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Version: 7.0.0



Takes a FeatureCollection of points and calculates the median center, algorithimically. The median center is understood as the point that is requires the least total travel from all other points.

Turfjs has four different functions for calculating the center of a set of data. Each is useful depending on circumstance.

@turf/center finds the simple center of a dataset, by finding the midpoint between the extents of the data. That is, it divides in half the farthest east and farthest west point as well as the farthest north and farthest south.

@turf/center-of-mass imagines that the dataset is a sheet of paper. The center of mass is where the sheet would balance on a fingertip.

@turf/center-mean takes the averages of all the coordinates and produces a value that respects that. Unlike @turf/center, it is sensitive to clusters and outliers. It lands in the statistical middle of a dataset, not the geographical. It can also be weighted, meaning certain points are more important than others.

@turf/center-median takes the mean center and tries to find, iteratively, a new point that requires the least amount of travel from all the points in the dataset. It is not as sensitive to outliers as @turf/center-mean, but it is attracted to clustered data. It, too, can be weighted.


Harold W. Kuhn and Robert E. Kuenne, “An Efficient Algorithm for the Numerical Solution of the Generalized Weber Problem in Spatial Economics,” Journal of Regional Science 4, no. 2 (1962): 21–33, doi:10.1111/j.1467-9787.1962.tb00902.x.

James E. Burt, Gerald M. Barber, and David L. Rigby, Elementary Statistics for Geographers, 3rd ed., New York: The Guilford Press, 2009, 150–151.


featuresFeatureCollection<any>Any GeoJSON Feature Collection
options?ObjectOptional parameters (default {})
options.weight?stringthe property name used to weight the center
options.tolerance?numberthe difference in distance between candidate medians at which point the algorighim stops iterating. (default 0.001)
options.counter?numberhow many attempts to find the median, should the tolerance be insufficient. (default 10)



var points = turf.points([
[0, 0],
[1, 0],
[0, 1],
[5, 8],
var medianCenter = turf.centerMedian(points);


$ npm install @turf/center-median

import { centerMedian } from "@turf/center-median";
const result = centerMedian(...);
$ npm install @turf/turf

import * as turf from "@turf/turf";
const result = turf.centerMedian(...);