Geographic information analysis

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Title:

Personal Author:

O'Sullivan, David

Publication Information:

New York : John Wiley & Sons, 2003

ISBN:

9780471211761

Subject Term:

Geographic information systems

Spatial analysis (Statistics)

Added Author:

Unwin, David John

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... 1:FCOMP_L	FSK30000001011	G70.212 O88 2002	Open Access Book	Gift Book	Searching... Unknown
Searching... PSZ JB	30000010023243	G70.212 O88 2002	Open Access Book	Book	Searching... Unknown

Clear, up-to-date coverage of methods for analyzing geographical information in a GIS context

Geographic Information Analysis presents clear and up-to-date coverage of the foundations of spatial analysis in a geographic information systems environment. Focusing on the universal aspects of spatial data and their analysis, this book covers the scientific assumptions and limitations of methods available in many geographic information systems.

Throughout, the fundamental idea of a map as a realization of a spatial stochastic process is central to the discussion. Key spatial concepts are covered, including point pattern, line objects and networks, area objects, and continuous fields. Analytical techniques for each of these are addressed, as are methods for combining maps, exploring multivariate data, and performing computationally intensive analysis. Appendixes provide primers on basic statistics and linear algebra using matrices.

Complete with chapter objectives, summaries, "thought exercises," a wealth of explanatory diagrams, and an annotated bibliography, Geographic Information Analysis is a practical book for students, as well as a valuable resource for researchers and professionals in the industry.

Author Notes

David O'Sullivan is Assistant Professor of Geography at The Pennsylvania State University in University Park, Pennsylvania.

Preface	p. ix
1 Geographic Information Analysis and Spatial Data	p. 1
Chapter Objectives	p. 1
1.1 Introduction	p. 2
1.2 Spatial Data Types	p. 4
1.3 Scales for Attribute Description	p. 11
1.4 GIS Analysis, Spatial Data Manipulation, and Spatial Analysis	p. 17
1.5 Conclusion	p. 22
Chapter Review	p. 23
References	p. 24
2 The Pitfalls and Potential of Spatial Data	p. 26
Chapter Objectives	p. 26
2.1 Introduction	p. 27
2.2 The Bad News: The Pitfalls of Spatial Data	p. 28
2.3 The Good News: The Potential of Spatial Data	p. 34
2.4 Preview: The Variogram Cloud and the Semivariogram	p. 45
Chapter Review	p. 49
References	p. 49
3 Fundamentals: Maps as Outcomes of Processes	p. 51
Chapter Objectives	p. 51
3.1 Introduction	p. 52
3.2 Processes and the Patterns They Make	p. 53
3.3 Predicting the Pattern Generated by a Process	p. 58
3.4 More Definitions	p. 64
3.5 Stochastic Processes in Lines, Areas, and Fields	p. 66
3.6 Conclusion	p. 73
Chapter Review	p. 75
References	p. 75
4 Point Pattern Analysis	p. 77
Chapter Objectives	p. 77
4.1 Introduction	p. 78
4.2 Describing a Point Pattern	p. 79
4.3 Density-Based Point Pattern Measures	p. 81
4.4 Distance-Based Point Pattern Measures	p. 88
4.5 Assessing Point Patterns Statistically	p. 95
4.6 Two Critiques of Spatial Statistical Analysis	p. 108
4.7 Conclusion	p. 110
Chapter Review	p. 112
References	p. 113
5 Practical Point Pattern Analysis	p. 115
Chapter Objectives	p. 115
5.1 Point Pattern Analysis versus Cluster Detection	p. 116
5.2 Extensions of Basic Point Pattern Measures	p. 123
5.3 Using Density and Distance: Proximity Polygons	p. 126
5.4 Note on Distance Matrices and Point Pattern Analysis	p. 129
5.5 Conclusion	p. 132
Chapter Review	p. 132
References	p. 133
6 Lines and Networks	p. 135
Chapter Objectives	p. 135
6.1 Introduction	p. 136
6.2 Representing and Storing Linear Entities	p. 137
6.3 Line Length: More Than Meets the Eye	p. 142
6.4 Connection in Line Data: Trees and Graphs	p. 152
6.5 Statistical Analysis of Geographical Line Data	p. 161
6.6 Conclusion	p. 163
Chapter Review	p. 164
References	p. 165
7 Area Objects and Spatial Autocorrelation	p. 167
Chapter Objectives	p. 167
7.1 Introduction	p. 168
7.2 Types of Area Object	p. 169
7.3 Geometric Properties of Areas	p. 173
7.4 Spatial Autocorrelation: Introducing the Joins Count Approach	p. 180
7.5 Fully Worked Example: The 2000 U.S. Presidential Election	p. 192
7.6 Other Measures of Spatial Autocorrelation	p. 196
7.7 Local Indicators of Spatial Association	p. 203
Chapter Review	p. 205
References	p. 206
8 Describing and Analyzing Fields	p. 209
Chapter Objectives	p. 209
8.1 Introduction	p. 210
8.2 Modeling and Storing Field Data	p. 213
8.3 Spatial Interpolation	p. 220
8.4 Derived Measures on Surfaces	p. 234
8.5 Conclusion	p. 242
Chapter Review	p. 243
References	p. 244
9 Knowing the Unknowable: The Statistics of Fields	p. 246
Chapter Objectives	p. 246
9.1 Introduction	p. 247
9.2 Review of Regression	p. 248
9.3 Regression on Spatial Coordinates: Trend Surface Analysis	p. 256
9.4 Statistical Approach to Interpolation: Kriging	p. 265
9.5 Conclusion	p. 281
Chapter Review	p. 282
References	p. 283
10 Putting Maps Together: Map Overlay	p. 284
Chapter Objectives	p. 284
10.1 Introduction	p. 285
10.2 Polygon Overlay and Sieve Mapping	p. 287
10.3 Problems in Simple Boolean Polygon Overlay	p. 302
10.4 Toward a General Model: Alternatives to Boolean Overlay	p. 304
10.5 Conclusion	p. 311
Chapter Review	p. 312
References	p. 312
11 Multivariate Data, Multidimensional Space, and Spatialization	p. 315
Chapter Objectives	p. 315
11.1 Introduction	p. 316
11.2 Multivariate Data and Multidimensional Space	p. 317
11.3 Distance, Difference, and Similarity	p. 323
11.4 Cluster Analysis: Identifying Groups of Similar Observations	p. 327
11.5 Spatialization: Mapping Multivariate Data	p. 336
11.6 Reducing the Number of Variables: Principal Components Analysis	p. 343
11.7 Conclusion	p. 352
Chapter Review	p. 353
References	p. 355
12 New Approaches to Spatial Analysis	p. 356
Chapter Objectives	p. 356
12.1 Introduction	p. 357
12.2 Geocomputation	p. 361
12.3 Spatial Models	p. 370
12.4 Conclusion	p. 378
Chapter Review	p. 379
References	p. 380
A The Elements of Statistics	p. 384
A.1 Introduction	p. 384
A.2 Describing Data	p. 388
A.3 Probability Theory	p. 392
A.4 Processes and Random Variables	p. 396
A.5 Sampling Distributions and Hypothesis Testing	p. 402
A.6 Example	p. 406
Reference	p. 411
B Matrices and Matrix Mathematics	p. 412
B.1 Introduction	p. 412
B.2 Matrix Basics and Notation	p. 412
B.3 Simple Mathematics	p. 415
B.4 Solving Simultaneous Equations Using Matrices	p. 420
B.5 Matrices, Vectors, and Geometry	p. 425
Reference	p. 430
Index	p. 431

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Author Notes

Table of Contents