Páez / Dall`erba / Gallo Progress in Spatial Analysis

Methods and Applications
2010
ISBN: 978-3-642-03326-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 492 Seiten

Reihe: Advances in Spatial Science

Space is increasingly recognized as a legitimate factor that influences many processes and conceptual frameworks, including notions of spatial coherence and spatial heterogeneity that have been demonstrated to provide substance to both theory and explanation. The potential and relevance of spatial analysis is increasingly understood by an expanding sphere of cogent disciplines that have adopted the tools of spatial analysis. This book brings together major new developments in spatial analysis techniques, including spatial statistics, econometrics, and spatial visualization, and applications to fields such as regional studies, transportation and land use, political and economic geography, population and health. Establishing connections to existing and emerging lines of research, the book also serves as a survey of the field of spatial analysis and its links with related areas.

Autoren/Hrsg.

Dall`erba, Sandy

Páez, Antonio

Gallo, Julie

Buliung, Ron N.

Dall'erba, Sandy

Inhaltsverzeichnis

1;Foreword;6
2;Contents;8
3;List of Figures;11
4;List of Tables;17
5;Contributors;23
6;Progress in Spatial Analysis: Introduction;27
6.1;1 Background;27
6.2;2 Theory and Methods;28
6.3;3 Thematic Applications;31
6.3.1;3.1 Spatial Analysis of Land Use and Transportation Systems;31
6.3.2;3.2 Economic and Political Geography;34
6.3.3;3.3 Spatial Analysis of Population and Health Issues;35
6.3.4;3.4 Regional Applications;37
7;Part I Theory and Methods;40
7.1;Omitted Variable Biases of OLS and Spatial Lag Models;41
7.1.1;1 Introduction;41
7.1.2;2 Spatial Dependencies and OLS Bias;42
7.1.3;3 A Comparison with Spatial Lag Models;48
7.1.4;4 Conclusion;50
7.1.5;References;52
7.2;Topology, Dependency Tests and Estimation Bias in Network Autoregressive Models;53
7.2.1;1 Introduction;53
7.2.2;2 Literature Review;54
7.2.2.1;2.1 Monte Carlo Simulation and the Properties of Tests for Dependence;54
7.2.2.2;2.2 Network Topology;56
7.2.2.3;2.3 Behaviour of the Likelihood Ratio Test When W is Dense;57
7.2.3;3 Experimental Design;60
7.2.3.1;3.1 Simulating Networks with Tunable Degree Distribution and Clustering Coefficient;60
7.2.3.2;3.2 Monte Carlo Simulations;61
7.2.4;4 Simulation Results;63
7.2.4.1;4.1 Likelihood Ratio Tests;63
7.2.4.2;4.2 Power of Test and Sample Size;64
7.2.4.3;4.3 Power of Test, Mean Degree and Sample Size;65
7.2.4.4;4.4 Power of Test and Clustering;67
7.2.4.5;4.5 Power of Test and Matrix Density;68
7.2.4.6;4.6 Estimation Bias;69
7.2.4.7;4.7 Estimation Bias and Sample Size;70
7.2.4.8;4.8 Estimation Bias and Mean Degree Distribution;71
7.2.4.9;4.9 Estimation Bias and Clustering Coefficient;72
7.2.4.10;4.10 Estimation Bias and Matrix Density;72
7.2.5;5 Regression Analysis;73
7.2.5.1;5.1 Logistic Regression for LR Test Results;73
7.2.6;6 Conclusions;77
7.2.7;References;80
7.3;Endogeneity in a Spatial Context: Properties of Estimators;82
7.3.1;1 Introduction;82
7.3.2;2 Endogeneity and Spatial Econometric Models;83
7.3.3;3 The Omitted Variable Case;86
7.3.4;4 Simultaneity and Measurement Errors;91
7.3.5;5 Conclusions;94
7.3.6;References;95
7.4;Dealing with Spatiotemporal Heterogeneity:The Generalized BME Model;97
7.4.1;1 Introduction;97
7.4.2;2 Method;98
7.4.2.1;2.1 Structural Stage;99
7.4.2.2;2.2 Specificatory Stage;101
7.4.2.3;2.3 Integration Stage;101
7.4.3;3 Numerical Experiments;102
7.4.3.1;3.1 Experiment 1;103
7.4.3.2;3.2 Experiment 2;107
7.4.3.3;3.3 Experiment 3;108
7.4.4;4 Discussion;110
7.4.5;5 Conclusions;110
7.4.6;References;111
7.5;Local Estimation of Spatial Autocorrelation Processes;114
7.5.1;1 Introduction;114
7.5.2;2 Instability in Parameters of Spatial Autocorrelation;115
7.5.3;3 Main Characteristics of the Monte Carlo Experiment;120
7.5.4;4 Diagnostic Measures and Maximum Likelihood Estimation When There Is Instability in the Mechanisms of Spatial Interaction;121
7.5.5;5 Local Estimation in the Cases of Stabilityand Instability in the DGP;125
7.5.5.1;5.1 The Zoom Estimation When the DGP Is Stable;126
7.5.5.2;5.2 The Zoom Estimation When the DGP Is Not Stable;129
7.5.6;6 A Proposal to Identify Spatial Regimes in the Parameterof Spatial Interaction;130
7.5.7;7 Conclusions;135
7.5.8;References;135
8;Part II Spatial Analysis of Land Use and Transportation Systems;138
8.1;``Seeing Is Believing'': Exploring Opportunities for theVisualization of Activity–Travel and Land Use Processesin Space–Time;139
8.1.1;1 Introduction;139
8.1.2;2 The ``Art and Science'' of Visualization;140
8.1.2.1;2.1 A Brief Note on ``Tools'';142
8.1.3;3 Geovisualizing Transportation and Land Use Processes;143
8.1.3.1;3.1 Regional Context and Data Sources;144
8.1.4;4 Activity–Travel Processes;145
8.1.4.1;4.1 People and Cars in Space and Time;145
8.1.4.2;4.2 Spatio-Demographic Travel Indicators;151
8.1.5;5 Land Use Processes;154
8.1.5.1;5.1 Transportation Network Coverage;154
8.1.5.2;5.2 Commercial Development in Space–Time;158
8.1.6;6 Conclusion;163
8.1.7;References;164
8.2;Pattern-Based Evaluation of Peri-Urban Developmentin Delaware County, Ohio, USA: Roads, Zoningand Spatial Externalities;168
8.2.1;1 Introduction;168
8.2.2;2 Study Area and Data;170
8.2.3;3 Processes Underlying Peri-Urban Development;171
8.2.3.1;3.1 Timing of Development;171
8.2.3.2;3.2 Spatial Influences;172
8.2.4;4 Methods;174
8.2.4.1;4.1 Landscape Pattern Analysis of Development 1988–2003;174
8.2.4.2;4.2 Survival Models;176
8.2.4.3;4.3 Survival Model Estimation;177
8.2.5;5 Results;179
8.2.6;6 Simulations;183
8.2.7;7 Discussion;184
8.2.8;8 Conclusion;185
8.2.9;References;186
8.3;Demand for Open Space and Urban Sprawl: The Case of Knox County, Tennessee;189
8.3.1;1 Introduction;189
8.3.2;2 Empirical Model;191
8.3.2.1;2.1 Step 1 – Estimation of the Marginal Implicit Price of Open Space;191
8.3.2.2;2.2 Step 2 – Open-Space Demand Estimation;196
8.3.3;3 Study Area and Data;197
8.3.4;4 Empirical Results;200
8.3.5;5 Conclusions;207
8.3.6;References;209
8.4;Multilevel Models of Commute Times for Men and Women;212
8.4.1;1 Introduction;212
8.4.2;2 Review of the Literature;212
8.4.3;3 Data;215
8.4.4;4 Methodology;216
8.4.5;5 Findings;219
8.4.5.1;5.1 Descriptive Statistics for the Household- and MSA-Level Dependent and Independent Variables;219
8.4.5.2;5.2 Men-Only Multilevel Model;222
8.4.5.3;5.3 Women-Only Multilevel Model;224
8.4.5.4;5.4 Pooled Men–Women Multilevel Model;225
8.4.5.5;5.5 Analysis of MSA-Level Residuals from Multilevel Models;226
8.4.5.6;5.6 Proportion of Variance Between and Within MSAs;227
8.4.6;6 Discussion;229
8.4.7;7 Conclusions;230
8.4.8;References;231
8.5;Walkability as a Summary Measure in a Spatially Autoregressive Mode Choice Model: An Instrumental Variable Approach;233
8.5.1;1 Introduction;233
8.5.2;2 Econometric Model;235
8.5.3;3 Data and Instrumental Variables;237
8.5.4;4 Discussion;240
8.5.5;5 Conclusion;243
8.5.6;References;244
9;Part III Economic and Political Geography;246
9.1;Employment Density in Ile-de-France: Evidence from Local Regressions;247
9.1.1;1 Introduction;247
9.1.2;2 Data and Spatial Weights Matrix;249
9.1.3;3 Global Results;252
9.1.4;4 Local Results;256
9.1.5;5 Conclusion;263
9.1.6;References;264
9.2;The Geographic Dimensions of Electoral Polarizationin the 2004 U.S. Presidential Vote;266
9.2.1;1 Introduction;266
9.2.2;2 Entrenchment: Geography's Role in Political Polarization;268
9.2.3;3 Data;274
9.2.4;4 Methods;277
9.2.5;5 Results;280
9.2.5.1;5.1 The Spatial Clustering of Votes and Covariates;280
9.2.5.2;5.2 Aggregate-Level Regression Results;284
9.2.6;6 Geographically Weighted Regression;288
9.2.7;7 Conclusion;295
9.2.8;References;295
9.3;Gender Wage Differentials and the Spatial Concentrationof High-Technology Industries;299
9.3.1;1 Introduction;299
9.3.2;2 High-Tech Cities and the Gender Gap;301
9.3.3;3 Data and Variables;304
9.3.3.1;3.1 Non-High-Tech Variables;304
9.3.3.2;3.2 High-Tech Variables;305
9.3.4;4 Empirical Framework;307
9.3.4.1;4.1 Estimating the Wage Equation with Endogenous Regressors;308
9.3.4.2;4.2 Estimating the Gender Wage Gap;309
9.3.5;5 Empirical Results;313
9.3.5.1;5.1 Benchmark and IV Models;313
9.3.5.2;5.2 Examining Instrument Validity;315
9.3.6;6 Decomposition of the Gender Wage Gap;317
9.3.7;7 Conclusions;318
9.3.8;References;319
9.4;Fiscal Policy and Interest Rates: The Role of Financialand Economic Integration;322
9.4.1;1 Introduction;322
9.4.2;2 Crowding Out and Spillover;324
9.4.3;3 A Spatial Test for Crowding Out;325
9.4.3.1;3.1 Specification;328
9.4.3.2;3.2 Data;329
9.4.4;4 Results for the Baseline Model;331
9.4.4.1;4.1 Spillover on Financial Markets: The Spatial Lag Model;331
9.4.4.2;4.2 Financial and Real Economic Integration;333
9.4.4.3;4.3 Some Control Variables;333
9.4.4.4;4.4 Time Variation in the Crowding Out Effect;335
9.4.5;5 Some Robustness Checks;338
9.4.5.1;5.1 Global or Local Linkages;338
9.4.5.2;5.2 Different Weight Matrices;340
9.4.5.3;5.3 Alternative Data Definitions;343
9.4.6;6 Conclusions;344
9.4.7;References;345
10;Part IV Spatial Analysis of Population and Health Issues;348
10.1;Spatial Models of Health Outcomes and Health Behaviors:The Role of Health Care Accessibility and Availability;349
10.1.1;1 Introduction;349
10.1.2;2 Background;351
10.1.2.1;2.1 Literature Review;351
10.1.2.2;2.2 Modeling the Link Between Health Status and Accessibility;353
10.1.3;3 Empirical Analysis;355
10.1.3.1;3.1 Study Area;355
10.1.4;4 Data and Measurements;356
10.1.4.1;4.1 Health Outcomes;357
10.1.4.2;4.2 Health Behavior Variables;359
10.1.4.3;4.3 Measures of Spatial Accessibility;359
10.1.4.4;4.4 Control Variables;361
10.1.4.5;4.5 Exploratory Spatial Data Analysis;361
10.1.4.6;4.6 Estimation Results;365
10.1.5;5 Summary and Conclusions;369
10.1.6;References;371
10.2;Immigrant Women, Preventive Health and Place in Canadian CMAs;373
10.2.1;1 Introduction;373
10.2.2;2 Methods;376
10.2.3;3 Results;379
10.2.3.1;3.1 Lifetime Uptake;380
10.2.3.2;3.2 Regular Pap Testing;383
10.2.4;4 Discussion;386
10.2.5;References;388
10.3;Is Growth in the Health Sector Correlated with Later-Life Migration?;391
10.3.1;1 Introduction;391
10.3.2;2 Data and Empirical Model;395
10.3.2.1;2.1 Migration Cohorts;397
10.3.2.2;2.2 Control Variables;398
10.3.2.3;2.3 Spatial Econometric Issues;400
10.3.2.4;2.4 Heteroskedastic-Spatial Autocorrelation Robust Standard Error Estimation;401
10.3.2.5;2.5 Model Specification;402
10.3.3;3 Results and Discussion;403
10.3.3.1;3.1 Model Specification Results;403
10.3.3.2;3.2 Important Control Variables;404
10.3.3.3;3.3 The Relationship Between In-Migrating Seniors and Concentration of Medical Professionals Appears Limited;404
10.3.3.4;3.4 Spatial Heterogeneity of Other Demographic and Economic Factors and Concentration of Medical Professionals;408
10.3.3.5;3.5 Agglomeration and Deglomeration of Office-Based Surgical and Medical Specialists;409
10.3.4;4 Conclusions;409
10.3.5;References;411
11;Part V Regional Applications;414
11.1;Evolution of the Influence of Geography on the Locationof Production in Spain (1930–2005);415
11.1.1;1 Introduction;415
11.1.2;2 Theoretical Principles and Background;416
11.1.2.1;2.1 First Nature;416
11.1.2.2;2.2 Second Nature;418
11.1.2.3;2.3 The Spanish Case;419
11.1.3;3 Data and Model;420
11.1.3.1;3.1 Data;420
11.1.3.2;3.2 Model;423
11.1.4;4 Evolution of the Spatial Distribution of GDP per Area;425
11.1.5;5 Influence of Geography on the Location of Production;431
11.1.5.1;5.1 Filtering Gross Second Nature from First Nature Elements;431
11.1.5.2;5.2 Second Nature Effects on GDP per Area;433
11.1.5.3;5.3 First and Second Nature Joint Effect on GDP per Area;439
11.1.5.4;5.4 First Nature Net Effect on GDP per Area;443
11.1.6;6 Conclusions;444
11.1.7;References;446
11.2;Comparative Spatial Dynamics of Regional Systems;449
11.2.1;1 Introduction;449
11.2.2;2 Theoretical and Methodological Motivations;451
11.2.2.1;2.1 Space, Time and Regional Inequality;451
11.2.2.2;2.2 Distribution Dynamics and Spatial Pattern Analysis;452
11.2.3;3 Empirical Motivation: Regional Inequality in China and the United States;453
11.2.4;4 Comparative Spatial Dynamics;455
11.2.4.1;4.1 Inequality and Spatial Dependence;455
11.2.4.2;4.2 Distance-Based Local Markov Transition;458
11.2.4.3;4.3 LISA Time Path;459
11.2.4.4;4.4 Space–Time Covariance Matrix;463
11.2.4.5;4.5 Inferential Issues;464
11.2.5;5 Summary;468
11.2.6;References;468
11.3;Growth and Spatial Dependence in Europe;472
11.3.1;1 Introduction;472
11.3.2;2 A Spatially Augmented Neoclassical Growth Model;473
11.3.2.1;2.1 Production Function and Spatial Externalities;473
11.3.2.2;2.2 Transitional Dynamics and Local Convergence;475
11.3.3;3 Data and Spatial Weight Matrix;477
11.3.4;4 Analysis and Results;478
11.3.4.1;4.1 Empirical Model and Spatial Econometric Framework;478
11.3.4.2;4.2 A Spatial Conditional Convergence Model;483
11.3.5;5 Conclusion;486
11.3.6;References;488
12;Author Index;490
13;Subject Index;496