Preface |
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Introduction |
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1 | (26) |
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Spatial Statistics in ArcGIS |
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27 | (1) |
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Measuring geographic distributions |
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28 | (2) |
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30 | (3) |
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33 | (2) |
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Modeling spatial relationships |
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35 | (3) |
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38 | (1) |
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39 | (4) |
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40 | (3) |
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Spatial Statistics in SAS |
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43 | (1) |
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Spatial statistics and SAS |
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43 | (1) |
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SAS spatial analysis built-ins |
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44 | (1) |
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SAS implementation examples |
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45 | (6) |
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51 | (2) |
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51 | (2) |
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Spatial Econometric Functions in R |
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53 | (2) |
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Spatial models and spatial statistics |
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55 | (2) |
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Classes and methods in modelling using R |
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57 | (3) |
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Issues in prediction in spatial econometrics |
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60 | (5) |
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Boston housing values case |
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65 | (3) |
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68 | (5) |
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69 | (4) |
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GeoDa: An Introduction to Spatial Data Analysis |
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73 | (3) |
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76 | (2) |
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Mapping and geovisualization |
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78 | (2) |
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80 | (2) |
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Spatial autocorrelation analysis |
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82 | (2) |
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84 | (2) |
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86 | (5) |
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87 | (4) |
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STARS: Space-Time Analysis of Regional Systems |
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91 | (1) |
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92 | (1) |
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92 | (6) |
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98 | (11) |
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109 | (4) |
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111 | (2) |
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Space-Time Intelligence System Software for the Analysis of Complex Systems |
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113 | (2) |
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An approach to the analysis of complex systems |
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115 | (1) |
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116 | (1) |
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Exploratory space-time analysis |
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117 | (2) |
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119 | (3) |
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122 | (3) |
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123 | (2) |
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125 | (2) |
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Open source code versus black-box software |
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127 | (1) |
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128 | (3) |
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Affordability and user-friendliness |
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131 | (1) |
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132 | (3) |
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133 | (2) |
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GeoSurveillance: GIS-based Exploratory Spatial Analysis Tools for Monitoring Spatial Patterns and Clusters |
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135 | (2) |
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Structure of GeoSurveillance |
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137 | (1) |
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138 | (4) |
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Illustration of GeoSurveillance |
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142 | (6) |
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148 | (3) |
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149 | (2) |
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Web-based Analytical Tools for the Exploration of Spatial Data |
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151 | (1) |
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152 | (6) |
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158 | (5) |
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163 | (7) |
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170 | (5) |
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171 | (4) |
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PySAL: A Python Library of Spatial Analytical Methods |
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175 | (2) |
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177 | (3) |
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180 | (11) |
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191 | (6) |
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191 | (6) |
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PART B Spatial Statistics and Geostatistics |
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The Nature of Georeferenced Data |
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197 | (2) |
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From geographical reality to the spatial data matrix |
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199 | (5) |
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Properties of spatial data in the spatial data matrix |
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204 | (4) |
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Implications of spatial data properties for data analysis |
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208 | (6) |
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214 | (5) |
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214 | (5) |
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Exploratory Spatial Data Analysis |
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219 | (1) |
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Plotting and exploratory data analysis |
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220 | (4) |
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224 | (5) |
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Exploring point patterns and geostatistics |
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229 | (7) |
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236 | (13) |
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249 | (6) |
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250 | (5) |
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255 | (2) |
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Attributes and uses of the concept of spatial autocorrelation |
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257 | (2) |
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Representation of spatial autocorrelation |
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259 | (3) |
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Spatial autocorrelation measures and tests |
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262 | (10) |
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Problems in dealing with spatial autocorrelation |
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272 | (2) |
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Spatial autocorrelation software |
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274 | (5) |
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275 | (4) |
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279 | (1) |
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Global measures of spatial clustering |
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280 | (9) |
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Local measures of spatial clustering |
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289 | (8) |
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297 | (4) |
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298 | (3) |
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301 | (2) |
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Types of spatial filtering |
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303 | (9) |
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Eigenfunction spatial filtering and generalized linear models |
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312 | (1) |
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Eigenfunction spatial filtering and geographically weighted regression |
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313 | (2) |
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Eigenfunction spatial filtering and geographical interpolation |
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315 | (1) |
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Eigenfunction spatial filtering and spatial interaction data |
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316 | (1) |
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317 | (2) |
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317 | (2) |
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The Variogram and Kriging |
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319 | (1) |
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The theory of geostatistics |
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319 | (2) |
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321 | (6) |
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327 | (4) |
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Case study: The variogram |
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331 | (6) |
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Geostatistical prediction: Kriging |
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337 | (7) |
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344 | (11) |
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350 | (5) |
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Part C Spatial Econometrics |
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Spatial Econometric Models |
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355 | (5) |
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Estimation of spatial lag models |
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360 | (5) |
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Estimates of parameter dispersion and inference |
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365 | (1) |
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Interpreting parameter estimates |
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366 | (8) |
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374 | (3) |
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374 | (3) |
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Spatial Panel Data Models |
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377 | (1) |
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Standard models for spatial panels |
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378 | (4) |
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Estimation of panel data models |
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382 | (7) |
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Estimation of spatial panel data models |
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389 | (10) |
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Model comparison and prediction |
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399 | (4) |
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403 | (6) |
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405 | (4) |
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Spatial Econometric Methods for Modeling Origin-Destination Flows |
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409 | (1) |
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410 | (6) |
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Problems that plague empirical use of conventional spatial interaction models |
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416 | (15) |
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431 | (4) |
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432 | (3) |
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Spatial Econometric Model Averaging |
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435 | (1) |
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The theory of model averaging |
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436 | (4) |
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The theory applied to spatial regression models |
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440 | (4) |
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Model averaging for spatial regression models |
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444 | (6) |
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450 | (8) |
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458 | (3) |
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459 | (2) |
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Geographically Weighted Regression |
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461 | (1) |
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462 | (5) |
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467 | (2) |
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469 | (3) |
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472 | (2) |
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Bayesian hierarchical models as an alternative to GWR |
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474 | (3) |
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Bladder cancer mortality example |
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477 | (10) |
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484 | (3) |
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Expansion Method, Dependency, and Multimodeling |
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487 | (1) |
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488 | (5) |
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493 | (3) |
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496 | (5) |
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501 | (6) |
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502 | (5) |
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507 | (2) |
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Multilevel framework: A necessity for understanding ecological effects |
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509 | (1) |
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A typology of multilevel data structures |
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510 | (1) |
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The distinction between levels and variables |
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511 | (1) |
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512 | (1) |
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Multilevel statistical models |
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513 | (8) |
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Exploiting the flexibility of multilevel models to incorporating `realistic' complexity |
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521 | (2) |
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523 | (6) |
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524 | (5) |
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Part D The Analysis of Remotely Sensed Data |
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ARTMAP Neural Network Multisensor Fusion Model for Multiscale Land Cover Characterization |
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Background: Multiscale characterization of land cover |
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529 | (1) |
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Approaches for multiscale land cover characterization |
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530 | (2) |
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Research methodology and data |
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532 | (2) |
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534 | (6) |
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540 | (5) |
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541 | (4) |
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Model Selection in Markov Random Fields for High Spatial Resolution Hyperspectral Data |
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545 | (4) |
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Restoration, segmentation and classification of HSRH images |
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549 | (1) |
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Adjacency selection in Markov random fields |
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550 | (4) |
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A study of adjacency selection from hyperspectral data |
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554 | (6) |
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560 | (5) |
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561 | (4) |
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Geographic Object-based Image Change Analysis |
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565 | (1) |
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566 | (2) |
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Imagery and pre-processing requirements |
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568 | (1) |
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569 | (2) |
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571 | (1) |
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572 | (3) |
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575 | (1) |
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576 | (2) |
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578 | (7) |
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579 | (6) |
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Part E Applications in Economic Sciences |
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The Impact of Human Capital on Regional Labor Productivity in Europe |
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585 | (1) |
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Framework and methodology |
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586 | (6) |
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Application of the methodology |
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592 | (3) |
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595 | (4) |
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596 | (3) |
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Income Distribution Dynamics and Cross-Region Convergence in Europe |
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599 | (2) |
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601 | (7) |
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608 | (14) |
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622 | (7) |
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623 | (3) |
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626 | (3) |
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A Multi-Equation Spatial Econometric Model, with Application to EU Manufacturing Productivity Growth |
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629 | (1) |
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630 | (2) |
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Incorporating technical progress variations |
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632 | (5) |
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637 | (2) |
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639 | (3) |
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642 | (2) |
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644 | (9) |
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645 | (2) |
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647 | (6) |
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Part F Applications in Environmental Sciences |
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A Fuzzy k-Means Classification and a Bayesian Approach for Spatial Prediction of Landslide Hazard |
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653 | (2) |
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Overview of current prediction methods |
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655 | (3) |
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658 | (8) |
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Application of the modeling approach |
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666 | (13) |
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679 | (6) |
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680 | (5) |
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Incorporating Spatial Autocorrelation in Species Distribution Models |
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685 | (2) |
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687 | (4) |
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691 | (6) |
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697 | (6) |
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699 | (4) |
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A Web-based Environmental Decision Support System for Environmental Planning and Watershed Management |
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703 | (1) |
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704 | (1) |
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Design and implementation of WEDSS |
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705 | (7) |
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712 | (3) |
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715 | (6) |
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716 | (5) |
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Part G Applications in Health Sciences |
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Spatio-Temporal Patterns of Viral Meningitis in Michigan, 1993--2001 |
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721 | (2) |
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723 | (2) |
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725 | (5) |
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730 | (7) |
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734 | (3) |
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Space-Time Visualization and Analysis in the Cancer Atlas Viewer |
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737 | (2) |
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739 | (3) |
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742 | (8) |
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750 | (3) |
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751 | (2) |
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Exposure Assessment in Environmental Epidemiology |
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753 | (2) |
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755 | (2) |
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Features and architecture of Time-GIS |
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757 | (2) |
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759 | (6) |
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765 | (4) |
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766 | (3) |
List of Figures |
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769 | (10) |
List of Tables |
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779 | (6) |
Subject Index |
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785 | (8) |
Author Index |
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793 | (12) |
Contributing Authors |
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805 | |