The most obvious failure of policy is in those neighborhoods which are experiencing “displacement without development”: decreases in both population and income. 12 of the cities in the study fit this profile.
I’m going to be throwing a lot of data out there in this series, so I wanted to start with a few caveats about my process, and about data analysis in general.
Accurately describing the social dynamics of the city through data analysis is a persistent challenge, and the geospatial dynamics aren’t much easier to get at. A simple-sounding question like, “how has the proportion of Blacks in this neighborhood changed over time?” runs into all kinds of problems with the definition of terms and the availability of data.
As I’ve ridden around doing field work, I’ve noticed that many cities have a Way, or a Boulevard, or a Drive, or a Parkway, named for Martin Luther King, and that those streets are often located in neighborhoods which were predominantly African-American in the 70s and 80s when the dedications were made. Like gentrification itself, the answer is complex. I generated a lot of rich data to analyze, so this post will be the first of several digging into it. But here’s a teaser.
Here are some maps I’m working on, trying to combine OPD police stop data for bikes and pedestrians with demographic info. It’s not good. In every police beat in Oakland, the majority of pedestrian police stops are of non-whites; in most beats, it’s above 80%. Bike stops are not much better.
