Fast visualization of large spatiotemporal datasets
Interested? Try the live demos! All of these datasets are running off of a single machine with 16GB of RAM. For the demos not tagged as "tablet-friendly", you will need a WebGL capable browser. We have tested it on Chrome and Firefox, but ourselves use Chrome and OS X for development.
Download our implementation of the Nanocubes technology here.
Full source code is hosted on GitHub. Please file any bugs, feature requests, etc. as issues thru GitHub.
The Nanocubes technology builds on data cube technology. Until recently, data cubes took a very large amount of space. This means they could not be stored in main memory, so their computation and access for large datasets did not mix well with interactive visualization. Our main innovation is an algorithm for hierarchical data cubes that has very modest memory requirements. So it is just like a data cube, but it's tiny! We thought "Nanocubes" sounded better than "tiny cubes".
This project uses a litany of open-source projects and software, for which we are incredibly grateful. In no particular order, we want to acknowledge our use of: Bootstrap, Bootstrap Tour, jQuery, Underscore.js, d3, OpenStreetMap, Leaflet.js, and Lux.
In addition, we wish to acknowledge the comments, suggestions and help of Stephen North, Drew Skau, Hadley Wickham, Luiz Scheidegger, Chris Volinsky, Simon Urbanek, Robert Kosara, John Moeller, David Kormann, T.J. Jankun-Kelly and Steve Haroz.