arpitnarechania / resiliency-app Goto Github PK

View Code? Open in Web Editor NEW

Resiliency is an ensemble binning method that considers how frequently a geographic entity (e.g., county) falls in a particular bin across multiple comparable data binning methods. This application helps users visualize and interact with the outputs of Resiliency on a variety of datasets.

License: MIT License

JavaScript 2.57% TypeScript 73.36% HTML 19.33% SCSS 4.74%

resiliency-app's Introduction

Resiliency App

Resiliency is an ensemble technique that considers how frequently a geographic entity (e.g., county) falls in a particular bin across multiple comparable data binning methods.

Setup

Open the command line/terminal on your machine and navigate to this project's top-level directory (i.e. where this file is).
Download and install node, npm from https://nodejs.org/en/download/. We developed and tested the app on {Node, NPM}: {v14.15.5, 6.14.11}. Optionally, use the nvm (Node Version Manager) to quickly install and use different versions of node via the command line.
npm install -g @angular/[email protected] to install the desired angular-cli used to run ng-* commands.
npm install - installs required libraries from package.json.

Run

ng serve - compile and serve the application locally
Open the browser at http://localhost:4200
Enjoy!

Build and Deployment

ng build --configuration production --build-optimizer --output-hashing=all - build the app and push the output into angular.json > outputPath directory (default value = "dist/").
ng build --configuration production --build-optimizer --baseHref=/resiliency-app/ --output-hashing=all - build the app for deploying at a public URL with /resiliency-app/ base prefix.

Credits

Resiliency 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}
}