|Auto mode share (y-axis) vs residential density (x-axis, people per square kilometer)|
|Shares of population living in each dwelling type (RoC=Rest of Canada, apart from Québec)|
For example, here is Sudbury Town station, about 15 km west of London's CBD:
|Overhead view of Sudbury Town station, 15 km west of London's CBD|
|Closer view of station|
|Google Street View from Sudbury Town station|
|This is right next to the station|
|A bit further out|
|Still a bit further out|
|Overhead view of Tsutsujigaoka station|
|A closer view of the station and the high-density node around it|
|View from station's northern exit... contrast with Sudbury Town's|
|Right next to station, tall condo towers, often with retail on ground floor|
|Low-rise single-family area away from the station|
|Just for curiosity's sake, the danchis down south|
A thought experimentNow that I've illustrated the two models, of uniform density and concentrated density, let's do a thought experiment. Let's take two neighborhoods, with a major destination point at the center (retail, transit station, whatever), one of them has uniform density with 2- or 3-story buildings, with 10 000 people per square kilometer (much less in the commercial center) and another with tall condos/apartments in the center, surrounded by lower-density single-family areas. In effect, the density will vary between 30 000 and 6 000 people per square kilometer.
|To the left, the uniformly dense neighborhood, to the right, the one with varying density|
Uniform density: 26% probability of walking to center
Varying density: 36% probability of walking to center
The reason is simple: though overall the second neighborhood is less dense, since it's much denser in the center, more people live really close to the central area than in the uniformly dense neighborhood, where density is even lower in the center than farther out because building height is the same everywhere but retail occupies a large part of the floor area in the center. And since the probability of walking is much higher if distance is smaller, then it's much better if you can get as many people to live as near to the concentration of destinations as possible.
|Cumulative population living within distance of center|
Visually, it means this: If I plot the density of neighborhoods over the cumulative population of the two neighborhoods:
Of course, this works if the higher density areas are near the commercial area/train station/other common destinations of trips. If density varies, but is less near the center and higher at the fringe (like, you know, Radisson station in Montréal), this is far worse than the uniform density case.
And indeed, this impact of density distribution seems to have measurable impacts. Whereas Tokyo's subway lines have respectively riderships of 12 and 8 million riders per km of track, London's Tube only has a ridership of 3 million riders per km of track per year. Another impact is that buses in Tokyo have very low ridership, but in London, since people live on average farther from subway stations due to the absence of high-density multi-family housing near most of them, buses are more used than subways, however bus trips are very short (1,6 mile on average in 2001 according to this data... sounds like a last mile problem).