Advances in Geographic Information Science

By: Daniel A. Griffith, Jean H. Paul Paelinck

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Paperback | 12 January 2011

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300 Pages

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22.86 x 15.88 x 1.91

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Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree "spatial analysis with measurement"-some may remember the debate after WWII about "theory without measurement" versus "measurement without theory"-several general themes have emerged in the pertaining literature. But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.

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Non-Fiction Earth Sciences, Geography, Environment, Planning Geography Economics Society & Culture Social Issues & Processes Migration, Immigration & Emigration Business & Management Management & Management Techniques

Non-standard Spatial Statistics
Introduction: Spatial Statistics	p. 3
Individual Versus Ecological Analyses	p. 5
Introduction	p. 5
Spatial Autocorrelation Effects	p. 5
Aggregation Impacts	p. 6
The Syracuse Data	p. 8
Previous Findings for Syracuse	p. 10
Spatial Autocorrelation in the Syracuse Data	p. 11
Spatial Autocorrelation in the Syracuse Data: LN(BLL + 1) Values	p. 11
Spatial Autocorrelation in the Syracuse Data: Appraised House Value	p. 13
Spatial Autocorrelation in the Syracuse Data: Other Sources	p. 16
Bayesian Analysis Using Gibbs Sampling (BUGS) and Model Prediction Experiments	p. 17
Results for the 2000 Census Tracts	p. 20
Discussion and Implications	p. 22
Statistical Models for Spatial Data: Some Linkages and Communalities	p. 25
Introduction	p. 25
Background: Quantifying Spatial Autocorrelation	p. 26
The Moran Scatterplot	p. 27
The Semivariogram Plot	p. 28
Specifications of Spatial Autoregressive and Geostatistical Models	p. 28
Spatial Autoregressive Models	p. 29
Geostatistical Models	p. 31
Linkages Between Spatial Autoregression and Geostatistics	p. 32
A Major Commonality of Spatial Autoregression and Geostatistics	p. 33
Implications for Quantitative Human Geography	p. 35
Frequency Distributions for Simulated Spatially Autocorrelated Random Variables	p. 37
Introduction	p. 37
The Normal Probability Model	p. 38
Simulating Spatially Autocorrelated Normal RVs	p. 39
Simulation Results for an Ideal Regular Hexagonal Surface Partitioning	p. 41
Simulation Results for the China County Geographic Configuration	p. 45
Implications	p. 48
The Poisson Probability Model	p. 50
Simulating Spatially Autocorrelated Poisson RVs	p. 52
Simulation Results for an Ideal Regular Hexagonal Surface Partitioning	p. 55
Simulation Results for the China County Geographic Configuration	p. 56
Implications	p. 60
The Binomial Probability Model, N > 1	p. 62
Simulating Spatially Autocorrelated Binomial RVs	p. 63
Simulation Results for an Ideal Regular Hexagonal Surface Partitioning	p. 65
Simulation Results for the China County Geographic Configuration	p. 68
Implications	p. 70
Discussion	p. 71
Understanding Correlations Among Spatial Processes	p. 75
Introduction	p. 75
Two Illustrative Examples	p. 75
Geostatistical Semivariogram Model Implications	p. 77
Spatial Autoregressive Model Implications	p. 82
Variance and Covariance Inflation Attributable to Spatial Autocorrelation	p. 85
Effective Sample Size as a Function of ¿_X and ¿_Y	p. 87
Spatial Filtering Model Implications	p. 89
Correlation Coefficient Decomposition	p. 90
Variance Inflation	p. 93
Discussion	p. 93
Spatially Structured Random Effects: A Comparison of Three Popular Specifications	p. 97
Introduction	p. 97
Modeling Spatial Structure	p. 97
Linear Mixed Models	p. 99
Generalized Linear Mixed Models	p. 105
Degrees of Freedom for GLMM Random Effects	p. 110
Extensions to Space-Time Data Sets	p. 111
Discussion and Implications	p. 114
Spatial Filter Versus Conventional Spatial Model Specifications: Some Comparisons	p. 117
Introduction	p. 117
Background	p. 117
Variation and Covariation Considerations for Poisson Random Variables	p. 120
Heterogeneity in Counts Data	p. 121
Spatial Autocorrelation in Poisson Random Variables	p. 124
Spatial Autocorrelation-induced Correlation Inflation	p. 126
Principal Spatial Statistical Model Specifications	p. 130
The Log-normal Approximation	p. 130
A Winsorized Auto-Poisson Model	p. 131
A Proper CAR Model Specification via GeoBUGS	p. 134
Spatial Filter Model Specifications	p. 136
The Log-normal Approximation Spatial Filter Model	p. 136
A Poisson Spatial Filter Model	p. 137
A Spatial Filter Model Specification via BUGS	p. 139
Discussion	p. 140
Cross-validation Results for the Poisson Spatial Filter Model	p. 141
A Simulation Experiment Based Upon the Poisson Spatial Filter Model	p. 141
Impacts of Incorporating Additional Information	p. 143
Implications for Data Mapping	p. 144
Concluding Comments	p. 147
The Role of Spatial Autocorrelation in Prioritizing Sites Within a Geographic Landscape	p. 151
Introduction: The Problem	p. 151
The Murray Superfund Site: Part I	p. 152
State-of-the-Art Practice	p. 153
A Spatial Methodology: Stage 1, Spatial Sampling Data Collection and Preprocessing	p. 154
The Murray Superfund Site: Part II	p. 156
A Spatial Methodology: Stage 2, Spatial Statistics for Calculating UCLs	p. 159
The Murray Superfund Site: Part III	p. 161
A Spatial Methodology: Stage 3, Prioritizing Subregions for Remediation	p. 163
The Murray Superfund Site: Part IV	p. 163
A Spatial Methodology: Stage 4, Covariation of Contaminants and Joint Pollutant Analyses	p. 164
The Murray Superfund Site: Part V	p. 168
Implications	p. 170
General Conclusions: Spatial Statistics	p. 171
Non-standard Spatial Econometrics
Introduction: Spatial Econometrics	p. 177
A Mixed Linear-Logarithmic Specification for Lotka-Volterra Models with Endogenously Generated SDLS-Variables	p. 179
Lotka-Volterra Models	p. 179
A General Specification	p. 179
Applications	p. 180
Simultaneous Dynamic Least Squares (SDLS) Estimation	p. 181
Mixed Specification	p. 182
Equations	p. 182
Stability	p. 183
Application	p. 183
Conclusion	p. 186
Selecting Spatial Regimes by Threshold Analysis	p. 189
Method	p. 189
Spatial Income Generating Model	p. 190
A Spatial Activity Complex Model	p. 193
Conclusion	p. 197
Appendix	p. 197
Finite Automata	p. 199
A Finite Automaton Bi-regional Dynamic Model	p. 199
An Empirical Application	p. 203
Conclusion	p. 205
Learning from Residuals	p. 207
Residuals	p. 207
Multiple Regimes	p. 209
Spatial Interpolation	p. 212
Composite Parameters	p. 213
Conclusion	p. 215
Verhulst and Poisson Distributions	p. 217
Robust Estimation in the Binary Case: A Linear Logistic Estimator (LLE)	p. 217
A Logistic Dynamic Share Model	p. 219
A Linear Poisson Distribution Estimator	p. 221
Conclusion	p. 225
Qualireg, A Qualitative Regression Method	p. 227
Qualiflex	p. 227
Qualireg	p. 230
Spatial Setting	p. 231
Conclusion	p. 233
Filtering Complexity for Observational Errors and Spatial Bias	p. 235
Complexity, Estimation and Testing	p. 235
Filtering for Observational Errors	p. 237
Further Filtering for Spatial Bias	p. 240
Conclusions	p. 241
General Spatial Econometric Conclusions	p. 243
Epilogue	p. 245
References	p. 247
Author Index	p. 257
Subject Index	p. 259
Table of Contents provided by Ingram. All Rights Reserved.

Advances in Geographic Information Science

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