(Over)Simplifying Calgary too

One of the good side effects of scripting multi-stage pipelines to build a visualization like my over-simplified map of Buenos Aires is that to process a data source in a completely different format only requires you to write a pre-processing script — everything else remains the same.

While I had used CSV data for the Buenos Aires map, I got KML files for the equivalent land use data for the City of Calgary. The pipeline I had written expected use types tied to single points mapped into a fixed grid, so I wrote a small Python script to extract the polygons defined in the KML file, overlay a grid over them, and assign to each grid point the land use value of the polygon that contained id.

After that the analysis was straightforward. Here's the detailed map of land uses (with less resolution than the original data, as the polygons have been projected on the point grid):

calgary-complex_sectors

Here's the smoothed-out map:

calgary-simple_sectors

This is how we split it into a puzzle of more-or-less single-use sectors:

calgary-simple_nodes

And here's how it looks when you forget the geometry and only care about labels and relative (click to read the labels):

calgary-labels

Unlike Buenos Aires, I've never been to Calgary, but a quick look at online maps seem to support the above as a first approximation to the city geography. I'd love to hear how from somebody who actually knows the city whether and how it matches their subjective map of the city.