# An overview of the Spatial Statistics toolbox

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, connectivity, and/or other spatial relationships) directly into their mathematics.

The tools in the Spatial Statistics toolbox allow you to summarize the salient characteristics 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.

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).

Toolset

Description

These tools evaluate if features, or the values associated with features, form a clustered, dispersed, or random spatial pattern.

These tools may be used to identify statistically significant hot spots, cold spots, or spatial outliers.

These tools address questions such as: Where's the center? What's the shape and orientation? How dispersed are the features?

These tools model data relationships using regression analyses or construct spatial weights matrices.

These tools may be used to render analysis results.

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.

Spatial Statistics toolsets