An overview of the Spatial Statistics toolbox
(ArcGIS, ArcToolbox) Tools
· 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.
· Toolset of the Spatial Statistics toolbox:
1. Analyzing
Patterns
These tools evaluate if features, or the values associated with features,
form a clustered, dispersed, or random spatial pattern.
2. Mapping
Clusters
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.
3. Measuring
Geographic Distributions
These tools address questions such as Where's the center? What's the
shape and orientation? How dispersed are the features?
4. Modeling
Spatial Relationships
These tools model data relationships using regression analyses or
construct spatial weights matrices.
5. 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.
Thank You
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