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<p>Foreword ix</p> <p>Preface xi</p> <p>Introduction xv</p> <p><b>Chapter 1. Methodological Context</b> <b>1</b></p> <p>1.1. A systemic approach to health 1</p> <p>1.2. Risk and public health 5</p> <p>1.3. Epidemiology 9</p> <p>1.4. Health geography 10</p> <p>1.5. Spatial analysis for epidemiology and health geography 11</p> <p>1.6. Geographic information systems 16</p> <p>1.7. Book structure 18</p> <p><b>Chapter 2. Spatial Analysis of Health Phenomena: General Principles</b> <b>21</b></p> <p>2.1. Spatial analysis in epidemiology and health geography 21</p> <p>2.1.1. Spatial distribution of a health phenomenon 21</p> <p>2.1.2. Spatial analysis in epidemiology 23</p> <p>2.1.3. Spatial and statistical dependence 28</p> <p>2.1.4. Causal relationships, explanatory factors, confounding factors 29</p> <p>2.1.5. Uncertainty in event localization 30</p> <p>2.1.6. Health data are often aggregated into geographical units 30</p> <p>2.2. Spatial analysis terminology and formalism 32</p> <p>2.2.1. Objects, attributes, events 33</p> <p>2.2.2. Localization and spatial domain 34</p> <p>2.2.3. The formalism of descriptive analysis 36</p> <p>2.2.4. The formalism of the explanatory analysis 39</p> <p>2.3. General approach of spatial analysis in epidemiology 42</p> <p>2.3.1. The approach of descriptive analysis 42</p> <p>2.3.2. The approach of explanatory analysis 44</p> <p>2.3.3. Spatial analysis methods 45</p> <p>2.3.4. Spatial analysis and health geography 46</p> <p>2.4. Required knowledge on epidemiology and statistics 47</p> <p>2.4.1. Epidemiology 47</p> <p>2.4.2. Statistical analysis 48</p> <p>2.4.3. Methods for model adjustment. 52</p> <p>2.4.4. Several distributions and models 58</p> <p><b>Chapter 3. Spatial Data in Health</b> <b>63</b></p> <p>3.1. Introduction 63</p> <p>3.2. Health data 64</p> <p>3.2.1. Various types of data for individuals 64</p> <p>3.2.2. Individual and aggregated health data 65</p> <p>3.2.3. Description of the healthcare system 66</p> <p>3.3. Spatialization of epidemiological data 66</p> <p>3.3.1. Localization in space 66</p> <p>3.3.2. Localization in time 68</p> <p>3.3.3. Localization in time and space 68</p> <p>3.3.4. Data aggregated according to a spatial criterion 68</p> <p>3.3.5. Ethics and localization 69</p> <p>3.4. Sources of data 70</p> <p>3.4.1. Epidemiological data 70</p> <p>3.4.2. Geographical and environmental data 71</p> <p>3.4.3. Access to geographical data 72</p> <p><b>Chapter 4. Cartographic Representations and Synthesis Tools</b> <b>75</b></p> <p>4.1. Introduction 75</p> <p>4.1.1. Why use mapping methods? 75</p> <p>4.1.2. How to use mapping? 76</p> <p>4.2. Cartographic representations 78</p> <p>4.2.1. Mapping events or health status 78</p> <p>4.2.2. Mapping rates: prevalence, incidence, risk and odds ratio 78</p> <p>4.2.3. Mapping flows and spatial relationships 82</p> <p>4.2.4. Mapping limitations 83</p> <p>4.2.5. Mapping rate significance 89</p> <p>4.2.6. Rate adjustment 90</p> <p>4.3. Descriptive statistics and visual synthesis tools 93</p> <p>4.3.1. Average points, median points 93</p> <p>4.3.2. Standard deviational ellipses 95</p> <p>4.4. Interpolations and trend surfaces 97</p> <p>4.4.1. Interpolations and continuous representation 97</p> <p>4.4.2. Directions and gradients 103</p> <p>4.4.3. Anamorphoses 103</p> <p>4.5. Spatio-temporal animations 104</p> <p>4.5.1. What and how 104</p> <p>4.5.2. Animated mapping 105</p> <p><b>Chapter 5. Spatial Distribution Analysis</b> <b>109</b></p> <p>5.1. Introduction 109</p> <p>5.1.1. “Direct” methods for spatial analysis 109</p> <p>5.1.2. Continuous space, point pattern, subsets 113</p> <p>5.2. Global spatial analyses 115</p> <p>5.2.1. Geographical location, extent, orientation 115</p> <p>5.2.2. Centrality 118</p> <p>5.2.3. Spatial dependence of values 120</p> <p>5.2.4. Bivariate spatial analysis 133</p> <p>5.2.5. Global pattern of the phenomenon and search for a geometric model 138</p> <p>5.3. Local spatial analyses 139</p> <p>5.3.1. Local indicators of spatial association (LISA) 140</p> <p>5.3.2. Spatial scan-based search for singularities 145</p> <p>5.3.3. Analyses around a source point 151</p> <p>5.4. Example: emergence and diffusion of avian influenza 153</p> <p>5.4.1. Introduction 153</p> <p>5.4.2. Mapping 155</p> <p>5.4.3. Analysis of the spatial distribution of cases 157</p> <p>5.4.4. Spatio-temporal analyses 165</p> <p>5.4.5. Analyses of risk factors 172</p> <p><b>Chapter 6. Spatial Analysis of Risk</b> <b>177</b></p> <p>6.1. Introduction 177</p> <p>6.2. Aggregation-based spatial analyses 177</p> <p>6.2.1. Spatial aggregation operation 179</p> <p>6.2.2. Statistical analysis 183</p> <p>6.2.3. Spatial analysis of aggregation. 195</p> <p>6.2.4. Spatial belonging 198</p> <p>6.3. Statistical modeling of spatial data 198</p> <p>6.3.1. Statistical correlations and spatial relationships 199</p> <p>6.3.2. Statistical modeling 200</p> <p>6.3.3. Spatial models 201</p> <p>6.3.4. Spatial heterogeneity of parameters 204</p> <p>6.4. An example: analysis of tuberculosis risk factors 207</p> <p>6.4.1. Epidemiological and socio-economic data 208</p> <p>6.4.2. Analysis of the statistical and spatial distribution of rates 209</p> <p>6.4.3. Statistical modeling of SMR and incidence 213</p> <p><b>Chapter 7. Space–time Analyses and Modeling</b> <b>219</b></p> <p>7.1. Time–distance relationships 219</p> <p>7.2. Mobile mean points 220</p> <p>7.3. Spatio-temporal autocorrelation and clusters 222</p> <p>7.3.1. Global spatio-temporal autocorrelation 222</p> <p>7.3.2. Local spatio-temporal autocorrelation 222</p> <p>7.3.3. Spatio-temporal clusters 222</p> <p>7.3.4. Statistical modeling: GTWR 223</p> <p>7.4. Emergence, diffusion, pathway 224</p> <p>7.5. Spatio-temporal modeling of health phenomena 226</p> <p>7.5.1. Process modeling and simulation 226</p> <p>7.5.2. The deterministic approach of SEIR models 229</p> <p>7.5.3. SEIR models and localization 231</p> <p>7.5.4. Non-deterministic approach of multi-agent models 232</p> <p>Glossary 235</p> <p>References 237</p> <p>Index 247</p>

<b>Marc Souris</b> is a Senior Researcher at Institut de Recherche pour le Développement (IRD), France. His work mainly concerns research, innovation, software and teaching within data science, particularly applied to geography and epidemiology. He is the lead developer of the SavGIS software package.

Localization is involved everywhere in epidemiology: health phenomena often involve spatial relationships among individuals and risk factors related to geography and environment. Therefore, the use of localization in the analysis and comprehension of health phenomena is essential. This book describes the objectives, principles, methods and tools of spatial analysis and geographic information systems applied to the field of health, and more specifically to the study of the spatial distribution of disease and health–environment relationships. It is a practical introduction to spatial and spatio-temporal analysis for epidemiology and health geography, and takes an educational approach illustrated with real-world examples.<br /><br />Epidemiology and Geography presents a complete and straightforward overview of the use of spatial analysis in epidemiology for students, public health professionals, epidemiologists, health geographers and specialists in health–environment studies.

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