Counting Squares
15 Jul 2008
One of the last projects I had a substantial hand in formulating and designing while at Refractions was a project for providing provincial-level environmental summaries, using the best possible high resolution data. The goal is to be able to answer questions like:
- What is volume of old-growth pine in BC? By timber supply area? In caribou habitat?
- What young forest areas on on south facing slopes of less than 10%, within 200 meters of water?
- What is the volume of fir in areas affected by mountain pine beetle?
- How much bear habitat is more than 5km from a road but not in an existing protected area? Where is it?
This is all standard GIS stuff, but we wanted to make answering these questions the matter of a few mouse gestures, with no data preparation required, so that a suitably motivated environmental scientist or forester could figure out how to do the analysis with almost no training.
Getting there involves solving two problems: what kind of engine can generate fairly complicated summaries based on arbitrary summary areas, and; how do you make that engine maximally usable with minimal interface complexity.
The solution to the engine was double-barreled.
First, to enable arbitrary summary areas, move from vector analysis units to a province-wide raster grid. For simplicity, we chose one hectare (100m x 100m), which means about 90M cells for all the non-ocean area in the jurisdiction. Second, to enable a roll-up engine on those cells, put all the information into a database, in our case PostgreSQL. Data entering the system is pre-processed, rasterized onto the hectare grid, and then saved in a master table that has one row for each hectare. At this point, each hectare in the province has over 100 variables associated with it in the system.
To provide a usable interface on the engine, we took the best of breed everywhere we could: Google Web Toolkit as the overall UI framework; OpenLayers as a mapping component; server-side Java and Tomcat for all the application logic. The summary concept was very similar to OLAP query building, so we stole the ideas for the working of that tab from the SQL Server OLAP query interface.
The final result is Hectares BC, which is one of the cooler things I have been involved in, going from a coffee shop “wouldn’t this be useful” discussion to a prototype, to a funding proposal, to the completed pilot in about 24 months.