Newly-available data, visualized! The San Francisco County Transportation Authority just released some of and Lyft usage in the City by the Bay. Combined, the two rideshare services logged more than 200,000 daily rides (!!) on typical Fridays in the fall of 2016 — and that’s just counting trips entirely within the city limits. unprecedented data summaries Uber I was working with the Transportation Authority on some other data visualization tasks when they asked if the platform we were building could also be used to help explore this new dataset. This is what we came up with. The “TNCs Today” data explorer In the public sector, rideshare services are called Transportation Network Companies, or “TNCs”. The official report and the data itself are … on the SFCTA website . Or click here to go directly to the SFCTA “TNCs Today” Data Explorer There is a lot to see: You can select views by day of week, and you can explore either all-day totals or focus on trips during a specific hour. Clicking on any block on the map will pop up a daily graph of pickups and dropoffs for that area. You can then switch to different days of the week to see how Mondays differ from Fridays, for example. Try out the 2D and 3D views: 3D really shows the striking patterns of TNC activity in the city across different days of the week, while the 2D view makes it a bit easier to click and explore individual locations. Fridays have the most daily trips on average. You can easily see the commute “humps” during the AM and PM rush hours — when traffic is already at its worst. You can also see a lot of evening and late-night trips, which aren’t as prevalent mid-week. The data is an estimate of pickups and dropoffs by TNC drivers. The researchers analyzed the lat/long coordinate and timestamp of each when they announce, or This isn’t exactly the same as passenger origins and destinations, but in terms of TNC impacts on roadway traffic and congestion, it’s probably sufficient to know when and where the driver’s vehicle has started and ended moving. And as a cyclist, I don’t much care whether the car blocking the Valencia Street bike lane is picking up or dropping off passengers: same difference. driver “I’ve accepted a trip” “I’m now available.” Caveats: the dataset represents the average of several weeks of data collection during fall 2016, summarized into one-hour buckets by day of week. Only trips with both ends inside the San Francisco city limits are captured; thus this is likely a of the total vehicle trips by Uber and Lyft in the city. low estimate Notable nuggets in the data Sundays and Mondays have the lowest number of TNC trips, with progressively more trips on Tuesdays, Wednesdays and Thursdays, and the most trips on Fridays and Saturdays Trips in Ubers and Lyfts go up and up as the week progresses. Weekdays have a predictable commute pattern with two peaks in the AM and PM rush. Fridays and Saturdays have much more evening travel than other days do, extending very late into the nighttime Uber and Lyft trips are far more frequent in the northeast quadrant of the city, basically north of Cesar Chavez and east of Divisadero, on all days and at all times of day Notable tourist attractions such as Fisherman’s Wharf, the Golden Gate Bridge, and GG Park museums are easily visible, and have very different time-of-day distributions than downtown Weekend hotspots show up on Friday and Saturday nights: the Castro, Mission/Valencia, North Beach, the ballpark, and many others Lots of late-night trips to and from the Castro on Friday nights. 🍸🍸 There are some weird things, too. Some areas have crazy spikes at just one particular time of day, while others seem to have a lot of TNC trips even though there is nothing paticularly unique about those locations. Maybe one of you can come up with some explanations for these hotspots; or perhaps they are just noise in the dataset. What does it all mean? Well, the SF Transportation Authority is quick to point out: no policy judgments about this data are being made at this time. The data is now simply “out there”. Leveraging open source in the public sector We had already settled on a fully open-source stack of technologies as a base for the agency’s upcoming data visualization efforts. These were more than just “free tools” — the combination of these components resulted in something far more flexible, and just as powerful, as any off-the-shelf product I could have envisioned. The database is with . For this project, only block-level summaries (called ) were provided, so we weren’t dealing with any sort of “Big Data” here. Any database would have sufficed for storage, but the PostGIS extension allows us to do cool stuff like geocoding, spatial buffers, paths, and offsets. PostGIS is awesome. Back end. PostgreSQL PostGIS spatial extensions “traffic analysis zones” The front-end needed to talk to the database somehow; for this we chose for its dead-simple RESTful API and easy configuration. PostgREST sits behind the agency’s reverse-proxy, which provides security and flexibility for assigning URL endpoints. PostgREST NGINX We leveraged as many pre-built libraries as possible to get this thing up and running quickly. Furthermore, San Francisco wanted a modern web tool that would be easy for staff to maintain, so I couldn’t pick any esoteric niche products. After experimenting with some alternatives we settled on: Front end. for serving the static site. All the code was on GitHub already, so it just made sense to keep using them for hosting. A static site means no servers to maintain or get hacked. If I could just build static sites for everything from now on, I would. Bummer it’s so hard to get SSL support on custom domains, though. GitHub Pages for templating and reactive elements; what a pleasure learning and using this framework. Vue.js for the interactive 2D/3D map. I originally wanted to stick to fully open-source , but we really liked the 3D capabilities in Mapbox. If our data hadn’t been so pretty in 3D, we would have used Leaflet. Mapbox GL JS Leaflet Interactive charts using Morris.js for the shiny pretty buttons Semantic UI , piped through and for broad browser support (but not browsers: we drew the line at IE11) Modern ES2015 JavaScript webpack babel all After testing the first few iterations, I started worrying that we’d be getting more attention from the interwebs than we had originally envisioned for this internal tool. To avoid Hacker News overloading our database server we refactored things to load a static zipfile containing the main dataset, instead of fetching a giant GeoJSON query from the database for every page hit. We’ll see how this approach holds up over the coming days. Ultimately, this whole thing ended up being less than 1,000 lines of JavaScript code. Woohoo! What’s Next? San Francisco expects to continue research on the effects TNCs are having in the city, and has plenty of other transportation data as well. We’ll be building a full data portal which explores several different datasets in the months ahead. In the meanwhile, have fun playing around with the tool! Credits None of this would have been possible without the vision and financial support of staff and leadership at the San Francisco County Transportation Authority — which means , if you pay sales taxes in San Francisco! Thank you for your generous support. you I stand on the shoulders of the giants who’ve produced this enormous ecosystem of fantastic open-source tools, free for the taking. All of the code I’ve written is — I hope you find ways to remix it and use it in your city, too. available on GitHub About Me Hi! I’m Billy Charlton, founder of and former Director of Data at the in Seattle. I mostly hang around in the transportation planning field, since it has such a direct impact on our cities and on our daily lives. I’m currently living in San Francisco. I can do data visualizations for your city, too! Because LLC Puget Sound Regional Council My contact info is on and I’m on and too. my site GitHub Twitter Cheers!
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