Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS

ISBN: 978-0-471-46899-8
464 pages
October 2005

Description

Statistical Analysis and Modeling of Geographic Information with ArcView GIS is an update to Lee and Wong's Statistical Analysis with ArcView GIS, featuring expanded coverage of classical statistical methods, probability and statistical testing, new student exercises to facilitate classroom use, new exercises featuring interactive ArcView Avenue scripts, and a new overview of compatible spatial analytical functions in ArcGIS 9.0.
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PREFACE.

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.

6.3 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.

INDEX.

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Author Information

David W. S. Wong, PhD, is Professor and Chair of the Earth Systems and GeoInformation Sciences Program at George Mason University in Fairfax, Virginia.

Jay Lee, PhD, is Professor and Chair of the Department of Geography at Kent State University in Kent, Ohio.

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* Gives students direct access to powerful statistical techniques, and teaches the reader how to actually implement the theory with the included software

* Comes with ArcView Avenue scripts and data sets so students can implement the examples in the book to help them further understand the concepts being taught

* Book and software are able to stand-alone as self-directed lab exercises

* Scripts are compatible with user-created data sets so they have a useful life after the user is done with the exercises in the book

* Completely updated with new examples and exercises, and features a new chapter on polygon pattern analyzers

* Expanded coverage includes new chapters on classic statistical methods

* CD includes the scripts that accompany the book as well as new instructor support material such as PowerPoint slides and structured labs built around the exercises in the book.

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