node-red-contrib-tak-registration/node_modules/@turf/nearest-neighbor-analysis/dist/cjs/index.d.cts

A Node-RED node to register to TAK and to help wrap files as datapackages to send to TAK

import { Feature, Polygon, FeatureCollection, GeoJsonProperties } from 'geojson';
import { Units, AreaUnits } from '@turf/helpers';

/**
 * Nearest neighbour statistics.
 *
 * @typedef {object} NearestNeighborStatistics
 * @property {(Units & AreaUnits)} units
 * @property {string} arealUnits
 * @property {number} observedMeanDistance
 * @property {number} expectedMeanDistance
 * @property {number} numberOfPoints
 * @property {number} zScore
 */
interface NearestNeighborStatistics {
    units: Units & AreaUnits;
    arealUnits: string;
    observedMeanDistance: number;
    expectedMeanDistance: number;
    numberOfPoints: number;
    zScore: number;
}
/**
 * Nearest neighbour study area polygon feature.
 *
 * @typedef {object} NearestNeighborStudyArea
 * @extends Feature<Polygon>
 * @property {GeoJsonProperties} properties
 * @property {NearestNeighborStatistics} properties.nearestNeighborAnalysis
 */
interface NearestNeighborStudyArea extends Feature<Polygon> {
    properties: {
        nearestNeighborAnalysis: NearestNeighborStatistics;
        [key: string]: any;
    };
}
/**
 * Nearest Neighbor Analysis calculates an index based the average distances
 * between points in the dataset, thereby providing inference as to whether the
 * data is clustered, dispersed, or randomly distributed within the study area.
 *
 * It returns a {@link Feature}<{@link Polygon}> of the study area, with the results of
 * the analysis attached as part of of the `nearestNeighborAnalysis` property
 * of the study area's `properties`. The attached
 * [_z_-score](https://en.wikipedia.org/wiki/Standard_score) indicates how many
 * standard deviations above or below the expected mean distance the data's
 * observed mean distance is. The more negative, the more clustered. The more
 * positive, the more evenly dispersed. A _z_-score between -2 and 2 indicates
 * a seemingly random distribution. That is, within _p_ of less than 0.05, the
 * distribution appears statistically significantly neither clustered nor
 * dispersed.
 *
 * **Remarks**
 *
 * - Though the analysis will work on any {@link FeatureCollection} type, it
 * works best with {@link Point} collections.
 *
 * - This analysis is _very_ sensitive to the study area provided.
 * If no {@link Feature}<{@link Polygon}> is passed as the study area, the function draws a box
 * around the data, which may distort the findings. This analysis works best
 * with a bounded area of interest within with the data is either clustered,
 * dispersed, or randomly distributed. For example, a city's subway stops may
 * look extremely clustered if the study area is an entire state. On the other
 * hand, they may look rather evenly dispersed if the study area is limited to
 * the city's downtown.
 *
 * **Bibliography**
 *
 * Philip J. Clark and Francis C. Evans, “Distance to Nearest Neighbor as a
 * Measure of Spatial Relationships in Populations,” _Ecology_ 35, no. 4
 * (1954): 445–453, doi:[10.2307/1931034](http://doi.org/10.2307/1931034).
 *
 * @function
 * @param {FeatureCollection<any>} dataset FeatureCollection (pref. of points) to study
 * @param {Object} [options={}] Optional parameters
 * @param {Feature<Polygon>} [options.studyArea] polygon representing the study area
 * @param {Units & AreaUnits} [options.units='kilometers'] unit of measurement for distances and, squared, area.
 * @param {GeoJsonProperties} [options.properties={}] properties
 * @returns {NearestNeighborStudyArea}  A polygon of the study area or an approximation of one.
 * @example
 * var bbox = [-65, 40, -63, 42];
 * var dataset = turf.randomPoint(100, { bbox: bbox });
 * var nearestNeighborStudyArea = turf.nearestNeighborAnalysis(dataset);
 *
 * //addToMap
 * var addToMap = [dataset, nearestNeighborStudyArea];
 */
declare function nearestNeighborAnalysis(dataset: FeatureCollection<any>, options?: {
    studyArea?: Feature<Polygon>;
    units?: Units & AreaUnits;
    properties?: GeoJsonProperties;
}): NearestNeighborStudyArea;

export { type NearestNeighborStatistics, type NearestNeighborStudyArea, nearestNeighborAnalysis as default, nearestNeighborAnalysis };