By: Ryan McCullough
As head of Research and Innovation at one of the Pacific Northwest’s largest family-owned general contractors, Ryan McCullough uses AR, machine-learning, collaborative cloud-based design, and more to understand cities and change how buildings are made. McCullough used Tippecanoe to create a light-weight, fast loading dot plot map comparing property owners and renters. The map was styled using Studio.
While exploring US Census data, I started to wonder what the balance between renters and homeownership looks like geographically. Certainly, there are parts of cities known for having lots of condos, or apartments, or single family homes, but I was curious what this looks like at a larger scale. That’s why I built this nationwide map of every property renter and every property owner.
I pulled 2010 US Census data from the US Census API and TIGER/Line geometry database and grabbed both census variables of interest and their associated geometries. For this map, I pulled these variables: total population, population who rented their home, and population who owned their home. This was also a low priority project, so I coded it up the cheap way (brute force), set it running on a Friday evening, and enjoyed the weekend.
When I checked back on Monday, the output ended up being nearly 40GB of geojson files containing point features. My next question naturally was, “How on earth are we going to load this into anything?”
Fortunately, vector map tiling is a magical thing! Mapbox maintains a program for just such situations, called Tippecanoe. Tippecanoe takes in huge quantities of geojson geometries and converts them to the Mapbox Vector Tiles format, a highly efficient protobuf encoded SQLite database. This lets you serve your data as small digestible vector tiles, and will help to ensure the texture and density of the data is preserved across all zoom levels.
The resulting vector tile database, called a .mbtiles file, was around 2GB in size and uploaded to Mapbox’s tile servers easily.
All that was left then was to apply some styling in Studio. To help the points read well across zoom levels, their diameter is a function of zoom as well as their opacity. That way at low zooms, overlapping points create brighter regions.
This lets us compare structures of density and ownership in very dense locations like Manhattan:
And locations that have a visible spread from urban to suburban, like Washington DC:
Jump into the map, zoom and pan around the entire United States, and start asking questions. What historical forces, policies, and timelines created particular shapes and conglomerations of one type or the other? Or, by the same token, what gave rise to those areas with no discernible structure at all? I will admit I have no answers yet, but it’s an interesting jumping off point.
Check out the finished map here, and see my more detailed walkthrough of the project.
Got a big data file you need to manage? Get started with Tippecanoe.
Ryan McCullough (@mcculloughrt) | Twitter
How I built it: Mapping every homeowner and renter with Tippecanoe and Studio was originally published in Points of interest on Medium, where people are continuing the conversation by highlighting and responding to this story.