How arrests reduce near repeats: Breaking the Chain paper published

My paper (with colleagues Jordan Riddell and Cory Haberman), Breaking the chain: How arrests reduce the probability of near repeat crimes, has been published in Criminal Justice Review. If you cannot access the peer reviewed version, always feel free to email and I can send an offprint PDF copy. (For those not familiar, it is totally OK/legal for me to do this!) Or if you don’t want to go to that trouble, I have a pre-print version posted here.

The main idea behind the paper is that crimes often have near-repeat patterns. That is, if you have a car break in on 100 1st St on Monday, the probability you have another car break in at 200 1st St later in the week is higher than typical. This is most often caused by the same person going and committing multiple offenses in a short time period. So a way to prevent that would on its face be to arrest the individual for the initial crime.

I estimate models showing the reduction in the probability of a near repeat crime if an arrest occurs, based on publicly available Dallas PD data (paper has links to replication code). Because near repeat in space & time is a fuzzy concept, I estimate models showing reductions in near repeats for several different space-time thresholds.

So here the model is Prob[Future Crime = I(time < t & distance < d)] ~ f[Beta*Arrest + sum(B_x*Control_x)] where the f function is a logistic function, and I plot the Beta estimates given different time and space look aheads. Points indicate statistical significance, so you can see they tend to be negative for many different crime and different specifications (with a linear coefficient of around -0.3).

Part of the reason I pursued this is that the majority of criminal justice responses to near repeat patterns in the past were target hardening or traditional police patrol. Target hardening (e.g. when a break in occurs, go to the neighbors and tell them to lock their doors) does not appear to be effective, but traditional patrol does (see the work of Rachel/Robert Santos for example).

It seems to me ways to increase arrest rates for crimes is a natural strategy that is worthwhile to explore for police departments. Easier said than done, but one way may be to prospectively identify incidents that are likely to spawn near repeats and give them higher priority in assigning detectives. In many urban departments, lower level property crimes are never assigned a detective at all.

Open Data and Reproducible Criminology Research

This is part of a special issue put together by Jonathan Grubb and Grant Drawve on spatial approaches to community violence. Jon and Grant specifically asked contributors to discuss a bit about open data standards and replication materials. I repost my thoughts on that here in full:

In reference to reproducibility of the results, we have provided replication materials. This includes the original data sources collated from open sources, as well as python, Stata, and SPSS scripts used to conduct the near-repeat analysis, prepare the data, generate regression models, and graph the results. The Dallas Police Department has provided one of the most comprehensive open sources of crime data among police agencies in the world (Ackerman & Rossmo, 2015; Wheeler et al., 2017), allowing us the ability to conduct this analysis. But it also identifies one particular weakness in the data as well – the inability to match the time stamp of the occurrence of an arrest to when the crime occurred. It is likely the case that open data sources provided by police departments will always need to undergo periodic revision to incorporate more information to better the analytic potential of the data.

For example, much analysis of the arrest and crime relationship relies on either aggregate UCR data (Chamlin et al., 1992), or micro level NIBRS data sources (Roberts, 2007). But both of these data sources lack specific micro level geographic identifiers (such as census tract or addresses of the events), which precludes replicating the near repeat analysis we conduct. If however NIBRS were to incorporate address level information, it would be possible to conduct a wide spread analysis of the micro level deterrence effects of arrests on near repeat crimes across many police jurisdictions. That would allow much broader generalizability of the results, and not be dependent on idiosyncratic open data sources or special relationships between academics and police departments. Although academic & police practitioner relationships are no doubt a good thing (for both police and academics), limiting the ability to conduct analysis of key policing processes to the privileged few is not.

That being said, currently both for academics and police departments there are little to no incentives to provide open data and reproducible code. Police departments have some slight incentives, such as assistance from governmental bodies (or negative conditions for funding conditional on reporting). As academics we have zero incentives to share our code for this manuscript. We do so simply because that is a necessary step to ensure the integrity of scientific research. Relying on the good will of researchers to share replication materials has the same obvious disadvantage that allowing police departments to pick and choose what data to disseminate does – it can be capricious. What a better system to incentivize openness may look like we are not sure, but both academics and police no doubt need to make strides in this area to be more professional and rigorous.

Podcast and Video Shout Outs

So y’all know I really enjoy blogs. So much so I think they often have a higher value added than traditional peer review papers. There are other mediums I would like to recognize, and those are Podcasts and video tutorials. So while I like to do lab tutorials (pretty much like my blog posts in which I step through some code), I know many students would prefer I do videos and lectures. And I admit some of these I have seen done quite well on Coursera for example.

Another source I have been consuming quite a bit lately are Podcasts. These often take the form of an interview. So are not technical in nature, but are more soft story telling, such as talking about a particular topical area the interviewee is expert in, or that persons career path. So here are my list of these resources I have personally learned from and enjoyed.

None of these I have listened/watched 100% of the offerings, but have listened/watch multiple episodes (and will continue to listen/watch more)! These are very criminal justice focused, so would love to branch out to data science and health care resources if folks have suggestions!

Podcasts

Reducing Crime – Jerry Ratcliffe interviews a mix of academics and folks working in the criminal justice field. I have quite a few of these episodes I found personally very informative. John Eck, Kim Rossmo, and Phil Goff were perhaps my favorites of academics. Danny Murphy and Thomas Abt were really good as well (for my favorite non-academics offhand).

Niro Knowledge – Nicholas Roy is a current crime analyst, and interviews other crime analysts and academics. Favorite interviews so far are Cynthia Lum and Renee Mitchell. Similar to reducing crime is typically more focused on a particular topic of interest to the person being interviewed (e.g. Renee talked about her work on crime harm indices).

Analyst Talk – This is a podcast hosted by Jason Elder where he interviews crime analysts from all over about their careers. Annie Thompson and my former colleague Shelagh Dorn’s are my favorite so far, but I also need to listen in sometime on Sean Bair’s series of talks as well.

Abt Podcasts – This I only came across a week ago, but have listened to several on data science, CJ, and social determinants of health. These are a bit different than the other podcasts here, they are shorter and have two individuals from different fields discuss social science relevant to the chosen topic.

Videos

Canadian Society of Evidence Based Policing – Has many interviews of academics in crim/cj. I have an interview with them (would not recommend, I need to work on sitting still!) I really enjoyed the Peter Neyroud interview though is my favorite.

UARK CASDAL – These are instructional videos uploaded by Grant Drawve, mostly around doing crime analysis in Excel, but also has a few in ArcGIS.

StatQuest with Josh Starmer – This is one of the few non crim/cj examples I watch regularly. As interview questions at my work place for entry data scientists we often ask folks to explain machine learning models (such as random forests or XGBoost) in some simple terms. These videos are excellent resources to get you to understand the basics of the mathematics behind the techniques.

Again let me know if of podcasts/video series I am missing out on in the comments!

Reproducible research and code review for journals

Recently came across two different groups broaching the subject of code reviews and reproducible research more broadly for criminal justice. There are certainly aspects of either that make it difficult in the context of peer review. But I am not one to let the perfect be the enemy of the good, so I will layout the difficulties and give some comments on potential good enough solutions that still make marked improvements on the current state of affairs in crim/cj research.

Reproducible Research

So what do I mean by reproducible research? Jeromy Anglim on crossvalidated has a good breakdown on different ways we may apply the term. So to some it may mean if you did a hot spots policing experiment, can I replicate the same crime reduction results in another city.

These are important to publish (simply because social science experiments will inevitably have quite a bit of variance), but this is often not what we are talking about when we talk about replication. We are often talking about a much smaller in scope goal – if I give you the exact same data, can you reproduce the tables/figures in the manuscript you used to make your inferences?

One problem that is often the case with CJ research is that we are working with sensitive data. If I do analysis on a survey of a sensitive topic, I often cannot share the data. But, I do not believe that should entirely put a spike in the question of reproducible data. I have broken down different levels that are possible in making research more reproducible:

  1. A Sharing data and code files to reproduce the paper results
  2. B Sharing code files and simulated data that illustrate the results
  3. C Sharing the plain-text log files showing the code and results of tables/figures

So I have not seen C proposed anywhere, but it is a dead simple solution that almost everyone should be able to accommodate. It simply involves typing log using "output.txt", text at the top of your Stata file, or OUTPUT EXPORT /PDF DOCUMENTFILE="output.pdf" at the end of your SPSS analysis (or could be done via the GUI), etc. These are the log/output files used to generate the results you report in the paper, and typically contain both the commands run, as well as the resulting tables. These files can quite easily not contain privileged information (in fact they won’t be default most of the time, unless you printed out individual names in a table for example in intermediate results).

To accomplish C does take some modicum of wherewithal in terms of writing code, but it is a pretty low bar. So I see no reason why all quantitative analyses cannot require at least this step right now. I realize it is not foolproof – a bad actor could go and edit the results (same as they could edit the results without this information). But it ups the level of effort to manipulate results by quite a bit, and more importantly has the potential to catch more mundane transcription errors that occur quite frequently.

Sometimes I want more details on the code used, the nature of the data etc. (Most quasi-experimental design for example can be summed up as shape your data in a special way and run a particular regression model.) For people like me who care about that, B helps with that, in that I can see the code front-to-back, can actually go and inspect the shape and values in a particular rectangular dataset, and see how the code interacts with those objects. The only full on example of this I am aware of is a recent example paper in Nature Behavior that shares the code using simulated data.

B is also very similar to people who release statistical packages to reproduce their code. So if you release an R package that conducts your new fancy technique, even if you can’t share your data it is really good for people to be able to view the underlying code even by itself to understand the technique better and in conjunction build on your work more. If you do a new technique, it is a crazy ton of work to replicate that on your own, so most people will not bother.

A is most of the way there to the gold standard – if you can share both the data and the code used to reproduce the analysis. Both A and B take a significant amount of knowledge of statistical programming to accomplish. Most people in our field do not have the skills to write an analysis front-to-back that can run in a series of scripts though. To get to A/B grad programs in crim/cj need to spend crazy more time on teaching these skills, which is near zero now almost across the board.

One brief thing to mention about A is that the boundary is difficult to define. So for example, I share code to reproduce analysis in my 311 and crime at micro places in DC paper (paper link, code). But this starts from a dataset that has the street units in DC and all of the covariates already compiled. But where did that dataset come from? I created it by compiling many different sources, so the base dataset is itself very difficult to replicate. Again not letting the perfect be the enemy of the good, I think just starting from your compiled dataset, and replicating the tables/graphs in the manuscript is better than letting the fuzzy boundary prevent you from sharing anything.

Code Reviews for Journal Submissions

The hardest part of A is that even after you share your data, some journals want to be able to run the code locally to entirely reproduce your results. So while I have shared data code (A above) for many papers, see this spreadsheet, they have not been externally vetted by any of those journals. This vetting is the standard in some economic journals now I believe, and would not be surprised in some poli-sci journals as well. This is a very hard problem though, and requires significant resources from both the journal and the researcher to be able to do that.

The biggest hurdle is that even if you share your data/code, your particular system may be idiosyncratic. You may have different R libraries installed than me. You may have different versions of python packages. I may have used a program on Windows to do some analysis you cannot do on a Mac. You may rely on some paid API I cannot access.

These are often solvable problems, but take quite a bit of time to work out. A comparable example to my work is when data scientists say ‘going to production’. This often involves taking some analysis I did on my local machine, and making it run autonomously on my companies servers. There are some things that make it more or less difficult than the typical academic situation, but I think it is broadly comparable. To go to production for a project will typically take me 3-6 months at 50% of my time, so maybe something like 300 hours for a lowish end estimate. And that is just the time it takes from the researchers end, from the journals end it will also take a significant amount of time to compile every ones code and verify the results.

Because of this, I don’t think the fully reproducible re-run my code and generate the exact same tables are feasible in the current way we do academic research and peer review. But again that is why I list C above – we shouldn’t let the perfect be the enemy of the good.

Validating New Empirical Techniques

The code review above is not really code review in the sense that someone looks at your code and says this is correct, it is simply just saying can I get the same results as you. You may want peer review to accomplish the task of not only saying is it reproducible, but is it valid/correct? There are a few things towards this end I would like to see more often in crim/cj. I realize we are not statistics, so cannot often ask for formal proofs. But there are simpler things we can do to verify the results. These are the responsibility of the researcher to provide, not the reviewer to script up on their own to validate someone elses work.

One, illustrate the technique using a very simplified example. So for instance, in my p-median patrol areas paper, I show an example of constructing the linear program with only four areas. You should be able to calculate what the result should be by hand, so can verify the correctness of your algorithm. This has the added benefit of being a very good pedagogical way to describe your method.

Two, illustrate the technique on a larger sample of simulated data in which you again know the correct result. For one example of this, I showed how to estimate group based trajectory models using deep learning libraries. Again your model/method should be able to recover the correct result (which you know) given the simulated fake data.

Three, validate the result using real data compared to the current standard. For crime mapping papers, this means comparing forecasts compared to RTM, or simpler regression models, or simply prior crime = future crime on out of sample data. Amazingly many machine learning papers in CJ do not do out of sample predictions. If it is an inferential procedure, comparing the results to some other status quo technique is similar, such as showing conformal prediction intervals have smaller widths (so more statistical power) than placebo results for synthetic control designs (at least for that example with state panel level crime data).

You may not have all three of these examples in any particular paper, but I think for very new techniques 1 or 2 is necessary. 3 is often a by-product on the analysis anyway. So I do not believe any of these asks are that onerous. If you have the skills to create some new technique, you should be able to accomplish 1 or 2.

I do not have any special advice in terms of the reviewers perspective. When I do code reviews at work, what we do is go line by line, and my co-workers give high level design advice. E.g. you should use a config file for this instead of defining it inline, you should turn this block into a function, you should make a class to open/close the database connections etc. The code reviews do not validate the technical correctness, so if I queried the wrong data they wouldn’t know in the code review. The proof is in the pudding so to speak, so if my results are performing really badly in the real world I know I am doing something wrong. (And the obverse, if my results are on the mark and making money I am pretty sure I did nothing terribly wrong.)

Because there are not these real world mechanisms to validate code in peer reviewed papers, my suggestions for 1/2/3 are the closest I think we can get in many circumstances. That and simply making your code available will dramatically improve the reproducibility and validity of your research compared to the current status quo in our field.

My online course lab materials and musings about online teaching

I often refer folks to the courses I have placed online. Just for an update for everyone, if you look at the top of my website, I have pages for each of my courses at the header of my page. Several of these are just descriptions and syllabi, but the few lab based courses I have done over the years I have put my materials entirely online. So those are:

And each of those pages links to a GitHub page where all the lab goodies are stored.

The seminar in research focuses on popular quasi-experimental designs in CJ, and has code in R/Stata/SPSS for the weekly lessons. (Will need to update with python, I may need to write my own python margins library though!)

Grad GIS is mostly old ArcGIS tutorials (I don’t think I will update ArcPro, will see when Eric Piza’s new book comes out and just suggest that probably). Even though the screenshots are perhaps old at this point though the ideas/workflow are not. (It also has some tutorials on other open source tools, such as CrimeStat, Jerry’s Near Repeat Calculator, GeoDa, spatial regression analysis in R, and Mallesons/Andresens SPPT tool are examples I remember offhand.)

Undergrad Crime Analysis is mostly focused on number crunching relevant to crime analysts in Excel, although has a few things in Access (making SQL queries), and making a BOLO in publisher.

So for folks self-learning of course use those resources however you want. My suggestion is to skim through the syllabus, see if you want to learn about any particular lesson, and then jump right to that one. No need to slog through the whole course if you are just interested in one specific thing.

They are also freely available to any instructors who want to adapt those materials for their own courses as well.


One of the things that has disappointed me about the teaching response to Covid is instead of institutions taking the opportunity to really invest in online teaching, people are just running around with their heads cut off and offering poor last minute hybrid courses. (This is both for the kiddos as well as higher education.)

If you have ever taken a Coursera course, they are a real production! And the ones I have tried have all been really well done; nice videos, interactive quizzes with immediate feedback, etc. A professor on their own though cannot accomplish that, we would need investment from the University in filming and in scripting the webpage. But once it is finished, it can be delivered to the masses.

So instead of running courses with a tiny number of students, I think it makes more sense for Universities to actually pony up resources to help professors make professional looking online courses. Not the nonsense with a bad recorded lecture and a discussion board. It is IMO better to give someone a semester sabbatical to develop a really nice online course than make people develop them at the last minute. Once the course is set up, you really only need to administer the course, which takes much less work.

Another interested party may be professional organizations. For example, the American Society of Criminology could make an ad-hoc committee to develop a model curriculum for an intro criminology course. You can see in my course pages I taught this at one point – there is no real reason why every criminology teacher needs to strike out on their own. This is both more work for the individual teacher, as well as introduces quite a bit of variation in the content that crim/cj students receive.

Even if ASC started smaller, say promoting individual lessons, that would be lovely. Part of the difficulty in teaching a broad course like Intro to Criminology is that I am not an expert on all of criminology. So for example if someone made a lesson plan/video for bio-social criminology, I would be more apt to use that. Think instead of a single textbook, leveraging multi-media.


It is a bit ironic, but one of the reasons I was hired at HMS was to internally deliver data science training. So even though I am in the private sector I am still teaching!

Like I said previously, you are on your own for developing teaching content at the University. There is very little oversight. I imagine many professors will cringe at my description, but one of the things I like at HMS is the collaboration in developing materials. So I initially sat down with my supervisor and project manager to develop the overall curricula. Then for individual lessons I submit my slides/lab portion to my supervisor to get feedback, and also do a dry run in front of one of my peers on our data science team to get feedback. Then in the end I do a recorded lecture – we limit to something like 30 people on WebEx so it is not lagging, but ultimately everyone in the org can access the video recording at a later date.

So again I think this is a better approach. It takes more time, and I only do one lecture at a time (so take a month or two to develop one lecture). But I think that in the end this will be a better long term investment than the typical way Uni’s deliver courses.

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!

Publishing in Peer Review?

I am close, but not quite, entirely finished with my current crim/cj peer reviewed papers. Only one paper hangs on, the CCTV clearance paper (with Yeondae Jung). Rejected twice so far (once on R&R from Justice Quarterly), and has been under review in toto around a year and a half so far. It will land somewhere eventually, but who knows where at this point. (The other pre-prints I have on my CV but are not in peer review journals I am not actively seeking to publish anymore.)

Given the typical lags in the peer review process, if you look at my CV I will appear active in terms of publishing in 2020 (6 papers) and 2021 (4 papers and a book). But I have not worked on any peer review paper in earnest since I started working at HMS in December 2019, only copy-editing things I had already produced. (Which still takes a bit of work, for example my Cost of Crime hot spots paper took around 40 hours to respond to reviewers.)

At this point I am not sure if I will pursue any more peer reviewed publications directly in criminology/criminal justice. (Maybe as part of a team in giving support, but not as the lead.) Also we have discussed at my workplace pursuing publications, but that will be in healthcare related projects, not in Crim/CJ.

Part of the reason is that the time it takes to do a peer review publication is quite a bit relative to publishing a simple blog post. Take for instance my recent post on incorporating harm weights into the WDD test. I received the email question for this on Wednesday 11/18, thought about how to tackle the problem overnight, and wrote the blog post that following Thursday morning before my CrimCon presentation, (I took off work to attend the panel with no distractions). So took me around 3 hours in total. Many of my blog posts take somewhat longer, but I definitely do not take any more than 10-20 hours on an individual one (that includes the coding part, the writing part is mostly trivial).

I have attempted to guess as to the relative time it takes to do a peer reviewed publication based on my past work. I averaged around 5 publications per year, worked on average 50 hours a week while I was an academic, and spent something like I am guessing 60% to 80% (or more) of my time on peer review publications. Say I work 51 weeks a year (I definitely did not take any long vacations!, and definitely still put in my regular 50 hours over the summertime), that is 51*50=2550 hours. So that means around (2550*0.6)/5 ~ 300 or (2550*0.8)/5 ~ 400 so an estimate of 300 to 400 hours devoted to an individual peer review publication over my career. This will be high (as it absorbs things like grants I did not get), but is in the ballpark of what I would guess (I would have guessed 200+).

So this is an average. If I had recorded the time, I may have had a paper only take around 100 hours (I don’t think I could squeeze any out in less than that). I have definitely had some take over 400 hours! (My Mapping RTM using Machine Learning I easily spent over 200 hours just writing computer code, not to brag, it was mostly me being inefficient and chasing a few dead ends. But that is a normal part of the research process.)

So it is hard for me to say, OK here is a good blog post that took me 3 hours. Now I should go and spend another 300 to write a peer review publication. Some of that effort to publish in peer review journals is totally legitimate. For me to turn those blog posts into a peer review article I would need a more substantive real-life application (if not multiple real-life applications), and perhaps detailed simulations and comparisons to other techniques for the methods blog posts. But a bunch is just busy work – the front end lit review and answering petty questions from peer reviewers is a very big chunk of that 300 hours (and has very little value added).

My blog posts typically get many more views than my peer review papers do, so I have very little motivation to get the stamp of approval for peer review. So my blog posts take far less time, are more wide read, and likely more accessible than peer reviewed papers. Since I am not on the tenure track and do not get evaluated by peer reviewed publications anymore, there is not much motivation to continue them.

I do have additional ideas I would like to pursue. Fairness and efficiency in siting CCTV cameras is a big one on my mind. (I know how to do it, I just need to put in the work to do the analysis and write it up.) But again, it will likely take 300+ hours for me to finish that project. And I do not think anyone will even end up using it in the end – peer reviewed papers have very little impact on policy. So my time is probably better spent writing a few blog posts and playing video games with all the extra time.

If you are an editor reading this, I still do quite a few peer reviews (so feel free to send me those). I actually have more time to do those promptly since I am not hustling writing papers! I have actually debated on whether it is worth it to start my own peer reviewed journal, or maybe contribute to editing an already existing journals (just joined the JQC editorial board). Or maybe start writing my own crime analysis or methods text books. I think that would be a better use of my time at this point than pursuing individual publications.

Lit reviews are (almost) functionally worthless

The other day I got an email from ACJS about the most downloaded articles of the year for each of their journals. For The Journal of Criminal Justice Education it was a slightly older piece, How to write a literature review in 2012 by Andrew Denney & Richard Tewksbury, DT from here on. As you can guess by the title of my blog post, it is not my most favorite subject. I think it is actually an impossible task to give advice about how to write a literature review. The reason for this is that we have no objective standards by which to judge a literature review – whether one is good or bad is almost wholly subject to the discretion of the reader.

The DT article I don’t think per se gives bad advice. Use an outline? Golly I suggest students do that too! Be comprehensive in your lit review about covering all relevant work? Well who can argue with that!

I think an important distinction to make in the advice DT give is the distinction between functional actions and symbolic actions. Functional in this context means an action that makes the article better accomplish some specific function. So for example, if I say you should translate complicated regression models to more intuitive marginal effects to make your results more interpretable for readers, that has a clear function (improved readability).

Symbolic actions are those that are merely intended to act as a signal to the reader. So if the advice is along the lines of, you should do this to pass peer review, that is on its face symbolic. DT’s article is nearly 100% about taking symbolic actions to make peer reviewers happy. Most of the advice doesn’t actually improve the content of the manuscript (or in the most charitable interpretation how it improves the manuscript is at best implicit). In DT’s section Why is it important this focus on symbolic actions becomes pretty clear. Here is the first paragraph of that section:

Literature reviews are important for a number of reasons. Primarily, literature reviews force a writer to educate him/herself on as much information as possible pertaining to the topic chosen. This will both assist in the learning process, and it will also help make the writing as strong as possible by knowing what has/has not been both studied and established as knowledge in prior research. Second, literature reviews demonstrate to readers that the author has a firm understanding of the topic. This provides credibility to the author and integrity to the work’s overall argument. And, by reviewing and reporting on all prior literature, weaknesses and shortcomings of prior literature will become more apparent. This will not only assist in finding or arguing for the need for a particular research question to explore, but will also help in better forming the argument for why further research is needed. In this way, the literature review of a research report “foreshadows the researcher’s own study” (Berg, 2009, p. 388).

So the first argument, a lit review forces a writer to educate themselves, may offhand seem like a functional objective. It doesn’t make sense though, as lit. reviews are almost always written ex post research project. The point of writing a paper is not to educate yourself, but educate other people on your research findings. The symbolic motivation for this viewpoint becomes clear in DT’s second point, you need to demonstrate credibility to your readers. In terms of integrity if the advice in DT was ‘consider creating a pre-analysis plan’ or ‘release data and code files to replicate your results’ that would be functional advice. But no, it is important to wordsmith how smart you are so reviewers perceive your work as more credible.

Then the last point in the paragraph, articulating the need for a particular piece of research, is again a symbolic action in DT’s essay. You are arguing to peer reviewers about the need for a particular research question. I understand the spirit of this, but think back to what function does this serve? It is merely a signal to reviewers to say, given finite space in a journal, please publish my paper over some other paper, because my topic is more important.

You actually don’t need a literature review to demonstrate a topic is important and/or needed – you can typically articulate that in a sentence or two. For a paper I reviewed not too long ago on crime reductions resulting from CCTV installations in a European city, I was struck by another reviewers critique saying that the authors “never really motivate the study relative to the literature”. I don’t know about you, but the importance of that study seems pretty obvious to me. But yeah sure, go ahead and pad that citation list with a bunch of other studies looking at the same thing to make some peer reviewers happy. God forbid you simply cite a meta-analysis on prior CCTV studies and move onto better things.

What should a lit review accomplish?

So again I don’t think DT give bad advice – mostly vapid but not obviously bad. DT focus on symbolic actions in lit reviews because as lit reviews are currently performed in CJ/Crim journals, they are almost 100% symbolic. They serve almost no functional purpose other than as a signal to reviewers that you are part of the club. So DT give about the best advice possible navigating a series of arbitrary critiques with no clear standard.

As an example for this position that lit reviews accomplish practically nothing, conduct this personal experiment. The next peer review article you pick up, do not read the literature review section. Only read the abstract, and then the results and conclusion. Without having read the literature review, does this change the validity of a papers findings? It for the most part does not. People get feelings hurt by not being cited (including myself), but even if someone fails to cite some of my work that is related it pretty much never impacts the validity of that persons findings.

So DT give advice about how peer review works now. No doubt those symbolic actions are important to getting your paper published, even if they do not improve the actual quality of the manuscript in any clear way. I rather address the question about what I think a lit review should look like – not what you should do to placate three random people and the editor. So again I think the best way to think about this is via articulating specific functions a lit review accomplishes in terms of improving the manuscript.

Broadening the scope abit to consider the necessity of citations, the majority of citations in articles are perfunctory, but I don’t think people should plagiarize. So when you pull a very specific piece of information from a source, I think it is important to cite that work. Say you are using a survey instrument developed by someone else, citing the work that establishes that instruments reliability and validity, as well as the original population those measures were established on, is certainly useful information to the reader. Sources of information/measures, a recent piece saying the properties of your statistical model are I think other good examples of things to cite in your work. Unfortunately I cannot give a bright line here, I don’t cite Gauss every time I use the normal distribution. But if I am using a code library someone else developed that is important, inasmuch as that if someone wants to do a similar project they could use the same library.

In terms of discussing relevant results in prior studies, again the issue is the boundary of what is relevant is very difficult to articulate. If there is a relevant meta-analysis on a topic, it seems sufficient to me to simply state the results of the meta-analysis. Why do I think that is important though? It helps inform your priors about the current study. So if you say a meta-analysis effect size is X, and the current study has an effect size much larger, it may give you pause. It is also relevant if you are generalizing from the results of the study, it is just another piece of evidence in addition to the meta-analysis, not an island all by itself.

I am not saying discussing prior specific results are not needed entirely, but they do not need to be extensive. So if studies Z, Y, X are similar to yours but all had null results, and you think it was because the sample sizes were too small, that is relevant and useful information. (Again it changes your priors.) But it does not need to be belabored on in detail. The current standard of articulating different theoretical aspects ad-nauseum in Crim/CJ journals does not improve the quality of manuscripts. If you do a hot spots policing experiment, you do not need to review all the different minutia of general deterrence theory. Simply saying this experiment is likely to only accomplish general deterrence, not specific deterrence, seems sufficient to me personally.

When you propose a book you need to say ‘here are some relevant examples’ – I think the same idea would be sufficient for a lit review. OK here is my study, here are a few additional studies I think the reader may be interested in that are related. This accomplishes what contemporary lit reviews do in a much more efficient manner – citing more articles makes it much more difficult to pull out the really relevant related work. So admit this does not improve the quality of the current manuscript in a specific way, but helps the reader identify other sources of interest. (I as a reader typically go through the citation list and note a few articles I am interested in, this helps me accomplish that task much quicker.)

I’ve already sprinkled a few additional pieces of advice in this blog post (marginal effect estimates, pre-analysis plans, sharing data code), although you may say they don’t belong in the lit review. Whatever, those are things that actually improve either the content of the manuscript or the actual integrity of the research, not some spray paint on your flowers.

Relevant Other Work

Mapping attitudes paper published

My paper (joint work with Jasmine Silver, Rob Worden, and Sarah McLean), Mapping attitudes towards the police at micro places, has been published in the most recent issue of the Journal of Quantitative Criminology. Here is the abstract:

Objectives: We examine satisfaction with the police at micro places using data from citizen surveys conducted in 2001, 2009 and 2014 in one city. We illustrate the utility of this approach by comparing micro- and meso-level aggregations of policing attitudes, as well as by predicting views about the police from crime data at micro places.

Methods: In each survey, respondents provided the nearest intersection to their address. Using that geocoded survey data, we use inverse distance weighting to map a smooth surface of satisfaction with police over the entire city and compare the micro-level pattern of policing attitudes to survey data aggregated to the census tract. We also use spatial and multi-level regression models to estimate the effect of local violent crimes on attitudes towards police, controlling for other individual and neighborhood level characteristics.

Results: We demonstrate that there are no systematic biases for respondents refusing to answer the nearest intersection question. We show that hot spots of dissatisfaction with police do not conform to census tract boundaries, but rather align closely with hot spots of crime. Models predicting satisfaction with police show that local counts of violent crime are a strong predictor of attitudes towards police, even above individual level predictors of race and age.

Conclusions: Asking survey respondents to provide the nearest intersection to where they live is a simple approach to mapping attitudes towards police at micro places. This approach provides advantages beyond those of using traditional neighborhood boundaries. Specifically, it provides more precise locations police may target interventions, as well as illuminates an important predictor (i.e., nearby violent crimes) of policing attitudes.

And this was one of my favorites to make maps. We show how to take surveys and create analogs of hot spot maps of negative sentiment towards police. We do this via asking individuals to list their closest intersection (to still give some anonymity), and then create inverse distance weighted maps of negative attitudes towards police.

We also find in this work that nearby crimes are the biggest factor in predicting negative sentiment towards police. This hints that past results aggregating attitudes to neighborhoods is inappropriate, and that police reducing crime is likely to have the best margin in terms of making people more happy with the police in general.

As always, feel free to reach out for a copy of the paper if you cannot access JQC. (Or you could go a view the pre-print.)

Amending the WDD test to incorporate Harm Weights

So I received a question the other day about amending my and Jerry Ratcliffe’s Weighted Displacement Difference (WDD) test to incorporate crime harms (Wheeler & Ratcliffe, 2018). This is a great idea, but unfortunately it takes a small bit of extra work compared to the original (from the analysts perspective). I cannot make it as simple as just piping in the pre-post crime weights into that previous spreadsheet I shared. The reason is a reduction of 10 crimes with a weight of 10 has a different variance than a reduction of 25 crimes with a weight of 4, even though both have the same total crime harm reduction (10*10 = 4*25).

I will walk through some simple spreadsheet calculations though (in Excel) so you can roll this on your own. HERE IS THE SPREADSHEET TO DOWNLOAD TO FOLLOW ALONG. What you need to do is to calculate the traditional WDD for each individual crime type in your sample, and then combine all those weighted WDD’s estimates in the end to figure out your crime harm weighted estimate in the end (with confidence intervals around that estimated effect).

Here is an example I take from data from Worrall & Wheeler (2019) (I use this in my undergrad crime analysis class, Lab 6). This is just data from one of the PFA areas and a control TAAG area I chose by hand.

So first, go through the motions for your individual crimes in calculating the point estimate for the WDD, and then also see the standard error of that estimate. Here is an example of piping in the data for thefts of motor vehicles. The WDD is simple, just pre-post crime counts. Since I don’t have a displacement area in this example, I set those cells to 0. Note that the way I calculate this, a negative number is a good thing, it means crime went down relative to the control areas.

Then you want to place those point estimates and standard errors in a new table, and in those same rows assign your arbitrary weight. Here I use weights taken from Ratcliffe (2015), but these weights can be anything. See examples in Wheeler & Reuter (2020) for using police cost of crime estimates, and Wolfgang et al. (2006) for using surveys on public perceptions of severity. Many of the different indices though use sentencing data to derive the weights. (You could even use negative weights and the calculations here all work, say you had some positive data on community interactions.)

Now we have all we need to calculate the harm-weighted WDD test. The big thing here to note is that the variance of Var(x*harm_weight) = Var(x)*harm_weight^2. So that allows me to use all the same machinery as the original WDD paper to combine all the weights in the end. So now you just need to add a few additional columns to your spreadsheet. The point estimate for the harm reduction is simply the weight multiplied by the point estimate for the crime reduction. The variance though you need to square the standard error, and square the weight, and then multiply those squared results together.

Once that is done, you can pool the harm weighted stats together, see the calculations below the table. Then you can use all the same normal distribution stuff from your intro stats class to calculate z-scores, p-values, and confidence intervals. Here are what the results look like for this particular example.

I think this is actually a really good idea to pool results together. Many place based police interventions are general, in that you might expect them to reduce multiple crime types. Various harm scores are a good way to pool the results, instead of doing many individual tests. A few caveats though, I have not done simulations like I did in the WDD peer reviewed paper, I believe these normal approximations will do OK under the same circumstances though that we suggest it is reasonable to do the WDD test. You should not do the WDD test if you only have a handful of crimes in each area (under 5 in any cell in that original table is a good signal it is too few of crimes).

These crime count recommendations I think are likely to work as well for weighted crime harm. So even if you give murder a really high weight, if you have fewer than 5 murders in any of those original cells, I do not think you should incorporate it into the analysis. The large harm weight and the small numbers do not cancel each other out! (They just make the normal approximation I use likely not very good.) In that case I would say only incorporate individual crimes that you are OK with doing the WDD analysis to begin with on their own, and then pool those results together.

Sometime I need to grab the results of the hot spots meta-analysis by Braga and company and redo the analysis using this WDD estimate. I think the recent paper by Braga and Weisburd (2020) is right, that modeling the IRR directly makes more sense (I use the IRR to do cost-benefit analysis estimates, not Cohen’s D). But even that is one step removed, so say you have two incident-rate-ratios (IRRs), 0.8 and 0.5, the latter is bigger right? Well, if the 0.8 study had a baseline of 100 crimes, that means the reduction is 100 - 0.8*100 = 20, but if the 0.5 study had a baseline of 30 crimes, that would mean a reduction of 30 - 0.5*30 = 15, so in terms of total crimes is a smaller effect. The WDD test intentionally focuses on crime counts, so is an estimate of the actual number of crimes reduced. Then you can weight those actual crime decreases how you want to. I think worrying about the IRR could even be one step too far removed.

References

CrimCon Roundtable: Flipping a Criminal Justice PhD to an alt-academic Data Science Career

This Thursday 11/19/2020 at 1 PM Eastern, I will be participating in a roundtable for the online CrimCon event. This is free for everyone to zoom in, and here is the link to the program, I am on Stream 3!

The title is above — I have been a private sector data scientist at HMS for not quite a year now. I wanted to organize a panel to help upcoming PhD’s in criminal justice get some more exposure to potential data science positions, outside the traditional tenure track. Here is the abstract:

Tenure-track positions in academia are becoming more challenging to obtain, and only a small portion of junior faculty continue in academia to the rank of full professor. Therefore, students may opt to explore alternate options to obtain employment after their PhD is finished. These alternatives to the tenure track are often called “alt-academic” jobs. This roundtable will be focused on discussing various opportunities that exist for PhD’s in criminal justice and behavioral sciences spanning the public sector, the private sector, and non-profits/think tanks. Panelists will also discuss gaps in the typical PhD curriculum, with the goal of aiding current students to identify steps they can take to make themselves more competitive for alt-academic roles.

And here are each of the panelists bios:

Dr. Andrew Wheeler is currently a Data Scientist at HMS working on problems related to predictive modeling and optimization in relation to health insurance claims. Before joining HMS, he received a PhD degree in Criminal Justice from SUNY Albany. While in academia his research focused on collaborating with police departments for various problems including; evaluating crime reduction initiatives, place based and person based predictive modeling, data analytics for crime analysis, and developing models for the efficient and fair delivery of police resources.

Dr. Jennifer Gonzalez is the Senior Director of Population Health at the Meadows Mental Health Policy Institute, where she manages the Institute’s research and data portfolio. She earned her doctoral degree in epidemiology and a M.S. degree in criminal justice. Before joining MMHPI, Dr. Gonzalez was a tenured associate professor at the University of Texas School of Public Health, where she maintained a portfolio of more than $10 million in research funding and published more than one hundred interdisciplinary articles focused on the health of those who come into contact with—and work within—the criminal justice system.

Dr. Kyleigh Clark-Moorman is a Senior Research Associate for the Public Safety Performance Project at The Pew Charitable Trusts, a non-profit public policy organization. Kyleigh began working at Pew in 2019 and completed her PhD in Criminology and Criminal Justice at the University of Massachusetts, Lowell in May 2020. As an early career researcher, Dr. Clark-Moorman’s work has been published in Criminal Justice and Behavior, Criminal Justice Studies, and the Journal of Criminal Justice. In her role at Pew, Kyleigh is responsible for research design and data analysis focused on various criminal justice topics while also working with external partners to produce high-impact reports and analyses to raise awareness and drive public policy.

Matt Vogel is Associate Professor in the School of Criminal Justice at the University at Albany, SUNY and the Director of the Laboratory for Decision Making in Criminology and Criminal Justice. Matt regularly assists local agencies with data and evaluation needs. Some of his ongoing collaborations include assessments of racial representation on capital juries in Missouri, a longitudinal evaluation of a school-based violence reduction program, and the implementation of a police-hospital collaboration to help address retaliatory violence in St. Louis. Prior to joining the faculty at UAlbany, Matt worked in the Department of Criminology and Criminal Justice at the University of Missouri – St. Louis and held a long-term visiting appointment with the Faculty of Architecture at TU Delft (the Netherlands).

If you have any upfront questions you would like addressed by the panel, always feel free to send me a pre-emptive email (or comment below).


Update: The final roundtable is now posted on Youtube. See below for the panels thoughts on pursuing non-tenure track jobs with your social science Phd.