# 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?

Tool | Description |
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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. 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 files (.swm).
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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