@turf/distance-weight
pNormDistance
calcualte the Minkowski p-norm distance between two features.
Parameters
feature1
point featurefeature2
point featurep
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, 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 module individually:
$ npm install @turf/distance-weight
Or install the Turf module that includes it as a function:
$ npm install @turf/turf