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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010047018 | G70.212 L43 2001 | Open Access Book | Book | Searching... |
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Summary
Summary
Statistical analysis of geographic data has been greatly enhanced in recent years with the advent of Geographical Information Systems (GIS) software. Yet GIS users have struggles to synchronize their applications of spatial information with practical, quantitative statistics. ArcView, one of the most powerful GIS-compatible systems, has become the most popular software among geographers precisely because of its capacity for spatial-quantitative synthesis. Now geographers Jay Lee and David Wong have produced the first handbook for applied ArcView use, bringing the theoretical underpinnings of classical statistics into the earth science environment.
Employing points, lines, and polygons to model real-world geographic forms, this easy-to-use resource provides geographers with a valuable bridge between theory and the software necessary to apply it. It contains sections on point distribution, point pattern analysis, linear features, network analysis, and spatial autocorrelation analysis. Statistical Analysis with ArcView GIS also features:
Examples that show steps of statistical calculations-as well as ways to interpret the results.
More than 100 illustrations, including statistical charts, maps, and ArcView screen captures.
Helpful end-of-chapter references.
Suitable for professionals as well as students of geography, this book is an important tool for anyone involved in the statistical analysis of GIS data.
Author Notes
Jay Lee, PhD, is Associate Professor of Geography at Kent State University in Ohio
David W. S. Wong, PhD, is Associate Professor of Earth Sciences at George Mason University in Fairfax, Virginia
Table of Contents
| 1 Attribute Descriptors |
| 1.1 Central Tendency |
| 1.2 Dispersion and Distribution |
| 1.3 Relationship |
| 1.4 Trend |
| 2 Point Descriptors |
| 2.1 The Nature of Point Features |
| 2.2 Central Tendency of Point Distributions |
| 2.3 Dispersion of Point Distributions |
| 2.4 Application Examples |
| 2.4.1 References |
| 3 Pattern Detectors |
| 3.1 Scale, Extent, and Projection |
| 3.2 Quadrat Analysis |
| 3.3 Nearest Neighbor Analysis |
| 3.4 Spatial Autocorrelation |
| 3.5 Application Examples |
| 3.5.1 References |
| 4 Line Descriptors |
| 4.1 The Nature of Linear Features |
| 4.2 Characteristics and Attributes of Linear Features |
| 4.3 Directional Statistics |
| 4.4 Network Analysis |
| 4.5 Application Examples |
| 4.5.1 References |
| 5 Pattern Descriptors |
| 5.1 Spatial Relationships |
| 5.2 Spatial Autocorrelation |
| 5.3 Spatial Weights Matrices |
| 5.4 Types of Spatial Autocorrelation Measures and Some Notations |
| 5.5 Joint Count Statistics |
| 5.6 Moran and Geary Indices |
| 5.7 General G-Statistic |
| 5.8 Local Spatial Autocorrelation Statistics |
| 5.9 Moran Scatterplot |
| 5.10 Application Examples |
| 5.11 Summary |
| 5.11.1 References |
| Index |
