Skip to content

jsgeoda is a javascript library for spatial data analysis using webAssembly

Notifications You must be signed in to change notification settings

GeoDaCenter/jsgeoda

Folders and files

NameName
Last commit message
Last commit date

Latest commit

6bffa96 · Sep 5, 2023

History

54 Commits
Sep 5, 2023
Nov 25, 2020
Jul 28, 2021
Sep 5, 2023
Sep 5, 2023
Sep 5, 2023
Nov 25, 2020
Nov 25, 2020
Jun 9, 2021
Jun 4, 2021
Jul 30, 2021
Jul 30, 2021
Jun 5, 2021
Sep 5, 2023
May 18, 2021
Jun 9, 2021
Sep 5, 2023

Repository files navigation

jsgeoda

Build jsgeoda

jsgeoda is the first javascript library for spatial data analysis with functionalities of choropleth mapping, spatial weights, local indicators of spatial association, spatial clustering, and cluster analysis. With a few lines of code, you can run spatial data analysis in a web browser or in Node.js:

import jsgeoda from 'jsgeoda';

const geoda = await jsgeoda.New();

// get geojson 
const response = await fetch("./natregimes.geojson");
const ab = response.arrayBuffer();

// read geojson in jsgeoda
const nat = geoda.readGeoJSON(ab);

// create Queen contiguity weights
const w = geoda.getQueenWeights(nat);

// get values of variable "HR60"
const hr60 = geoda.getColumn("HR60");

// apply local Moran statistics on variable "HR60"
const lm = geoda.localMoran(w, hr60);

Installation

$ npm i --save jsgeoda

Documentation

https://jsgeoda.libgeoda.org

Hands-On Tutorials:

  1. Hello jsgeoda!

https://codesandbox.io/s/1-hello-jsgeoda-foq4j

  1. Load spatial data

https://codesandbox.io/s/2-load-spatial-data-dgcux

  1. Choropleth Mapping

https://codesandbox.io/s/3basicmapping-lcguj

  1. Spatial Weights

https://codesandbox.io/s/4spatial-weights-owi84

  1. Univariate LISA

https://codesandbox.io/s/5lisaunivariate-zhhop

  1. Multivariate LISA

https://codesandbox.io/s/6lisamultivariate-4w3hk

  1. Spatial Clustering

https://codesandbox.io/s/7spatialclustering-uvz12

jsgeoda

Authors

Xun Li, Luc Anselin

About

jsgeoda is a javascript library for spatial data analysis using webAssembly

Resources

Stars

Watchers

Forks