Epidemiology and Geography

Principles, Methods and Tools of Spatial Analysis

Authors: Marc Souris Publisher: Wiley Publication date: 2019 Publication language: Angielski Number of pages: 284 Publication formats: EAN: 9781119597445 ISBN: 9781119597445 Category: Computer science Publisher's index: - Bibliographic note: -

Description

TOC

• Cover 2
• Half-Title Page 4
• Title Page 6
• Copyright Page 7
• Contents 8
• Foreword 12
• Preface 14
• Introduction: Software and Databases 18
  • I.1. Software 18
    • I.1.1. QGIS 18
    • I.1.4. R 19
    • I.1.2. ArcGIS 19
    • I.1.3. SavGIS 19
    • I.1.7. GWR4 20
    • I.1.5. GeoDA 20

• Cover 2
• Half-Title Page 4
• Title Page 6
• Copyright Page 7
• Contents 8
• Foreword 12
• Preface 14
• Introduction: Software and Databases 18
  • I.1. Software 18
    • I.1.1. QGIS 18
    • I.1.4. R 19
    • I.1.2. ArcGIS 19
    • I.1.3. SavGIS 19
    • I.1.7. GWR4 20
    • I.1.5. GeoDA 20
    • I.1.6. SaTScanTM 20
    • I.1.8. Gama 21
  • I.2. Data for the examples 21
• 1. Methodological Context 22
  • 1.1. A systemic approach to health 22
  • 1.2. Risk and public health 26
  • 1.3. Epidemiology 30
  • 1.4. Health geography 31
  • 1.5. Spatial analysis for epidemiology and health geography 32
  • 1.6. Geographic information systems 37
  • 1.7. Book structure 39
• 2. Spatial Analysis of Health Phenomena: General Principles 42
  • 2.1. Spatial analysis in epidemiology and health geography 42
    • 2.1.1. Spatial distribution of a health phenomenon 42
    • 2.1.2. Spatial analysis in epidemiology 44
    • 2.1.3. Spatial and statistical dependence 49
    • 2.1.4. Causal relationships, explanatory factors, confounding factors 50
    • 2.1.6. Health data are often aggregated into geographical units 51
    • 2.1.5. Uncertainty in event localization 51
  • 2.2. Spatial analysis terminology and formalism 53
    • 2.2.1. Objects, attributes, events 54
    • 2.2.2. Localization and spatial domain 55
    • 2.2.3. The formalism of descriptive analysis 57
    • 2.2.4. The formalism of the explanatory analysis 60
  • 2.3. General approach of spatial analysis in epidemiology 63
    • 2.3.1. The approach of descriptive analysis 63
    • 2.3.2. The approach of explanatory analysis 65
    • 2.3.3. Spatial analysis methods 66
    • 2.3.4. Spatial analysis and health geography 67
  • 2.4. Required knowledge on epidemiology and statistics 68
    • 2.4.1. Epidemiology 68
    • 2.4.2. Statistical analysis 69
    • 2.4.3. Methods for model adjustment 73
    • 2.4.4. Several distributions and models 79
  • 3. Spatial Data in Health 84
    • 3.1. Introduction 84
    • 3.2. Health data 85
      • 3.2.1. Various types of data for individuals 85
      • 3.2.2. Individual and aggregated health data 86
      • 3.2.3. Description of the healthcare system 87
    • 3.3. Spatialization of epidemiological data 87
      • 3.3.1. Localization in space 87
      • 3.3.2. Localization in time 89
      • 3.3.3. Localization in time and space 89
      • 3.3.4. Data aggregated according to a spatial criterion 89
      • 3.3.5. Ethics and localization 90
    • 3.4. Sources of data 91
      • 3.4.1. Epidemiological data 91
      • 3.4.2. Geographical and environmental data 92
      • 3.4.3. Access to geographical data 93
    • 4. Cartographic Representations and Synthesis Tools 96
      • 4.1. Introduction 96
        • 4.1.1. Why use mapping methods? 96
        • 4.1.2. How to use mapping? 97
      • 4.2. Cartographic representations 99
        • 4.2.2. Mapping rates: prevalence, incidence, risk and odds ratio 99
        • 4.2.1. Mapping events or health status 99
        • 4.2.3. Mapping flows and spatial relationships 103
        • 4.2.4. Mapping limitations 104
        • 4.2.5. Mapping rate significance 110
        • 4.2.6. Rate adjustment 111
      • 4.3. Descriptive statistics and visual synthesis tools 114
        • 4.3.1. Average points, median points 114
        • 4.3.2. Standard deviational ellipses 116
      • 4.4. Interpolations and trend surfaces 118
        • 4.4.1. Interpolations and continuous representation 118
        • 4.4.2. Directions and gradients 124
        • 4.4.3. Anamorphoses 124
      • 4.5. Spatio-temporal animations 125
        • 4.5.1. What and how 125
        • 4.5.2. Animated mapping 126
      • 5. Spatial Distribution Analysis 130
        • 5.1. Introduction 130
          • 5.1.1. “Direct” methods for spatial analysis 130
          • 5.1.2. Continuous space, point pattern, subsets 134
        • 5.2. Global spatial analyses 136
          • 5.2.1. Geographical location, extent, orientation 136
          • 5.2.2. Centrality 139
          • 5.2.3. Spatial dependence of values 141
          • 5.2.4. Bivariate spatial analysis 154
          • 5.2.5. Global pattern of the phenomenon and search for a geometric model 159
        • 5.3. Local spatial analyses 160
          • 5.3.1. Local indicators of spatial association (LISA) 161
          • 5.3.2. Spatial scan-based search for singularities 166
          • 5.3.3. Analyses around a source point 172
        • 5.4. Example: emergence and diffusion of avian influenza 174
          • 5.4.1. Introduction 174
          • 5.4.2. Mapping 176
          • 5.4.3. Analysis of the spatial distribution of cases 178
          • 5.4.4. Spatio-temporal analyses 186
          • 5.4.5. Analyses of risk factors 193
        • 6. Spatial Analysis of Risk 198
          • 6.2. Aggregation-based spatial analyses 198
            • 6.2.1. Spatial aggregation operation 200
            • 6.2.2. Statistical analysis 204
            • 6.2.3. Spatial analysis of aggregation 216
            • 6.2.4. Spatial belonging 219
          • 6.1. Introduction 198
          • 6.3. Statistical modeling of spatial data 219
            • 6.3.1. Statistical correlations and spatial relationships 220
            • 6.3.2. Statistical modeling 221
            • 6.3.3. Spatial models 222
            • 6.3.4. Spatial heterogeneity of parameters 225
          • 6.4. An example: analysis of tuberculosis risk factors 228
            • 6.4.1. Epidemiological and socio-economic data 229
            • 6.4.2. Analysis of the statistical and spatial distribution of rates 230
            • 6.4.3. Statistical modeling of SMR and incidence 234
          • 7. Space–time Analyses and Modeling 240
            • 7.1. Time–distance relationships 240
            • 7.2. Mobile mean points 241
            • 7.3. Spatio-temporal autocorrelation and clusters 243
              • 7.3.2. Local spatio-temporal autocorrelation 243
              • 7.3.3. Spatio-temporal clusters 243
              • 7.3.1. Global spatio-temporal autocorrelation 243
              • 7.3.4. Statistical modeling: GTWR 244
            • 7.4. Emergence, diffusion, pathway 245
            • 7.5. Spatio-temporal modeling of health phenomena 247
              • 7.5.1. Process modeling and simulation 247
              • 7.5.2. The deterministic approach of SEIR models 250
              • 7.5.3. SEIR models and localization 252
              • 7.5.4. Non-deterministic approach of multi-agent models 253
            • Glossary 256
            • References 258
            • Index 268
            • Other titles from iSTE in Information Systems, Web and Pervasive Computing 274
            • EULA 283

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