Geographic Information Analysis / Edition 2

Geographic Information Analysis / Edition 2

by David O'Sullivan, David Unwin
     
 

Geographic Information Analysis provides up-to-date coverage of the foundations of spatial data analysis through visualization and maps. This book covers key spatial concepts, including point pattern, line objects and networks, area objects, and continuous fields, as well as such new subjects as local statistics. With crucial methods for analyzing geographical

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Overview

Geographic Information Analysis provides up-to-date coverage of the foundations of spatial data analysis through visualization and maps. This book covers key spatial concepts, including point pattern, line objects and networks, area objects, and continuous fields, as well as such new subjects as local statistics. With crucial methods for analyzing geographical information, this is an essential reference for professionals as well as a useful text for the classroom.

Product Details

ISBN-13:
9780470288573
Publisher:
Wiley
Publication date:
03/29/2010
Edition description:
New Edition
Pages:
432
Sales rank:
759,612
Product dimensions:
6.40(w) x 9.30(h) x 1.00(d)

Table of Contents

Preface to the Second Edition xi

Acknowledgments xv

Preface to the First Edition xvii

1 Geographic Information Analysis and Spatial Data 1

Chapter Objectives 1

1.1 Introduction 2

1.2 Spatial Data Types 5

1.3 Some Complications 10

1.4 Scales for Attribute Description 18

1.5 GIS and Spatial Data Manipulation 24

1.6 The Road Ahead 28

Chapter Review 29

References 29

2 The Pitfalls and Potential of Spatial Data 33

Chapter Objectives 33

2.1 Introduction 33

2.2 The Bad News: The Pitfalls of Spatial Data 34

2.3 The Good News: The Potential of Spatial Data 41

Chapter Review 52

References 53

3 Fundamentals-Mapping It Out 55

Chapter Objectives 55

3.1 Introduction: The Cartographic Tradition 56

3.2 Geovisualization and Analysis 58

3.3 The Graphic Variables of Jacques Bertin 60

3.4 New Graphic Variables 63

3.5 Issues in Geovisualization 65

3.6 Mapping and Exploring Points 66

3.7 Mapping and Exploring Areas 72

3.8 Mapping and Exploring Fields 80

3.9 The Spatialization of Nonspatial Data 84

3.10 Conclusion 86

Chapter Review 87

References 87

4 Fundamentals-Maps as Outcomes of Processes 93

Chapter Objectives 93

4.1 Introduction: Maps and Processes 94

4.2 Processes and the Patterns They Make 95

4.3 Predicting the Pattern Generated by a Process 100

4.4 More Definitions 106

4.5 Stochastic Processes in Lines, Areas, and Fields 108

4.6 Conclusions 116

Chapter Review 117

References 118

5 Point Pattern Analysis 121

Chapter Objectives 121

5.1 Introduction 121

5.2 Describing a Point Pattern 123

5.3 Assessing Point Patterns Statistically 139

5.4 Monte Carlo Testing 148

5.5 Conclusions 152

Chapter Review 154

References 154

6 Practical Point Pattern Analysis 157

Chapter Objectives 157

6.1 Introduction: Problems of Spatial Statistical Analysis 158

6.2 Alternatives to Classical Statistical Inference 161

6.3 Alternatives to IRP/CSR 162

6.4 Point Pattern Analysis in the Real World 166

6.5 Dealing with Inhomogeneity 168

6.6 Focused Approaches 172

6.7 Cluster Detection: Scan Statistics 173

6.8 Using Density and Distance: Proximity Polygons 177

6.9 A Note on Distance Matrices and Point Pattern Analysis 180

Chapter Review 182

References 183

7 Area Objects and Spatial Autocorrelation 187

Chapter Objectives 187

7.1 Introduction: Area Objects Revisited 188

7.2 Types of Area Objects 188

7.3 Geometric Properties of Areas 191

7.4 Measuring Spatial Autocorrelation 199

7.5 An Example: Tuberculosis in Auckland, 2001-2006 206

7.6 Other Approaches 210

Chapter Review 212

References 213

8 Local Statistics 215

Chapter Objectives 215

8.1 Introduction: Think Geographically, Measure Locally 216

8.2 Defining the Local: Spatial Structure (Again) 218

8.3 An Example: The Getis-Ord Gi and Gi* Statistics 219

8.4 Inference with Local Statistics 223

8.5 Other Local Statistics 226

8.6 Conclusions: Seeing the World Locally 234

Chapter Review 235

References 236

9 Describing and Analyzing Fields 239

Chapter Objectives 239

9.1 Introduction: Scalar and Vector Fields Revisited 240

9.2 Modeling and Storing Field Data 243

9.3 Spatial Interpolation 250

9.4 Derived Measures on Surfaces 263

9.5 Map Algebra 270

9.6 Conclusions 273

Chapter Review 274

References 275

10 Knowing the Unknowable: The Statistics of Fields 277

Chapter Objectives 277

10.1 Introduction 278

10.2 Regression on Spatial Coordinates: Trend Surface Analysis 279

10.3 The Square Root Differences Cloud and the (Semi-) Variogram 287

10.4 A Statistical Approach to Interpolation: Kriging 293

10.5 Conclusions 311

Chapter Review 312

References 313

11 Putting Maps Together-Map Overlay 315

Chapter Objectives 315

11.1 Introduction 316

11.2 Boolean Map Overlay and Sieve Mapping 319

11.3 A General Model for Alternatives to Boolean Overlay 326

11.4 Indexed Overlay and Weighted Linear Combination 328

11.5 Weights of Evidence 331

11.6 Model-Driven Overlay Using Regression 334

11.7 Conclusions 336

Chapter Review 337

References 337

12 New Approaches to Spatial Analysis 341

Chapter Objectives 341

12.1 The Changing Technological Environment 342

12.2 The Changing Scientific Environment 345

12.3 Geocomputation 346

12.4 Spatial Models 355

12.5 The Grid and the Cloud: Supercomputing for Dummies 363

12.6 Conclusions: Neogeographic Information Analysis? 365

Chapter Review 367

References 368

Appendix A Notation, Matrices, and Matrix Mathematics 373

A.1 Introduction 373

A.2 Some Preliminary Notes on Notation 373

A.3 Matrix Basics and Notation 376

A.4 Simple Matrix Mathematics 379

A.5 Solving Simultaneous Equations Using Matrices 384

A.6 Matrices, Vectors, and Geometry 389

A.7 Eigenvectors and Eigenvalues 391

Reference 393

Index 395

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