Title:
Statistical analysis of geographic information with ArcView GIS And ArcGIS
Personal Author:
Publication Information:
Hoboken, N.J. : John Wiley & Sons, 2005
Physical Description:
1 CD-ROM ; 12 cm.
ISBN:
9780471468998
General Note:
Accompanies text of the same title : (G70.212 W66 2005)
Added Author:
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010116645 | CP 8265 | Computer File Accompanies Open Access Book | Compact Disc Accompanies Open Access Book | Searching... |
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Summary
Summary
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.
Author Notes
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.
Table of Contents
| Preface |
| Introduction |
| 1.1 Why Statistics and Sampling? |
| 1.2 What is so Special about Spatial Data? |
| 1.2.1 MAUP- The Modifiable Areal Unit Problems |
| 1.2.2 Spatial Autocorrelation |
| 1.3 Spatial Data and the Needs For Spatial Analysis/Statistics |
| 1.4 Fundamentals in Spatial Analysis and Statistics |
| 1.4.1 Scales of Measurement |
| 1.4.2 Mathematical Notations |
| 1.4.3 Scale, Extent, and Projection |
| 1.5 ArcView Notes - Data Model and Examples |
| 1.5.1 Data Model Used In Arcview GIS |
| 1.5.2 Random Sampling - The Generic Way |
| 1.5.3 Random and Systematic Point Sampling - An Extended Function |
| 1.6 References Cited |
| 1.7 Exercises |
| Part 1 Classical Statistics |
| Distribution Descriptors: One Variable (Univariate) |
| 2.1 Measures of Central Tendency |
| 2.1.1 Mode |
| 2.1.2 Median |
| 2.1.3 Mean |
| 2.1.4 Grouped or Weighted Mean |
| 2.2 Measures of Dispersion |
| 2.2.1 Range, Minimum, Maximum and Percentiles |
| 2.2.2 Mean Deviation |
| 2.2.3 Variance and Standard Deviation |
| 2.2.4 Weighted Variance and Weighted Standard Deviation |
| 2.2.5 Coefficient of Variation |
| 2.3 ArcView Examples |
| 2.3.1 Descriptive Statistics |
| 2.4 Higher Moments Statistics |
| 2.4.1 Skewness and Kurtosis |
| 2.5 ArcView Examples |
| 2.5.1 Statistical Charts |
| 2.5.2 Additional Statistics |
| 2.6 Application Example |
| 2.7 Summary |
| 2.8 References Cited |
| 2.9 Exercises |
| Relationship Descriptors: Two Variables (Bivariate) |
| 3.1 Correlation Analysis |
| 3.2 Correlation: Nominal Scale |
| 3.2.1 Nominal Scale and Binary: Phi Coefficient |
| 3.2.2 Nominal Scale and Polychotomous: Chi-Square Statistic |
| 3.3 Correlation: Ordinal Scale |
| 3.4 Correlation: Interval / Ratio Scale |
| 3.5 Trend Analysis |
| 3.5.1 Simple Linear Regression Models |
| 3.5.2 Coefficient of Determination |
| 3.5.3 Empirical Examples |
| 3.6 ArcView Notes |
| 3.7 Application Examples |
| 3.8 Reference Cited |
| 3.9 Exercises.Hypothesis Testers |
| 4.1 Probability Concepts |
| 4.2 Probability Functions |
| 4.2.1 The Binomial Distribution |
| 4.2.2 Poisson Distribution |
| 4.2.3 The Normal Distribution |
| 4.2.4 ArcView Notes |
| 4.3 Central Limit Theorem and Confidence Intervals |
| 4.4 Hypothesis Testing |
| 4.4.1 ArcView Notes |
| 4.4.2 Types of Error |
| 4.5 Parametric Test Statistics |
| 4.5.1 Difference in Variances |
| 4.5.2 ArcView Note |
| 4.6 Difference in Means |
| 4.6.1 Small Sample Size |
| 4.6.2 Large Sample Size |
| 4.6.3 ArcView Note |
| 4.7 Difference between a mean and a fixed value |
| 4.7.1 ArcView Note |
| 4.8 Significance of Pearson''s correlation coefficient |
| 4.8.1 ArcView Note |
| 4.9 Significance of Regression Parameters |
| 4.9.1 ArcView Note |
| 4.10 Testing Non-Parametric Statistics: Chi-Square Statistics, C |
| 2 |
| 4.10.1 ArcView Note |
| 4.11 Spearman''s Rank Coefficient |
| 4.11.1 ArcView Notes |
| 4.12 Kolmogorov-Smirnov Test |
| 4.12.1 ArcView Note |
| 4.13 Summary |
| 4.14 Reference used in this chapter |
| 4.15 Exercises |
| Part 2 Spatial Statistics |
| Point Pattern Descriptors |
| 5.1 The Nature of Point Features |
| 5.2 Central Tendency of Point Distributions |
| 5.2.1 Mean Center |
| 5.2.2 Weighted Mean Center |
| 5.2.3 Median Center |
| 5.3 Dispersion and Orientation of Point Distributions |
| 5.3.1 Standard Distance |
| 5.3.2 Standard Deviational Ellipses |
| 5.4 ArcView Notes |
| 5.5 Application Examples |
| 5.6 References Cited |
| 5.7 Exercises |
| Point Pattern Analyzers |
| Scale and Extent |
| Quadrat Analysis |
| 6.2.1 General Concepts in Quadrat Analysis |
| 6.2.2 Comparing the Observed with the Expected Distributions using K-S test |
| 6.2.3 Comparing the observed with the expected using variance-mean ratio |
| 6.2.4 ArcView Example: Quadrat Analysis of Northeast Ohio Cities |
| 6.3 Ordered Neighbor Analysis |
| 6.3.1 Nearest Neighbor Statistic |
| 6.3.2 Testing for pattern using Nearest Neighbor Statistic |
| 6.3.3 Higher Ordered Neighbor Statistics |
| 6.3.4 Boundary Adjustments of the Nearest Neighbor Statistics |
| 6.3.5 ArcView Example: Nearest neighbor analysis of Northeast Ohio Cities |
| 6.4 K-Function |
| 6.4.1 ArcView Example: K-Function Analysis of Northeast Ohio Cities |
| 6.5 Spatial Autocorrelation of Points |
| 6.5.1 Measures for Spatial Autocorrelation |
| 6.5.2 Significance Testing of Spatial Autocorrelation Measures |
| 6.5.3 ArcView Example: Spatial Autocorrelation Analysis of Northeast Ohio Cities |
| 6.6 Application Examples |
| 6.7 References Cited |
| 6.8 Exercises |
| Line Pattern Analyzers |
| 7.1 The Nature of Linear Features: Vectors and Networks |
| 7.2 Characteristics and Attributes of Linear Features |
| 7.2.1 Geometric Characteristics of Linear Features |
| 7.2.2 Spatial Attributes of Linear Features: Length |
| 7.2.3 Spatial Attributes of Linear Features: Orientation and Direction |
| 7.2.4 ArcView Example: Linear Attributes |
| 7.3 Directional Statistics |
| 7.3.1 Exploring Statistics for Liner Features |
| 7.3.2 Directional Mean |
| 7.3.3 Circular Variance |
| 7.3.4 ArcView Example: Directional Statistics |
| 7.4 Network Analysis |
| 7.4.1 Spatial Attribute of Network Features: Connectivity or Topology |
| 7.4.2 Assessing Connectivity Level |
| 7.4.3 Evaluating Accessibility |
| 7.4.4 ArcView Example: Network Analysis |
| 7.5 Application Examples |
| 7.5.1 Length Attribute Analysis of Linear Features |
| 7.5.2 Application Example for Directional Statistics |
| 7.5.3 Application Example for Network Analysis |
| 7.6 References Cited |
| 7.7 Exercises |
| Polygon Pattern Analyzers |
| 8.1 Introduction |
| 8.2 Spatial Relationships |
| 8.3 Spatial Dependency |
| 8.4 Spatial Weights Matrices |
| 8.4.1 Neighborhood Definitions |
| 8.4.2 Binary Connectivity Matrix |
| 8.4.3 Stochastic or Row Standardized Weights Matrix |
| 8.4.4 Centroid Distances |
| 8.4.5 Nearest Distances |
| 8.4.6 ArcView Eaxmple |
| 8.1 Spatial Weights Matrices |
| 8.5 Spatial Autocorrelation Statistics and Notations |
| 8.6 Joint Count Statistics |
| 8.6.1 Free Sampling |
| 8.6.2 Randomization Sampling |
| 8.6.3 ArcView Examples |
| 8.2 Joint Count Statistics.(** modify the sections in the original manuscript **) |
| 8.7 Global Statistics |
| 8.7.1 Moran''s I |
| 8.7.2 Geary''s Ratio |
| 8.7.3 General G Statistic |
| 8.7.4 ArcView Example |
| 8.3 Global Statistics for Spatial Autocorrelation |
| 8.8 Local Spatial Autocorrelation Statistics |
| 8.8.1 Local Indicators of Spatial Association (LISA) |
| 8.8.2 Local G-Statistics |
| 8.9 Moran Scatterplot |
| 8.10 ArcView Example |
| 8.4 Local Spatial Autocorrelation Statistics and Moran Scatterplot |
| 8.11 Bivariate Spatial Autocorrelation |
| 8.12 Application Examples |
| 8.13 Summary |
| 8.14 References Cited |
| 8.15 Exercises |
