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

ACKNOWLEDGEMENTS Firstly to my supervisor Dr. Perry Yang, who guided me throughout my research candidature with wisdom and patience, who supported me in all ways possible, and whom I deeply respect. To my co-supervisor Prof. Heng Chye Kiang, who provided me the opportunity to pursue my candidature in NUS and supported my research continually. To my colleagues Li Wenjing for his constant support and readiness in solving mathematical and technical problems, and almost every other issue in my work, and Li Ze for his inputs on problems in geometry and mathematics. To my thesis committee members, Dr. Stephen Wittkopf and Dr. Huang Bo, for their supports and inputs. To Dean Prof. Cheong Hin Fatt and Vice Dean (Research) Assoc. Prof. Michael Chew Yit Lin and SDE staffs for their administrative supports. To Singapore Millennium Foundation, its manager John De Roza, and other staffs for assisting with financial supports through Singapore Millennium Scholarship. To Dr. Johannes Widodo for his advises and inputs on research methodology, architectural and urban design theories. To 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 . TABLE OF CONTENTS . SUMMARY . LIST OF TABLES . LIST OF FIGURES . LIST OF SYMBOLS 13 LIST OF RELATED PUBLICATIONS . 17 CHAPTER INTRODUCTION . 18 1.1. BACKGROUND AND MOTIVATION . 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. IDENTIFICATION OF KNOWLEDGE GAP 27 1.3. OBJECTIVES AND PRIORITIES . 28 1.4. RESEARCH QUESTIONS AND HYPOTHESES . 29 1.5. STRUCTURE OF DISSERTATION 32 1.6. DEFINITIONS REFLECTING METHODOLOGY . 33 1.7. SCOPE AND LIMITATIONS 37 1.8. IMPORTANCE AND POTENTIAL CONTRIBUTION 38 CHAPTER VISIBILITY ANALYSIS AND THREE-DIMENSIONAL PERCEPTIONS OF URBAN SPACE 41 2.1. INTRODUCTION TO REVIEWS OF VISIBILITY ANALYSES AND 3D PERCEPTIONS OF URBAN SPACE 41 2.2. HUMAN THREE-DIMENSIONAL PERCEPTIONS OF URBAN SPACE 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’ AND VISIBILITY ANALYSIS . 50 2.3.1. MEANINGS OF ‘VISIBILITY’ . 50 2.3.2. VISIBILITY ANALYSIS AND HUMAN PERCEPTION AND COGNITION 52 2.4. REVIEW AND CRITIQUE OF PRECEDING 2D VISIBILITY ANALYSES 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. SPATIAL AND TEMPORAL INDICATORS OF URBAN SPACE 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. DISCUSSIONS OF PRECEDING SPHERICAL VISIBILITY ANALYSES 81 2.6.1. SKY-ORIENTED, SURFACE-BASED 3D ANALYSES 82 2.6.2. SPACE-ORIENTED, VOLUME-BASED 3D ANALYSES 83 2.7. CONCLUSION TO REVIEWS OF VISIBILITY ANALYSES AND SPATIAL AND TEMPORAL PERCEPTIONS . 85 CHAPTER DEVELOPMENT OF GIS-BASED VIEWSPHERE ANALYST AND VIEWSPHERE INDICES . 88 3.1. INTRODUCTION TO VIEWSPHERE DEVELOPMENT 88 3.2. CONCEPTUAL 89 3.3. VIEWSPHERE DEFINITION 91 3.4. MATHEMATICAL DEVELOPMENT 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. COMPUTATIONAL DEVELOPMENT 105 3.6. VIEWSPHERE AND OTHER SPHERICAL ANALYSES 108 3.7. METHODOLOGY FOR VIEWSPHERE APPLICATION 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. TEST CASE: SINGAPORE MANAGEMENT UNIVERSITY 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. CONCLUSION TO VIEWSPHERE DEVELOPMENT AND TESTS . 134 CHAPTER SPACE VIEWSPHERE INDICES AS INDICATORS OF PERCEPTIONS OF URBAN 138 4.1. INTRODUCTION TO VIEWSPHERE’S MEANINGS INVESTIGATION . 138 4.2. HYPOTHETICAL MEANINGS OF VIEWSPHERE INDICES 138 4.2.1. VIEWSPHERE INDICES AND SPATIAL PERCEPTIONS 138 4.2.2. VIEWSPHERE INDICES AND TEMPORAL PERCEPTION 142 4.3. RESEARCH METHODOLOGY 145 4.3.1. PREDICTION PROCESS: FROM URBAN GEOMETRY TO PERCEPTION . 145 4.3.2. RANDOM & GROUP SAMPLE SELECTION FOR EXPERIMENT AND 146 4.3.3. QUESTIONNAIRE DESIGN 148 4.3.4. VIEWSPHERE ANALYSIS ON 3D MODELS 149 4.4. QUANTITATIVE VALIDATION OF MEANINGS 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. FROM SPATIAL INDICATORS TO PERCEPTUAL INDICES: REGRESSION MODELS AND CLASSIFICATIONS . 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. CONCLUSIONS ABOUT VIEWSPHERE’S MEANINGS . 194 CHAPTER APPLICATIONS OF VIEWSPHERE ANALYSIS AND INDICES IN URBAN DESIGN CASE STUDIES . 199 5.1. INTRODUCTION TO VIEWSPHERE’S APPLICATIONS FOR URBAN DESIGN . 199 5.2. APPLICATION TO ANALYSE SPATIAL PERCEPTIONS OF SINGAPORE DISTRICTS’ URBAN SPACES AND STREETS . 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. APPLICATION TO ANALYSE PERCEPTUAL IMPACTS OF URBAN DESIGN 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. APPLICATION TO ANALYSE PERCEPTUAL IMPACTS OF PLANNING STRATEGIES 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 (VSIMIN) . 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. APPLICATION TO ANALYSE PERCEPTUAL IMPACT OF DENSITY AND TYPOLOGY VARIATIONS . 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. CONCLUSIONS ABOUT VIEWSPHERE’S APPLICATIONS FOR URBAN DESIGN . 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 CONCLUDING REMARKS AND LIMITATIONS 265 6.1. CONCLUDING REMARKS 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. POTENTIAL FUTURE WORKS . 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 nonGIS 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 largescale urban design and development will provide a more relevant result than planar 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 Perception of enclosure comparison with vertical angle ( ), D/H ratio, and VSI 74 Table Statistical correlations (Linear R square and Pearson’s) between GPR ( p) and VSI. . 103 Table Comparative analysis of spherical-based analyses (Yang & Putra, 2003) . 109 Table Evaluation using 2D indices. The results are identical between (a) and (b) . 127 Table Evaluation using 3D VSI. 3D indices show significant differences between (a) and (b) . 128 Table Descriptive statistics of spatial and temporal perceptions from six locations 157 Table Descriptive statistics (average) of selected visibility parameters from six locations 157 Table Descriptive statistics (coefficient of variance) of selected visibility parameters from six locations . 157 Table 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 VSImin 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 VSImin 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 Ambient optic array from a person' s visual system (Gibson, 1986) 46 Figure Line of Sight (LoS) studies on visibility and archaeological significance (Fisher, 1995) 56 Figure Isovist application for deconstructing architectural space (Hanson, 1994) . 57 Figure Isovist application for Virtual Tate Gallery, London (Batty et al., 1998) . 58 Figure ‘Isovist graph’ or ‘visibility graph’ (Turner et al., 2001) 59 Figure Isovist application for public safety in urban space (Desyllas, Connoly & Hebbert, 2003) 60 Figure Total viewshed analysis on DEM for determining different preferred paths (Lee & Stucky, 1998) . 61 Figure Visual quality mapping of skylines by overlaying viewshed analysis and human visual ergonomics and psychology parameter analysis (He &Tsou, 2002) . 62 Figure 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 LoSij components: Oi, Tij, and Qij . 94 Figure 16 Invisible and Visible parts of line of sight LoSij 94 Figure 17 3D transformation from Line of Sight LoSij to Viewsphere array VSij 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 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 rn = 500, N0 = 120, and Ei = 98.91%, (c) VoS with rn = 1000 and N0 = 120, and Ei = 98.96%, (d) VoS with rn = 1000 and N0 = 180, and Ei = 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 VSImin for the proposals NA (top), A (left) and B (right) . 132 Figure 38 Charts of 3D evaluations of 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 & . 152 Figure 42 Photos of Orchard Path (a) & (b) . 152 Figure 43 Plan view of Rochor Path . 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 and Tanjong Pagar Path 154 Figure 46 Photos of CBD’s Path (a) & Tanjong Pagar’s Path (b) . 154 Figure 47 Plan view of Chinatown Path . 155 Figure 48 Photos of Chinatown: (a) Path 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 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 VSImax 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 VSImin 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 61 VoS range (in million m3) and urban space and street typologies . 215 10 Dim dTargetHeightForVis As Double geomutil.CreateVerticalLOSPatches bIsVis, fPoint, tPoint, pVisPolyline, pInVisPolyline, pVisPatch, pInVisPatch, dTargetHeightForVis If (Not pVisPatch.IsEmpty) Then pColor.Green = 255 pColor.Red = pFillSym.Color = pColor AddGraphic m_pApp, pVisPatch, pFillSym, False, False End If If (Not pInVisPatch.IsEmpty) Then pColor.Green = pColor.Red = 255 pFillSym.Color = pColor ' AddGraphic m_pApp, pInVisPatch, pFillSym, False, False ' if viewsphere then off End If End If 'notify doc it' s been changed - so it knows to ask the user if they want to save on exit Dim pDoc As IBasicDocument Set pDoc = m_pApp.Document pDoc.UpdateContents DoEvents 'since the added graphic gets selected the doc sends a message to the status bar and that 'won' t happen until after this routine returns - effectively overwriting this tool' s 'message (see next section). DoEvents flushes the doc' s message so the overwrite problem 'goes away. VIEWSPHERE INDICES: DEFINITION The final calculation will take place here, before the result of VSI index calculations shown on status bar. Dim sTarVizVol As String Dim sViewsphere As String Dim dViewsphere As Double Dim sObstRadMax As String Dim sObstRadAve As String Dim sObstRadMin As String Dim dObstRadAve As Double Dim dVSImax As Double Dim dVSIave As Double Dim dVSImin As Double Dim sVSImax As String Dim sVSIave As String 298 Dim sVSImin As String VIEWSPHERE INDICES: VSI Below is the code for calculating the standard VSI index (VSI) dViewsphere = dTotVol / ((2 * PI * (dRadius ^ 3) * Sin(dBetaRad)) / 3) ' ok VSIave requires more codes. The calculation uses arrays of obstruction point height, and arrays of radius distance of obstruction point. A separate set of variables is defined here, different from the variables of standard VSI calculation. The average obstruction radius is calculated from the total obstruction radius divided by number of LoS. Several variables for calculating VSImin is also written below, where the minimum obstruction radius should not be equal to zero. A set of variables used for calculating Sky View Factor (SVF) is also defined here. dObstRadAve = dObstRadTot / iNLoS If dObstRadMin = Then dObstRadMin = End If Dim dVisVolAve As Double Dim dInVisVolAve As Double Dim dTotVolAve As Double Dim dVisVolMin As Double Dim dTotVolMin As Double Dim dSegSVF As Double Dim dTotSVF As Double Dim dSVF As Double VIEWSPHERE INDICES: VOLUME FOR VSIave A routine for each LoS is performed afterwards, to determine whether the distance between obstruction point and observer point (dVisLength) for each LoS is beyond the average obstruction radius (dObstRadAve). If it’s beyond the average obstruction radius, only the VoS part inside the average sphere is included in the calculation of VSIave. The resulting volumes (dTotVolAve) are then accumulated. For i = To iNLoS If (dVisLengthArr(i) < dObstRadAve) Then dVisVolAve = (dVisHeightArr(i) * (dVisLengthArr(i) ^ 2) * Sin((2 * PI) / iNLoS)) / Else dVisVolAve = (((2 * PI) / iNLoS) * (dObstRadAve ^ 3) * Sin(Atn(dVisHeightArr(i) / dVisLengthArr(i)))) / End If 299 Below the invisible part of visible gross-volume is calculated, using the invisible block values stored in the array. Then the invisible net-volume can be calculated by visible volume minus invisible volume. The accumulation of the net-volume is then used to get the VSIave index. For j = To l If dInVisRadArr(i, j) < dObstRadAve Then dInVisVolAve = dInVisVolAve + dInVisBlkArr(i, j) End If Next j dTotVolAve = dTotVolAve + dVisVolAve - dInVisVolAve dVisVolAve = dInVisVolAve = VIEWSPHERE INDICES: VOLUME FOR VSImin Below the accumulated volume of VSImin index is calculated, based on the shortest obstruction radius available. If (dVisLengthArr(i) > 0) Then dVisVolMin = (((2 * PI) / iNLoS) * (dObstRadMin ^ 3) * Sin(Atn(dVisHeightArr(i) / dVisLengthArr(i)))) / End If dTotVolMin = dTotVolMin + dVisVolMin dVisVolMin = VIEWSPHERE INDICES: SKY VIEW FACTOR Below is the calculation for Sky View Factor (SVF) according to Li Wenjing’s review. It’s basically accumulating the value of SVF from each segment, in dTotSVF. The final result shows that there is a strong relationship between and SVF and VSImin, although the based calculation is different. It can be said, that: (1 – VSImin) = SVF ' Sky View Factor dSegSVF = (1 - Sin(dVisBetaRadArr(i))) dTotSVF = dTotSVF + dSegSVF dSegSVF = Next i VIEWSPHER INDICES: FINAL CALCULATION Below are the final calculation of VSImax, VSIave, VSImin, and SVF dVSImax = dTotVol / ((2 * PI * (dObstRadMax ^ 3) * Sin(dBetaRad)) / 3) dVSIave = dTotVolAve / ((2 * PI * (dObstRadAve ^ 3) * Sin(dBetaRad)) / 3) dVSImin = dTotVolMin / ((2 * PI * (dObstRadMin ^ 3) * Sin(dBetaRad)) / 3) 300 dSVF = dTotSVF / iNLoS PRESENTING RESULTS IN MESSAGE BOX The results are transferred to string variables, for writing them on status bar and message box. The format of those string variables is defined in these codes, and written on the status bar and message box. This marks the end of Viewsphere calculation. sTarVizVol = Format(dTotVol, "#.##") sViewsphere = Format(dViewsphere, "#.####") sObstRadMax = Format(dObstRadMax, "#.###") sObstRadAve = Format(dObstRadAve, "#.###") sObstRadMin = Format(dObstRadMin, "#.###") sVSImax = Format(dVSImax, "#.####") sVSIave = Format(dVSIave, "#.####") sVSImin = Format(dVSImin, "#.####") sSVF = Format(dSVF, "#.####") m_pApp.StatusBar.Message(0) = "Total vol. = " & sTarVizVol & " ; Viewsphere (VSI) = " & sViewsphere & _ " ; MaxRad = " & sObstRadMax & " ; VSImax = " & sVSImax & _ " ; AveRad = " & sObstRadAve & " ; VSIave = " & sVSIave & _ " ; MinRad = " & sObstRadMin & " ; VSImin = " & sVSImin & _ " ; Sky View Factor = " & sSVF MsgBox ("Total vol. = " & sTarVizVol & " ; Viewsphere (VSI) = " & sViewsphere & (Chr(13)) & _ " ; MaxRad = " & sObstRadMax & " ; VSImax = " & sVSImax & (Chr(13)) & _ " ; AveRad = " & sObstRadAve & " ; VSIave = " & sVSIave & (Chr(13)) & _ " ; MinRad = " & sObstRadMin & " ; VSImin = " & sVSImin) & (Chr(13)) & _ " ; Sky View Factor (SVF) = " & sSVF) ' End calculation Exit Sub EH: MsgBox "Error on MouseDown: " & Err.Description End Sub 301 APPENDIX B SURVEYS OF SPATIAL AND TEMPORAL PERCEPTIONS OF URBAN SPACE Figure 98 Survey Questionnaire Form for Experiment (June 2004) Figure 99 Survey Questionnaire Form for Experiment (August 2004) 302 Table 42 Survey results (Experiment 1) from Orchard path 1. Code descriptions are in questionnaire form (Figure 103) NO 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Q1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Q2 3 20 5 3.5 10 10 10 15 10 15 10 15 15 15 10 4.5 4.5 20 3.5 Q3 10 10 3 3.5 5 10 10 15 5 10 8 10 4.5 1.5 20 3.5 Age 20 20 60 20 40 10 30 10 10 40 20 30 30 40 10 10 20 20 20 20 40 30 T 20 20 20 20 20 25 30 30 Sex M M M M M M M M M F M M M M M F M F M F M M M M M F F M F F M Ethnic 1 6 5 1 6 1 4 5 1 1 1 Activity 3 4 2 2 4 3 2 4 3 3 3 NO 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 Q1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Q2 3.5 10 10 15 15 15 15 10 10 30 15 5 10 8 10 20 10 10 5 15 15 Q3 3.5 10 10 7.5 7.5 10 5 10 10 20 7 10 10 5 10 10 7 10 2.5 2.5 10 10 Age 25 25 25 25 20 25 25 30 30 30 10 10 40 50 30 20 20 30 30 20 20 10 20 30 20 20 30 20 20 50 60 Sex M M M M M F F M M M M M F M M M M M M M M M M M M M M M M M M Ethnic 1 1 1 1 4 2 1 1 5 5 5 1 1 Acti vity 2 2 3 4 3 3 2 2 4 3 3 3 3 303 Table 43 Survey results (Experiment 1) from Orchard path 2. Code descriptions are in questionnaire form (Figure 103) NO 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Q1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Q2 10 10 5 1.5 5 5 5 5 5 5 10 10 10 10 2.5 2.5 Q3 15 15 5 1.5 10 10 12 10 10 15 10 5 10 10 15 15 10 10 Age 30 30 10 10 20 20 10 20 10 10 20 40 10 10 10 10 20 20 10 10 25 20 25 35 20 20 25 20 20 20 Sex M M M M M M M M M M M M M M M M M M M M M M M M F F M M M M Ethnic 4 1 1 1 1 1 1 1 1 1 2 1 Activity 2 3 3 2 2 2 2 2 2 2 2 4 NO 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Q1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 Q2 5 3 7 10 10 10 3 2.5 2.5 10 10 20 20 5 3 5 Q3 5 15 3 7 10 10 10 5 5 2.5 15 20 15 10 10 5 5 5 Age 30 30 25 25 25 25 25 25 25 45 20 20 20 20 10 10 30 30 10 10 10 20 20 40 50 40 20 20 20 30 Sex M M F M M F F F F M M M M M M M M M M M M F F M M M F F M M Ethnic 3 1 2 2 1 2 3 2 2 5 1 1 Acti vity 2 2 2 2 2 2 3 2 3 3 3 3 3 304 Table 44 Survey results (Experiment 1) from Rochor path. Code descriptions are in questionnaire form (Figure 103) NO Q1 Q2 3.5 20. 20. 10. 10. Q3 3.5 15. 15. 10. 10. Age 20 7.0 10. 5.0 10. 4.0 1 10 1 11 5.0 12. 5.0 12. 12 13 14 15 3.5 10. 10. 16 17 Sex M Ethnic Activity 50 M 50 M 20 M 20 F 7.0 60 M 5.0 5.0 15. 5.5 20 50 M M 1 3 10 30 M M 1 2 30 M 20 M 3.5 30 M 7.5 20 M 7.5 20 M 5.0 5.0 30 M 5.0 20 M 18 5.0 5.0 10. 10 M 19 20 1 5.0 6.0 30 10 M M 1 21 22 1 M M 1 2 1 5.0 4.0 10. 5.0 5.0 50 25 23 24 25 5.0 8.0 10. 4.0 10. 5.0 5.0 50 25 25 F M M 1 2 26 F M 28 29 1 30 M M 1 2 30 31 1 5.0 10. 10. 5.0 15. 6.0 40 27 5.0 10. 10. 5.0 15. 8.0 50 25 M M T T NO 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 Acti vity M 20 M 20 M 7.0 20 M 5.0 25 F 5.0 5.0 5.0 5.0 20 20 M M 1 3 1 5.0 5.0 20 20 M M 1 3 2.5 5.0 5.0 15. 50 F 5.0 20 M 5.0 10. 8.0 30 F 5.0 20 M 30 F 60 M 7.5 10. 10. 5.0 10. 10. 8.0 20 M 7.5 20 M 1 9.0 3.0 7.5 12. 5.0 40 40 M M 1 1 5.0 7.0 7.0 8.0 40 20 F M 3 2 1 F M M 2 20 M 4.0 7.0 4.0 5.5 10. 10. 20 40 30 7.0 5.0 9.0 10. 20 M 1 4.0 5.0 20. 8.0 7.0 10. 20 20 M M 2 60 M Q1 Q2 6.5 Q3 6.5 Age 35 7.0 35 7.0 10. 7.0 10. 10. 7.0 10. 1 1 Sex M Ethnic 305 Table 45 Survey results (Experiment 1) from CBD path. Code descriptions are in questionnaire form (Figure 103) NO Q1 Q2 2.5 1 1 5.5 7.0 10. 3.0 10. Ethnic Activity F M 1 40 30 M M 30 Q3 5.0 10. 7.0 Age 30 20 40 1 5.0 10. 1.0 5.0 5.0 10. 10. 10. 1.0 10 7.0 11 12 1 5.0 5.0 13 14 15 1 7.0 9.0 2.5 Sex M NO Acti vity F M 30 30 M M 2 20 M 5.5 30 M 30 30 M M 1 2 60 M 9.0 5.0 5.0 6.0 10. 10. 9.0 60 20 M M 5.0 5.0 5.0 5.0 5.0 5.0 40 40 30 M M M 2 2 3 5 5 5 Acti vity 2 2 2 4 4 16 Q1 Q2 5.0 Q3 4.0 Age 50 2 17 18 1 2.0 5.0 50 30 1 2 19 20 1 5.0 4.0 M 21 5.0 4.0 3.0 15. 5.0 10. 30 F 22 4.0 60 40 M M 1 2 23 24 1 3.0 3.0 4.0 25 F 25 8.5 5.0 7.0 15. 4.0 2.5 50 25 F F 2 26 27 1 25 35 30 F M M 2 28 29 30 1 Sex M Ethnic Table 46 Survey results (Experiment 1) from Chinatown path. Code descriptions are in questionnaire form (Figure 103) NO 10 11 12 13 14 15 Q1 1 1 1 1 1 1 1 Q2 30 30 5 10 5 10 10 15 15 20 Q3 30 30 10 10 5 5 5 15 15 20 Age 50 50 20 20 30 30 30 30 40 30 50 50 10 40 40 Sex M F M F M M F F F M F M M M M Ethnic 5 5 3 5 3 Activity 4 4 2 2 3 4 NO 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Q1 1 1 1 1 1 1 1 Q2 10 2.5 2.5 5 45 45 2.5 10 10 2.5 Q3 6.5 2.5 5 10 5 2.5 Age 20 20 25 35 20 40 30 20 50 50 30 40 40 50 Sex M F F F F M M M M F M M M M M Ethnic 306 Table 47 Survey results (Experiment 2) from Tanjong Pagar path SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f ?A 15 10 5 6 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Dea 30 60 6 6 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Lena 10 15 5 ?B 15 10 6 6 6 Adi Putra 20 18 6 7 Doddy 30 27 4 Monica 15 20 6 5 Aditya Prashida 30 30 5 3 Dodo 15 15 5 5 Reza 15 10 4 5 Enrico 30 25 5 4 Rizki 25 30 5 Albert Pranata 10 15 5 6 Gito 6 6 Triana Eka 40 50 5 Ambia AK 7 4 6 Hami 15 20 2 Arin 20 20 4 4 4 Indy 15 15 5 6 Average 19.04348 20.86957 5.217391 5.217391 5.043478 4.913043 5.086957 5.130435 Budi Bakti 20 25 7 7 Karla 30 30 4 6 Charlie 10 10 4 4 Karyadi 15 12 6 6 STD 8.967001 13.16781 0.850482 0.902347 1.14726 1.124643 1.276111 1.179536 Coefficient 0.47087 0.630958 0.163009 0.17295 0.227474 0.22891 0.250859 0.229909 307 Table 48 Survey results (Experiment 2) from CBD path SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition 20 15 5 Aditya Prashida 45 60 5 Adrianto 30 20 6 Albert Pranata 10 15 6 Ambia AK 15 25 Anida Dyah P 10 Ardi 30 30 4 Arin 25 25 3 4 3e 3f 5 7 6 Q: 3a 3b 3c 3d Budi Bakti 15 15 6 Charlie 12 5 Dea 15 45 4 Dian 0 Doddy 25 20 4 Dodo 15 15 4 Enrico 25 23 5 Faizal RS 20 10 5 Gito 4 7 Hami 25 20 6 6 3e 3f 3 6 Q: 3a 3b 3c 3d ? Q: 3a 3b 3c 3d Indy 15 15 6 Karla 30 30 4 Karyadi 15 15 6 Lena 10 15 6 Monica 10 10 7 Petrina 30 15 4 Raymond 25 15 Reza 20 10 5 3e 3f 5 6 5 Q: 3a 3b 3c 3d Rifqi Muhandra 20 30 6 Rika 20 15 6 Rizki 25 25 4 Sukmah 20 15 7 Triana Eka 30 25 5 Average 19.70968 19.03226 4.870968 4.903226 5.419355 5.193548 STD 9.205772 11.83916 1.384243 1.135924 1.057487 1.077632 Coefficient 0.467069 0.622057 0.284182 0.231669 0.195132 0.207494 3e 3f 5 5 4.645161 4.903226 1.305077 1.075634 0.280954 0.219373 308 Table 49 Survey results (Experiment 2) from Chinatown path Aditya Prashida 15 10 4 Adrianto 10 10 3 Albert Pranata 10 15 Ambia AK 8 4 Anida Dyah P 15 3 Ardi 15 15 Arin 25 25 4 4 4 5 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d ?A 15 10 4 ?B 20 12 3 Adi Putra 12 3e 3f 6 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d Budi Bakti 15 10 3 Charlie 10 3 Dea 30 60 6 Dian 30 10 2 Doddy 20 25 5 Dodo 15 15 3 Enrico 20 20 4 Faizal RS 10 4 Gito 4 5 3e 3f 4 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d Hami 15 15 Indy 30 20 3 Jeanny 30 20 3 Karla 30 30 4 Karyadi 10 10 3 Lena 2 Monica 10 1 Petrina 15 20 4 Rangga 15 10 3 3e 3f 6 3 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d Raymond 10 6 Reza 25 10 3 Rifqi Muhandra 10 3 Rika 10 4 Rizki 20 15 Sukmah 15 10 5 Triana Eka 20 15 Yuri 10 10 3e 3f 7 309 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d Raymond 10 6 Reza 25 10 3 Rifqi Muhandra 10 3 Rika 10 4 Rizki 20 15 Sukmah 15 10 5 Triana Eka 20 15 Yuri 10 10 3e 3f 7 Q: 3a 3b 3c 3d 3e 3f Average 15.80556 13.97222 4.805556 3.555556 3.083333 4.666667 4.111111 4.638889 STD 7.756359 9.929713 1.064208 1.106976 1.024695 1.473577 1.785302 1.476536 Coefficient 0.490736 0.710675 0.221454 0.311337 0.332334 0.315766 0.434263 0.318295 310 Table 50 Survey results (Experiment 2) from Orchard path SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f ?A 20 20 7 7 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Ardi 120 30 7 7 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Enrico 35 30 5 5 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Lena 15 10 6 6 Adi Putra 17 15 7 7 ?B 14 6 6 Arin 25 25 5 5 Aditya Prashida 20 15 6 6 Aubrey 60 60 6 5 Faizal RS 10 7 6 Monica 15 10 7 7 Adrianto 120 120 6 Budi Bakti 20 20 6 6 Febri 20 20 5 5 Rangga 15 30 6 7 Gito 3 6 6 Albert Pranata 10 10 6 6 Ambia AK 20 12 7 Charlie 20 10 4 Dea 15 60 7 6 6 Dian 240 60 5 5 Hami 45 45 6 6 Indy 240 270 7 5 Jeanny 15 6 4 Raymond 30 10 7 7 Reza 15 10 6 Andhika Pradana 10 5 Doddy 35 30 4 5 4 Karla 45 45 5 6 Rifqi Muhandra 10 5 Anida Dyah P 20 10 5 4 Dodo 15 15 6 Karyadi 15 15 6 6 Rika 10 6 6 6 Rizki 150 45 7 6 311 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Sukmah 18 15 7 5 Triana Eka 120 50 7 6 Yuri 25 20 7 7 Average 42.894737 31.421053 5.7631579 5.8684211 5.8684211 5.7105263 5.2105263 5.6842105 STD 58.99555 45.76889 1.076386 1.069758 0.963413 0.956002 1.398027 0.873182 Coefficient 1.375356 1.456631 0.18677 0.182291 0.164169 0.167411 0.268308 0.153615 312 Table 51 Survey results (Experiment 2) from Rochor path SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f ?B 20 15 6 6 Adi Putra 10 10 5 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Ardi 15 15 5 Arin 20 20 5 5 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Hami 20 25 6 Indy 15 15 5 5 SAMPLE NAME Sense of Time Sense of Time Openness Spaciousness Scale Visibility Amount Visibility Distance Space Definition Q: 3a 3b 3c 3d 3e 3f Rika 10 15 Rizki 15 20 5 6 Aditya Prashida 25 20 6 6 Adrianto 20 20 6 6 Budi Bakti 10 10 5 4 Aubrey 15 15 6 5 Karla 30 30 3 Sukmah 15 10 5 6 Agnatasya 20 20 7 5 Charlie 10 4 Karyadi 15 12 5 Triana Eka 15 10 6 Albert Pranata 30 15 4 Lena 10 15 7 7 Yuri 20 15 6 7 Dea 30 90 6 3 Doddy 25 21 4 Monica 15 15 7 7 Average 17 18 5 5 5 Ambia AK 30 20 4 6 Dodo 15 15 6 6 Rangga 15 10 4 3 Faizal RS 10 10 4 5 Reza 20 15 5 STD 6.55959 14.13697 1.029798 1.227622 1.04727 1.343668 1.244342 1.050633 Anida Dyah P 20 20 5 4 Gito 8 5 6 Rifqi Muhandra 10 10 5 Coefficient 0.376177 0.795049 0.198515 0.241006 0.209454 0.296534 0.248868 0.203759 313 [...]... traditional to computer -based design process and analysis 26 1.2 IDENTIFICATION OF KNOWLEDGE GAP Developments of visibility analyses of urban geometrical space have started from twodimensional 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... analysis, and urban design analysis, in comparison with various traditional planar and spherical visibility analyses of urban geometrical space 4 To establish the meanings of VoS and VSI based on their relationships with human spatial 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... 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 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 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, and exactly and quantitatively translate users degree of certain perception, such as sense of. .. 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. .. continues with formulation of research objectives, research questions and design of research hypotheses 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... 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... 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... 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 . 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. POTENTIAL FUTURE WORKS 275 BIBLIOGRAPHY 277 APPENDIX A 286 APPENDIX B 302 5 SUMMARY A PERCEPTUAL EVALUATION OF URBAN SPACE USING GIS-BASED 3D VOLUMETRIC VISIBILITY ANALYSIS. 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