@turf/distance-weight

pNormDistance

calcualte the Minkowski p-norm distance between two features.

Parameters

• feature1 Feature<Point> point feature
• feature2 Feature<Point> point feature
• p p-norm 1=<p<=infinity 1: Manhattan distance 2: Euclidean distance (optional, default 2)

Returns number

distanceWeight

Parameters

• fc FeatureCollection<any> FeatureCollection.

• options Object? option object.

  • options.threshold number If the distance between neighbor and target features is greater than threshold, the weight of that neighbor is 0. (optional, default 10000)
  • options.p number Minkowski p-norm distance parameter. 1: Manhattan distance. 2: Euclidean distance. 1=<p<=infinity. (optional, default 2)
  • options.binary boolean If true, weight=1 if d <= threshold otherwise weight=0. If false, weight=Math.pow(d, alpha). (optional, default false)
  • options.alpha number distance decay parameter. A big value means the weight decay quickly as distance increases. (optional, default -1)
  • options.standardization boolean row standardization. (optional, default false)

Examples

var bbox = [-65, 40, -63, 42];
var dataset = turf.randomPoint(100, { bbox: bbox });
var result = turf.distanceWeight(dataset);

Returns Array<Array<number>> distance weight matrix.

This module is part of the Turfjs project, an open source module collection dedicated to geographic algorithms. It is maintained in the Turfjs/turf repository, where you can create PRs and issues.

Installation

Install this single module individually:

$ npm install @turf/distance-weight

Or install the all-encompassing @turf/turf module that includes all modules as functions:

$ npm install @turf/turf