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Geographic Information Analysis
Synopses & Reviews
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.
"Geographic Information Analyses presents the spatial analytical foundation of geographic information systems which are instrumental to the emergent study of GIScience. This book covers spatial concepts such as points, lines, areas and surfaces, and analytical techniques such as projection, nearest neighbor, fractals, triangulation, and geostatistics.
About the Author
DAVID O SULLIVAN, PhD, is Assistant Professor of Geography at The Pennsylvania State University in University Park, Pennsylvania.
DAVID J. UNWIN, MPhil, formerly Professor of Geography at Birkbeck College in the University of London, UK, is currently Director of Learning Programmes at UKeUniversities Worldwide. He is also the author of Computer Programming for Geographers (with J. A. Dawson) and coeditor of Visualization in Geographic Information Systems (with Hilary M. Hearnshaw), both published by Wiley.
Table of Contents
1. Geographic Information Analysis and Spatial Data.
1.2 Spatial Data Types.
1.3 Scales for Attribute Description.
1.4 GIS Analysis, Spatial Data Manipulation, and Spatial Analysis.
2. The Pitfalls and Potential of Spatial Data.
2.2 The Bad News: The Pitfalls of Spatial Data.
2.3 The Good News: The Potential of Spatial Data.
2.4 Preview: The Variogram Cloud and the Semivariogram.
3. Fundamentals: Maps as Outcomes of Processes.
3.2 Processes and the Patterns They Make.
3.3 Predicting the Pattern Generated by a Process.
3.4 More Definitions.
3.5 Stochastic Processes in Lines, Areas, and Fields.
4. Point Pattern Analysis.
4.2 Describing a Point Pattern.
4.3 Density-Based Point Pattern Measures.
4.4 Distance-Based Point Pattern Measures.
4.5 Assessing Point Patterns Statistically.
4.6 Two Critiques of Spatial Statistical Analysis.
5. Practical Point Pattern Analysis.
5.1 Point Pattern Analysis versus Cluster Detection.
5.2 Extensions of Basic Point Pattern Measures.
5.3 Using Density and Distance: Proximity Polygons.
5.4 Note on Distance Matrices and Point Pattern Analysis.
6. Lines and Networks.
6.2 Representing and Storing Linear Entities.
6.3 Line Length: More Than Meets the Eye.
6.4 Connection in Line Data: Trees and Graphs.
6.5 Statistical Analysis of Geographical Line Data.
7. Area Objects and Spatial Autocorrelation.
7.2 Types of Area Object.
7.3 Geometric Properties of Areas.
7.4 Spatial Autocorrelation: Introducing the Joins Count Approach.
7.5 Fully Worked Example: The 2000 U.S. Presidential Election.
7.6 Other Measures of Spatial Autocorrelation.
7.7 Local Indicators of Spatial Association.
8. Describing and Analyzing Fields.
8.2 Modeling and Storing Field Data.
8.3 Spatial Interpolation.
8.4 Derived Measures on Surfaces.
9. Knowing the Unknowable: The Statistics of Fields.
9.2 Review of Regression.
9.3 Regression on Spatial Coordinates: Trend Surface Analysis.
9.4 Statistical Approach to Interpolation: Kriging.
10. Putting Maps Together: Map Overlay.
10.2 Polygon Overlay and Sieve Mapping.
10.3 Problems in Simple Boolean Polygon Overlay.
10.4 Toward a General Model: Alternatives to Boolean Overlay.
11. Multivariate Data, Multidimensional Space, and Spatialization.
11.2 Multivariate Data and Multidimensional Space.
11.3 Distance, Difference, and Similarity.
11.4 Cluster Analysis: Identifying Groups of Similar Observations.
11.5 Spatialization: Mapping Multivariate Data.
11.6 Reducing the Number of Variables: Principal Components Analysis.
12. New Approaches to Spatial Analysis.
12.3 Spatial Models.
A. The Elements of Statistics.
A.2 Describing Data.
A.3 Probability Theory.
A.4 Processes and Random Variables.
A.5 Sampling Distributions and Hypothesis Testing.
B. Matrices and Matrix Mathematics.
B.2 Matrix Basics and Notation.
B.3 Simple Mathematics.
B.4 Solving Simultaneous Equations Using Matrices.
B.5 Matrices, Vectors, and Geometry.
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