Geographic Information Analysis

by O'Sullivan, David; Unwin, David

• ISBN13: 9780470288573
• ISBN10: 0470288574
• Edition: 2nd
• Format: Hardcover
• Copyright: 2010-03-29
• Publisher: Wiley

Summary

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.

Author Biography

David O'Sullivan PhD, is Associate Professor of Geography at the University of Auckland, New Zealand. David J. Unwin, MPhil, formerly professor of geography at Birkbeck College in the University of London, UK, is now retired. He is also the co-author of Computer Programming for Geographers (with J.A. Dawson) and coeditor of Visualization in Geographical Information Systems (with Hilary M. Hearnshaw), both published by Wiley.

Table of Contents

Preface to the Second Edition	p. xi
Acknowledgments	p. xv
Preface to the First Edition	p. xvii
Geographic Information Analysis and Spatial Data	p. 1
Chapter Objectives	p. 1
Introduction	p. 2
Spatial Data Types	p. 5
Some Complications	p. 10
Scales for Attribute Description	p. 18
GIS and Spatial Data Manipulation	p. 24
The Road Ahead	p. 28
Chapter Review	p. 29
References	p. 29
The Pitfalls and Potential of Spatial Data	p. 33
Chapter Objectives	p. 33
Introduction	p. 33
The Bad News: The Pitfalls of Spatial Data	p. 34
The Good News: The Potential of Spatial Data	p. 41
Chapter Review	p. 52
References	p. 53
Fundamentals-Mapping It Out	p. 55
Chapter Objectives	p. 55
Introduction: The Cartographic Tradition	p. 56
Geovisualization and Analysis	p. 58
The Graphic Variables of Jacques Bertin	p. 60
New Graphic Variables	p. 63
Issues in Geovisualization	p. 65
Mapping and Exploring Points	p. 66
Mapping and Exploring Areas	p. 72
Mapping and Exploring Fields	p. 80
The Spatialization of Nonspatial Data	p. 84
Conclusion	p. 86
Chapter Review	p. 87
References	p. 87
Fundamentals-Maps as Outcomes of Processes	p. 93
Chapter Objectives	p. 93
Introduction: Maps and Processes	p. 94
Processes and the Patterns They Make	p. 95
Predicting the Pattern Generated by a Process	p. 100
More Definitions	p. 106
Stochastic Processes in Lines, Areas, and Fields	p. 108
Conclusions	p. 116
Chapter Review	p. 117
References	p. 118
Point Pattern Analysis	p. 121
Chapter Objectives	p. 121
Introduction	p. 121
Describing a Point Pattern	p. 123
Assessing Point Patterns Statistically	p. 139
Monte Carlo Testing	p. 148
Conclusions	p. 152
Chapter Review	p. 154
References	p. 154
Practical Point Pattern Analysis	p. 157
Chapter Objectives	p. 157
Introduction: Problems of Spatial Statistical Analysis	p. 158
Alternatives to Classical Statistical Inference	p. 161
Alternatives to IRP/CSR	p. 162
Point Pattern Analysis in the Real World	p. 166
Dealing with Inhomogeneity	p. 168
Focused Approaches	p. 172
Cluster Detection: Scan Statistics	p. 173
Using Density and Distance: Proximity Polygons	p. 177
A Note on Distance Matrices and Point Pattern Analysis	p. 180
Chapter Review	p. 182
References	p. 183
Area Objects and Spatial Autocorrelation	p. 187
Chapter Objectives	p. 187
Introduction: Area Objects Revisited	p. 188
Types of Area Objects	p. 188
Geometric Properties of Areas	p. 191
Measuring Spatial Autocorrelation	p. 199
An Example: Tuberculosis in Auckland, 2001-2006	p. 206
Other Approaches	p. 210
Chapter Review	p. 212
References	p. 213
Local Statistics	p. 215
Chapter Objectives	p. 215
Introduction: Think Geographically, Measure Locally	p. 216
Defining the Local: Spatial Structure (Again)	p. 218
An Example: The Getis-Ord Gi and Gi* Statistics	p. 219
Inference with Local Statistics	p. 223
Other Local Statistics	p. 226
Conclusions: Seeing the World Locally	p. 234
Chapter Review	p. 235
References	p. 236
Describing and Analyzing Fields	p. 239
Chapter Objectives	p. 239
Introduction: Scalar and Vector Fields Revisited	p. 240
Modeling and Storing Field Data	p. 243
Spatial Interpolation	p. 250
Derived Measures on Surfaces	p. 263
Map Algebra	p. 270
Conclusions	p. 273
Chapter Review	p. 274
References	p. 275
Knowing the Unknowable: The Statistics of Fields	p. 277
Chapter Objectives	p. 277
Introduction	p. 278
Regression on Spatial Coordinates: Trend Surface Analysis	p. 279
The Square Root Differences Cloud and the (Semi-) Variogram	p. 287
A Statistical Approach to Interpolation: Kriging	p. 293
Conclusions	p. 311
Chapter Review	p. 312
References	p. 313
Putting Maps Together-Map Overlay	p. 315
Chapter Objectives	p. 315
Introduction	p. 316
Boolean Map Overlay and Sieve Mapping	p. 319
A General Model for Alternatives to Boolean Overlay	p. 326
Indexed Overlay and Weighted Linear Combination	p. 328
Weights of Evidence	p. 331
Model-Driven Overlay Using Regression	p. 334
Conclusions	p. 336
Chapter Review	p. 337
References	p. 337
New Approaches to Spatial Analysis	p. 341
Chapter Objectives	p. 341
The Changing Technological Environment	p. 342
The Changing Scientific Environment	p. 345
Geocomputation	p. 346
Spatial Models	p. 355
The Grid and the Cloud: Supercomputing for Dummies	p. 363
Conclusions: Neogeographic Information Analysis?	p. 365
Chapter Review	p. 367
References	p. 368
Notation, Matrices, and Matrix Mathematics	p. 373
Introduction	p. 373
Some Preliminary Notes on Notation	p. 373
Matrix Basics and Notation	p. 376
Simple Matrix Mathematics	p. 379
Solving Simultaneous Equations Using Matrices	p. 384
Matrices, Vectors, and Geometry	p. 389
Eigenvectors and Eigenvalues	p. 391
Reference	p. 393
Index	p. 395
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