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GIS Statistical Analysis


Statistics is the science that deals with collecting, analyzing, interpreting, and presenting data; informing decision-making. However, statistics alone are not relevant without being associated with a specific place.

Statistical analysis helps you extract additional information from your GIS data, such as how attribute values are distributed, whether there are spatial trends in the data, or whether the features form spatial patterns.

In GIS some of the statistical analysis techniques described in this document are most well-suited for interactive applications, such as ArcMap, that allow you to visualize and analyze data .

Uses of statistical Analysis in GIS

  • Statistical analysis is used to explore your data.
  • Another use of statistical analysis is to summarize data.
  • It is also used to identify and confirm spatial patterns.

Statistical Analysis in ArcMap

Statistics Toolset

he Statistics toolset contains tools that perform standard statistical analysis, such as; mean, minimum, maximum, and standard deviation, on attribute data as well as tools that calculate area, length, and count statistics for overlapping and neighboring features.

FrequencyCreates a new table containing unique field values and the number of occurrences of each unique field value.
Summary StatisticsCalculates summary statistics for field in a table.
Tabulate IntersectionIntersection between two feature classes and cross-tabulates the area, length, or count of the intersecting features.

Spatial Statistics Toolbox

The Spatial Statistics toolbox contains statistical tools for analyzing spatial distributions, patterns, processes, and relationships.

This tools allow you to summarize the salient characteristics of a spatial distribution, identify statistically significant spatial clusters, assess overall patterns of clustering or dispersion, identify an appropriate scale of analysis, explore spatial relationships, and more.

Analyzing PatternsThe analyzing tools evaluate if features, or the values associated with features, form a clustered, dispersed, or random spatial pattern.
Mapping ClustersThese tools may be used to identify statistically significant hot spots, cold spots, or spatial outliers.
Measuring Geographic DistributionsThese tools address questions such as Where’s the center? What’s the shape and orientation? How dispersed are the features?
Modeling Spatial RelationshipsThese tools model data relationships using regression analyses or construct spatial weights matrices.
RenderingThese tools may be helpful for rendering analysis results.
UtilitiesThe utility tools perform a variety of miscellaneous functions, such as; computing areas, assessing minimum distances, exporting variables and geometry, converting spatial weights files, and more.
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