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Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS

ISBN-10: 0471468991
ISBN-13: 9780471468998
Edition: 2005
List price: $150.00 Buy it from $114.41
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Description: The fully revised and updated book on statistical and spatial analyses in a GIS environment It2s been four years since the publication of the groundbreaking Statistical Analysis with ArcView GIS, and ArcView continues to be one of the most popular  More...

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Book details

List price: $150.00
Copyright year: 2005
Publisher: John Wiley & Sons, Incorporated
Publication date: 10/20/2005
Binding: Hardcover
Pages: 464
Size: 6.00" wide x 9.50" long x 0.75" tall
Weight: 1.694
Language: English

The fully revised and updated book on statistical and spatial analyses in a GIS environment It2s 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

Prof. Chaowei Yang is the Director of the Joint Center for Intelligent Spatial Computing at George Mason University.Prof. David Wong is the Chair of Earth Systems and GeoInformation Sciences at George Mason University.Prof. Ruixin Yang is a Professor at George Mason University.Qianjun Miao is the Deputy Director General of the HeiLongJiang Bureau of Surveying and Mapping (HLJBSM)

Preface
Introduction
Why Statistics and Sampling?
What is so Special about Spatial Data?
MAUP- The Modifiable Areal Unit Problems
Spatial Autocorrelation
Spatial Data and the Needs For Spatial Analysis/Statistics
Fundamentals in Spatial Analysis and Statistics
Scales of Measurement
Mathematical Notations
Scale, Extent, and Projection
ArcView Notes - Data Model and Examples
Data Model Used In Arcview GIS
Random Sampling - The Generic Way
Random and Systematic Point Sampling - An Extended Function
References Cited
Exercises
Classical Statistics
Distribution Descriptors: One Variable (Univariate)
Measures of Central Tendency
Mode
Median
Mean
Grouped or Weighted Mean
Measures of Dispersion
Range, Minimum, Maximum and Percentiles
Mean Deviation
Variance and Standard Deviation
Weighted Variance and Weighted Standard Deviation
Coefficient of Variation
ArcView Examples
Descriptive Statistics
Higher Moments Statistics
Skewness and Kurtosis
ArcView Examples
Statistical Charts
Additional Statistics
Application Example
Summary
References Cited
Exercises
Relationship Descriptors: Two Variables (Bivariate)
Correlation Analysis
Correlation: Nominal Scale
Nominal Scale and Binary: Phi Coefficient
Nominal Scale and Polychotomous: Chi-Square Statistic
Correlation: Ordinal Scale
Correlation: Interval / Ratio Scale
Trend Analysis
Simple Linear Regression Models
Coefficient of Determination
Empirical Examples
ArcView Notes
Application Examples
Reference Cited
Exercises.Hypothesis Testers
Probability Concepts
Probability Functions
The Binomial Distribution
Poisson Distribution
The Normal Distribution
ArcView Notes
Central Limit Theorem and Confidence Intervals
Hypothesis Testing
ArcView Notes
Types of Error
Parametric Test Statistics
Difference in Variances
ArcView Note
Difference in Means
Small Sample Size
Large Sample Size
ArcView Note
Difference between a mean and a fixed value
ArcView Note
Significance of Pearson''s correlation coefficient
ArcView Note
Significance of Regression Parameters
ArcView Note
Testing Non-Parametric Statistics: Chi-Square Statistics, C
ArcView Note
Spearman''s Rank Coefficient
ArcView Notes
Kolmogorov-Smirnov Test
ArcView Note
Summary
Reference used in this chapter
Exercises
Spatial Statistics
Point Pattern Descriptors
The Nature of Point Features
Central Tendency of Point Distributions
Mean Center
Weighted Mean Center
Median Center
Dispersion and Orientation of Point Distributions
Standard Distance
Standard Deviational Ellipses
ArcView Notes
Application Examples
References Cited
Exercises
Point Pattern Analyzers
Scale and Extent
Quadrat Analysis
General Concepts in Quadrat Analysis
Comparing the Observed with the Expected Distributions using K-S test
Comparing the observed with the expected using variance-mean ratio
ArcView Example: Quadrat Analysis of Northeast Ohio Cities
Ordered Neighbor Analysis
Nearest Neighbor Statistic
Testing for pattern using Nearest Neighbor Statistic
Higher Ordered Neighbor Statistics
Boundary Adjustments of the Nearest Neighbor Statistics
ArcView Example: Nearest neighbor analysis of Northeast Ohio Cities
K-Function
ArcView Example: K-Function Analysis of Northeast Ohio Cities
Spatial Autocorrelation of Points
Measures for Spatial Autocorrelation
Significance Testing of Spatial Autocorrelation Measures
ArcView Example: Spatial Autocorrelation Analysis of Northeast Ohio Cities
Application Examples
References Cited
Exercises
Line Pattern Analyzers
The Nature of Linear Features: Vectors and Networks
Characteristics and Attributes of Linear Features
Geometric Characteristics of Linear Features
Spatial Attributes of Linear Features: Length
Spatial Attributes of Linear Features: Orientation and Direction
ArcView Example: Linear Attributes
Directional Statistics
Exploring Statistics for Liner Features
Directional Mean
Circular Variance
ArcView Example: Directional Statistics
Network Analysis
Spatial Attribute of Network Features: Connectivity or Topology
Assessing Connectivity Level
Evaluating Accessibility
ArcView Example: Network Analysis
Application Examples
Length Attribute Analysis of Linear Features
Application Example for Directional Statistics
Application Example for Network Analysis
References Cited
Exercises
Polygon Pattern Analyzers
Introduction
Spatial Relationships
Spatial Dependency
Spatial Weights Matrices
Neighborhood Definitions
Binary Connectivity Matrix
Stochastic or Row Standardized Weights Matrix
Centroid Distances
Nearest Distances
ArcView Eaxmple
Spatial Weights Matrices
Spatial Autocorrelation Statistics and Notations
Joint Count Statistics
Free Sampling
Randomization Sampling
ArcView Examples
Joint Count Statistics.(** modify the sections in the original manuscript **)
Global Statistics
Moran''s I
Geary''s Ratio
General G Statistic
ArcView Example
Global Statistics for Spatial Autocorrelation
Local Spatial Autocorrelation Statistics
Local Indicators of Spatial Association (LISA)
Local G-Statistics
Moran Scatterplot
ArcView Example
Local Spatial Autocorrelation Statistics and Moran Scatterplot
Bivariate Spatial Autocorrelation
Application Examples
Summary
References Cited
Exercises

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