SKATER

Spatial C(K)luster Analysis by Tree Edge Removal (SKATER) is an optimized algorithm to prune the minimum spanning tree into several clusters that their values of selected variables are as similar as possible while retaining the contiguity structure. For more information, please read https://geodacenter.github.io/workbook/9c_spatial3/lab9c.html#skater

skater()

function skater(
    WeightResult w,
    Number k, 
    Array vals,
    Number min_bound, 
    Array bound_vals,
    String scale_method,
    String distance_type)

Arguments

Name	Type	Description
weights	WeightsResult	The weights object WeightsResult
k	Number	The number of clusters
values	Array	The list of numeric vectors of selected variable
min_bound	Number	The minimum value that the sum value of bounding variable int each cluster should be greater than
bound_vals	Array	The numeric vector of selected bounding variable
scale_method	String	The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}
distance_method	String	The distance method: {"euclidean", "manhattan"}

Return

Type	Description
ClusteringResult	The Clustering object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Examples

Node.js

const jsgeoda = require('jsgeoda');
const fs = require('fs');

// load data
const data = fs.readFileSync('./data/natregimes.geojson').buffer;

// create jsgeoda instance
const geoda = await jsgeoda.New();

// load geojson in jsgeoda
const nat = geoda.read_geojson(data);

// create a queen contiguity weights
const w = geoda.queen_weights(nat);

// get values
const hr60 = geoda.get_col(nat, "HR60");
const ue60 = geoda.get_col(nat, "UE60");

// set minimum bound
const po60 = geoda.get_col(nat, "PO60");

// apply skater
const skater = geoda.skater(w, 10, [hr60, ue60], 17845200, po60);

React

Try it yourself in the playground (jsgeoda + deck.gl):