If you’ve been following the continuing saga of my exploration of local crime statistics [1, 2, 3, 4], here’s an update. The police department has provided a spreadsheet containing a complete dump of reported crimes back to 2002, including the location (address) information I was looking for.
This dataset includes about 15000 rows, which is far too many to show on a map without some fancy filtering. But while pondering what to do about that, I realized I could try to answer two questions that folks have been asking:
1. Is there more crime in the past few years?
2. If so, is the increase localized to the downtown area?
The second question arises because the police department relocated, in 2006, from the center of town to a peripheral address. It’s been suggested that there is, as a result, less of a police presence downtown, and thus more crime.
The answer to the first question appears to be yes. As Martin Wattenberg observes, in his comments on that visualization, there’s a striking seasonal pattern: strong dips in winter, weak dips in summer. He asks:
Is this weather-related (potential criminals thinking “It’s too cold to mug anyone” in January)? Are there population changes in Keene, like tourism or college students, that would cause this?
I think he’s right on both counts. It’s cold here in winter, and it’s a college town.
More broadly though, the 2006 peak is noticeably higher than prior years’ peaks, and though we’re only in the middle of 2007, it’s tracking the 2006 pattern. Clearly crime is up since 2006.
But does the likelikehood of downtown crime correlate with the relocation of the police department? According to this chart, there is — if anything — a reverse correlation:
Here’s how I made that chart:
1. Geocode the addresses to latitude/longitude locations.
2. Compute the distance of each location from the town center.
3. Group the locations into zones.
4. Chart the percent of crimes in each zone.
I’ll reflect in a separate entry on the nature of that process, and on ways it could be made more accessible to the less technically inclined. But if this result proves to be a valid, it’s a nice example of citizen use of public data. And of course if someone else’s analysis of the data (and of my methods) were to challenge my result and prove something different, that would be even better!