Wednesday, 16 July 2014

Relative crime levels by district correlate with the relative number of Uber rides

"You’ll notice that we do the most San Francisco trips in the downtown and SoMa areas. These also happen to be large, densely populated regions, so that’s to be expected. So in our spatial demand predictions we clearly need to take into account population density.
But there’s a catch. While neighborhood population density might account for some of the variance in our demand, we also need to take into account where people are hanging out, going to work, etc. This is different from census data. Where people live, where people work, and where people play are (usually) in very different neighborhoods in a densely populated city.
So we needed a simple surrogate metric for where people are. We could do that by counting the number of businesses or bars or whatever in a neighborhood… but we had a better idea.
We hypothesized that crime would be a proxy for non-residential population density.
According to the data from San Francisco Crimespotting (HUGE shout-out to Stamen Design for the data; you guys are awesome!), there were 75,488 crimes in San Francisco since Uber’s launch on 2010 June 01. These crime data are broken down into 12 categories: murder, robbery, aggravated assault, simple assault, arson, theft, vehicle theft, burglary, vandalism, narcotics, alcohol, and prostitution.
If it looks kind of like the trips map to you, that’s because the two are decently correlated (r = 0.56, p < 0.001). (For you math sticklers, crime and trip data are log distributed by neighborhood, so all correlations are Spearman rank correlations, but log-log Pearson correlations give approximately the same results).
Neighborhoods with more crime (more people hanging out) have more Uber rides.
But we also wanted to know if any specific crimes might be better predictors of rides than others.
To examine this we looked at the correlation between the number of each type of crime and the number of trips we’ve done in each neighborhood. All types of crime except murder, vehicle theft, and arson were positively correlated with number of trips. After correcting for multiple comparisons, four crimes remained significantly correlated (p < 0.05, Bonferroni corrected):
In other words:
The parts of San Francisco that have the most prostitution, alcohol, theft, and burglary also have the most Uber rides! Party hard but be safe, Uberites!"
Note - the key point is that crime levels are a good proxy for non-residential population density

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