Geographic Information Analysis

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2010-03-29
  • Publisher: Wiley

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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 Editionp. xi
Acknowledgmentsp. xv
Preface to the First Editionp. xvii
Geographic Information Analysis and Spatial Datap. 1
Chapter Objectivesp. 1
Introductionp. 2
Spatial Data Typesp. 5
Some Complicationsp. 10
Scales for Attribute Descriptionp. 18
GIS and Spatial Data Manipulationp. 24
The Road Aheadp. 28
Chapter Reviewp. 29
Referencesp. 29
The Pitfalls and Potential of Spatial Datap. 33
Chapter Objectivesp. 33
Introductionp. 33
The Bad News: The Pitfalls of Spatial Datap. 34
The Good News: The Potential of Spatial Datap. 41
Chapter Reviewp. 52
Referencesp. 53
Fundamentals-Mapping It Outp. 55
Chapter Objectivesp. 55
Introduction: The Cartographic Traditionp. 56
Geovisualization and Analysisp. 58
The Graphic Variables of Jacques Bertinp. 60
New Graphic Variablesp. 63
Issues in Geovisualizationp. 65
Mapping and Exploring Pointsp. 66
Mapping and Exploring Areasp. 72
Mapping and Exploring Fieldsp. 80
The Spatialization of Nonspatial Datap. 84
Conclusionp. 86
Chapter Reviewp. 87
Referencesp. 87
Fundamentals-Maps as Outcomes of Processesp. 93
Chapter Objectivesp. 93
Introduction: Maps and Processesp. 94
Processes and the Patterns They Makep. 95
Predicting the Pattern Generated by a Processp. 100
More Definitionsp. 106
Stochastic Processes in Lines, Areas, and Fieldsp. 108
Conclusionsp. 116
Chapter Reviewp. 117
Referencesp. 118
Point Pattern Analysisp. 121
Chapter Objectivesp. 121
Introductionp. 121
Describing a Point Patternp. 123
Assessing Point Patterns Statisticallyp. 139
Monte Carlo Testingp. 148
Conclusionsp. 152
Chapter Reviewp. 154
Referencesp. 154
Practical Point Pattern Analysisp. 157
Chapter Objectivesp. 157
Introduction: Problems of Spatial Statistical Analysisp. 158
Alternatives to Classical Statistical Inferencep. 161
Alternatives to IRP/CSRp. 162
Point Pattern Analysis in the Real Worldp. 166
Dealing with Inhomogeneityp. 168
Focused Approachesp. 172
Cluster Detection: Scan Statisticsp. 173
Using Density and Distance: Proximity Polygonsp. 177
A Note on Distance Matrices and Point Pattern Analysisp. 180
Chapter Reviewp. 182
Referencesp. 183
Area Objects and Spatial Autocorrelationp. 187
Chapter Objectivesp. 187
Introduction: Area Objects Revisitedp. 188
Types of Area Objectsp. 188
Geometric Properties of Areasp. 191
Measuring Spatial Autocorrelationp. 199
An Example: Tuberculosis in Auckland, 2001-2006p. 206
Other Approachesp. 210
Chapter Reviewp. 212
Referencesp. 213
Local Statisticsp. 215
Chapter Objectivesp. 215
Introduction: Think Geographically, Measure Locallyp. 216
Defining the Local: Spatial Structure (Again)p. 218
An Example: The Getis-Ord Gi and Gi* Statisticsp. 219
Inference with Local Statisticsp. 223
Other Local Statisticsp. 226
Conclusions: Seeing the World Locallyp. 234
Chapter Reviewp. 235
Referencesp. 236
Describing and Analyzing Fieldsp. 239
Chapter Objectivesp. 239
Introduction: Scalar and Vector Fields Revisitedp. 240
Modeling and Storing Field Datap. 243
Spatial Interpolationp. 250
Derived Measures on Surfacesp. 263
Map Algebrap. 270
Conclusionsp. 273
Chapter Reviewp. 274
Referencesp. 275
Knowing the Unknowable: The Statistics of Fieldsp. 277
Chapter Objectivesp. 277
Introductionp. 278
Regression on Spatial Coordinates: Trend Surface Analysisp. 279
The Square Root Differences Cloud and the (Semi-) Variogramp. 287
A Statistical Approach to Interpolation: Krigingp. 293
Conclusionsp. 311
Chapter Reviewp. 312
Referencesp. 313
Putting Maps Together-Map Overlayp. 315
Chapter Objectivesp. 315
Introductionp. 316
Boolean Map Overlay and Sieve Mappingp. 319
A General Model for Alternatives to Boolean Overlayp. 326
Indexed Overlay and Weighted Linear Combinationp. 328
Weights of Evidencep. 331
Model-Driven Overlay Using Regressionp. 334
Conclusionsp. 336
Chapter Reviewp. 337
Referencesp. 337
New Approaches to Spatial Analysisp. 341
Chapter Objectivesp. 341
The Changing Technological Environmentp. 342
The Changing Scientific Environmentp. 345
Geocomputationp. 346
Spatial Modelsp. 355
The Grid and the Cloud: Supercomputing for Dummiesp. 363
Conclusions: Neogeographic Information Analysis?p. 365
Chapter Reviewp. 367
Referencesp. 368
Notation, Matrices, and Matrix Mathematicsp. 373
Introductionp. 373
Some Preliminary Notes on Notationp. 373
Matrix Basics and Notationp. 376
Simple Matrix Mathematicsp. 379
Solving Simultaneous Equations Using Matricesp. 384
Matrices, Vectors, and Geometryp. 389
Eigenvectors and Eigenvaluesp. 391
Referencep. 393
Indexp. 395
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