BinGuru
BinGuru is a Javascript package with an API to several established data binning / data classification methods, often used for visualizing data on choropleth maps. It also includes an implementation of a new, consensus binning method, 'Resiliency'.
Install
Usage
import { BinGuru } from "binguru";
let rawData = [1, 45, 65, 23, 65, 87, 54, 45, 31, 21, 12, 12, 98, 56, 76, null, null, "nan", undefined, "", "null"];
let binCount = 5;
let binExtent = 10;
let precision = 2;
let binGuruObj = new BinGuru(rawData=rawData, binCount=binCount, binExtent=binExtent, precision=precision);
let bins = binGuruObj.fisherJenks();
console.log(bins);
API and Demo
Check out this Observable Notebook.
Build and Publish
- Install dependencies:
npm install
- Build the package:
npm run build
- Set version:
npm version prerelease --preid=<version>
- Dry run:
npm publish --dry-run
- Publish to the registry:
npm publish
Credits
BinGuru was created by
Arpit Narechania, Alex Endert, and Clio Andris of the Georgia Tech Visualization Lab. We thank the members of the Georgia Tech Visualization Lab for their support and constructive feedback.
Citations
@InProceedings{narechania2023resiliency,
author = {Narechania, Arpit and Endert, Alex and Andris, Clio},
title = {{Resiliency: A Consensus Data Binning Method}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {55:1--55:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
year = {2023},
volume = {277},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
doi = {10.4230/LIPIcs.GIScience.2023.55}
}
License
The software is available under the MIT License.
Contact
If you have any questions, feel free to open an issue or contact Arpit Narechania.