Was 80, now 83

When I first saw walkscore.com I was impressed with their simple but clever approach to creating a walk index.

Ranking walkability is an interesting topic for me: I've had the opportunity to work on a few different sprawl and walkability models in the past, but these often tended to be complex. And I don't mean they used tricky or especially unintuitive algorithms (although do believe this type of analysis is often ruined by unintuitive modeling). I mean complex in terms of the data sources. In the world of ranking walkability, many sources (assessor, zoning, sidewalk attributes, etc) that the more academic models tend to rely on are just not comparable from city to city.

To me, one of the key features of the new version of walkscore is that it keeps true to it's initial approach by keeping it simple (business listings and census data), and using a straightforward methodology. By focusing on amenities and where people live, can a model like this be used as a proxy for walkability? Or, perhaps more accurately, for don't-need-a-car-ability?

I think so, but check out the rankings yourself. Of course, I might be biased, since I did have the opportunity to assist with the analysis and visualizations (which turned out to be a truly fun project -- thanks Matt and Jesse).

If you have any questions on the methodology, be sure to stop by the FOSS4G conference later this year, where I'll be giving a talk on some of the methods for data acquisition, analysis, and integration of heatmaps.


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