New book: Micro geographic analysis of Chicago homicides, 1965-2017

In joint work with Chris Herrmann and Dick Block, we now have a book out – Understanding Micro-Place Homicide Patterns in Chicago (1965 – 2017). It is a Springer Brief book, so I recommend anyone who has a journal article that is too long that this is a potential venue for the work. (Really this is like the length of three journal articles.)

A few things occurred to prompt me to look into this. First, Chicago increased a big spike of homicides in 2016 and 2017. Here is a graph breaking them down between domestic related homicides and all other homicides. You can see all of the volatility is related to non-domestic homicides.

So this (at least to me) begs the question of whether those spiked homicides show similar characteristics compared to historical homicides. Here we focus on long term spatial patterns and micro place grid cells in the city, 150 by 150 meter cells. Dick & Carolyn Block had collated data, including the address where the body was discovered, using detective case notes starting in 1965 (ending in 2000). The data from 2000 through 2017 is the public incident report data released by Chicago PD online. Although Dick and Carolyn’s public dataset is likely well known at this point, Dick has more detailed data than is released publicly on ICPSR and a few more years (through 2000). Here is a map showing those homicide patterns aggregated over the entire long time period.

So we really have two different broad exploratory analyses we employed in the work. One was to examine homicide clustering, and the other was to examine temporal patterns in homicides. For clustering, we go through a ton of different metrics common in the field, and I introduce even one more, Theil’s decomposition for within/between neighborhood clustering. This shows Theil’s clustering metric within neighborhoods in Chicago (based on the entire time period).

So areas around the loop showed more clustering in homicides, but here it appears it is somewhat confounded with neighborhood size – smaller neighborhoods appear to have more clustering. This is sort of par for the course for these clustering metrics (we go through several different Gini variants as well), in that they are pretty fickle. You do a different temporal slice of data or treat empty grid cells differently the clustering metrics can change quite a bit.

So I personally prefer to focus on long term temporal patterns. Here I estimated group based trajectory models using zero-inflated Poisson models. And here are the predicted outputs for those grid cells over the city. You can see unlike prior work David Weisburd (Seattle), myself (Albany), or Martin Andresen (Vancouver) has done, they are much more wavy patterns. This may be due to looking over a much longer horizon than any of those prior works though have.

The big wave, Group 9, ends up being clearly tied to former large public housing projects, which their demolitions corresponds to the downturn.

I have an interactive map to explore the other trajectory groups here. Unfortunately the others don’t show as clear of patterns as Group 9, so it is difficult to answer any hard questions about the uptick in 2016/2017, you could find evidence of homicides dispersing vs homicides being in the same places but at a higher intensity if you slice the data different ways.

Unfortunately the analysis is never ending. Chicago homicides have again spiked this year, so maybe we will need to redo some analysis to see if the more current trends still hold. I think I will migrate away from the clustering metrics though (Gini and Theil), they appear to be too volatile to say much of anything over short term patterns. I think there may be other point pattern analysis that are more diagnostic to really understand emerging/changing spatial patterns.

The coffee next to the cover image is Chris Herrmann’s beans, so go get yourself some as well at Fellowship Coffee!