Visualizing Transitland data using Python and GeoPandas

by Kuan Butts


Recently, I posted the above image on Twitter. It generated some positive responses, so I went ahead and generated a few more, one for each continent as well as a few “special requests.” Also included was a script that would allow someone to recreate the same scenes themselves. This post will provide more context around the steps listed out in that notebook, as well as some notes about how tools such as GeoPandas and Shapely can help make the process of exploring Transitland’s API more visual and, perhaps, easier to peruse.

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Help us expand our worldwide listings of transit operators

by Steven Vance

map of operator service bounding boxes displayed on South Korea A map showing “placeholder” operator records in Transitland in South Korea. Open a map of all placeholder operator records.

The Transitland Feed Registry lists maps, schedules, stops, and route information for 879 transit feeds containing over 2,275 transit operators. Our current coverage of the United States, Canada, Mexico, and most countries in Europe is excellent, and we have very good coverage of the most populated areas in Australia and New Zealand.

Transitland, however, still has no information about public-transit service in other places around the world. Now we’re creating “placeholder” records for transit operators for which Transitland does not — yet — have complete stop/route/schedule data.

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Introducing Quality Issues

Transitland has issues. No, we’re not talking about bugs, missing features, or flaws (we hope!) but a whole new way of tracking, viewing, and even a little editing of the qualitative concerns we find in the transit data we receive. We call these “Quality Issues.”

Transit data is inherently tricky given all the spatial, temporal, and operational components that have to come together. Whether they are assembled and edited manually, or produced as the combined output of several different programs and processes, sometimes mistakes are made. Just to give a few examples, we’ve seen outdated and invalid URLs, stops that are too far from their routes, and trip shapes that don’t match the trip’s stop patterns or even go in the wrong direction.

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