I made a few maps this week, just for fun, using Boston’s parcel and assessment data.

All of these used my Datasette GIS stack to filter and join the relevant data and Felt to visualize it.

Age of Boston’s buildings

Assessments include a column called YR_BUILT (and also a YR_REMODEL which could be interesting). Joining this to parcel boundaries gave me a map of Boston’s buildings colored by age. Darker purple is the oldest parcels, and some go back to the 1700s. It’s an old city.

Value of residential units

I initially made a map of all property values, but that isn’t very interesting when you include houses and also universities and an airport. Those big public works are expensive.

But filtering out everything but residential units – coded as R1, R2, R3, R4 and CD – makes for an intersting map.

There are still things missing here: Mixed use buildings are a separate category, as well as large apartment buildings. I wanted to map places an individual might realistically own.

Value density

While I was working on this, Jeffrey Baker in the Felt community Slack posted a map of Alameda County, CA, colored by “value density,” or assessment value divided by area. It’s a smart way to handle the problem I had with the range of values in Boston.

Is it a huge surprise to see more concentrated land value downtown? No. But it’s an interesting way to look at the city, and to think about the economic tradeoffs of living on its edges.

All the code I used is here. Open an issue if there’s something you’d like to see mapped.