binguru

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

• npm install binguru

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"]; // Input array of numbers, strings, nulls, nans, undefineds.
let binCount = 5; // Desired number of bins (inconsequential for certain binning methods, e.g., boxPlot).
let binExtent = 10; // Desired bin interval (only applicable for certain binning methods, e.g., definedInterval).
let precision = 2; // Desired rounding off precision.
let binGuruObj = new BinGuru(rawData=rawData, binCount=binCount, binExtent=binExtent, precision=precision); // Initialize an instance of BinGuru

let bins = binGuruObj.fisherJenks(); // Call an endpoint, e.g., fisherJenks() to bin using the FisherJenks / Natural Breaks binning method first.
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

@article{narechania2023resiliency,
  title = {{Resiliency: A Consensus Binning Method}},
  author = {{Narechania}, Arpit and {Endert}, Alex and {Andris}, Clio},
  journal={GIScience 2023 Short Paper Proceedings},
  doi = {},
  year = {2023}
}

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.