Chicago Homicides 2017, a Follow-Up Look with R
Guest blog post by Steve Miller.
A year ago, I posted an article on the disturbing 57% increase in Chicago homicides for 2016. There’s been no shortage of loaded commentary since, including strong statements by the POTUS. A bit more balanced prerspective was provided by fivethiryeight.
There can be no denying the off-the-charts Chicago homicide numbers — 493 in 2015, 777 in 2016. But while Chicago had far more murders than other U.S. cities, New York and LA included, the murder rate (homicides/100000 population) was exceeded by several other metropolises, including Detroit, New Orleans, St Louis, and Baltimore.
With daily access to Chicago data (after a 7 day lag), I kept up with Chicago crime throughout 2017, looking for rays of hope in the data. Yet while there was a decline of over 100 homicides from 2016 to 2017, I was in no way inclined to claim even a minor victory. After all, the final 2017 number was still 175 more than in 2015 — so the decline could simply be regression to the mean.
What follows is a look at the 2001 through January 2017 Chicago homicide data, embellished by additional numbers from wikipedia.
The technologies deployed are JupyterLab with an R 3.4 kernel. The scripts are driven primarily through the R data.table and tidyverse packages. Hopefully, readers will see just how powerful these tools are in collaboration. Notable is that neither data.table nor tidyverse is a part of “core” R; each is an addon maintained by the energetic R ecosystem.
The full article, with commented source code (in R) and charts, is available here.