mostly the form or the representation of large urban objects, such as buildings The term

geometrical indicates that only geometrical shape, in three-dimensional sense, will be

discussed in this dissertation Texture, colour, material, or other intrinsic and visible components of urban form will not be discussed in this dissertation

Accordingly, the term urban space in this dissertation implies immediately to the

geometrical space outside, or unoccupied by the urban geometrical form Although urban space is shaped by urban geometrical form surrounding it, it is not a physical or visible entity, but is perceivable only through human visual perception and cognition process The property

of urban space which becomes the focus of this study is the volume of visible urban space, which can be represented graphically by Viewsphere array, and quantitatively by Volume of

Sight VoS These properties will be discussed thoroughly throughout the dissertation’s body

The definition of visibility or visual perception can be preliminary understood from simple possible starting questions proposed to an individual, such as “how much can you see (perceive visually)?”, which implies quantity-related perception, and “how far can you see?”, which implies distance-related perception In the simple term, visibility in this dissertation refers to the amount of space and form visible from an individual’s vantage point However, answers to these questions most likely will not imply discreet and measurable quantitative understanding of one’s visibility perception, but will signify that human’s visibility perception is based on subjective-comparative judgment between different settings, thus will fall into subjectively fuzzy, ordinal, and non-quantitative answers such as ‘more visible’ or

‘less visible.’ It mainly shows that human visual perception is less dependable as scientific data for quantitative research, and therefore needs to be represented by a set of quantitative indicators in order to investigate the relationships between visual perception and scalar dimensions of urban built-environment Since visual perception is not easily translatable to a quantitative indicator, the main challenge is how visibility perception can be ‘captured’ in a discreet quantitative measure of “visibility amount”, and vice versa

The term ‘visibility’ in this dissertation implies to the amount of physical environmental surface visible from an observer point This type of visibility is not to be mistaken with other

visible, or the degree of view unobstructed by any occluding object, or “the less obstruction objects you see, the higher your visibility” It should not be mistaken also with other visibility definition such as ‘the amount of light photons’ which is actually refers to ‘brightness’, or related to the atmospheric quality, or the ability to see as ‘empty’ or ‘thorough’ as possible (i.e empty space = ‘full’ visibility) Our definition of visibility therefore is closer to “the more visible volume of ambient optic array reflected from obstruction objects you see, thus the more ambient optic array you perceive, the higher your visibility.”

The term visibility analysis implies of a methodology, applied and materialized as a tool,

most often a computerized tool, which is able to indicate whether the targeted points are visible or invisible from a vantage point This visibility analysis should not be mistaken with any non-computerized tool, such as binoculars or similar apparatus The tradition of visibility analysis discussed in CHAPTER 2 defined that it must have digital data to compute on, mathematical algorithms translatable to programming code, and quantitative results with visualizations to show them The dissertation will adopt this definition exclusively in using the term visibility analysis

The term spatial indicator means a characteristic or property of urban form or urban

space that is measurable and represent-able as quantitative index The quantitative index can

be nominal (i.e metric index) or proportional (i.e ratio or percentage index) An example of spatial indicator is the apparent dimension of urban space, such as width or length Changes in urban space, which are represented by changes in spatial indicator, may imply changes of its

perception In other words, a value of spatial indicator may mean or represent a certain degree

of spatial perception of certain spatial characteristics

Perceptual indices or metrics are the quantitative indices of a spatial indicator that

implies or represents the degree of any perception, which in this dissertation may be spatial, environmental or temporal These perceptual indices may only represent the perceptions if

they have proven and established relationship(s) to one or more meanings, expressed by sufficient and observable statistical relationship between them Perceptual indices focus on

perceptual ‘performances’ of urban form, or the impact of urban form and space on human

perceptions A certain value of perceptual index should be translatable directly to an ordinal (but not necessarily quantitative) degree or classification of certain spatial or temporal perception In order to achieve such direct translation, a predictive regression model that leads

to perceptual classification must be defined

Predictive or Regression Model is the algorithm or mathematical model derived from

statistical regression analysis between the results of a perceptual index and a spatial or temporal perception They must have sufficiently significant statistical correlation in order to have valid regression model The model can be linear or curved (parabolic or quadratic, logarithmic, etc.) depending on the nature of highest possible regression relationship discovered The model is useful to predict a degree of spatial perception from a value of perceptual index, and vice versa A comprehensive list of these pairing values comprises a

perceptual classification

A perceptual classification is a table of predicted pairing values between a spatial

indicator and spatial perception, which in total comprise a ‘range’ or ‘scale’ of corresponding values The perceptual classification is useful to interpret contour pattern maps on an urban dataset of the particular spatial indicator to pattern maps of human spatial or temporal perception The whole research involving meanings of visibility indices, including spatial indicator, perceptual indices, predictive or regression model, and perceptual classification will

be discussed in CHAPTER 4

In discussing temporal perception of urban space, two concepts are involved The first concept is the ‘current awareness’ of passing time, which is the perception of time in reference to an on-going activity The second concept is the ‘retrospective awareness’ of passed duration of time, which usually refers to an already passed activity The term temporal perception in this dissertation will refer to the second concept only, which is the perception of retrospective duration of time

1.7 SCOPE AND LIMITATIONS

This dissertation will focus on developing and applying new volumetric visibility analysis through conceptual, mathematical, and computational development Previous visibility analyses in general can only analyse modelled urban geometry, and not building surface materials, texture, transparency, etc No visibility analyses, including the one developed in this dissertation, are capable of analysing non-geometric (i.e trees & vegetation) and non-static objects (i.e human & vehicles) There are other variants of ‘visibility analysis’ group that analyse ambience environmental factors, such as atmospheric condition, natural, artificial lighting, and shadow Although these factors affect perceptions of urban space, they will not

be discussed in this dissertation

Another focus of this dissertation is on contribution of newly developed analysis, in terms of improvement in three-dimensional computation, compared to existing planar (2D) or spherical (3D) analysis Improving 3D digital urban model, such as GIS-based 3D modelling technology which is still limited to 2.5D, is not within the scope of this dissertation

The new analysis, which can only conduct analysis from ground-level urban space, is still in the process of continuous development, such as from interior space or multi-storey levels

Although this dissertation exhibits the usefulness of the new analysis as evaluation tool in urban design process, it should not be considered as a straight-forward designing tool, since it can’t evaluate many urban design and perceptual factors mentioned earlier

The dissertation’s theoretical focus is on pedestrian’s perception of urban space, based on Gibson’s theory of direct ecological perception Other theories of perception, such

as Gestalt, Steven’s power, Transactional or Information system theories, which are or can be contradictive with Gibson’s, will not be discussed thoroughly Perceptions discussed are direct ecological perceptions, thus cognition process discussed by other theories will not be covered in this dissertation Based on Gibson’s direct perception theory, discussion of samples’ perceptions in this dissertation does not take into account perceiver’s cognitive

memory, spatial familiarity, mental and psychological condition, social, economic, and educational background, temporal awareness, travel mode and intentions, etc

This scope of this dissertation stays within the field of architecture and urban design, thus the discussions in this dissertation will maintain the centrality of these research fields Discussions may relate to peripheral fields of geographic information science, environmental psychology, cognitive science and psycho-physical theories Although human perceptions will be discussed, which certainly involves the greater area of cognitive process, this research should focus only to make one small step closer in empirically relating quantitative 3D visibility indicators with surveyed human spatial and temporal perception The impacts of this research to peripheral fields should require further studies in different research fields

1.8 IMPORTANCE AND POTENTIAL CONTRIBUTION

The completion of this research will contribute in establishing new volumetric and potentially three-dimensional visibility analysis of urban geometrical space, in particular by the computational development of Viewsphere Analyst The theoretical contribution will bring a fundamental contribution in the realm of visibility analysis studies, from predominantly planar to volumetric methodology A theoretical stand is taken that urban space is not only an abstract, fuzzy, residual perception from its surfaces, but can be discretely defined by its visibility as a three-dimensional volumetric and spatial entity represented by the “viewsphere array” This concept is based on Gibson’s ambient optic array theory, as viewsphere array is potentially the spatial representation of collective ambient optic array in a specific setting (Gibson, 1986) Although Gibson himself argued that there is no ‘physical’ reality of the conception of abstract ‘space’, but studies development of viewsphere array reveals that a spatial reality of ‘space’ can be defined using Gibson’s own methodology through Viewsphere computational analysis

The outcome of this computational analysis is a set of volumetric indices, including

Volume of Sight VoS and ViewSphere Indices VSI, which are hypothetically quantitative

perceptual indices derived from characteristics of urban geometrical space These indices provide volumetric alternatives for the existing planar indices Latest developments have

revealed that VoS and VSI have close relationship with existing indicators of sky view

proportion, scale, degree of visibility, degree of enclosure, retrospective duration of time, and perceived density Viewsphere has solved the limitation of previous 2D enclosure indices, which is known as the ‘infinite urban canyon’ situation (Oke, 1987) This limitation made the 2D indices ineffective in handling irregular environmental surface in real urban situation Viewsphere has overcomed it by analysing ‘enclosure’ as a single volumetric index computable in any irregular settings of geometric urban space

The Viewsphere analysis is being developed to complement the existing visual analyses, such as 2D isovists, 2D viewshed, and Sky View Factor (SVF), in the view of understanding urban space Viewsphere can actually perform these 2D analyses, applying the same visibility principle with only minor modifications on computational programming Viewsphere analysis also contributes as a contemporary alternative of more three-dimensional sky-oriented analyses (Sky Opening by Teller, 2003; Sky View Factor by Ratti, 2002) and space-oriented analyses (Spatial Openness Index “SOI” by Fisher-Gewirtzman, Burt & Tzamir, 2003)

The application of Viewsphere Analyst and its indices have the potential to contribute to various applications related to urban environment (although only the first application will be discussed in this dissertation):

Urban design & planning & management, for analysis of pedestrians’ spatial,

environmental & temporal perceptions, analysis of urban space geometric typology, impact analysis of urban development scenarios, and assessment, classification & management of public space

Urban microclimatic, using Sky View Factor (SVF) for urban heat island and thermal

comfort analysis

Real estate, for multi-storey property valuation in relation with view and ‘scenery’ Tourism, for identifying locations with scenic view or grand vista (max VoS)

Archaeology, for identifying historically strategic sites

Telecommunication, for coverage and path loss analysis in broadcast infrastructure

planning

Security, surveillance and defence, for assessment of crime prone areas, analysis of

visibility and strategic value of urban battlefield, for identifying strategic locations for control and hiding, useful for allocation of surveillance devices or troops

Organizations and authorities which contribute to the shaping of urban environment will be benefited much by this methodological and technological contribution