Skip to content

jonnydubowsky/AgentMaps

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

AgentMaps - Social Simulations on Interactive Maps

AgentMaps is a Javascript library for building and visualizing dynamic social systems on maps. It is based on the Leaflet interactive mapping library. Given a neighborhood, AgentMaps lets you quickly and easily:

  • Build units along the streets.
  • Spawn agents onto the map.
  • Schedule them to move between places on the map.
  • Change their appearance and properties.

AgentMaps lets you turn this:

into something like this:

You can install it via npm (npm install agentmaps) and bundle it yourself, or you can get a premade bundle here to include directly in a webpage. You'll need to include Leaflet separately.

Documentation

Docs for people who want to use AgentMaps are available here.

Docs people who want to understand its internals are here.

A basic walkthrough for creating an AgentMaps simulation can be found here.

Demos

Simple: Shows all the different ways agents can travel around a map.

Epidemic: Agents commute between different parts of a neighborhood while an infection spreads between them.

You can find the corresponding code under /demos in the gh-pages branch here.

Authors

  • Andrew - came up with & built AgentMaps

Acknowledgements

I've only had a few extended conversations talking and thinking about this project outloud over the last few months, and those probably influenced how I went forward with it. The people I've had those discussions with are:

  • I. ("Wheels") Errati
  • M. ("dont ask me, ask gagan") Singh

Thank you to anyone who somehow benefits from this.

AgentMaps: Geospatial Agent-based Modeling and Simulation for JavaScript.

About

Make social simulations on interactive maps with Javascript!

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • JavaScript 100.0%