This a Data Science for Social Good project that estimates the association between alley and street light outages and crime in Chicago over the period April 2012 - July 2013.
The Chicago Department of Transportation (CDOT) asked us to investigate the relationship between alley and street light outages and crime around the city. CDOT can use the findings to determine if shortening outage durations might decrease crime. The geographic detail we provide can be used to determine if certain outages should be prioritized in order to reduce crime.
Read more about the problem in the wiki
The fundamental challenge for this study is that different areas may be both high crime and susceptible to outages. Thus, raw correlations may tend to overestimate the impact of outages upon crime.
We therefore estimate the association between outages and crime by comparing crime rates during outages in blocks affected by outages to crime rates in the same blocks immediately before and after the outage. This method has the advantage that each outage-affected block serves as its own control.
The approach also partially adjusts for variation in crime rates over time by comparing crime rates in time periods close to each other. However, we also examine a model that includes further adjustments for the crime time trend.
Specifically, we use Poisson Generalized Linear Models, which are useful for studying counts and rates. Crime rates are modeled as a function of an outage indicator variable, fixed effects for each outage area, and a full set of monthly indicator variables to adjust estimates for the crime time trend.
Read more about our methodology in the wiki
This project uses data from the City of Chicago Data Portal, which contains a variety of useful City of Chicago datasets. We believe the Data Portal allows for a rich set of important questions to be studied in a way that they could not be studied otherwise. Further, as this project demonstrates, the data can be used to study questions that matter and that are important for department planning.
Read more about what data we use in the wiki
- The analysis lives in the
codedirectory. This includes Python code that determines the number crimes occurring before, during, and after outages as well as R code that estimates our models. - Various charts used for the project's report are in the
resultsfolder. - Our datasets are in the
datafolder.
Please see each folder for further documentation.
You'll need to install python and R. You'll also need the following R libraries: plyr, doBy, reshape, glmmML.
To get involved, please check the issue tracker.
Please contact Zach Seeskin at z-seeskin@u.northwestern.edu with any comments or questions.
Copyright (C) 2013 Data Science for Social Good Fellowship at the University of Chicago
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