# A geographic analysis of local crime data

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!

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## 10 thoughts on “A geographic analysis of local crime data”

1. My dad (a lawyer from Austin, TX) always regards police stations as the area of town with the most crime. “They don’t police their own backyard,” he says, “and this is where you end up when they let you out of the drunk tank.”

Maybe there’s some truth to that. Is crime up in the area around the new station?

2. Hamish Harvey says:

Hi Jon,

You want, using this data, to answer the question “Is there more crime in the past few years?” But can you? What you have is data on _recorded_ crime, so you can surely only answer questions on that.

So you may be able to find trends in the data (are they statistically significant?) on recorded crime, but how do you move from that to actual crime? How many laws have creating new crimes, or guidelines which encourage the police to resource and record differently?

Another possible explanation for the dip in winter recorded crime: the police don’t like getting out of their cars.

Several causes may combine to generate a trend or pattern, of course. E.g. there may be less mugging _and_ the police stay in their cars more.

Cheers,
Hamish

3. maybe i’m missing it, but is the full dataset available? or are you not allowed to publish that data?

i remember my adviser doing something with the lapd and he had to sign some privacy contract. they then handed him the data on a memory stick that required a key.

4. David French says:

An early effort at producing crime hotspot maps in NZ flagged up police stations as being crime centres because the station was the place of the crime report and there was often no specific address associated with the crime incident itself. Your analysis may be reflecting a similar quality issue in the underlying data.

5. “Is crime up in the area around the new station?”

Heh. Great question, I’ll look into it.

“Another possible explanation for the dip in winter recorded crime: the police don’t like getting out of their cars.”

:-)

“the full dataset available? or are you not allowed to publish that data?”

There’s a possible privacy concern about some of it, so I’m going slowly. But the data behind this analysis is just dates and locations, so I can put that up, and will.

“the station was the place of the crime report and there was often no specific address associated with the crime incident itself”

In this case the addresses are all real street addresses…

“Your analysis may be reflecting a similar quality issue in the underlying data.”

…but, great point. There are all kinds of questions that can and should be asked about the nature and quality of officially-reported data. It’s only once you start to explore the data, in these ways, that you can even begin to ask those questions.

6. Is it possible that there are fewer muggings during winter because there are fewer people on the streets to mug (i.e they’re all indoors so there is less trade for the muggers)?

7. I like to go for a long walk in my small town (pop 7,000) every night. During the summer nights there tend to be many “characters” wandering around and gathering socially in certain locations I try to avoid. In the winter, which can be very cold here (northern New Hampshire), these same characters are nowhere to be seen and most of the people who are out seem to be those who enjoy the outdoors and keeping healthy – which usually are not these same “characters” I mentioned earlier.

My theory is that many muggers, drug users, and the like are not about to be inconvenienced by the cold of winter so they stay inside. Anyone who has the strength of character to enjoy being outdoors in the winter weather (which really is very beautiful) probably also has the strength of character to hold down careers and work on self improvement. In effect, winter is a great filter of sorts that separates types of people.