New working paper: The effect of housing demolitions on crime in Buffalo, New York

I have a new working paper up, The effect of housing demolitions on crime in Buffalo, New York. This is in conjunction with my colleagues Dae-Young Kim and Scott Phillips, who are at SUNY Buffalo. Below is the abstract.

Objectives: From 2010 through 2015, the city of Buffalo demolished over 2,000 residences. This study examines whether those demolitions resulted in crime reductions.

Methods: Analysis was conducted at micro places matching demolished parcels to comparable control parcels with similar levels of crime. In addition, spatial panel regression models were estimated at the census tract and quarterly level, taking into account demographic characteristics of neighborhoods.

Results: We find that at the micro place level, demolitions cause a steep drop in reported crime at the exact parcel, and result in additional crime decreases at buffers of up to 1,000 feet away. At the census tract level, results indicated that demolitions reduced Part 1 crimes, but the effect was not statistically significant across different models.

Conclusions: While concerns over crime and disorder are common for vacant houses, the evidence that housing demolitions are an effective crime reduction solution is only partially supported by the analyses here. Future research should compare demolitions in reference to other neighborhood revitalization processes.

As always, if you have feedback/comments let me know.

And here are a few maps from the paper!

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Paper: The Effect of 311 Calls for Service on Crime in D.C. at Microplaces published

My paper, The Effect of 311 Calls for Service on Crime in D.C. at Microplaces, was published online first at Crime & Delinquency. Here is the link to the published paper. If you do not have access to a library where you can get the paper always feel free to email and I will send an off-print. But I also have the pre-print posted on SSRN. Often the only difference between my pre-prints and the finished version is the published paper is shorter!

As a note, I’ve also posted all of the data and code to replicate my findings. The note is unfortunately buried at the end of the paper, instead of the beginning.

This was the first paper published from my dissertation. I have pre-prints out for two others, What we can learn from small units and Local and Spatial Effect of Bars. Hopefully you will see those two in print the near future as well!

New working paper: Choosing Representatives to Deliver the Message in a Group Violence Intervention

I have a new preprint up on SSRN, Choosing Representatives to Deliver the Message in a Group Violence Intervention. This is what I will be presenting at ACJS next Friday the 24th. Here is the abstract:

Objectives: The group based violence intervention model is predicated on the assumption that individuals who are delivered the deterrence message spread the message to the remaining group members. We focus on the problem of who should be given the initial message to maximize the reach of the message within the group.

Methods: We use social network analysis to create an algorithm to prioritize individuals to deliver the message. Using a sample of twelve gangs in four different cities, we identify the number of members in the dominant set. The edges in the gang networks are defined by being arrested or stopped together in the prior three years. In eight of the gangs we calculate the reach of observed call-ins, and compare these with the sets defined by our algorithm. In four of the gangs we calculate the reach for a strategy that only calls-in members under supervision.

Results: The message only needs to be delivered to around 1/3 of the members to reach 100% of the group. Using simulations we show our algorithm identifies the minimal dominant set in the majority of networks. The observed call-ins were often inefficient, and those under supervision could be prioritized more effectively.

Conclusions: Group based strategies should monitor their potential reach based on who has been given the message. While only calling-in those under supervision can reach a large proportion of the gang, delivering the message to those not under supervision will likely be needed to reach 100% of the group.

And here is an image of the observed reach for one of the gang networks using both call-ins and custom notifications.

The paper has the gang networks available at this link, and uses Python to do the network analysis and SPSS to draw the graphs.

If you are interested in applying this to your work let me know! Not only do I think this is a good idea for focused deterrence initiatives for criminal justice agencies, but I think the idea can be more widely applied to other fields in social sciences, such as public health (needle clean/dirty exchange programs) or organizational studies (finding good leaders in an organization to spread a message).

Paper – Replicating Group Based Trajectory Models of Crime at Micro-Places in Albany, NY published

My article on estimating crime trajectories in Albany from 2000 through 2014 has been published in the latest issue of JQC.

That link is permanent, but Springer gifts me a temporary free pdf link for everyone for up to four weeks. So grab that if you are interested.

Also note though that I have the pre-print posted on SSRN. Since that is Albany PD’s data, I cannot provide code to replicate the analysis. But, I have produced a series of blog posts showing to to replicate the trajectory and the point pattern analysis on your own data if you are interested, see

Here is the cross Ripley’s L plot testing for clustering between the different trajectory groupings.

Also always feel free to send me an email if you have questions about the findings and paper.

ASC 2016 – Quantifying the Local and Spatial Effects of Alcohol Outlets on Crime

This year at the American Society of Criminology I will be presenting some work from my dissertation, Quantifying the Local and Spatial Effects of Alcohol Outlets on Crime. I have the working paper posted on SSRN, and that also has a link to download data and code to reproduce the findings in the paper.

I will be presenting at the panel Alcohol and Crime on Wednesday at 9:30 (at the Cambridge room on the 2nd level).

Here is the abstract:

This paper estimates the relationship between alcohol outlets and crime at micro place street units in Washington, D.C. Three specific additions to this voluminous literature are articulated. First, the diffusion effect of alcohol outlets is larger than the local effect. This has important implications for crime prevention. The second is that in this sample the effects of on-premise and off-premise outlets are very similar in magnitude. I argue this is evidence in favor of routine activities theory, in opposition to theories which emphasize individual alcohol consumption. The final is that alcohol outlets have large effects on burglary, despite the fact that alcohol outlets cannot increase the number of vulnerable targets, as it can with interpersonal crimes. I discuss how this can either be interpreted as evidence that alcohol outlets self-select into already crime prone areas, or potentially that the presence of motivated offenders’ matters much more than increasing the number of potential victims.

The most interesting finding is the fact that I estimate the diffusion effect of alcohol outlets is larger than the local effect. I then show that this is the case for some other papers as well, it is just interpreting the regression model is tricky. Here is a diagram showing what happens. The idea is the regression coefficient for the spatial lag is one orange dot, and the local effect is the blue dot. Adding a bar though diffuses to multiple places, so when adding up all the smaller orange dots, they result in more crime than the one bigger blue dot.

Roadblocks in Buffalo update (plus more complaints about peer-review!)

I’ve updated the roadblocks in Buffalo manuscript due to a rejection and subsequent critiques. So be prepared about my complaints of the peer-review!

I’ve posted the original manuscript, reviews and a line-by-line response here. This was reviewed at Policing: An International Journal of Police Strategies & Management. I should probably always do this, but I felt compelled to post this review by the comically negative reviewer 1 (worthy of an article on The Allium).

The comment of reviewer 1 that really prompted me to even bother writing a response was the critique of the maps. I spend alot of time on making my figures nice and understandable. I’m all ears if you think they can be improved, but best be prepared for my response if you critique something silly.

So here is the figure in question – spot anything wrong?

The reviewer stated it did not have legend, so it does not meet "GIS standards". The lack of a legend is intentional. When you open google maps do they have a legend? Nope! It is a positive thing to make a graphic simple enough that it does not need a legend. This particular map only has three elements: the outline of Buffalo, the streets, and the points where the roadblocks took place. There is no need to make a little box illustrating these three things – they are obvious. The title is sufficient to know what you are looking at.

Reviewer 2 was more even keeled. The only thing I would consider a large problem in their review was they did not think we matched comparable control areas. If true I agree it is a big deal, but I’m not quite sure why they thought this (sure balance wasn’t perfect, but it is pretty close across a dozen variables). I wouldn’t release the paper if I thought the control areas were not reasonable.

Besides arbitrary complaints about the literature review this is probably the most frustrating thing about peer-reviews. Often you will get a list of two dozens complaints, with most being minor and fixable in a sentence (if not entirely arbitrary), but the article will still be rejected. People have different internal thresholds for what is or is not publishable. I’m on the end that even with worts most of the work I review should still be published (or at least the authors given a chance to respond). Of the 10 papers I’ve reviewed, my record is 5 revise-and-resubmits, 4 conditional accepts, and 1 rejection. One of the revise-and-resubmits I gave a pretty large critique of (in that I didn’t think it was possible to improve the research design), but the other 4 would be easily changed to accept after addressing my concerns. So worst case scenario I’ve given the green light to 8/10 of the manuscripts I’ve reviewed.

Many reviewers are at the other end though. Sometimes comically so, in that given the critiques nothing would ever meet their standards. I might call it the golden-cow peer review standard.

Even though both of my manuscripts have been rejected from PSM, I do like their use of a rubric. This experience makes me wonder what if the reviewers did not give a final reject-accept decision – just the editors took the actual comments and made their own decision. Editors do a version of this currently, but some are known to reject if any of the reviewers give a rejection no matter what the reviewers actually say. It would force the editor to use more discretion if the reviewers themselves did not make the final judgement. It also forces reviewers to be more clear in their critiques. If they are superficial the editor will ignore them, whereas the final accept-reject is easy to take into account even if the review does not state any substantive critiques.

I don’t know if I can easily articulate what I think is a big deal and what isn’t though. I am a quant guy, so the two instances I rejected were for model identification in one and for sample selection biases in the other. So things that could not be changed essentially. I haven’t read a manuscript that was so poor I considered it to be unsalvagable in terms of writing. (I will do a content analysis of reviews I’ve recieved sometime, but almost all complaints about the literature review are arbitrary and shouldn’t be used as reasons for rejection.)

Often times I write abunch of notes on the paper manuscript my first read, and then when I go to write up the critique specifically I edit them out. This often catches silly initial comments of mine, as I better understand the manuscript. Examples of silly comments in the reviews of the roadblock paper are claiming I don’t conduct a pre-post analysis (reviewer 1), and asking for things already stated in the manuscript (reviewer 2 asking for how long the roadblocks were and whether they were "high visibility"). While it is always the case things could be explained more clearly, at some point the reviewer(s) needs to be more careful in their reading of the manuscript. I think my motto of "be specific" helps with this. Being generic helps to conceal silly critiques.

Preprint – A Quasi-Experimental Evaluation Using Roadblocks and Automatic License Plate Readers to Reduce Crime in Buffalo, NY

I have a new preprint article posted on SSRN – A Quasi-Experimental Evaluation Using Roadblocks and Automatic License Plate Readers to Reduce Crime in Buffalo, NY. This is some work I have been conducting with Scott Phillips out at SUNY Buffalo (as well as Dae-Young Kim, although he is not on this paper).

Here is the abstract:

Purpose: To evaluate the effectiveness of a hot spots policing strategy: using automated license plate readers at roadblocks.

Design: Different roadblock locations were chosen by the Buffalo Police Department every day over a two month period. We use propensity score matching to identify a set of control locations based on prior counts of crime and demographic factors before the intervention took place. We then evaluate the reductions in Part 1 crimes, calls for service, and traffic accidents at roadblock locations compared to control locations.

Findings: We find modest reductions in Part 1 violent crimes (10 over all roadblock locations and over the two months) using t-tests of mean differences. We find a 20% reduction in traffic accidents using fixed effects negative binomial regression models. Both results are sensitive to the model used though, and the fixed effects models predict increases in crimes due to the intervention.

Research Limitations: The main limitations are the quasi-experimental nature of the intervention, the short length of the intervention, and that many micro places have low baseline counts of crime.

Originality/Value: This adds to literature on hot spots policing – in particular on the use of automated license plate readers and traffic enforcement at hot spots of crime. While the results are mixed, it provides some evidence that the intervention has potential to reduce crime.

And here is one figure from the paper, showing how street units are defined, and given the intersection the road block was stationed on how we determined the treated street units:

Feedback is always welcome!

Testing day-of-week crime randomness paper published

My paper, Testing Serial Crime Events for Randomness in Day of Week Patterns with Small Samples, was recently published in the Journal of Investigative Pyschology and Offender Profiling. Here is the pre-print version on SSRN if you can’t get access to that journal.

The main idea behind the paper was if you had a series of a few crime events that you know are linked to the same offender, can we tell if those patterns are random with respect to the day of the week? We know spatial patterns are often clustered, but police responses such as surveillance are conditioned not only on a spatial location, but take place during certain days and times. I wanted to know when I could go to command staff and say, yeah you should BOLO on Saturday. Or just as importantly say in response, no the observed patterns could easily happen if the offender were just randomly picking days.

In the paper I show that if you have only 3 events and they all occur on the same day, you would reject the null that crimes have an equal probability across all seven days of the week at a p-value of less than 0.05. I also show that the exact test I propose has pretty good power for as few as 8 events in the series. So if you have, say 10 events and you fail to reject the null that each day of the week has equal probability of being chosen, it is pretty good evidence that a police response should not have any preference for a particular day.

To illustrate how one would use the test, I have a simple spreadsheet posted here (in the zip file has my other SPSS code to reproduce the results in the paper) in which you can type in the days of the week that the crimes are occurring on, and it calculates the hypothesis test.

The spreadsheet contains both the G-test and Kuiper’s V test. If you don’t read the paper and understand the difference, just use the G-test and ignore the Kuiper’s V results. For crime analysts, this is basically the minimum of what you need to know.


For analysts who are more into the nitty gritty, I also have some R code that is a bit more flexible, and calculates the exact test for varying numbers of bins and provides some code to conduct power analysis. So you can either download the code from GitHub and insert it to define the functions, or simply copy-paste it into the console. The only library dependency is the partitions library, so make sure that is installed before following along.

So if you have downloaded the code, you can use something like below to insert the functions and load the partitions library.

library(partitions)
mydir <- "C:\\Users\\andrew.wheeler\\Dropbox\\Documents\\BLOG\\ExactTest_Weekdays"
setwd(mydir)
source("Exact_Dist.R")

Now, say you had a series of crimes that had 4 on Saturday, 3 on Tuesday, and 1 on Sunday. You can test this for randomness by simply using:

crime <- c(1,0,3,0,0,0,4)
res <- SmallSampTest(d=crime)
res

Which prints at the console:

Small Sample Test Object 
Test Type is G 
Statistic is 15.5455263389754 
p-value is:  0.0182662  
Data are:  1 0 3 0 0 0 4 
Null probabilities are:  0.14 0.14 0.14 0.14 0.14 0.14 0.14 
Total permutations are:  3003  

This defaults to using the likelihood ratio G-test, but you can also use Kuiper’s V, the chi-square test, or the Komolgrov-Smirnov test. Also you can change the null hypothesis to not equal probability in the bins. I default to the G-test in my paper because it is more powerful than the more typical chi-square after 8 crimes for 7 day-of-week bins, but equal in power to the chi-square for smaller sample sizes. So to do the chi-square test on the same data, use:

resChi <- SmallSampTest(d=crime, type="Chi")
resChi
chisq.test(crime) #for comparison to base R 
chisq.test(crime, simulate.p.value = TRUE, B = 10000)

Which you can see the test statistic mimics base R’s chisq.test, and the p-value is slightly higher than the asymptotic p-value (the exact test should always have a higher p-value than the asympotic distribution, and here it is lower than the simulated p-value). This situation the simulation approach would have been fine. I prefer the exact approach when feasible though, because it is exact, and you don’t need to worry about convergence for the simulation (which most everyone simply picks a large number and hopes for the best).

I’ve also made some code that allows for easy evaluation of the power of the exact test. Coding wise it was easiest to simply use the original object created with the test, so I know it invites post-hoc power analysis – forgive me for my slothness in coding practices. So say you wanted to do apriori power analysis with the Kuiper’s V test for 10 bins and 15 observations (so over 1.3 million permutations, i.e. n <- 15; m <- 10; choose(n+m-1,m-1)). You can simply make an original object (with any observed values across the bins).

test10_data <- c(15,rep(0,9))
test10_perm <- SmallSampTest(d=test10_data, type="KS")
#takes around a minute

The default null is equal probability across the bins, and to do a power analysis you have to specify an alternative. Lets say for the alternative there is equal probability in 5 of the bins, and zero probability in the other 5. (Most of the work is done in making the original permutation object, the power analysis is quite fast, hence why I coded it to work this way.)

p_alt <- c(rep(1/5,5),rep(0,5))
Pow_test <- PowAlt(SST=test10_perm,p_alt=p_alt)
Pow_test

This prints out at the console:

Power for Small Sample Test 
Test statistic is: KS  
Power is: 0.1822815  
Null is: 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1  
Alt is: 0.2 0.2 0.2 0.2 0.2   0   0   0   0   0  
Alpha is: 0.05  
Number of Bins: 10  
Number of Observations: 15  

So for this alternative there is quite low power, only 0.18. But if we change it to only have mass in four of the bins, the power goes way up to over 0.99.

> p_alt2 <- c(rep(1/4,4),rep(0,6))
> Pow_test2 <- PowAlt(SST=test10_perm,p_alt=p_alt2)
> Pow_test2
Power for Small Sample Test 
Test statistic is: KS  
Power is: 0.9902265  
Null is: 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1  
Alt is: 0.25 0.25 0.25 0.25   0   0   0   0   0   0  
Alpha is: 0.05  
Number of Bins: 10  
Number of Observations: 15 

So this shows how the exact test R code can be extended beyond just 7 day-of-week bins. I have not done really any exploration of the power of the KS test or differing numbers of bins though.

New working paper: What We Can Learn from Small Units of Analysis

I’ve posted a new working paper, What We Can Learn from Small Units of Analysis to SSRN. This is a derivative of my dissertation (by the same title). Below is the abstract:

This article provides motivation for examining small geographic units of analysis based on a causal logic framework. Local, spatial, and contextual effects are confounded when using larger units of analysis, as well as treatment effect heterogeneity. I relate these types of confounds to all types of aggregation problems, including temporal aggregation, and aggregation of dependent or explanatory variables. Unlike prior literature critiquing the use of aggregate level data, examples are provided where aggregation is unlikely to hinder the goals of the particular research design, and how heterogeneity of measures in smaller units of analysis is not a sufficient motivation to examine small geographic units. Examples of these confounds are presented using simulation with a dataset of crime at micro place street units (i.e. street segments and intersections) in Washington, D.C.

As always, if you have comments or critiques let me know.

Cartography and GIS special issue on Crime Mapping

My paper, Visualization techniques for journey to crime flow data, has been recently published in a special issue in CaGIS on crime mapping. Always feel free to email me for off-prints of published papers, but the pre-print of this one I posted on SSRN as well.

There is an annoying error that crept into the paper, in that the footnote linking to the results to replicate the maps and graphs says "REDACTED FOR ANONYMITY" – which is my fault for not pointing it out to the copy-editor. The files are available here. They are certainly not easy to walk through, so if you want help replicating any of the maps for your own data and can’t figure out my code feel free to send me an email. I would like to make an R package to make maps like below eventually, but that is just not going to happen in the forseeable future.