OpenTimes: Free travel times between U.S. Census geographies
Today I’m launching OpenTimes, a free database of pre-computed, point-to-point travel times between major U.S. Census geographies. In addition to letting you visualize travel isochrones, OpenTimes also lets you download massive amounts of travel time data for free and with no limits. Visit the dedicated about page to learn more about the project.
The primary goal here is to enable research and fill a gap I noticed in the open-source spatial ecosystem. Researchers (social scientists, economists) use large travel time matrices to quantify things like access to healthcare, but they often end up paying Google or Esri for the necessary data. By pre-calculating times between commonly-used research geographies (i.e. Census) and then making those times easily accessible via SQL, I hope to make large-scale accessibility research cheaper and simpler.
OpenTimes covers all 50 states (and D.C.), 3 travel modes (driving, biking, and walking), and 6 Census geographies. Here’s what the actual data looks like as a table:
| origin_id | destination_id | duration_sec |
|---|---|---|
| 060750328021 | 060750328021 | 0 |
| 060750328021 | 060750328023 | 284.1 |
| 060750328021 | 060750328022 | 322.5 |
| 060750328021 | 060750326023 | 479.7 |
| … | … | … |
And here’s that same data on the homepage map:
OpenTimes also has some interesting technical stuff going on, most of which I haven’t seen replicated elsewhere:
- The entire OpenTimes backend is just static Parquet files on Cloudflare’s R2. There’s no RDBMS or running service, just files and a CDN. The whole thing costs about $10/month to host and costs nothing to serve. In my opinion, this is a great way to serve infrequently updated, large public datasets at low cost (as long as you partition the files correctly).
- All travel times were calculated by pre-building the inputs (OSM, OSRM networks) and then distributing the compute over hundreds of GitHub Actions jobs. This worked shockingly well for this specific workload (and was also completely free).
- The query layer uses a single DuckDB database file with views that point to static Parquet files via HTTP. This lets you query a table with hundreds of billions of records after downloading just the ~5MB pointer file.
- The map frontend uses a Javascript Parquet library called hyparquet to query the same static Parquet files as DuckDB. Once the Parquet files are cached the map is actually super responsive.
I built most of OpenTimes during a 6-week stint at the Recurse Center, where it was my main project. Many thanks to the wonderful folks there!