It is the implied authority of geospatially precise ‘bike route recommendations’ that puts me off; my travelling ontology doesn’t recognize such routes.
Bike routes to me, where not literally demarcated by bollards or boundary paint, are a loose, conceptual topology of best-paths contingent on weather, health and my day’s ambition. There are rare edges that are fairly static and these can be mapped: Spring Grove can be for racers and relaxation, snowing or scorched. But why transpose it literally? “Spring Grove”, as I mean it, is a heuristic referencing the whole street, perhaps even to the whole Mill Creek valley east of the tracks, not a geocoded centerline. Ol’ Colerain sliced by the highway is a good ride too, and I take it sometimes if I feel like looking at something different.
How to communicate such useful, abstract edges?
A hand-rendered, schematic map is so clearly subjective it openly invites criticism from the viewer’s own ideosyncratic subjectivity. This is ideal. The point of bike-route maps cannot be to convey authority but to connote personal suggestion.
Here is my morning’s attempt at a bicycle edges map, from memory and a half-hour.
Now to digitize and make it look decent…
What’s all this about a West//East divide? I’d like to propose a distinct Car-Free-Cincinnatian spatial identity that apparently fails to recognize any but the central neighborhoods and places well-connected by transit. I couldn’t for the life of me recall how to bike to Xavier, NKU, or College Hill because I so often take transit to those places. My concept of the city seems to have a very tightly connected core with more distant neighborhoods dangling from abstract transit lines but no street names. This may more accurately be my winter version of the city. Come summer I’m much more likely to bike laterally.
I was taking another look at the old ridership dataset SORTA shared with me last January, when I realized: there are a good many stops that have an average daily ridership of exactly zero.
There are really a lot of them, and they’re pretty evenly distributed. About 1,000 of them by my count, compared to ~2,650 with at least some daily riders(above in black). I seem to have missed this before by immediately visualizing all the stops with circles sized according to their total ridership…naturally, these stops simply failed to render.
Click the image above (or here) for a PDF that will let you look up close at the locations of ghost stops throughout the whole system. Red dots are ghost stops, black circles are stops with riders on an average day; their area is proportional to the number of riders. The average day, including weekends, has ~46,100 passenger trips, not counting TANK.
It’s important to note that the presence of these low-to-no rider stops may not be hurting anything if we’re OK with the lines serving them being there in the first place. If no one is getting on or off, the bus probably isn’t slowing down by stopping there.
Scary factoid of the day: Greater Cincinnati has about 9,000 cul-de-sacs, or streets that end bulbously. Generally, such streets are part of a dendritic hierarchy, a branching development pattern very common in post-car/war/car-war urban development.
8,894 Cul-de-sacs. Data is from OSM
I grew up on a cul-de-sac, but we’ll not go there: too much baggage. Also, it’s an unpleasant trip and there’s no transit.
These cul-de-sacs are interesting to me, if I can use a word like ‘interesting’ anywhere near such a lifeless thing, in part because they present an opportunity to do something inverted: the opportunity to make an intensity map of the very opposite of intensity, a map of the extremity of dullness. So far as transportation is concerned, this will also be a proxy for the degree of disconnection between things or more practically, the degree to which one might reasonably be scared to be outside the protective machinations of a car.
Here’s a density analysis of the location of cul-de-sacs:
Places you probably won’t go on a bus
The darker the color, the more and closer-together are the cul-de-sacs.
Let’s take a closer look at the most dis-intense spot, shall we?
I actually thought this cluster must have been an error in the data when I first noticed it.
Leave it to the golf-course-crowd to take the top spot in this contest. This kind of pattern is perfectly typical of affluent post-car suburbs: houses are located for maximum isolation from neighbors and no one wants to live on a street with ‘traffic’. Of course the obvious irony is that in keeping the traffic off their part of the street, they’ve ensured it everywhere else. It’s such a middle-class arms race isn’t it?
There’s an interesting counter-variable here, though it’s not as completely represented in the data: pedestrian crosswalks. Where there are many crosswalks close together, we should find the opposite characteristics: walkability, liveliness, places where you’d rather not be in a car. So where are the crosswalks?
Locations of 2,770 known crosswalks. Crosswalks are only somewhere near half to a third accounted for in this dataset, so this is not an accurate representation, but it’s the best I can do at the moment.
And then the reveal:
Kernel density of crosswalk locations, same scale and methods as with the cul-de-sacs above
This looks like it might actually line up well with the location of transit lines!
1km triweight kernel density of bus stop events(raster) compared against the contour lines from the crosswalks from above(slightly altered for legibility)
Not a terrible assumption! It’s not a superb fit, but you can definitely notice some areas that seem to have a rather strong correlation. Obviously, the most intense spot for both transit and crosswalks is right in downtown, which we’ve all seen, so I won’t bother with an aerial photo of that.
Interestingly though not surprisingly, crosswalks and cul-de-sacs appear to be somewhat mutually exclusive.
Only a few relatively minor areas demonstrate substantial overlap
It seems odd that anyone would have taken the time to actually enter in almost 9,000 cul-de-sacs around Cincinnati, though indeed there have been about 85,000 buildings already entered by hand. I rather suspected that they might have been added in the big TIGER imports from a few years back. If they were, that would mean we’d be able to compare against other US cities. I tried a few, but it looks like the data is really just too spotty for a any reliable analysis. Alas, Pittsburgh, Indy, Cleveland and the other cities I checked don’t seem quite ready to give up their
subhuman suburban secrets just yet.
Demonstrative Pittsburgh data problems: Clearly, there should be more cul-de-sacs on the right here.
Indy seems fairly complete, but something about this just doesn’t feel right to me. From what I know about the city, I don’t think there are enough cul-de-sacs in the data here. Maybe someone will tell me I’m wrong and that Indy just hasn’t experienced as much post-war growth as Cincinnati.
One of my long-term mapping goals is to tag my taxidermist boyfriend with a GPS and get exact locations of all the roadkill he picks up. My bet is that it would primarily lie within or along the edges of the cul-de-clusters identified here.
Here is a rather crude, though I think useful, visualization of service frequency at the stop level. Basically, I used the GTFS data from SORTA and TANK to calculate the number of times a bus stops at each stop every week. Since a week is the basic cycle period of transit(service is bad on Sunday, better on monday), this should give us a an idea of basic average frequency with the huge caveat that there’s enormous variation within each week.
Click the image to get a bigger version. There’s lot’s of interesting detail in there!
You may notice that frequency can appear vary in a single line where it doesn’t seem like it probably should:
In most cases, this is simply an artifact of the way I grouped stops that were next to each other and had exactly the same name. At least 2-3,000 stops of the 6,000 stops in the dataset can reasonably be thought of as pairs with one serving each direction of travel.