Crossings & Cul-de-sacs
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.
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Great maps! I have some vehicle miles traveled data that might be fun to overlay w/ this.
Cul-de-sacs can also be a big plus for livability – if they have connecting pedestrian and bike paths/cut throughs. This would be a form of “filtered permeability” (British origin term) or a “fused grid” (Canadian).
I would say it’s more complicated than that. Maintaining a pedestrian/bicycle grid while keeping cars in a dendritic pattern would decrease the number/speed of cars at the ends of the cul-de-sacs which I presume is what you’re getting at but would increase speed and traffic on the roads that cars would be funneled onto. It would also increase the total distance traveled by car, all things being equal, compared to an identical pattern where cars could make the same connections as other modes.
‘Better’ or more ‘liveable’ in spots, ‘worse’ in others.
Heh, yeah, I added tons of cul-de-sacs a few years ago. It’s a highly effective means of procrastination, as surely you’ve discovered by now.
Does your analysis account for cul-de-sacs with islands? They’re typically represented in OpenStreetMap either as ways that turn back on themselves or as separate, circular one-ways. Here’s an example in Landen: http://osm.org/go/ZTVWUyU~U-
Oh yes. So many hours spent late at night when I’m too dull to do anything else…
I didn’t account for the cul-de-sacs with islands; I only used tags on points. There weren’t too many of them and I suspected it would be reasonably difficult to find them all without getting a bunch of false positives.
I figure I’ve probably mapped (or fixed) a significant number of cul-de-sacs with islands, but perhaps they were mostly in the Dayton area.
The vast majority of the cul-de-sacs you see in OSM are the result of hand entry. TIGER data does include quite a few cul-de-sacs – as ginormous loops, triangles, or diamonds hanging off the end of the street. And cul-de-sacs with islands are often just barely unclosed. (I think I’ve cleaned up all the instances of these problems in the Cincinnati area.)
It’s pretty mind-boggling to think how big even this medium city is…I mean really: 9,000 cul-de-sacs and I keep finding new ones to map, even right in the city.
And then there’s the crosswalks–at first I thought I had most of them and then I went and added about 2,000 more just since I wrote this post! It’s incredible the amount of work that you, I, and a handful of others have put into mapping this place. It’s worth it though; The street network data at least is the best available for the region if you ask me. I wouldn’t try routing on anything else.
Your Cincinnati’s geospatial batman, Minh. Keep rockin’ on :-)