| Preface |
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xv | |
| Acknowledgements |
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xvii | |
| Introduction |
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1 | (1) |
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1 | (3) |
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What is spatial data analysis? |
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4 | (1) |
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5 | (3) |
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8 | (2) |
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10 | (5) |
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Part A The context for spatial data analysis |
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Spatial data analysis: scientific and policy context |
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15 | (28) |
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Spatial data analysis in science |
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15 | (7) |
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Generic issues of place, context and space in scientific explanation |
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16 | (1) |
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Location as place and context |
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16 | (2) |
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Location and spatial relationships |
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18 | (3) |
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21 | (1) |
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Place and space in specific areas of scientific explanation |
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22 | (14) |
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Defining spatial subdisciplines |
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22 | (2) |
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Examples: selected research areas |
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24 | (1) |
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Environmental criminology |
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24 | (2) |
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Geographical and environmental (spatial) epidemiology |
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26 | (3) |
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Regional economics and the new economic geography |
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29 | (2) |
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31 | (1) |
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32 | (1) |
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Spatial data analysis in problem solving |
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33 | (3) |
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Spatial data analysis in the policy area |
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36 | (4) |
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Some examples of problems that arise in analysing spatial data |
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40 | (1) |
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Description and map interpretation |
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40 | (1) |
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41 | (1) |
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41 | (1) |
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41 | (2) |
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The nature of spatial data |
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43 | (48) |
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The spatial data matrix: conceptualization and representation issues |
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44 | (10) |
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Geographic space: objects, fields and geometric representations |
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44 | (2) |
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Geographic space: spatial dependence in attribute values |
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46 | (1) |
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47 | (1) |
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48 | (2) |
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50 | (1) |
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51 | (3) |
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The spatial data matrix: its form |
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54 | (3) |
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The spatial data matrix: its quality |
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57 | (17) |
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58 | (1) |
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59 | (1) |
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Spatial representation: general considerations |
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59 | (2) |
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Spatial representation: resolution and aggregation |
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61 | (1) |
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61 | (2) |
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63 | (4) |
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67 | (3) |
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70 | (1) |
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71 | (3) |
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Quantifying spatial dependence |
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74 | (13) |
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Fields: data from two-dimensional continuous space |
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74 | (5) |
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Objects: data from two-dimensional discrete space |
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79 | (8) |
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87 | (4) |
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Part B Spatial data: obtaining data and quality issues |
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Obtaining spatial data through sampling |
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91 | (25) |
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91 | (2) |
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93 | (20) |
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The purpose and conduct of spatial sampling |
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93 | (3) |
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Design- and model-based approaches to spatial sampling |
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96 | (1) |
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Design-based approach to sampling |
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96 | (2) |
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Model-based approach to sampling |
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98 | (1) |
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99 | (1) |
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100 | (3) |
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Selected sampling problems |
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103 | (1) |
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Design-based estimation of the population mean |
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103 | (3) |
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Model-based estimation of means |
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106 | (1) |
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107 | (1) |
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Sampling to identify extreme values or detect rare events |
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108 | (5) |
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113 | (3) |
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Data quality: implications for spatial data analysis |
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116 | (65) |
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Errors in data and spatial data analysis |
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116 | (11) |
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Models for measurement error |
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116 | (1) |
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117 | (1) |
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Spatially correlated error models |
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118 | (1) |
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119 | (1) |
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119 | (3) |
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122 | (1) |
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Testing for outliers in large data sets |
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123 | (1) |
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124 | (3) |
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Data resolution and spatial data analysis |
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127 | (24) |
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Variable precision and tests of significance |
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128 | (1) |
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The change of support problem |
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129 | (1) |
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Change of support in geostatistics |
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129 | (2) |
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131 | (7) |
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Analysing relationships using aggregate data |
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138 | (3) |
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Ecological inference: parameter estimation |
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141 | (6) |
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Ecological inference in environmental epidemiology: identifying valid hypotheses |
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147 | (3) |
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The modifiable areal units problem (MAUP) |
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150 | (1) |
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Data consistency and spatial data analysis |
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151 | (1) |
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Data completeness and spatial data analysis |
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152 | (25) |
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154 | (2) |
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Approaches to analysis when data are missing |
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156 | (3) |
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Approaches to analysis when spatial data are missing |
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159 | (5) |
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Spatial interpolation, spatial prediction |
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164 | (10) |
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Boundaries, weights matrices and data completeness |
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174 | (3) |
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177 | (4) |
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Part C The exploratory analysis of spatial data |
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Exploratory spatial data analysis: conceptual models |
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181 | (7) |
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181 | (2) |
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Conceptual models of spatial variation |
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183 | (5) |
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183 | (1) |
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Spatial `rough' and `smooth' |
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184 | (1) |
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Scales of spatial variation |
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185 | (3) |
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Exploratory spatial data analysis: visualization methods |
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188 | (38) |
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Data visualization and exploratory data analysis |
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188 | (6) |
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Data visualization: approaches and tasks |
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189 | (3) |
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Data visualization: developments through computers |
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192 | (1) |
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Data visualization: selected techniques |
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193 | (1) |
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194 | (16) |
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Data preparation issues for aggregated data: variable values |
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194 | (5) |
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Data preparation issues for aggregated data: the spatial framework |
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199 | (1) |
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Non-spatial approaches to region building |
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200 | (1) |
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Spatial approaches to region building |
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201 | (2) |
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Design criteria for region building |
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203 | (3) |
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Special issues in the visualization of spatial data |
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206 | (4) |
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Data visualization and exploratory spatial data analysis |
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210 | (15) |
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Spatial data visualization: selected techniques for univariate data |
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211 | (1) |
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Methods for data associated with point or area objects |
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211 | (4) |
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Methods for data from a continuous surface |
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215 | (3) |
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Spatial data visualization: selected techniques for bi- and multi-variate data |
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218 | (1) |
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Uptake of breast cancer screening in Sheffield |
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219 | (6) |
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225 | (1) |
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Exploratory spatial data analysis: numerical methods |
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226 | (47) |
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227 | (10) |
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Resistant smoothing of graph plots |
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227 | (1) |
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Resistant description of spatial dependencies |
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228 | (1) |
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228 | (2) |
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Simple mean and median smoothers |
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230 | (1) |
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Introducing distance weighting |
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230 | (2) |
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232 | (2) |
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Non-linear smoothing: headbanging |
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234 | (2) |
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Non-linear smoothing: median polishing |
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236 | (1) |
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Some comparative examples |
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237 | (1) |
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The exploratory identification of global map properties: overall clustering |
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237 | (13) |
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242 | (5) |
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Clustering in a marked point pattern |
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247 | (3) |
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The exploratory identification of local map properties |
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250 | (15) |
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251 | (1) |
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251 | (8) |
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259 | (4) |
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263 | (2) |
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265 | (8) |
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265 | (3) |
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268 | (5) |
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Part D Hypothesis testing and spatial autocorrelation |
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Hypothesis testing in the presence of spatial dependence |
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273 | (16) |
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Spatial autocorrelation and testing the mean of a spatial data set |
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275 | (3) |
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Spatial autocorrelation and tests of bivariate association |
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278 | (11) |
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Pearson's product moment correlation coefficient |
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278 | (5) |
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Chi-square tests for contingency tables |
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283 | (6) |
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Part E Modelling spatial data |
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Models for the statistical analysis of spatial data |
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289 | (36) |
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292 | (20) |
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Models for large-scale spatial variation |
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293 | (1) |
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Models for small-scale spatial variation |
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293 | (1) |
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Models for data from a surface |
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293 | (4) |
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Models for continuous-valued area data |
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297 | (7) |
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Models for discrete-valued area data |
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304 | (2) |
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Models with several scales of spatial variation |
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306 | (1) |
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Hierarchical Bayesian models |
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307 | (5) |
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312 | (13) |
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Models for continuous-valued response variables: normal regression models |
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312 | (4) |
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Models for discrete-valued area data: generalized linear models |
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316 | (4) |
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Adding covariates to hierarchical Bayesian models |
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320 | (1) |
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Modelling spatial context: multi-level models |
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321 | (4) |
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Statistical modelling of spatial variation: descriptive modelling |
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325 | (25) |
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Models for representing spatial variation |
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325 | (13) |
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Models for continuous-valued variables |
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326 | (1) |
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Trend surface models with independent errors |
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326 | (1) |
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Semi-variogram and covariance models |
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327 | (4) |
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Trend surface models with spatially correlated errors |
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331 | (3) |
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Models for discrete-valued variables |
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334 | (4) |
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Some general problems in modelling spatial variation |
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338 | (1) |
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Hierarchical Bayesian models |
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339 | (11) |
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Statistical modelling of spatial variation: explanatory modelling |
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350 | (29) |
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Methodologies for spatial data modelling |
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350 | (8) |
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350 | (3) |
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353 | (2) |
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A general spatial specification |
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355 | (1) |
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Two models of spatial pricing |
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356 | (2) |
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A `data-driven' methodology |
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358 | (1) |
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Some applications of linear modelling of spatial data |
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358 | (20) |
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Testing for regional income convergence |
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359 | (2) |
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Models for binary responses |
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361 | (1) |
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A logistic model with spatial lags on the covariates |
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361 | (3) |
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Autologistic models with covariates |
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364 | (1) |
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365 | (2) |
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Bayesian modelling of burglaries in Sheffield |
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367 | (9) |
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Bayesian modelling of children excluded from school |
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376 | (2) |
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378 | (1) |
| Appendix I Software |
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379 | (2) |
| Appendix II Cambridgeshire lung cancer data |
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381 | (4) |
| Appendix III Sheffield burglary data |
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385 | (6) |
| Appendix IV Children excluded from school: Sheffield |
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391 | (3) |
| References |
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394 | (30) |
| Index |
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424 | |
