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An overview of the Spatial Statistics toolbox

Release 9.3
Last modified April 24, 2009
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Note: This topic was updated for 9.3.1.

The Spatial Statistics toolbox contains statistical tools for analyzing spatial distributions, patterns, processes, and relationships. While there may be similarities between spatial and non-spatial (traditional) statistics in terms of concepts and objectives, spatial statistics are unique in that they were developed specifically for use with geographic data. Unlike traditional non-spatial statistical methods, they incorporate space (proximity, area, connectivitity and/or other spatial relationships) directly into their mathematics.
The tools in the Spatial Statistics toolbox allow you to summarize the salient characterisitcs of a spatial distribution (determine the mean center or overarching directional trend, for example), identify statistically significant spatial clusters (hot spots/cold spots) or spatial outliers, assess overall patterns of clustering or dispersion, and model spatial relationships. In addition, for those tools written with python, the source code is available to encourage you to learn from, modify, extend and/or share these and other analysis tools with others. For more information about these tools and statistical analysis of geographic data in general, see The ESRI Guide to GIS Analysis, Volumes 1 and 2 (Volume 2 directly discusses the methods in the Spatial Statistics toolbox).

Note: whenever distance is a component of your analysis, which is almost always the case with spatial statistics, project your data using a Projected Coordinate System (rather than a Geographic Coordinate System based on degrees, minutes, and seconds).

The following toolsets are provided with the Spatial Statistics toolbox at ArcGIS 9.

Toolset Description
Analyzing Patterns These tools evaluate if features or similar attribute values form a clustered, uniform, or random pattern across the region.
Mapping Clusters These tools may be used to identify statistically significant hot spots, cold spots, or spatial outliers.
Measuring Geographic Distributions These tools address questions such as: Where's the center? What's the shape and orientation? How dispersed are the features?
Modeling Spatial Relationships These tools construct spatial weights matrices or model data relationships using regression analyses.
Rendering These tools may be used to render analysis results. New capabilities have, or will soon, make these tools unnecessary.
Utilities These utility tools perform a variety of miscellaneous functions: computing areas, assessing minimum distances, exporting variables and geometry, converting spatial weights files, and collecting coincident points.

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