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.
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.
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.
Inhaltsangabe
Preface. Introduction. 1. 1 Why Statistics and Sampling? 1.2 What is so Special about Spatial Data? 1.3 Spatial Data and the Needs For Spatial Analysis/Statistics. 1.4 Fundamentals in Spatial Analysis and Statistics. 1.5 ArcView Notes - Data Model and Examples. 1.6 References Cited. 1.7 Exercises. PART I. CLASSICAL STATISTICS. Distribution Descriptors: One Variable (Univariate). 2.1 Measures of Central Tendency. 2.2 Measures of Dispersion. 2.3 ArcView Examples. 2.4 Higher Moments Statistics. 2.5 ArcView Examples. 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. 3.3 Correlation: Ordinal Scale. 3.4 Correlation: Interval / Ratio Scale. 3.5 Trend Analysis. 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. 4.3 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 Non-Parametric Statistics: Chi-Square Statistics, Chi². 4.11 Spearman's Rank Coefficient. 4.12 Kolmogorov-Smirnov Test. 4.13 Summary. 4.14 Reference used in this chapter. 4.15 Exercises. PART II. SPATIAL STATISTICS. 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. 5.6 References Cited. 5.7 Exercises. Point Pattern Analyzers. Scale and Extent. Quadrat Analysis. 6.3 Ordered Neighbor Analysis. 6.4 K-Function. 6.5 Spatial Autocorrelation of Points. 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. 7.3. Directional Statistics. 7.4 Network Analysis. 7.5 Application Examples. 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. 8.5 Spatial Autocorrelation Statistics and Notations. 8.6 Joint Count Statistics. (** modify the sections in the original manuscript **). 8.7 Global Statistics. 8.8 Local Spatial Autocorrelation 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.
Preface. Introduction. 1. 1 Why Statistics and Sampling? 1.2 What is so Special about Spatial Data? 1.3 Spatial Data and the Needs For Spatial Analysis/Statistics. 1.4 Fundamentals in Spatial Analysis and Statistics. 1.5 ArcView Notes - Data Model and Examples. 1.6 References Cited. 1.7 Exercises. PART I. CLASSICAL STATISTICS. Distribution Descriptors: One Variable (Univariate). 2.1 Measures of Central Tendency. 2.2 Measures of Dispersion. 2.3 ArcView Examples. 2.4 Higher Moments Statistics. 2.5 ArcView Examples. 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. 3.3 Correlation: Ordinal Scale. 3.4 Correlation: Interval / Ratio Scale. 3.5 Trend Analysis. 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. 4.3 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 Non-Parametric Statistics: Chi-Square Statistics, Chi². 4.11 Spearman's Rank Coefficient. 4.12 Kolmogorov-Smirnov Test. 4.13 Summary. 4.14 Reference used in this chapter. 4.15 Exercises. PART II. SPATIAL STATISTICS. 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. 5.6 References Cited. 5.7 Exercises. Point Pattern Analyzers. Scale and Extent. Quadrat Analysis. 6.3 Ordered Neighbor Analysis. 6.4 K-Function. 6.5 Spatial Autocorrelation of Points. 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. 7.3. Directional Statistics. 7.4 Network Analysis. 7.5 Application Examples. 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. 8.5 Spatial Autocorrelation Statistics and Notations. 8.6 Joint Count Statistics. (** modify the sections in the original manuscript **). 8.7 Global Statistics. 8.8 Local Spatial Autocorrelation 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.
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