jtBaseApp/node_modules/@turf/center-median/dist/js/index.d.ts

import { FeatureCollection, Feature, Point, Position } from "@turf/helpers";
/**
 * Takes a {@link 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.
 *
 * **Bibliography**
 *
 * 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:{@link https://doi.org/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.
 *
 * @name centerMedian
 * @param {FeatureCollection<any>} features Any GeoJSON Feature Collection
 * @param {Object} [options={}] Optional parameters
 * @param {string} [options.weight] the property name used to weight the center
 * @param {number} [options.tolerance=0.001] the difference in distance between candidate medians at which point the algorighim stops iterating.
 * @param {number} [options.counter=10] how many attempts to find the median, should the tolerance be insufficient.
 * @returns {Feature<Point>} The median center of the collection
 * @example
 * var points = turf.points([[0, 0], [1, 0], [0, 1], [5, 8]]);
 * var medianCenter = turf.centerMedian(points);
 *
 * //addToMap
 * var addToMap = [points, medianCenter]
 */
declare function centerMedian(features: FeatureCollection<any>, options?: {
    weight?: string;
    tolerance?: number;
    counter?: number;
}): Feature<Point, {
    medianCandidates: Array<Position>;
    [key: string]: any;
}>;
export default centerMedian;