Work on Shootings in Dallas Published

I have two recent articles that examine racial bias in decisions to shoot using Dallas Police Data:

  • Wheeler, Andrew P., Scott W. Phillips, John L. Worrall, and Stephen A. Bishopp. (2018) What factors influence an officer’s decision to shoot? The promise and limitations of using public data. Justice Research and Policy Online First.
  • Worrall, John L., Stephen A. Bishopp, Scott C. Zinser, Andrew P. Wheeler, and Scott W. Phillips. (2018) Exploring bias in police shooting decisions with real shoot/don’t shoot cases. Crime & Delinquency Online First.

In each the main innovation is using control cases in which officers pulled their firearm and pointed at a suspect, but decided not to shoot. Using this design we find that officers are less likely to shoot African-Americans, which runs counter to most recent claims of racial bias in police shootings. Besides the simulation data of Lois James, this is a recurring finding in the recent literature — see Roland Fryer’s estimates of this as well (although he uses TASER incidents as control cases).

The reason for the two articles is that me and John through casual conversation found out that we were both pursuing very similar projects, so we decided to collaborate. The paper John is first author examined individual officer level outcomes, and in particular retrieved personnel complaint records for individual officers and found they did correlate with officer decisions to shoot. My article I wanted to intentionally stick with the publicly available open data, as a main point of the work was to articulate where the public data falls short and in turn suggest what information would be needed in such a public database to reasonably identify racial bias. (The public data is aggregated to the incident level — one incident can have multiple officers shooting.) From that I suggest instead of a specific officer involved shooting database, it would make more sense to have officer use of force (at all levels) attached to incident based reporting systems (i.e. NIBRS should have use of force fields included). In a nutshell when examining any particular use-of-force outcome, you need a counter-factual that is that use-of-force could happen, but didn’t. The natural way to do that is to have all levels of force recorded.

Both John and I thought prior work that only looked at shootings was fundamentally flawed. In particular analyses where armed/unarmed was the main outcome among only a set of shooting cases confuses cause and effect, and subsequently cannot be used to determine racial bias in officer decision making. Another way to think about it is that when only looking at shootings you are just limiting yourself to examining potentially bad outcomes — officers often use their discretion for good (the shooting rate in the Dallas data is only 3%). So in this regard databases that only include officer involved shooting cases are fundamentally limited in assessing racial bias — you need cases in which officers did not shoot to assess bias in officer decision making.

This approach of course has some limitations as well. In particular it uses another point of discretion for officers – when to draw their firearm. It could be the case that there is no bias in terms of when officers pull the trigger, but they could be more likely to pull their gun against minorities — our studies cannot deny that interpretation. But, it is also the case other explanations could explain why minorities are more likely to have an officer point a gun at them, such as geographic policing or even more basic that minorities call the police more often. In either case, at the specific decision point of pulling the trigger, there is no evidence of racial bias against minorities in the Dallas data.

I did not post pre-prints of this work due to the potentially contentious nature, as well as the fact that colleagues were working on additional projects based on the same data. I have posted the last version before the copy-edits of the journal for the paper in which I am first author here. If you would like a copy of the article John is first author always feel free to email.

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Paper published: The effect of housing demolitions on crime in Buffalo, New York

I have a new paper published with a few of my colleagues up in Buffalo, Dae-Young Kim and Scott Phillips. This work looks at the crime reduction effects of widespread demolitions in Buffalo, is titled The Effect of Housing Demolitions on Crime in Buffalo, New York, and was published at the Journal of Research in Crime & Delinquency. In short, at the micro level there is very strong evidence that demolitions reduce crime — the neighborhood level the evidence is not as strong. This is likely partly due to the neighborhood level analysis being underpowered, as several of the estimates between the two are very similar overall.

If you cannot get access to that published article, you can always send me an email for a copy, or you can download a pre-print version from SSRN.

Below is one of the images from the paper, a set of small-multiple maps showing demographic characteristics of Buffalo census tracts:

Someone could surely replicate this micro level result in other cities that have experienced widespread demolitions (like Detroit). But for long term city planners I would consider more rigorous designs that incorporate not only selective demolition, but other neighborhood investment strategies to improve neighborhoods over long term. That is, this research is good evidence of the near-term crime reduction effects of demolitions, but for the long haul leaving empty lots is not going to greatly improve neighborhoods.

New working paper: Modeling the Spatial Patterns of Intra-Day Crime Trends

I have a new working paper out with Cory Haberman, Modeling the Spatial Patterns of Intra-Day Crime Trends. Below is the abstract:

Several prior studies have found that despite theoretical expectations otherwise, facilities (such as on-premise alcohol outlets) have consistent effects on crime regardless of time of the day (Bernasco et al., 2017; Haberman & Ratcliffe, 2015). We explain these results by failure to account for the regular background wave of crime, which results from ubiquitous patterns of human routine activities. Using eight years of data on assaults and robberies in Seattle (WA), we demonstrate the regularity of the within-day crime wave for all areas of the city. Then using models to predict when a crime will most likely occur, we demonstrate how schools and on-premise alcohol outlets cause bumps in the background wave at particular times of the day, such as when school dismisses. But those bumps dissipate quite rapidly in space, and are relatively small compared to the amplitude of the regular background wave of crime. Although facilities have theoretical times in which they should have a greater influence on crime patterns, they are situated within a community of other human activity uses, making it difficult to uniquely identify their effects separately from other aspects of the built environment.

And here is a joyplot showing the changes in the hour of day wave depending on how close robberies are to a public high school or middle school:

You can see bumps very nearby schools at 7 am, then around noon and throughout the later afternoon, but are smoothed out when you get to around 2,000 feet away from schools.

The idea behind this paper is that several recent articles have not found much of a conditional relationship between crime generators and time of day. For example you would think bars only effect crime at nighttime when most people are at the bar, but several recent articles found the time of day does not make much of a difference (Bernasco et al., 2017; Haberman & Ratcliffe, 2015). We hypothesize this is because of the background wave of crime per hour of the day is much larger in magnitude than any local factor. An intuitive reason for this is that a place never has just a bar in isolation, there are other local land uses nearby that influence criminal patterns. You can see places nearby crime generators cause slight bumps in the background wave, but they are tiny compared to the overall amplitude of the general within day crime wave.

The article has a link to data and code to reproduce the findings. As always if you have feedback I am all ears.

Paper published: Evaluating Community Prosecution Code Enforcement in Dallas, Texas

Some work John Worrall and I collaborated on was just published in Justice Quarterly, Evaluating Community Prosecution Code Enforcement in Dallas, Texas. I have two links to share:

If you need access to the article always feel free to email.

Below is the abstract:

We evaluated a community prosecution program in Dallas, Texas. City attorneys, who in Dallas are the chief prosecutors for specified misdemeanors, were paired with code enforcement officers to improve property conditions in a number of proactive focus areas, or PFAs, throughout the city. We conducted a panel data analysis, focusing on the effects of PFA activity on crime in 19 PFAs over a six-year period (monthly observations from 2010 to 2015). Control areas with similar levels of pre-intervention crime were also included. Statistical analyses controlled for pre-existing crime trends, seasonality effects, and other law enforcement activities. With and without dosage data, the total crime rate decreased in PFA areas relative to control areas. City attorney/code enforcement teams, by seeking the voluntary or court-ordered abatement of code violations and criminal activity at residential and commercial properties, apparently improved public safety in targeted areas.

This was a neat program, as PFAs are near equivalents of hot spots that police focus on. So for the evaluation we drew control areas from Dallas PD’s Target Area Action Grid (TAAG) Areas:

New preprint: The accuracy of the violent offender identification directive (VOID) tool to predict future gun violence

I have a new preprint out, The accuracy of the violent offender identification directive (VOID) tool to predict future gun violence. This is work with Rob Worden and Jasmine Silver from our time at the Finn Institute. Below is the abstract:

We evaluate the Violent Offender Identification Directive (VOID) tool, a risk assessment instrument implemented within a police department to prospectively identify offenders likely to be involved with future gun violence. The tool uses a variety of static measures of prior criminal history that are readily available in police records management systems. The VOID tool is assessed for predictive accuracy by taking a historical sample and calculating scores for over 200,000 individuals known to the police at the end of 2012, and predicting 103 individuals involved with gun violence (either as a shooter or a victim) during 2013. Despite weights for the instrument being determined in an ad-hoc manner by crime analysts, the VOID tool does very well in predicting involvement with gun violence compared to an optimized logistic regression and generalized boosted models. We discuss theoretical reasons why such ad-hoc instruments are likely to perform well in identifying chronic offenders for all police departments.

There were just slightly over 100 violent gun offenders we were trying to pick out of over 200,000. The VOID tool did really well! Here is a graph comparing how many of those offenders VOID captured compared to a generalized boosted model (GBM), and two different logistic regression equations.

I have some of my thoughts in this article as to why a simple tool does just as well as more complicated regression and machine learning techniques, which is a common finding in recidivism studies as well. My elevator pitch for why that is is because most offenders are generalists, and for example you can basically swap prior arrests for robbery with prior arrests for motor vehicle theft — they both provide essentially the same signal for future potential criminality. See also discussion of this on Dan Simpson’s post on the Stat Modeling, Causal Inference and Social Science blog, which in turn makes me think the idea behind simple models can be readily applied to many decision points in the criminal justice field.

The simple takeaway from this for crime analysts making chronic offender lists is that don’t let the perfect be the enemy of the good. Analysts can likely create an ad-hoc weighting to prioritize chronic offenders and it will do quite well compared to fancier models.

I will be presenting this work at the ACJS conference in New Orleans on Saturday 2/17/18. It is a great session, with YongJei Lee, Jerry Ratcliffe, Bryanna Fox, and Stacy Sechrist (see session 384 in the ACJS program), so stop on by. If you want to catch up with me in New Orleans just send me an email. And as always if you have feedback on the draft I am all ears.

New preprint: Testing for Similarity in Area-Based Spatial Patterns: Alternative Methods to Andresen’s Spatial Point Pattern Test

I just posted another pre-print to SSRN, Testing for Similarity in Area-Based Spatial Patterns: Alternative Methods to Andresen’s Spatial Point Pattern Test. This is work with Wouter Steenbeek and Martin Andresen. Below is the abstract:

Andresen’s spatial point pattern test (SPPT) compares two spatial point patterns on defined areal units: it identifies areas where the spatial point patterns diverge and aggregates these local (dis)similarities to one global measure. We discuss the limitations of the SPPT and provide two alternative methods to calculate differences in the point patterns. In the first approach we use differences in proportions tests corrected for multiple comparisons. We show how the size of differences matter, as with large point patterns many areas will be identified by SPPT as statistically different, even if those differences are substantively trivial. The second approach uses multinomial logistic regression, which can be extended to identify differences in proportions over continuous time. We demonstrate these methods on identifying areas where pedestrian stops by the New York City Police Department are different from violent crimes from 2006 through 2016.

And here is an example map using our proportion differences test and graduated circles to identify places with larger differences in the percentages:

This is opposed to the traditional SPPT output, which just identifies whether two areas are different and does not focus on the size of the difference, like below:

You can see with a large sample size, basically everything is statistically different! (This uses over 4 million stops and over 800,000 violent crimes). Focusing on the magnitude of the differences gives a much clear indication of patterns.

The paper includes a dropbox link to download the data and code used to estimate the different techniques (it includes code in SPSS, R, and Stata). If you have any feedback as always let me know. This was submitted as a GISScience presentation for the 2018 ESRI User conference in July in San Diego, so I should have news about that presentation in the near future as well.

New preprint: A Gentle Introduction to Creating Optimal Patrol Areas

I have a new preprint posted, A Gentle Introduction to Creating Optimal Patrol Areas. Below is the abstract:

Models to create optimal patrol areas have been in existence for over 45 years, but police departments still regularly construct patrol areas in an ad-hoc fashion. This essay walks the reader through formulating an integer linear program to create a set number of patrol areas that have near equal call load and that are contiguous using simple examples. Then the technique is illustrated using a case study in Carrollton, TX. Creating optimal patrol areas not only have the potential to improve efficiency in response times, but can also encourage hot spots policing. Applications of linear programming can additionally be applied to a wide variety of problems within criminal justice agencies, and this essay provides a gentle introduction to understanding the mathematical notation of linear programming.

In this paper I introduce a very simple integer linear program to create patrol beats, and then build up the complexity into the fuller p-median problem with additional constraints applicable to making patrol areas. The constraints on making the call load equal that I introduce in the paper are the only real novel aspect of the paper (although no doubt someone else has done something similar previously), but I was a bit frustrated reading other linear programs to create patrol areas. Most work was concentrated in operations research journals and in my opinion was totally inaccessible to a typical crime analyst. So I frame the paper as an introduction to integer linear programs, walk though some simplified examples, and then apply that full model in Carrollton. I also provide an extensive walkthrough using the python program PuLP so others can replicate the work with their own data in the supplementary materials.

Here is my end example map of the optimal patrol areas in Carrollton.

You can see that my areas are not as nice and convex, although most applications of correcting for that introduce multiple objective functions and/or non-linear functions, making the problem much harder to estimate in practice (which was part of my pet-peeve with prior publications – none provided code to estimate the models described, with the exception of some of the work of Kevin Curtin).

Part of the reason I tackled this problem is that it comes up all the time on the IACA list-serve — how to make new patrol areas. If you are an analyst interested in applying this in your jurisdiction and would like help always feel free to contact me.

New working paper: Mapping attitudes towards the police at micro places

I have a new preprint posted, Mapping attitudes towards the police at micro places. This is work with Jasmine Silver, as well as Rob Worden and Sarah McLean. See the abstract:

We demonstrate the utility of mapping community satisfaction with the police at micro places using data from citizen surveys conducted in 2001, 2009 and 2014 in one city. In each survey, respondents provided the nearest intersection to their address. We use inverse distance weighting to map a smooth surface of satisfaction with police over the entire city, which shows broader neighborhood patterns of satisfaction as well as small area hot spots of dissatisfaction. Our results show that hot spots of dissatisfaction with police do not conform to census tract boundaries, but rather align closely with hot spots of crime and police activity. Models predicting satisfaction with police show that local counts of violent crime are the strongest predictors of attitudes towards police, even above individual level predictors of race and age.

In this article we make what are analogs of hot spot maps of crime, but measure dissatisfaction with the police.

One of the interesting findings is that these hot spots do not align nicely with census tracts (the tracts are generalized, we cannot divulge the location of the city). So the areas identified by each procedure would be much different.

As always, feel free to comment or send me an email if you have feedback on the article.

Monitoring homicide trends paper published

My paper, Monitoring Volatile Homicide Trends Across U.S. Cities (with coauthor Tom Kovandzic) has just been published online in Homicide Studies. Unfortunately, Homicide Studies does not give me a link to share a free PDF like other publishers, but you can either grab the pre-print on SSRN or always just email me for a copy of the paper.

They made me convert all of the charts to grey scale :(. Here is an example of the funnel chart for homicide rates in 2015.

And here are example fan charts I generated for a few different cities.

As always if you have feedback or suggestions let me know! I posted all of the code to replicate the analysis at this link. The prediction intervals can definately be improved both in coverage and in making their length smaller, so I hope to see other researchers tackling this as well.

Presentation at ASC: Crime Data Visualization for the Future

At the upcoming American Society of Criminology conference in Philadelphia I will be presenting a talk, Crime Data Visualization for the Future. Here is the abstract:

Open data is a necessary but not sufficient condition for data to be transparent. Understanding how to reduce complicated information into informative displays is an important step for those wishing to understand crime and how the criminal justice system works. I focus the talk on using simple tables and graphs to present complicated information using various examples in criminal justice. Also I describe ways to effectively evaluate the size of effects in regression models, and make black box machine learning models more interpretable.

But I have written a paper to go with the talk as well. You can download that paper here. As always, if you have feedback/suggestions let me know.

Here are some example graphs of plotting the predictions from a random forest model predicting when restaurants in Chicago will fail their inspections.

I present on Wednesday 11/15 at 11 am. You can see the full session here. Here is a quick rundown of the other papers — Marcus was the one who put together the panel.

  • A Future Proposal for the Model Crime Report – Marcus Felson
  • Crime Data Warehouses and the future of Big Data in Criminology – Martin Andresen
  • Can We Unify Criminal Justice Data, Like the Dutch and the Nordics? – Michael Mueller-Smith

So it should be a great set of talks.


I also signed up to present a poster, Mapping Attitudes Towards the Police at Micro Places. This is work with Albany Finn Institute folks, including Jasmine Silver, Sarah McLean, and Rob Worden. Hopefully I will have a paper to share about that soon, but for a teaser on that here is an example map from that work, showing hot spots of dissatisfaction with the police estimated via inverse distance weighting. Update: for those interested, see here for the paper and here for the poster. Stop on by Thursday to check it out!

And here is the abstract:

We demonstrate the utility of mapping community satisfaction with the police at micro places using data from citizen surveys conducted in 2001, 2009 and 2014 in one city. In each survey, respondents provided the nearest intersection to their address. We use inverse distance weighting to map a smooth surface of satisfaction with police over the entire city, which shows broader neighborhood patterns of satisfaction as well as small area hot spots of dissatisfaction. Our results show that hot spots of dissatisfaction with police do not conform to census tract boundaries, but rather align closely with hot spots of crime and police activity. Models predicting satisfaction with police show that local counts of violent crime are the strongest predictors of attitudes towards police, even above individual level predictors of race and age.