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 to encourage you to learn from, modify, extend, and/or share these and other analysis tools with others.
The tools in the Spatial Statistics toolbox will not work directly with an XY event layer (a layer created from a table containing x-coordinate and y-coordinate fields). Use the Copy Features tool to first convert the XY Event data into a feature class before you run your analysis.
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.
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. 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 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.
The Spatial Statistics Resources page at https://www.esriurl.com/spatialstats contains a variety of resources to help you use the Spatial Statistics and Space Time Pattern Mining tools, including the following:
- Hands-on tutorials and Learn lessons
- Workshop videos and presentations
- Training and web seminars
- Links to books, articles, and technical papers
- Sample scripts and case studies