Show HN: OpenTimes – Free travel times between U.S. Census geographies
55 comments
·March 17, 2025simonw
OK the way you're publishing the data with Parquet and making it accessible through DuckDB is spectacular.
Your README shows R and Python examples: https://github.com/dfsnow/opentimes?tab=readme-ov-file#using...
I got it working with the `duckdb` terminal tool like this:
INSTALL httpfs;
LOAD httpfs;
ATTACH 'https://data.opentimes.org/databases/0.0.1.duckdb' AS opentimes;
SELECT origin_id, destination_id, duration_sec
FROM opentimes.public.times
WHERE version = '0.0.1'
AND mode = 'car'
AND year = '2024'
AND geography = 'tract'
AND state = '17'
AND origin_id LIKE '17031%' limit 10;
anitil
Lately duckdb is becoming a serious competitor for my use of datasette because it's eliminating a step for most of my workflows - converting csv to sqlite.
I've been thinking about how to swap it in as a backend for datasette (maybe as a plugin?) but it seems inherently riskier as it needs to at very least be able to read a folder to list all the csvs available for my usecase. If I could hook that up with its native s3 support I'd be unstoppable (at work)
simonw
I have a medium term ambition to make Datasette backends a plugin mechanism, and the two I am most excited about are DuckDB and PostgreSQL.
dfsnow
Thanks! I hadn't seen anyone do it this way before with a very large, partitioned dataset, but it works shockingly well as long as you're not trying to `SELECT *` the entire table. Props to the DuckDB folks.
Eventually I plan to add some thin R and Python wrapper packages around the DuckDB calls just to make it easier for researchers.
simonw
I blogged a few more notes here: https://simonwillison.net/2025/Mar/17/opentimes/
kalendos
Nice! I know a couple of projects that have been using this pattern.
- https://bsky.app/profile/jakthom.bsky.social/post/3lbarcvzrc...
- https://bsky.app/profile/jakthom.bsky.social/post/3lb4y65z24...
Love this distribution pattern. Users can go to the Parquet files or attach to your "curated views" on a small DuckDB database file.
hatthew
Very cool! I love the interactive map, but have a couple UX suggestions:
I usually expect most features of a map to be zoom invariant, with the exception of level of detail. Having the colormap change is surprising, particularly that longer time buckets simply disappear as I zoom in. The two problems with this are that any time I zoom in or out I now have to double check the color key in case it's changed, and if I want to find a travel time for something far away I need to zoom in to find the destination and then back out to see the travel time. Perhaps you can let the user manually choose the colormap granularity/range, or find some way to have a colormap that works at all scales?
Second suggestion, related, is to display the travel time next to the geography ID in the bottom left corner. This would mitigate the issues with getting a good colormap, since I can then just hover over a geography to get its time anywhere that the colormap isn't sufficient.
dfsnow
These are great suggestions, thank you!
I played around with a single static colormap for all scales but couldn't find one that worked well/looked good. Perhaps I'll add a slider that lets you manually select the values of the legend.
The second suggestion is a no-brainer. I'll definitely get that added.
jonny_eh
Maybe it sticks to whatever colors are initially shown, and there's a button to "reset colors"?
c_moscardi
Amazing! GitHub actions to compute a giant skim matrix is an incredible hack.
I pretty regularly work with social science researchers who have a need for something like this... will keep it in mind. For a bit we thought of setting something like this up within the Census Bureau, in fact. I have some stories about routing engines from my time there...
kalendos
Totally! I've been using this pattern a lot and recently wrote about it.
https://davidgasquez.com/community-level-open-data-infrastru...
modriano
Thank you for this excellent post! I've been developing [my own platform](https://github.com/MattTriano/analytics_data_where_house) that curates a data warehouse mostly of census and socrata datasets but I haven't really had a good way to share the products with anyone as it's a bit too heavyweight. I've been trying to find alternate solutions to that issue (I'm currently building out a much smaller [platform](https://github.com/MattTriano/fbi_cde_data) to process the FBI's NIBRS datasets), and your post has given me a few great implementations to study and experiment with.
Thanks!
balderdash
This is a fantastic project , and well done! How are you thinking about implementing traffic (it feels like that’s obviously essential for most practical use cases)?
Also just so I’m clear, is the shading corresponding to a time the represent mean travel time for the area, or the max travel time? E.g. if you pick Walking, and a neighborhood next to Central Park in New York City, even Even though parts of Central Park are directly adjacent to that neighborhood and should be a short travel time. The entirety of Central Park is shaded with a longer travel time, Does this mean I could theoretically get to the furthest point away from the selected area in Central Park in that travel time or is that more of like a mean travel time within that area and some travel times will be shorter or longer?
pimlottc
Looks cool. Please allow high max zoom levels, it’s hard to see individual street details on mobile.
dfsnow
Good point, will do!
itissid
Travel time context in general could be useful for retrieval before ranking in searches like Yelp or Google maps like products for nearby events and places. My MVP use case is to configure a rerank based on commute "score".
For example there is just no way I'm going to commute from Brooklyn to Manhattan on a Monday night to eat out.
I live across the river from Manhattan and what I never understood was why yelp and Google maps upranks restaurants across the river on the other island even though it is highly improbable I would go there.
drchickensalad
I love how much this affects people on dating apps too, lol.
jarpineh
This is a wonderful project. Seemingly simple on the surface. I'd love to see some notes of the frontend implementation. I see there's OpenFreeMap as presumably the base map, which uses MBTiles. Then custom geometry on top from Pmtiles, that I assume is generated for the project. How the colormapping is done I didn't find yet. Actually lots to unpack here.
ctrlp
This is great! I've been thinking about building something like this for ages since I started using Smappen [0] for mapping travel times on road trips. Super useful way to travel if you're on an open-ended trip with flexibility.
ctrlp
Would be great to have multiple layers of times so you can plan out possible routes over days.
matthewfcarlson
I’ve been meaning to make a much more searchable realty system (think Redfin with significantly more filters) and can look across the whole country not just in a specific zip code. This would be awesome as you could compute travel time between points roughly then filter out results more accurately
lotsofpulp
Cool project! For me, seeing commutes time for 1 and 2 standard deviations would be useful too. Many times, people like to convey the lowest travel times, when I am more interested in the amount of time I need to budget to travel between two places to be on time 95% of the time.
ynac
Well done, dfsnow!
* some islands seem hamstrung by the approach - see Vashon Island for example.
* curious what other dataset you might incorporate for managing next level of magnitude smaller trips - e.g. getting a quarter mile to the store for a frozen pizza at the seventh inning stretch.
veilgen
This is an impressive project! Providing free access to such a massive dataset of pre-computed travel times could be a game-changer for researchers and policymakers who rely on spatial accessibility data but often face high costs with commercial providers.
The technical approach is also fascinating—using static Parquet files on R2 instead of a traditional database keeps costs low while maintaining accessibility via SQL and DuckDB. Offloading computation to GitHub Actions is a clever way to handle large-scale processing efficiently.
It'll be interesting to see how this evolves, especially with the potential addition of traffic-aware travel times. Great work!
Hi HN! Today I'm launching OpenTimes, a free database of roughly 150 billion pre-computed, point-to-point travel times between United States Census geographies. In addition to letting you visualize travel isochrones on the homepage, OpenTimes also lets you download massive amounts of travel time data for free and with no limits.
The primary goal here is to enable research and fill a gap I noticed in the open-source spatial ecosystem. Researchers (social scientists, economists, etc.) 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.
Some technical bits that may be of interest to HN folks:
- The entire OpenTimes backend is just static Parquet files on R2. There's no RDBMS or running service. The whole thing costs about $10/month to host and is free to serve.
- All travel times were calculated by pre-building the inputs (OSM, OSRM networks) and then distributing the compute over hundreds of GitHub Actions jobs.
- The query/SQL layer uses a setup I haven't seen before: a single DuckDB database file with views that point to static Parquet files via HTTP.
Finally, the driving times are optimistic since they don't (yet) account for traffic. This is something I hope to work on in the near future. Enjoy!