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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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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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14646899000
- 1. Auflage
- Seitenzahl: 464
- Erscheinungstermin: 1. Oktober 2005
- Englisch
- Abmessung: 240mm x 161mm x 29mm
- Gewicht: 770g
- ISBN-13: 9780471468998
- ISBN-10: 0471468991
- Artikelnr.: 15181391
- Verlag: Wiley & Sons
- Artikelnr. des Verlages: 14646899000
- 1. Auflage
- Seitenzahl: 464
- Erscheinungstermin: 1. Oktober 2005
- Englisch
- Abmessung: 240mm x 161mm x 29mm
- Gewicht: 770g
- ISBN-13: 9780471468998
- ISBN-10: 0471468991
- Artikelnr.: 15181391
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.
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.
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.
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.