I spent the last few days poking around in SORTA’s fresh ridership statistics, and I’ve compiled some maps, a couple charts, and a few anecdotes for your general edification. All of this is from data I got last week that I understand to be an average of a month of weekdays in January 2013. In no particular order…here we go!
The biggest stops for each neighborhood in the city by total daily riders:
Transit users familiar with any of these neighborhoods will probably have been able to guess where a lot of these stops would be if not their relative proportion, but it’s fun to put them all on a map.
Clifton in particular took me by surprise though. I would have guessed the stops in the business district in front of Sitwells would have won out over Cincy State.
Now of course just picking out the biggest stops by some arbitrary boundary is losing a lot of information. In this case, it’s failing to account for 86.5% of riders. Government Square for example has several stops in the thousands that haven’t shown up. Here’s what the same map looks like with all of the stops shown.
And zoomed in again
Heatmap of ridership intensity:
A heatmap is a slightly more complicated kind of thing because it’s not quite as intuitive how the total is calculated. In this case, the total number of of riders was applied to an area a couple thousand feet wide, directly in the center and progressively less intensely toward the edges of that circle. Stops nearby each other compound the total value as their circles overlap. So heatmaps aren’t great for deriving actual values, but they’re good for comparing approximate intensity and should make more sense of Downtown than the above.
That lone black spot is Downtown. It’s ridiculously dominant on pretty much any map of transit in Cincinnati. Let’s see what happens if we set the upper limit of our scale to ignore Downtown altogether.
Note that whereas the upper limit of the spectrum was 15,000 before, it’s now only 1,200. Transit activity Downtown is more than an order of magnitude more intense than anywhere else in the region.
I also broke the data down in a slightly less mathematically ambiguous, and slightly more designer-y way.
But I think it’s statistically kind of useless since, again, the boundaries are rather arbitrary. But let’s have one more boundary based map before we go right into some harder data.
Trip Density by Neighborhood:
This is the total number of trips per square mile by neighborhood, with half a trip counted for each boarding and the other half counted for each de-boarding. Thus if I took a bus from Downtown to Mt. Washington, 0.5 would accrue to each neighborhood(Assuming each were exactly 1 square mile in size). It’s possible that neighborhood boundaries running down the middle of roads served by transit make this not very useful at a fine level, but it looks generally right at a small scale, so I thought I’d share it. Now here’s the hard stuff.
Express Line Ridership Totals:
Non-Express Line Ridership Totals:
Totals for Express vs. Non-Express:
It looks like non-express lines account for the lion’s share of all trips. Express lines count for less than 10%, and that’s only on weekdays! I’m also told that CPS students(counted by their use of special passes) account for about 9% of all trips. Assuming they’re not taking any express lines, which seems like a fair assumption, that means they account for about 10% of all non-express trips. Anecdotally it would seem, with such numbers from CPS, that the reintroduction of free rides for University of Cincinnati students could cause a pretty large jump in total riders. With the high numbers near Cincy State, I’m curious how much they’re subsidizing passes for students. BTW, Cincy State claims a distinct proximity to the 25th and 30th largest stops…
30 Biggest stops:
And that’s it for now. Here’s the data! I’ve provided the original files from SORTA and most of what I was able to make from it, including a shapefile of all stops by line with ridership, and one of all stops grouped by name with the total of counts from all lines. There are also a few CSVs and a geo-tiff of the heatmap. 1 I’ll probably add a few more things to the data page later, so be sure to check back. If anyone is able to make anything interesting of the data, I really would love to see it!