I think his goal was to see if one could use an activity proxy to predict where most rides would occur. Crime data is a fairly natural proxy because it's readily available in an easy-to-use format.
It doesn't seem all that useful — it's fluffy — but it's amusing nonetheless and I don't regret reading it.
I'm curious what leaps of faith do you see in his reasoning? Crimes "caught" versus actual criminal activity seems like the obvious one, but I don't think there's really anything in the piece that makes that distinction necessary. (Police activity might actually be a better proxy for activity than criminal activity.)
Reported crimes probably correlate more strongly to police presence, so the root question may be, Are police officers more heavily patrolling areas where people also need rides, and how do they know?
That's exactly what I was thinking about. Here in San Francisco, the police were recently cracking down in North Beach (mostly around the strip clubs on Broadway). It seemed to be a pretty clear causal relationship in that case — more people meant more police meant more tickets and arrests.
It doesn't seem all that useful — it's fluffy — but it's amusing nonetheless and I don't regret reading it.
I'm curious what leaps of faith do you see in his reasoning? Crimes "caught" versus actual criminal activity seems like the obvious one, but I don't think there's really anything in the piece that makes that distinction necessary. (Police activity might actually be a better proxy for activity than criminal activity.)