The Spatial Statistics toolbox contains statistical tools for analyzing spatial distributions, patterns, processes, and relationships. While there may be similarities between spatial and nonspatial (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 nonspatial 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, group features based on attribute similarities, identify an appropriate scale of analysis, and explore spatial relationships. In addition, for those tools written with Python, the source code is available for you to learn from, and modify, extend, and share these and other analysis tools with others.
Note:
When using shapefiles, keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from nonshapefile inputs may store or interpret null values as zero. In some cases, nulls are stored as very large negative values in shapefiles. This can lead to unexpected results. See Geoprocessing considerations for shapefile output for more information.
| Toolset | Description |
|---|---|
| These tools evaluate whether features, or the values associated with features, form a clustered, dispersed, or random spatial pattern. | |
These tools assess the sensitivity of an analysis to different types of uncertainty by comparing the original analysis results to results from simulated data. | |
| These tools can be used to identify statistically significant hot spots, cold spots, or spatial outliers. There are also tools to identify or group features with similar characteristics. | |
| 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 create and analyze spatial components (Moran eigenvectors), such as creating component explanatory variables, filtering autocorrelation from a field, or recommending neighborhoods for spatial analysis. | |
| These tools perform a variety of miscellaneous functions, such as computing areas, assessing minimum distances, exporting variables and geometry, converting spatial weights files, and collecting coincident points. |
Additional resources
The Spatial Statistics Resources page contains a variety of resources to help you use the Spatial Statistics and Space Time Pattern Mining tools, including the following:
- Hands-on tutorials
- Workshop videos and presentations
- Training and web seminars
- Links to books, articles, and technical papers
- Sample scripts and case studies