David Bayley

David Bayley is most known in my research area, policing interventions to reduce crime, based on this opening paragraph in Police for the future:

The police do not prevent crime. This is one of the best kept secrets of modern life. Experts know it, the police know it, but the public does not know it. Yet the police pretend that they are society’s best defense against crime and continually argue that if they are given more resources, especially personnel, they will be able to protect communities against crime. This is a myth.

This quote is now paraded as backwards thinking, often presented before discussing the overall success of hot spots policing. If you didn’t read the book, you might come to the conclusion that this quote is a parallel to the nothing works mantra in corrections research. That take is not totally off-base: Police for the future was published in 1994, so it was just at the start of the CompStat revolution and hot spots policing. The evidence base was no doubt much thinner at that point and deserving of skepticism.

I don’t take the contents of David’s book as so hardlined on the stance that police cannot reduce crime, at least at the margins, as his opening quote suggests though. He has a chapter devoted to traditional police responses (crackdowns, asset forfeiture, stings, tracking chronic offenders), where he mostly expresses scientific skepticism of their effectiveness given their cost. He also discusses problem oriented approaches to solving crime problems, how to effectively measure police performance (outputs vs outcomes), and promotes evaluation research to see what works. Still all totally relevant twenty plus years later.

The greater context of David’s quote comes from his work examining police forces internationally. David was more concerned about professionalization of police forces. Part of this is better record keeping of crimes, and in the short term crime rates will often increase because of this. In class he mocked metrics used to score international police departments on professionalization that used crime as a measure that went into their final grade. He thought the function of the police was broader than reducing crime to zero.


I was in David’s last class he taught at Albany. The last day he sat on the desk at the front of the room and expressed doubt about whether he accomplished anything tangible in his career. This is the fate of most academics. Very few of us can point to direct changes anyone implemented in response to our work. Whether something works is independent of an evaluation I conduct to show it works. Even if a police department takes my advice about implementing some strategy, I am still only at best indirectly responsible for any crime reductions that follow. Nothing I could write would ever compete with pulling a single person from a burning car.

While David was being humble he was right. If I had to make a guess, I would say David’s greatest impact likely came about through his training of international police forces — which I believe spanned multiple continents and included doing work with the United Nations. (As opposed to saying something he wrote had some greater, tangible impact.) But even there if we went and tried to find direct evidence of David’s impact it would be really hard to put a finger on any specific outcome.

If a police department wanted to hire me, but I would be fired if I did not reduce crimes by a certain number within that first year, I would not take that job. I am confident that I can crunch numbers with the best of them, but given real constraints of police departments I would not take that bet. Despite devoting most of my career to studying policing interventions to reduce crime, even with the benefit of an additional twenty years of research, I’m not sure if David’s quote is as laughable as many of my peers frame it to be.

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Why I publish preprints

I encourage peers to publish preprint articles — journal articles before they go through the whole peer review process and are published. It isn’t normative in our field, and I’ve gotten some pushback from colleagues, so figured I would put on paper why I think it is a good idea. In short, the benefits (increased exposure) outweigh the minimal costs of doing so.

The good — getting your work out there

The main benefit of posting preprints is to get your work more exposure. This occurs in two ways: one is that traditional peer-review work is often behind paywalls. This prevents the majority of non-academics from accessing your work. This point about paywalls applies just the same to preventing other academics from reading your work in some cases. So while the prior blog post I linked by Laura Huey notes that you can get access to some journals through your local library, it takes several steps. Adding in steps you are basically losing out on some folks who don’t want to spend the time. Even through my university it is not uncommon for me to not be able to access a journal article. I can technically take the step of getting the article through inter-library loan, but that takes more time. Time I am not going to spend unless I really want to see the contents of the article.

This I consider a minor benefit. Ultimately if you want your academic work to be more influential in the field you need to write about your work in non-academic outlets (like magazines and newspapers) and present it directly to CJ practitioner audiences. But there are a few CJ folks who read journal articles you are missing, as well as a few academics who are missing your work because of that paywall.

A bigger benefit is actually that you get your work out much quicker. The academic publishing cycle makes it impossible to publish your work in a timely fashion. If you are lucky, once your paper is finished, it will be published in six months. More realistically it will be a year before it is published online in our field (my linked article only considers when it is accepted, tack on another month or two to go through copy-editing).

Honestly, I publish preprints because I get really frustrated with waiting on peer review. No offense to my peers, but I do good work that I want others to read — I do not need a stamp from three anonymous reviewers to validate my work. I would need to do an experiment to know for sure (having a preprint might displace some views/downloads from the published version) but I believe the earlier and open versions on average doubles the amount of exposure my papers would have had compared to just publishing in traditional journals. It is likely a much different audience than traditional academic crim people, but that is a good thing.

But even without that extra exposure I would still post preprints, because it makes me happy to self-publish my work when it is at the finish line, in what can be a miserably long and very much delayed gratification process otherwise.

The potential downsides

Besides the actual time cost of posting a preprint (next section I will detail that more precisely, it isn’t much work), I will go through several common arguments why posting preprints are a bad idea. I don’t believe they carry much weight, and have not personally experienced any of them.

What if I am wrong — Typically I only post papers either when I am doing a talk, or when it is ready to go out for peer review. So I don’t encourage posting really early versions of work. While even at this stage there is never any guarantee you did not make a big mistake (I make mistakes all the time!), the sky will not fall down if you post a preprint that is wrong. Just take it down if you feel it is a net negative to the scholarly literature (which is very hard to do — the results of hypothesis tests do not make the work a net positive/negative). If you think it is good enough to send out for peer review it is definitely at the stage where you can share the preprint.

What if the content changes after peer review — My experience with peer review is mostly pedantic stuff — lit. review/framing complaints, do some robustness checks for analysis, beef up the discussion. I have never had a substantive interpretation change after peer-review. Even if you did, you can just update the preprint with the new results. While this could be bad (an early finding gets picked up that is later invalidated) this is again something very rare and a risk I am willing to take.

Note peer review is not infallible, and so hedging that peer review will catch your mistakes is mostly a false expectation. Peer review does not spin your work into gold, you have to do that yourself.

My ideas may get scooped — This I have never personally had happen to me. Posting a preprint can actually prevent this in terms of more direct plagiarism, as you have a time-stamped example of your work. In terms of someone taking your idea and rewriting it, this is a potential risk (same risk if you present at a conference) — really only applicable for folks working on secondary data analysis. Having the preprint the other person should at least cite your work, but sorry, either presenting some work or posting a preprint does not give you sole ownership of an idea.

Journals will view preprints negatively — Or journals do not allow preprints. I haven’t come across a journal in our field that forbids preprints. I’ve had one reviewer note (out of likely 100+ at this point) that the pre-print was posted as a negative (suggesting I was double publishing or plagiarizing my own work). An editor that actually reads reviews should know that is not a substantive critique. That was likely just a dinosaur reviewer that wasn’t familiar with the idea of preprints (and they gave an overall positive review in that one case, so did not get the paper axed). If you are concerned about this, just email the editor for feedback, but I’ve never had a problem from editors.

Peer reviewers will know who I am — This I admit is a known unknown. So peer review in our crim/cj journals are mostly doubly blind (most geography and statistic journals I have reviewed for are not, I know who the authors are). If you presented the work at a conference you have already given up anonymity, and also the field is small enough a good chunk of work the reviewers can guess who the author is anyway. So your anonymity is often a moot point at the peer review stage anyway.

So I don’t know how much reviewers are biased if they know who you are (it can work both ways, if you get a friend they may be more apt to give a nicer review). It likely can make a small difference at the margins, but again I personally don’t think the minor risk/cost outweighs the benefits.

These negatives are no doubt real, but again I personally find them minor enough risks to not outweigh the benefits of posting preprints.

The not hard work of actually posting preprints

All posting a preprint involves is uploading a PDF file of your work to either your website or a public hosting service. My workflow currently I have my different components of a journal article in several word documents (I don’t use LaTex very often). (Word doesn’t work so well when it has one big file, especially with many pictures.) So then I export those components to PDF files, and stitch them together using a freeware tool PDFtk. It has a GUI and command line, so I just have a bat file in my paper directory that lists something like:

pdftk.exe TitlePage.pdf MainPaper.pdf TablesGraphs.pdf Appendix.pdf cat output CombinedPaper.pdf

So just a double click to update the combined pdf when I edit the different components.

Public hosting services to post preprints I have used in the past are Academia.edu, SSRN, and SoxArXiv, although again you could just post the PDF on your webpage (and Google Scholar will eventually pick it up). I use SocArXiv now, as SSRN currently makes you sign up for an account to download PDFs (again a hurdle, the same as a going through inter-library loan). Academia.edu also makes you sign up for an account, and has weird terms of service.

Here is an example paper of mine on SocArXiv. (Note the total downloads, most of my published journal articles have fewer than half that many downloads.) SocArXiv also does not bother my co-authors to create an account when I upload a paper. If we had a more criminal justice focused depository I would use that, but SocArXiv is fine.

There are other components of open science I should write about — such as replication materials/sharing data, and open peer reviewed journals, but I will leave those to another blog post. Posting preprints takes very little extra work compared to what academics are currently doing, so I hope more people in our field start doing it.

 

Plotting Predictive Crime Curves

Writing some notes on this has been in the bucket list for a bit, how to evaluate crime prediction models. A recent paper on knife homicides in London is a good use case scenario for motivation. In short, when you have continuous model predictions, there are a few different graphs I would typically like to see, in place of accuracy tables.

The linked paper does not provide data, so what I do for a similar illustration is grab the lower super output area crime stats from here, and use the 08-17 data to predict homicides in 18-Feb19. I’ve posted the SPSS code I used to do the data munging and graphs here — all the stats could be done in Excel though as well (just involves sorting, cumulative sums, and division). Note this is not quite a replication of the paper, as it includes all cases in the homicide/murder minor crime category, and not just knife crime. There ends up being a total of 147 homicides/murders from 2018 through Feb-2019, so the nature of the task is very similar though, predicting a pretty rare outcome among almost 5,000 lower super output areas (4,831 to be exact).

So the first plot I like to make goes like this. Use whatever metric you want based on historical data to rank your areas. So here I used assaults from 08-17. Sort the dataset in descending order based on your prediction. And then calculate the cumulative number of homicides. Then calculate two more columns; the total proportion of homicides your ranking captures given the total proportion of areas.

Easier to show than to say. So for reference your data might look something like below (pretend we have 100 homicides and 1000 areas for a simpler looking table):

 PriorAssault  CurrHom CumHom PropHom PropArea
 1000          1         1      1/100    1/1000
  987          0         1      1/100    2/1000
  962          2         4      4/100    3/1000
  920          1         5      5/100    4/1000
    .          .         .       .        .
    .          .         .       .        .
    .          .         .       .        .
    0          0       100    100/100 1000/1000

You would sort the PriorCrime column, and then calculate CumHom (Cumulative Homicides), PropHom (Proportion of All Homicides) and PropArea (Proportion of All Areas). Then you just plot the PropArea on the X axis, and the PropHom on the Y axis. Here is that plot using the London data.

Paul Ekblom suggests plotting the ROC curve, and I am too lazy now to show it, but it is very similar to the above graph. Basically you can do a weighted ROC curve (so predicting areas with more than 1 homicide get more weight in the graph). (See Mohler and Porter, 2018 for an academic reference to this point.)

Here is the weighted ROC curve that SPSS spits out, I’ve also superimposed the predictions generated via prior homicides. You can see that prior homicides as the predictor is very near the line of equality, suggesting prior homicides are no better than a coin-flip, whereas using all prior assaults does alittle better job, although not great. SPSS gives the area-under-the-curve stat at 0.66 with a standard error of 0.02.

Note that the prediction can be anything, it does not have to be prior crimes. It could be predictions from a regression model (like RTM), see this paper of mine for an example.

So while these do an OK job of showing the overall predictive ability of whatever metric — here they show using assaults are better than random, it isn’t real great evidence that hot spots are the go to strategy. Hot spots policing relies on very targeted enforcement of a small number of areas. The ROC curve shows the entire area. If you need to patrol 1,000 LSOA’s to effectively capture enough crimes to make it worth your while I wouldn’t call that hot spots policing anymore, it is too large.

So another graph you can do is to just plot the cumulative number of crimes you capture versus the total number of areas. Note this is based on the same information as before (using rankings based on assaults), just we are plotting whole numbers instead of proportions. But it drives home the point abit better that you need to go to quite a large number of areas to be able to capture a substantive number of homicides. Here I zoom in the plot to only show the first 800 areas.

So even though the overall curve shows better than random predictive ability, it is unclear to me if a rare homicide event is effectively concentrated enough to justify hot spots policing. Better than random predictions are not necessarily good enough.

A final metric worth making note of is the Predictive Accuracy Index (PAI). The PAI is often used in evaluating forecast accuracy, see some of the work of Spencer Chainey or Grant Drawve for some examples. The PAI is simply % Crime Captured/% Area, which we have already calculated in our prior graphs. So you want a value much higher than 1.

While those cited examples again use tables with simple cut-offs, you can make a graph like this to show the PAI metric under different numbers of areas, same as the above plots.

The saw-tooth ends up looking very much like a precision-recall curve, but I haven’t sat down and figured out the equivalence between the two as of yet. It is pretty noisy, but we might have two regimes based on this — target around 30 areas for a PAI of 3-5, or target 150 areas for a PAI of 3. PAI values that low are not something to brag to your grandma about though.

There are other stats like the predictive efficiency index (PAI vs the best possible PAI) and the recapture-rate index that you could do the same types of plots with. But I don’t want to put everyone to sleep.

Downloading your PDFs from CiteULike using python and selenium

CiteULike, an online bibliography manager, is unfortunately shutting down. They have a service to export your bibliography as a BibTex file, but this does not include the PDFs you have uploaded to the site. Having web access to the PDFs is one of the main reasons I liked CiteULike (along with the tag cloud).

I have too many PDFs to download them all manually (over 2,000), so I wrote a script in Python to download the PDFs. Unlike prior scraping examples I’ve written about, you need to have signed into your CiteULike account to be able to download the files. Hence I use the selenium library to mimic what you do normally in a web-browser.

So let me know what bibliography manager I should switch to. Really one of the main factors will be if I can automate the conversion, including PDFs (even if it just means pointing to where the PDF is stored on my local machine).

This is a good tutorial to know about even if you don’t have anything to do with CiteULike. There are various web services that you need to sign in or mimic the browser like this to download data repeatedly, such as if a PD has a system where you need to input a set of dates to get back crime incidents (and limit the number returned, so you need to do it repeatedly to get a full sample). The selenium library can be used in a similar fashion to this tutorial in that circumstance.

Weighted buffers in R

Had a request not so recently about implementing weighted buffer counts. The idea behind a weighted buffer is that instead of say counting the number of crimes that happen within 1,000 meters of a school, you want to give events that are closer to the school more weight.

There are two reasons you might want to do this for crime analysis:

  • You want to measure the amount of crime around a location, but you rather have a weighted crime count, where crimes closer to the location have a greater weight than those further away.
  • You want to measure attributes nearby a location (so things that predict crime), but give a higher weight to those closer to a location.

The second is actually more common in academic literature — see John Hipp’s Egohoods, or Liz Groff’s work on measuring nearby to bars, or Joel Caplan and using kernel density to estimate the effect of crime generators. Jerry Ratcliffe and colleagues work on the buffer intensity calculator is actually the motivation for the original request. So here are some quick code snippets in R to accomplish either. Here is the complete code and original data to replicate.

Here I use over 250,000 reported Part 1 crimes in DC from 08 through 2015, 173 school locations, and 21,506 street units (street segment midpoints and intersections) I constructed for various analyses in DC (all from open data sources) as examples.

Example 1: Crime Buffer Intensities Around Schools

First, lets define where our data is located and read in the CSV files (don’t judge me setting the directory, I do not use RStudio!)

MyDir <- 'C:\\Users\\axw161530\\Dropbox\\Documents\\BLOG\\buffer_stuff_R\\Code' #Change to location on your machine!
setwd(MyDir)

CrimeData <- read.csv('DC_Crime_08_15.csv')
SchoolLoc <- read.csv('DC_Schools.csv')

Now there are several ways to do this, but here is the way I think will be most useful in general for folks in the crime analysis realm. Basically the workflow is this:

  • For a given school, calculate the distance between all of the crime points and that school
  • Apply whatever function to that distance to get your weight
  • Sum up your weights

For the function to the distance there are a bunch of choices (see Jerry’s buffer intensity I linked to previously for some example discussion). I’ve written previously about using the bi-square kernel. So I will illustrate with that.

Here is an example for the first school record in the dataset.

#Example for crimes around school, weighted by Bisquare kernel
BiSq_Fun <- function(dist,b){
    ifelse(dist < b, ( 1 - (dist/b)^2 )^2, 0)
    }

S1 <- t(SchoolLoc[1,2:3])
Dis <- sqrt( (CrimeData$BLOCKXCOORD - S1[1])^2 + (CrimeData$BLOCKYCOORD - S1[2])^2 )
Wgh <- sum( BiSq_Fun(Dis,b=2000) )

Then repeat that for all of the locations that you want the buffer intensities, and stuff it in the original SchoolLoc data frame. (Takes less than 30 seconds on my machine.)

SchoolLoc$BufWeight <- -1 #Initialize field

#Takes about 30 seconds on my machine
for (i in 1:nrow(SchoolLoc)){
  S <- t(SchoolLoc[i,2:3])
  Dis <- sqrt( (CrimeData$BLOCKXCOORD - S[1])^2 + (CrimeData$BLOCKYCOORD - S[2])^2 )
  SchoolLoc[i,'BufWeight'] <- sum( BiSq_Fun(Dis,b=2000) )
}

In this example there are 173 schools and 276,621 crimes. It is too big to create all of the pairwise comparisons at once (which will generate nearly 50 million records), but the looping isn’t too cumbersome and slow to worry about building a KDTree.

One thing to note about this technique is that if the buffers are large (or you have locations nearby one another), one crime can contribute to weighted crimes for multiple places.

Example 2: Weighted School Counts for Street Units

To extend this idea to estimating attributes at places just essentially swaps out the crime locations with whatever you want to calculate, ala Liz Groff and her inverse distance weighted bars paper. I will show something alittle different though, in using the weights to create a weighted sum, which is related to John Hipp and Adam Boessen’s idea about Egohoods.

So here for every street unit I’ve created in DC, I want an estimate of the number of students nearby. I not only want to count the number of kids in attendance in schools nearby, but I also want to weight schools that are closer to the street unit by a higher amount.

So here I read in the street unit data. Also I do not have school attendance counts in this dataset, so I just simulate some numbers to illustrate.

StreetUnits <- read.csv('DC_StreetUnits.csv')
StreetUnits$SchoolWeight <- -1 #Initialize school weight field

#Adding in random school attendance
SchoolLoc$StudentNum <- round(runif(nrow(SchoolLoc),100,2000)) 

Now it is very similar to the previous example, you just do a weighted sum of the attribute, instead of just counting up the weights. Here for illustration purposes I use a different weighting function, inverse distance weighting with a distance cut-off. (I figured this would need a better data management strategy to be timely, but this loop works quite fast as well, again under a minute on my machine.)

#Will use inverse distance weighting with cut-off instead of bi-square
Inv_CutOff <- function(dist,cut){
    ifelse(dist < cut, 1/dist, 0)
}

for (i in 1:nrow(StreetUnits)){
    SU <- t(StreetUnits[i,2:3])
    Dis <- sqrt( (SchoolLoc$XMeters - SU[1])^2 + (SchoolLoc$YMeters - SU[2])^2 )
    Weights <- Inv_CutOff(Dis,cut=8000)
    StreetUnits[i,'SchoolWeight'] <- sum( Weights*SchoolLoc$StudentNum )
}   

The same idea could be used for other attributes, like sales volume for restaurants to get a measure of the business of the location (I think more recent work of John Hipp’s uses the number of employees).

Some attributes you may want to do the weighted mean instead of a weighted sum. For example, if you were using estimates of the proportion of residents in poverty, it makes more sense for this measure to be a spatially smoothed mean estimate than a sum. In this case it works exactly the same but you would replace sum( Weights*SchoolLoc$StudentNum ) with sum( Weights*SchoolLoc$StudentNum )/sum(Weights). (You could use the centroid of census block groups in place of the polygon data.)

Some Wrap-Up

Using these buffer weights really just swaps out one arbitrary decision for data analysis (the buffer distance) with another (the distance weighting function). Although the weighting function is more complicated, I think it is probably closer to reality for quite a few applications.

Many of these different types of spatial estimates are all related to another (kernel density estimation, geographically weighted regression, kriging). So there are many different ways that you could go about making similar estimates. Not letting the perfect be the enemy of the good, I think what I show here will work quite well for many crime analysis applications.

My Year Blogging in Review – 2018

The blog continues to grow in site views. I had a little north of 90,000 site views over the entire year. (If you find that impressive don’t be, a very large proportion are likely bots.)

The trend on the original count scale looks linear, but on the log scale the variance is much nicer. So I’m not sure what the best forecast would be.

I thought the demise had already started earlier in the year, as I actually saw the first year-over-year decreases in June and July. But the views recovered in the following months.

So based on that the slow down in growth I think is a better bet than the linear projection.

For those interested in extending their reach, you should not only consider social media and creating a website/blog, but also writing up your work for a more general newspaper. I wrote an article for The Conversation about some of my work on officer involved shootings in Dallas, and that accumulated nearly 7,000 views within a week of it being published.

Engagement in a greater audience is very bursty. Looking at my statistics for particular articles, it doesn’t make much sense to report average views per day. I tend to get a ton of views on the first few days, and then basically nothing after that. So if I do the top posts by average views per day it is dominated by my more recent posts.

This is partly due to shares on Twitter, which drive short term views, but do not impact longer term views as far as I can tell. That is a popular post on Twitter does not appear to predict consistent views being referred via Google searches. In the past year I get a ratio of about 50~1 referrals from Google vs Twitter, and I did not have any posts that had a consistent number of views (most settle in at under 3 views per day after the initial wave). So basically all of my most viewed posts are the same as prior years.

Since I joined Twitter this year, I actually have made fewer blog posts. Not including this post, I’ve made 29 posts in 2018.

2011  5
2012 30
2013 40
2014 45
2015 50
2016 40
2017 35
2018 29

Some examples of substitution are tweets when a paper is published. I typically do a short write up when I post a working paper — there is not much point of doing another one when it is published online. (To date I have not had a working paper greatly change from the published version in content.) I generally just like sharing nice graphs I am working on. Here is an example of citations over time I just quickly published to Twitter, which was simpler than doing a whole blog post.

Since it is difficult to determine how much engagement I will get for any particular post, it is important to just keep plugging away. Twitter can help a particular post take off (see these examples I wrote about for the Cross Validated Blog), but any one tweet or blog post is more likely to be a dud than anything.

Reasons Police Departments Should Consider Collaborating with Me

Much of my academic work involves collaborating and consulting with police departments on quantitative problems. Most of the work I’ve done so far is very ad-hoc, through either the network of other academics asking for help on some project or police departments cold contacting me directly.

In an effort to advertise a bit more clearly, I wrote a page that describes examples of prior work I have done in collaboration with police departments. That discusses what I have previously done, but doesn’t describe why a police department would bother to collaborate with me or hire me as a consultant. In fact, it probably makes more sense to contact me for things no one has previously done before (including myself).

So here is a more general way to think about (from a police departments or criminal justice agencies perspective) whether it would be beneficial to reach out to me.

Should I do X?

So no one is going to be against different evidence based policing practices, but not all strategies make sense for all jurisdictions. For example, while focussed deterrence has been successfully applied in many different cities, if you do not have much of a gang violence problem it probably does not make sense to apply that strategy in your jurisdiction. Implementing any particular strategy should take into consideration the cost as well as the potential benefits of the program.

Should I do X may involve more open ended questions. I’ve previously conducted in person training for crime analysts that goes over various evidence based practices. It also may involve something more specific, such as should I redistrict my police beats? Or I have a theft-from-vehicle problem, what strategies should I implement to reduce them?

I can suggest strategies to implement, or conduct cost-benefit analysis as to whether a specific program is worth it for your jurisdiction.

I want to do X, how do I do it?

This is actually the best scenario for me. It is much easier to design a program up front that allows a police department to evaluate its efficacy (such as designing a randomized trial and collecting key measures). I also enjoy tackling some of the nitty-gritty problems of implementing particular strategies more efficiently or developing predictive instruments.

So you want to do hotspots policing? What strategies do you want to do at the hotspots? How many hotspots do you want to target? Those are examples of where it would make sense to collaborate with me. Pretty much all police departments should be doing some type of hot spots policing strategy, but depending on your particular problems (and budget constraints), it will change how you do your hot spots. No budget doesn’t mean you can’t do anything — many strategies can be implemented by shifting your current resources around in particular ways, as opposed to paying for a special unit.

If you are a police department at this stage I can often help identify potential grant funding sources, such as the Smart Policing grants, that can be used to pay for particular elements of the strategy (that have a research component).

I’ve done X, should I continue to do it?

Have you done something innovative and want to see if it was effective? Or are you putting a bunch of money into some strategy and are skeptical it works? It is always preferable to design a study up front, but often you can conduct pretty effective post-hoc analysis using quasi-experimental methods to see if some crime reduction strategy works.

If I don’t think you can do a fair evaluation I will say so. For example I don’t think you can do a fair evaluation of chronic offender strategies that use officer intel with matching methods. In that case I would suggest how you can do an experiment going forward to evaluate the efficacy of the program.

Mutual Benefits of Academic-Practitioner Collaboration

Often I collaborate with police departments pro bono — which you may ask what is in it for me then? As an academic I get evaluated mostly by my research productivity, which involves writing peer reviewed papers and getting research grants. So money is not the main factor from my perspective. It is typically easier to write papers about innovative problems or programs. If it involves applying for a grant (on a project I am interested in) I will volunteer my services to help write the grant and design the study.

I could go through my career writing papers without collaborating with police departments. But my work with police departments is more meaningful. It is not zero-sum, I tend to get better ideas when understanding specific agencies problems.

So get in touch if you think I can help your agency!

CAN SEBP webcast on predictive policing

I was recently interviewed for a webcast by the Canadian Society of Evidence Based Policing on Predictive Policing.

I am not directly affiliated with any software vendor, so these are my opinions as an outsider, academic, and regular consultant for police departments on quantitative problems.

I do have some academic work on predictive policing applications that folks can peruse at the moment (listed below). The first is on evaluating the accuracy of a people predictions, the second is for addressing the problem of disproportionate minority contact in spatial predictive systems.

  • Wheeler, Andrew P., Robert E. Worden, and Jasmine R. Silver. (2018) The predictive accuracy of the Violent Offender Identification Directive (VOID) tool. Conditionally accepted at Criminal Justice and Behavior. Pre-print available here.
  • Wheeler, Andrew P. (2018) Allocating police resources while limiting racial inequality. Pre-print available here.

I have some more work on predictive policing applications in the pipeline, so just follow the blog or follow me on Twitter for updates about future work.

If police departments are interested in predictive policing applications and would like to ask me some questions, always feel free to get in contact. (My personal email is listed on my CV, my academic email is just Andrew.Wheeler at utdallas.edu.)

Most of my work consulting with police departments is ad-hoc (and much of it is pro bono), so if you think I can be of help always feel free to get in touch. Either for developing predictive applications or evaluating whether they are effective at achieving the outcomes you are interested in.

Monitoring Use of Force in New Jersey

Recently ProPublica published a map of uses-of-force across different jurisdictions in New Jersey. Such information can be used to monitor whether agencies are overall doing a good or bad job.

I’ve previously discussed the idea of using funnel charts to spot outliers, mostly around homicide rates but the idea is the same when examining any type of rate. For example in another post I illustrated its use for examining rates of officer involved shootings.

Here is another example applying it to lesser uses of force in New Jersey. Below is the rate of use of force reports per the total number of arrests. (Code to replicate at the end of the post.)

The average use of force per arrests in the state is around 3%. So the error bars show relative to the state average. Here is an interactive chart in which you can use tool tips to see the individual jurisdictions.

Now the original press release noted by Seth Stoughton on twitter noted that several towns have ratio’s of black to white use of force that are very high. Scott Wolfe suspected that was partly a function of smaller towns will have more variable rates. Basically as one is comparing the ratio between two rates with error, the error bars around the rate ratio will also be quite large.

Here is the chart showing the same type of funnel around the rate ratio of black to white use-of-force relative to the average over the whole sample (the black percent use of force is 3.2 percent of arrests, and the white percent use of force is 2.4, and the rate ratio between the two is 1.35). I show in the code how I constructed this, which I should write a blog post about itself, but in short there are decisions I could make to make the intervals wider. So the points that are just slightly above a ratio of 2 at around 10,000 arrests are arguably not outliers, those more to the top-right of the plot though are much better evidence. (I’d note that if one group is very small, you could always make these error bars really large, so to construct them you need to make reasonable assumptions about the size of the two groups you are comparing.)

And here is another interactive chart in which you can view the outliers again. The original press release, Millville, Lakewood, and South Orange are noted as outliers. Using arrests as the denominator instead of population, they each have a rate ratio of around 2. In this chart Millville and Lakewood are outside the bounds, but just barely. South Orange is within the bounds. So those aren’t the places I would have called out according to this chart.

That same twitter thread other folks noted the potential reliability/validity of such data (Pete Moskos and Kyle McLean). These charts cannot say why individual agencies are outliers — either high or low. It could be their officers are really using force at different rates, it could also be though they are using different definitions to reporting force. There are also potential other individual explanations that explain the use of force distribution as well as the ratio differences in black vs white — no doubt policing in Princeton vs Camden are substantively different. Also even if all individual agencies are doing well, it does not mean there are no potential problem officers (as noted by David Pyrooz, often a few officers contribute to most UoF).

Despite these limitations, I still think there is utility in this type of monitoring though. It is basically a flag to dig deeper when anomalous patterns are spotted. Those unaccounted for factors contribute to more points being pushed outside of my constructed limits (overdispersion), but more clearly indicate when a pattern is so far outside the norm of what is expected the public deserves some explanation of the pattern. Also it highlights when agencies are potentially doing good, and so can be promoted according to their current practices.

This is a terrific start to effectively monitoring police agencies by ProPublica — state criminal justice agencies should be doing this themselves though.

Here is the code to replicate the analysis.

Projecting spatial data in Python and R

I use my blog as sort of a scholarly notebook. I often repeatedly do a task, and then can’t find where I did it previously. One example is projecting crime data, so here are my notes on how to do that in python and R.

Commonly I want to take public crime data that is in spherical lat/lon coordinates and project it to some local projection. Most of the time so I can do simply euclidean geometry (like buffers within X feet, or distance to the nearest crime generator in meters). Sometimes you need to do the opposite — if I have the projected data and I want to plot the points on a webmap it is easier to work with the lat/lon coordinates. As a note, if you import your map data and then your points are not on the map (or in a way off location), there is some sort of problem with the projection.

I used to do this in ArcMap (toolbox -> Data Management -> Projections), but doing it these programs are faster. Here are examples of going back and forth for some Dallas coordinates. Here is the data and code to replicate the post.

Python

In python there is a library pyproj that does all the work you need. It isn’t part of the default python packages, so you will need to install it using pip or whatever. Basically you just need to define the to/from projections you want. Also it always returns the projected coordinates in meters, so if you want feet you need to do a conversions from meters to feet (or whatever unit you want). For below p1 is the definition you want for lat/lon in webmaps (which is not a projection at all). To figure out your local projection though takes a little more work.

To figure out your local projection I typically use this online tool, prj2epsg. You can upload a prj file, which is the locally defined projection file for shapefiles. (It is plain text as well, so you can just open in a text editor and paste into that site as well.) It will then tell you want EPSG code corresponds to your projection.

Below illustrates putting it all together and going back and forth for an example area in Dallas. I tend to write the functions to take one record at a time for use in various workflows, but I am sure someone can write a vectorized version though that will take whole lists that is a better approach.

import pyproj

#These functions convert to/from Dallas projection
#In feet to lat/lon
p1 = pyproj.Proj(proj='latlong',datum='WGS84')
p2 = pyproj.Proj(init='epsg:2276') #show how to figure this out, http://spatialreference.org/ref/epsg/ and http://prj2epsg.org/search 
met_to_feet = 3.280839895 #http://www.meters-to-feet.com/

#This converts Lat/Lon to projected coordinates
def DallConvProj(Lat,Lon):
    #always returns in meters
    if abs(Lat) > 180 or abs(Lon) > 180:
        return (None,None)
    else:
        x,y = pyproj.transform(p1, p2, Lon, Lat)
        return (x*met_to_feet, y*met_to_feet)

#This does the opposite, coverts projected to lat/lon
def DallConvSph(X,Y):
    if abs(X) < 2000000 or abs(Y) < 6000000:
        return (None,None)
    else:
        Lon,Lat = pyproj.transform(p2, p1, X/met_to_feet, Y/met_to_feet)
        return (Lon, Lat)

#check coordinates
x1 = -96.828295; y1 = 32.832521
print DallConvProj(Lat=y1,Lon=x1)

x2 = 2481939.934525765; y2 = 6989916.200679892
print DallConvSph(X=x2, Y=y2)

R

In R I use the library proj4 to do the projections for point data. R can read in the projection data from a file as well using the rgdal library.

library(proj4)
library(rgdal)

#read in projection from shapefile
MyDir <- "C:\\Users\\axw161530\\Dropbox\\Documents\\BLOG\\Projections_R_Python"
setwd(MyDir)
DalBound <- readOGR(dsn="DallasBoundary_Proj.shp",layer="DallasBoundary_Proj")
DalProj <- proj4string(DalBound)    

ProjData <- data.frame(x=c(2481939.934525765),
                       y=c(6989916.200679892),
                       lat=c(32.832521),
                       lon=c(-96.828295))
       
LatLon <- proj4::project(as.matrix(ProjData[,c('x','y')]), proj=DalProj, inverse=TRUE)
#check to see if true
cbind(ProjData[,c('lon','lat')],as.data.frame(LatLon))

XYFeet <- proj4::project(as.matrix(ProjData[,c('lon','lat')]), proj=DalProj)
cbind(ProjData[,c('x','y')],XYFeet)    

plot(DalBound)
points(ProjData$x,ProjData$y,col='red',pch=19,cex=2)

The last plot function shows that the XY point is within the Dallas basemap for the projected boundary. But if you want to project the boundary file as well, you can use the spTransform function. Here I have a simple example of tacking the projected boundary file and transforming to lat/lon, so can be superimposed on a leaflet map.

Additionally I show a trick I sometimes use for maps by transforming the boundary polygon to a polyline, as it provides easier styling options sometimes.

#transform boundary to lat/lon
DalLatLon <- spTransform(DalBound,CRS("+init=epsg:4326") )
plot(DalLatLon)
points(ProjData$lon,ProjData$lat,col='red',pch=19,cex=2)

#Leaflet useful for boundaries to be lines instead of areas
DallLine <- as(DalLatLon, 'SpatialLines')
library(leaflet)

BaseMapDallas <- leaflet() %>%
  addProviderTiles(providers$OpenStreetMap, group = "Open Street Map") %>%
  addProviderTiles(providers$CartoDB.Positron, group = "CartoDB Lite") %>%
  addPolylines(data=DallLine, color='black', weight=4, group="Dallas Boundary Lines") %>%
  addPolygons(data=DalLatLon,color = "#1717A1", weight = 1, smoothFactor = 0.5,
              opacity = 1.0, fillOpacity = 0.5, group="Dallas Boundary Area") %>%
  addLayersControl(baseGroups = c("Open Street Map","CartoDB Lite"),
                   overlayGroups = c("Dallas Boundary Area","Dallas Boundary Lines"),
                   options = layersControlOptions(collapsed = FALSE)) %>%
                   hideGroup("Dallas Boundary Lines")   
                      
BaseMapDallas

I have too much stuff in the blog queue at the moment, but hopefully I get some time to write up my notes on using leaflet maps in R soon.