ACKNOWLEDGMENTS.

1 INTRODUCTION.

1.1 Why Statistics and Sampling?

1.2 What Are Special about Spatial Data?

1.3 Spatial Data and the Need for Spatial Analysis/ Statistics.

1.4 Fundamentals of Spatial Analysis and Statistics.

1.5 ArcView Notes—Data Model and Examples.

PART I: CLASSICAL STATISTICS.

2 DISTRIBUTION DESCRIPTORS: ONE VARIABLE (UNIVARIATE).

2.1 Measures of Central Tendency.

2.2 Measures of Dispersion.

2.3 ArcView Examples.

2.4 Higher Moment Statistics.

2.5 ArcView Examples.

2.6 Application Example.

2.7 Summary.

3 RELATIONSHIP DESCRIPTORS: TWO VARIABLES (BIVARIATE).

3.1 Correlation Analysis.

3.2 Correlation: Nominal Scale.

3.3 Correlation: Ordinal Scale.

3.4 Correlation: Interval /Ratio Scale.

3.5 Trend Analysis.

3.6 ArcView Notes.

3.7 Application Examples.

4 HYPOTHESIS TESTERS.

4.1 Probability Concepts.

4.2 Probability Functions.

4.3 Central Limit Theorem and Confidence Intervals.

4.4 Hypothesis Testing.

4.5 Parametric Test Statistics.

4.6 Difference in Means.

4.7 Difference Between a Mean and a Fixed Value.

4.8 Significance of Pearson’s Correlation Coefficient.

4.9 Significance of Regression Parameters.

4.10 Testing Nonparametric Statistics.

4.11 Summary.

PART II: SPATIAL STATISTICS.

5 POINT PATTERN DESCRIPTORS.

5.1 The Nature of Point Features.

5.2 Central Tendency of Point Distributions.

5.3 Dispersion and Orientation of Point Distributions.

5.4 ArcView Notes.

5.5 Application Examples.

6 POINT PATTERN ANALYZERS.

6.1 Scale and Extent.

6.2 Quadrat Analysis.

6.3 Ordered Neighbor Analysis.

6.4 K-Function.

6.5 Spatial Autocorrelation of Points.

6.6 Application Examples.

7 LINE PATTERN ANALYZERS.

7.1 The Nature of Linear Features: Vectors and Networks.

7.2 Characteristics and Attributes of Linear Features.

7.3 Directional Statistics.

7.4 Network Analysis.

7.5 Application Examples.

8 POLYGON PATTERN ANALYZERS.

8.1 Introduction.

8.2 Spatial Relationships.

8.3 Spatial Dependency.

8.4 Spatial Weights Matrices.

8.5 Spatial Autocorrelation Statistics and Notations.

8.6 Joint Count Statistics.

8.7 Spatial Autocorrelation Global Statistics.

8.8 Local Spatial Autocorrelation Statistics.

8.9 Moran Scatterplot.

8.10 Bivariate Spatial Autocorrelation.

8.11 Application Examples.

8.12 Summary.

APPENDIX: ArcGIS Spatial Statistics Tools.

ABOUT THE CD-ROM.

INDEX.

It's been four years since the publication of the groundbreaking Statistical Analysis with ArcView GIS®, and ArcView continues to be one of the most popular desktop GIS among geographers and other GIS users because of its capabilities for spatial-quantitative synthesis. Now, David Wong and Jay Lee update their comprehensive handbook with Statistical Analysis of Geographic Information with ArcView GIS® and ArcGIS®. This revised and expanded guide features classic statistical methods supported by numerous new examples and worked problems.

Employing points, lines, and polygons to model real-world geographic forms, this easy-to-use resource provides geographers, researchers, and practitioners a valuable bridge between theory and the necessary software to apply it. It contains sections on point distribution, point pattern analysis, linear features, network analysis, and spatial autocorrelation analysis. This new edition:

• Covers a full range of statistical methods, including classical techniques, probability, and statistical testingFeatures dozens of new exercises for use with tools and procedures packaged as ArcView extensions and data sets
• Provides a CD-ROM offering immediate access to ready-to-use ArcView extensions for use with the book and with real-world data sets
• Includes updated discussions on implementing spatial analysis in ArcGIS 9.X