Geospatial Analysis and Modelling of Urban Structure and Dynamics

Cover......Page 1
Geospatial Analysis and Modelling of Urban Structure and Dynamics......Page 2
The GeoJournal Library......Page 3
ISBN 9048185718......Page 5
A Coming of Age: Geospatial Analysis and Modelling in the Early Twenty First Century......Page 6
Acknowledgments......Page 10
Editors......Page 12
Contributors......Page 13
Contents......Page 28
Contributors......Page 32
Part 1: Introduction......Page 35
1.1 GIS = GISystems, GIScience, GIServices and GIStudies......Page 36
1.3 Modeling Urban Complexity and Hierarchy......Page 38
1.4 Simulating and Modeling Urban Transportation Systems......Page 39
1.6 Studying Other Urban Problem Using Geospatial Analysis and Modeling......Page 41
1.7 Conclusion......Page 42
References......Page 43
Part 2: Individual-Based Data Capture for Modeling Urban Structure and Dynamics......Page 45
2.1 Introduction......Page 46
2.2 The Potential Implications of High Resolution Data for Socio-geographic Research......Page 48
2.3 Simulation of Social Residential Segregation......Page 53
2.4 Discussion and Conclusions......Page 57
References......Page 58
3.1 Introduction......Page 61
3.2 Space Syntax......Page 62
3.2.1.1 Axial Maps......Page 63
3.2.1.2 Metrics......Page 64
3.2.2.2 Gatecounts......Page 66
3.2.2.3 Static Snapshots......Page 68
3.2.2.4 Trails......Page 69
3.2.3 Empirical Results......Page 70
3.2.4 Criticism......Page 72
3.3 Pervasive Systems and Space Syntax......Page 73
3.4.1 Space Syntax and Application Development......Page 74
3.4.2 Space Syntax as an Explanatory Tool......Page 76
3.4.3 Space Syntax as a Modelling Tool......Page 78
References......Page 80
4.1 Introduction......Page 83
4.2.1 The City in Flux......Page 85
4.2.2 Decentralized Spatial Computing......Page 86
4.2.3 Safeguarding Privacy......Page 87
4.3.1 An LBS Scenario......Page 88
4.3.2 Experimental Methodology......Page 90
4.4 Experiment #1: Quality of Service versus Level of Privacy......Page 91
4.5 Experiment #2: Communication Network Effects......Page 94
4.6 Experiment #3: Goal Directed Movement......Page 96
4.7 Experiment #4: Push and Pull Queries......Page 97
4.8 Conclusions and Outlook......Page 100
References......Page 102
Part 3: Modeling Urban Complexity and Hierarchy......Page 105
Network Cities: A Complexity-Network Approach to Urban Dynamics and Development......Page 106
5.1 Introduction......Page 107
5.2 Methodology......Page 108
5.3 The Model......Page 109
5.4 Analysis......Page 111
5.4.1 The Parameter PV0......Page 112
5.4.2 The Parameter beta......Page 114
5.4.3 The Parameter PT......Page 115
5.5 Conclusions......Page 116
References......Page 117
6.1 Introduction......Page 120
6.2.1 Mathematical Models......Page 122
6.2.2 Basic Principles......Page 124
6.3.1 City Classification Based on Urban Numbers......Page 127
6.3.2 City Classification Based on Population Sizes......Page 131
6.4.1 Symmetry Analysis of the Two Classification Results for Regional Studies......Page 134
6.4.2 Analysis of Spatial Interactions in Urban Studies......Page 135
6.4.3 Town Classification in the Small-Scaled Region......Page 138
6.5 Concluding Remarks......Page 140
6.6.1 The Concept of ``City\'\' in China at Present......Page 143
6.6.2 The Equivalence Relation Between the 2n Rule and the Rank-Size Rule with an Exponent -1......Page 144
References......Page 145
Part 4: Simulating and Modeling Urban Transportation Systems......Page 147
7.1 Introduction......Page 148
7.2 Parking Models......Page 150
7.3.1 Infrastructure GIS......Page 151
7.3.3 Driver\'s Decision to Park on the Way to Destination......Page 153
7.3.5 Algorithm of Car Following......Page 155
7.4 Non-spatial (Point) Model of Parking......Page 156
7.5 Cruising for Commercial Parking......Page 157
7.6 Cruising Threshold......Page 159
7.7 Effects of Space on Cruising......Page 160
7.7.1 Equal Arrival and Egress Rates......Page 161
7.7.2 When the Point Approximation Becomes Sufficient?......Page 162
7.8 Conclusions......Page 163
References......Page 165
Multiscale Modeling of Virtual Urban Environments and Associated Populations......Page 166
8.1 Introduction......Page 167
8.2 Our VUE Design Pattern......Page 168
8.2.1 Locations......Page 169
8.2.3 Places/Transport Network Relationships......Page 170
8.2.4 Illustration......Page 171
8.3 Example of Creating a Multiscale VUE: The Case of Quebec-City......Page 172
8.4 The Synthetic Population of Quebec-city......Page 175
8.4.1 Estimation of Non-spatial Attributes......Page 177
8.4.2 Allocation of Spatial Attributes......Page 178
8.5.1 Pre-processing Data for TransNetSIM......Page 182
8.5.2 Simulation of Trips Using TransNetSIM......Page 184
8.5.3 TransNetSIM\'s Performance......Page 185
8.6 Conclusion......Page 187
References......Page 188
9.1 Introduction......Page 190
9.2 Topology of Urban Street Networks and Imageability......Page 193
9.3 The Tel Aviv Street Network: Structural Qualities and Imageability......Page 197
9.4 Conclusion......Page 200
References......Page 201
10.1 Introduction......Page 203
10.2.1 Urban Transportation and City Livability......Page 204
10.2.2 UrbanTransportation Studies in China......Page 205
10.3 Data Used for This Study......Page 206
10.3.2 Objective Measurements for Beijing\'s Urban Transportation......Page 207
10.4.1 Subjective Scores for Urban Transportation Convenience......Page 208
10.4.2 Objective Scores for Urban Transportation Convenience......Page 210
10.4.3 Link the Perceived Transportation Convenience with the Measured......Page 211
10.5 Analysis of Results......Page 212
10.6 Discussion and Conclusions......Page 217
References......Page 220
11.1 Introduction......Page 222
11.2.1 Modeling Multi-Modal Networks......Page 224
11.3 Modeling Transportation Networks in Urban Environments......Page 226
11.3.1.1 Conceptualizing the Floor Network......Page 227
11.3.1.2 Modeling Movement Between Floors......Page 230
Street Network......Page 231
Bus Routes......Page 232
Modeling Transfers Between Modes......Page 233
11.4.1 FloorNetwork......Page 234
11.4.3 FloorTurns......Page 235
11.4.5 Streets......Page 236
11.4.10 Streets_Walkways_Transfer......Page 237
11.4.11 Creating the Network Dataset......Page 238
11.5.1.1 Determining the Least-Effort Route Inside a Building......Page 239
11.5.1.3 Determining an ``All-Inside\'\' Least-Effort Route Between Two Buildings......Page 240
11.6 Conclusions......Page 242
References......Page 243
Part 5: Analyzing and Modeling Urban Grown, Urban Changes and Impacts......Page 246
Integration of Remote Sensing with GIS for Urban Growth Characterization......Page 247
12.1 Introduction......Page 248
12.2 Integrating Remote Sensing and GIS for Urban Growth Research......Page 249
12.3 The Case Study Site......Page 250
12.4.1 Data Acquisition and Assemblage......Page 252
12.4.2 Image Processing of Remotely Sensed Data......Page 255
12.4.3 Change Detection......Page 256
12.4.4 Spatial Statistical Analysis......Page 257
12.4.5 Predictive Modeling and Simulation......Page 258
12.5 Urban Spatial Growth......Page 260
12.6.1 High-Density Urban Use......Page 263
12.6.2 Low-Density Urban Use......Page 266
12.7 Future Urban Growth Scenario Simulation......Page 267
12.8 Conclusions......Page 271
References......Page 272
13.1 Introduction......Page 275
13.2.1 Study Area......Page 277
13.2.2 Data Sets......Page 278
13.2.3 Landscape Ecological Measures......Page 280
13.3 Results and Analysis......Page 281
13.4 Conclusions......Page 286
References......Page 287
14.1 Introduction and Motivation......Page 289
14.2 Historical and Current Perspectives on the Urban Rainfall Effects......Page 290
14.3 Modeling Studies of the Urban Rainfall Effect......Page 294
14.4.1.1 Model Configuration and Approach......Page 297
14.4.1.2 Results......Page 298
14.4.2.1 Configuration and Approach......Page 302
14.4.2.2 Results......Page 304
14.5 Recommendations to Improve Model Studies of the Urban Rainfall Effect......Page 308
References......Page 311
Interpolating a Consumption Variable for Scaling and Generalizing Potential Population Pressure on Urbanizing Natural Areas......Page 317
15.1 Introduction......Page 318
15.2 Scales of Urbanization......Page 319
15.3 Approach for the Analysis......Page 322
15.3.1 Related Literature......Page 324
15.3.2 Study Area......Page 325
15.3.3 Data Sources and Analysis......Page 326
15.4 Results......Page 330
15.5 Conclusions......Page 331
References......Page 332
16.1 Introduction......Page 335
16.2 Time and Spatio-Temporal Modeling......Page 338
16.3 Spatio-Temporal Ontology for Places......Page 339
16.4.1 Some Modeling Issues......Page 341
16.4.1.1 Vagueness and Identity......Page 342
16.4.2 Spatio-Temporal Place Model......Page 343
16.5 Analyzing Spatio-Temporal Relationship......Page 346
16.6 Discussions and Future Research......Page 349
References......Page 350
Part 6: Studying Other Urban Problems Using Geospatial Analysis and Modeling......Page 353
17.1 Introduction......Page 354
17.2 Networks of Urban Space in a Living City......Page 355
17.3 Geospatial Analysis and ``Urban Seeding\'\'......Page 358
17.4 Useful and Useless Urban Space......Page 359
17.5 Urban Complexity and Modular Decomposition......Page 361
17.6 Three Different Metaphors for a City......Page 362
17.7 Do We Wish to Connect to Our Neighbor?......Page 365
17.8 Respecting and Re-creating Complex Urban Fabric......Page 366
17.9 The Problem of Designing the City\'s Periphery......Page 368
17.10 Spatio-Temporal Scale Jumps and Their Implications......Page 371
17.11 Spontaneous Settlements: What We Can Learn from Them......Page 372
17.12 Conclusion......Page 374
References......Page 375
Analyzing Spatial Patterns of Late-Stage Breast Cancer in Chicago Region: A Modified Scale-Space Clustering Approach......Page 377
18.1 Late-Stage Breast Cancer and Risk Factors......Page 378
18.1.1 Data and Methods......Page 379
18.2 Modified Scale-Space Clustering Method (MSSC)......Page 381
18.3.1 Clustering Process by the MSSC Method......Page 383
18.3.2 Visualizing Local Maxima......Page 384
18.3.3 Level of Convergence......Page 386
18.3.4 Spatial Regression......Page 388
18.4 Discussion......Page 389
18.5 Summary......Page 391
References......Page 392
19.1 Introduction......Page 394
19.2.1 Research Strategies......Page 396
19.2.2 Empirical Challenges......Page 397
19.3.1 Job Accessibility Measures......Page 399
19.3.2 Factor Analysis......Page 400
19.3.3 Hedonic Modeling......Page 401
19.4.1 Study Area......Page 402
19.4.2 Job Accessibility......Page 404
19.4.4 Global Regression......Page 405
19.4.5 Local Regression......Page 406
19.5 Conclusions......Page 409
References......Page 410
20.1 Introduction......Page 413
20.2.1 Efficiency Versus Equity?......Page 415
20.2.2 Accessibility, Hurricane Relief Service Provision, and Transportation......Page 416
20.3 Relief Distribution System Architectures......Page 417
20.3.1 Model Overview......Page 418
20.4.1 Study Area and Data......Page 420
20.4.3 Efficiency and Equity Analysis......Page 422
20.4.3.1 Equity analysis with the p-center problem......Page 423
20.4.3.3 Comparisons of p-median and p-center Results......Page 426
20.4.3.4 Exploring Service Differences Across Income Groups......Page 430
20.5 Discussion and Conclusions......Page 431
References......Page 432
21.1 Introduction......Page 435
21.2.1 Geographically Weighted Regression......Page 437
21.2.2 Diagnostic Tools......Page 438
21.2.3 Visualization Tools......Page 440
21.3 Census Undercount in Franklin County, Ohio......Page 442
21.4 Conclusion......Page 454
References......Page 455
Why Does Location Matter?......Page 457
The World is Flat (and Rural)......Page 458
Looking Forward......Page 459
References......Page 461
Index......Page 463