@turf/distance-weight
pNormDistance
calcualte the Minkowski p-norm distance between two features.
Parameters
-
feature1
point feature -
feature2
point feature -
p
p-norm 1=<p<=infinity 1: Manhattan distance 2: Euclidean distance
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, default10000
) -
options.p
number Minkowski p-norm distance parameter. 1: Manhattan distance. 2: Euclidean distance. 1=<p<=infinity. (optional, default2
) -
options.binary
boolean If true, weight=1 if d <= threshold otherwise weight=0. If false, weight=Math.pow(d, alpha). (optional, defaultfalse
) -
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, defaultfalse
)
-
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 module individually:
$ npm install @turf/distance-weight
Or install the Turf module that includes it as a function:
$ npm install @turf/turf