The Criminal Topography of Chicago

Lantern slide of the street and boulevard system, "present and proposed," from the Plan of Chicago

A Map of Chicago, in Crimes

I recently stumbled upon this website, which is a huge repository of the City of Chicago’s formidable data-gathering.  This page was put together as part of the city’s new commitment to greater transparency, and includes everything from CTA bus records to Tax-Increment Financing districts to registers of public employees and their salaries.  One of the files was a listing of every crime report filled out in the city for the last 13 years.

Let me note here that such a file is a staggering achievement.  For each crime, there is recorded the location, the time, the address, the kind of crime, and a few other bits of miscellaneous information.  In total, the file encompasses 4 million crime reports, spread about over 13 years; an average of 350,000 per year.

This spreadsheet is colossal, is massive, not only in terms of its sheer information (1 GB in total), but in the implications of it.  How many questions can be asked with this data that were impossible or accessible to only a handful of academics before?

MapOfChicago

This picture is a map of the City of Chicago, in crimes.  Each dot here is a single crime.  The darker green bits are where multiple crimes have piled up in roughly the same place.  You can see that it recapitulates perfectly the known geography of the city.  Where there are people, there are crimes.

There are also some gaps in the map.  Some of these blank spaces are parks; presumably the report is assigned to the nearest street location, instead of properly putting it where it actually occurred.  Other gaps are rivers or industrial zones, where presumably little crime is committed.

Different Neighborhoods, Different Crimes

Yet, not all areas heave the same crimes.  I separated the data by the offense.  I noticed immediately that some crimes were disproportionately committed in certain areas (I’m sure you can imagine some hypotheses).  Here’s an example of that phenomenon, in which I’ve focused on three kinds of crimes: battery (as in assault and battery), deceptive practices, and narcotics.

DiffCrimes

You can still see the outline of the city here, and its idiosyncratic borders.  But you’ll note that the battery is found mostly on the southwest side of the city, along with much of the narcotics reports.  There are pockets of narcotics reports on the northside, in Rogers Park and other neighborhoods, but narcotics is primarily a southwestern offense.

In strong contrast to the narcotics pattern is the grouping for “Deceptive Practices.” Wikipedia (behold, my complete ignorance of the law) informs me that Deceptive Practices includes things like fraud, false advertising, and misrepresentation.  Such offenses are most often the province of businesses, and so the Loop is home to the greatest cluster of Deceptive Practices reports.  You can see rays of Deceptive Practices reports emanating from the Loop along the major thoroughfares (Milwaukee Avenue, for instance).  This pattern presents a corollary to the above law: where there are businesses, there are deceptive practices.

The Inequality of Arrests

*In an earlier version of this post, I wrote about no arrest crime reports, which I erroneously assumed were tickets.  They are not tickets, but it is not clear exactly what they are.

Another variable recorded in the dataset is called “arrest”.  In fact, this field doesn’t record arrests, but rather whether a crime report has been marked “cleared”.  Cleared reports are those for which an offender has been found and successfully prosecuted.  For some crimes, the clearance rates are near 100%, but in others, clearance rates appear to be much more variable.

One of the more variable categories of crimes are narcotics possession offenses.  This variability is curious, since I wouldn’t imagine officers devoting a lot of resources to filing reports on “unsolved” narcotics crimes.  However, I have very limited information as to why a low-level narcotics crime would go uncleared (see updates below).  I decided to model clearance probability for marijuana possession as a function of location.

I subset the data by the kind of crime and year (2013).  I selected narcotics reports, specifically those with less than 30g of cannabis (as described in the report; this was the lowest level).  I made a logistic regression model, incorporating latitude, longitude, and an interaction term between them.  There are fancier and cleverer ways of doing this modelling; I am not striving for mathematical precision but rather a rough overview.  Here’s the fitted probabilities of clearance, depending on where the crime took place.

arrest probability

I built a fully 3-dimensional version of this graph, which can be rotated and zoomed, here.  You should go play with it.  If you view the visualization head on, with longitude as the x-axis and latitude as the y, you’ll see a map very similar to the first graph on this page.  If you tilt it slightly, you’ll see that this graph can be thought of as a criminal topography of Chicago (as above), but warped or deformed by the probability of an arrest.

The takeaways of the model are largely as expected.  There are significant effects of latitude, longitude, and their interaction, such that the more southern or western the crime occurs, the more likely there is to be clearance.  The difference is not overwhelming, but stark nonetheless: on the northeast side of the city, the probability of clearance falls to ~80%.  Anywhere in the southern or western sides of the city, it is close to 100%.  That’s probably not an artifact or an accident: we know that African-Americans and Latinos are much more likely to be arrested, overall, and the southern and western parts of the city are where many African-American and Latino people live.

Even so, I ought to note that I haven’t (and can’t) control for all the necessary covariates.  As always, the task is more complex than it appears initially.  Police must arrest (and have more motivation to clear a report) when the offense is performed by people under 17, so it may be that in the south & west sides of the city, more clearances are occurring because there are more young people committing the crime.  As well, the police reports do not offer enough granularity to know whether the amount of weed is higher in the southern and western quadrants: the lowest level is simply less than 30g.  Since the technical cutoff for a ticket is only 15g, perhaps it is the case that there’s simply more offenders in the 15-30g window on the south and west sides.

I doubt that’s the case, though.  According to lots of published research, blacks and whites use marijuana at approximately similar rates.  So we are left with other, more uncomfortable reasons for the observed differences in clearance rates.

Whatever the cause, the pattern is clear, and serves as an example of the sort of discovery this data can give rise to.  I am usually skeptical of the notion of “big data” as any kind of transformative phenomenon, on the theory that big data is really just lots of small data put together.  But to the extent that governments embrace it, there may yet be some sea change, in that big data reduces the inherent information asymmetry between the people and their representatives.  The government of Chicago has a thousand maps of the city at their disposal at any moment, some flattering, some despicable; the thought that all of those maps might be laid bare and examined is exciting.

 

Update #1

In the first draft of this post, I assumed that many of the marijuana crime reports (<30g) without clearances corresponded to citations that were issued instead.  I made this assumption based on three facts: 1) the marijuana reports without arrests increased suddenly in the crime logs in the same year the decriminalization measure was passed; 2) the number of no-arrest low-level marijuana reports was quite similar to the reported number of tickets issued; and 3) I couldn’t think of a reason why there would be crime reports filed for such small amounts of weed unless there was also an offender present to prosecute.

Having now found a database of some of those (~300) tickets, that assumption appears not to be the case: the issued tickets do not seem to be recorded in the crime database, meaning that the no-arrest marijuana reports are potentially something different.  This conclusion begs the question: what, then, are the no-arrest marijuana reports?  As of right now, I don’t know.  I have emailed the appropriate contact at the Chicago data portal to find out.

The fact remains, however, that whatever the no-arrest marijuana reports are, they are distributed non-randomly throughout the city.  I have changed some language in the post to make clear that these no-arrest reports are not necessarily tickets, and I will update again when I find out what the reports actually are.

Update #2

I sought clarification about the no-arrest reports from the contact listed at the City of Chicago data portal.  Apparently, these no arrest reports are not tickets, and have little to do with whether there was an arrest made.  Instead, they denote whether the report was marked “Cleared-Closed”, as would happen after the incident went through the court system.  The body of the post has been updated accordingly.

As a result, the no arrest reports could be cases in which the offender couldn’t be found (or died?).  According to the person with whom I spoke, there may be additional reasons why the report would not be marked Cleared-Closed, but I wasn’t able to get an exhaustive list.

And that’s where I’m leaving it.  I’m not a journalist, and I don’t really know where to go next with this line of inquiry.  It does seem strange that these no arrest narcotics reports would show the pronounced geographic pattern that I describe in the post; I doubt police on the north side are finding unattended joints, and writing up reports on them, at a different rate than on the south side.  But in the absence of more an better information, I’ll leave these no arrest reports as an unsolved, albeit intriguing riddle.  Hopefully someone with more time and the skills to pursue the question will pick it up at some point.

*

I reproduce the full email chain with the City of Chicago contact below.

I wrote this email:

“I’m curious about what a certain classification of crime report corresponds to.  Specifically, I have noticed that not all offenses described as “30 or less grams of marijuana” also show arrests (arrest = true).  Are these cases in which there was a ticket issued instead?  If not, what occurred in these instances?”

And received this email in response:

“The field indicating whether an arrest was made is based on whether the incident has been marked as “Cleared – Closed” not necessarily whether an arrest was made. When a citation is issued instead of arrest for under 15 grams, no case report is generated.  ”

To which I replied:

“Thank you so much for your response. So in cases where the report was not “Cleared/Closed”, the perpetrator has not been found or charged? Is there any other reason why a case would not be cleared/closed?”

And got this back:

“There are actually a few other statuses, such as cleared/exceptionally closed and cleared open.

Cleared/exceptionally closed could be that we know who the offender is but the victim does not want to sign complaints, the offender is dead and therefore cannot be charged, or some other reason the CPD could not charge the offender.
Cleared open means we know who the offender is but do not have the person in custody yet.

Additionally, while looking at narcotic incidents which were not marked as having an arrest, some of those case reports are still in preliminary status and should not have been visible/available on the dataportal.”

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