An overview of the Measuring Geographic Distributions toolset

Measuring the distribution of a set of features allows you to calculate a value that represents a characteristic of the distribution, such as the center, compactness, or orientation. You can use this value to track changes in the distribution over time or compare distributions of different features.

The Measuring Geographic Distributions toolset addresses questions such as the following:

  • Where's the center?
  • What's the shape and orientation of the data?
  • How dispersed are the features?


Central Feature

Identifies the most centrally located feature in a point, line, or polygon feature class.

Directional Distribution

Creates standard deviational ellipses or ellipsoids to summarize the spatial characteristics of geographic features: central tendency, dispersion, and directional trends.

Linear Directional Mean

Identifies the mean direction, length, and geographic center for a set of lines.

Mean Center

Identifies the geographic center (or the center of concentration) for a set of features.

Median Center

Identifies the location that minimizes overall Euclidean distance to the features in a dataset.

Neighborhood Summary Statistics

Calculates summary statistics of one or more numeric fields using local neighborhoods around each feature. The local statistics include mean (average), median, standard deviation, interquartile range, skewness, and quantile imbalance, and all statistics can be geographically weighted using kernels to give more influence to neighbors closer to the focal feature. Various neighborhood types can be used, including distance band, number of neighbors, polygon contiguity, Delaunay triangulation, and spatial weights matrix (.swm) files. Summary statistics are also calculated for the distances to the neighbors of each feature.

Standard Distance

Measures the degree to which features are concentrated or dispersed around the geometric mean center.

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