CrimRxiv, Alt-Journal Contributions, and Mike Maltz’s Retrospective

As I’m sure followers of mine know, I am a big proponent of posting pre-prints. Spearheaded by Scott Jacques, he has started a specifically criminology focused pre-print server title CrimRxiv. It is still in beta but anyone can contribute a paper if they want.

One of the things me and Scott have been jamming about is how to leverage crimrxiv to make a journal that not only takes advantage of all the goodies on the internet, such as being able to embed interactive graphics or other rich media directly in a journal articles. But to really widen the scope of what ‘counts’ in terms of scholarly contribution. Why can’t things like a cool app, or a really good video lecture you edited, or a blog post illustrating code be put on the same level with journal articles?

Part of the reason I am writing this blog post is that I saw Michael Maltz recently publish a retrospective on his career on Academia.edu. This isn’t a typical journal article, but despite that there is no reason why you shouldn’t share such pieces. So I was able to convince Mike to post A Retrospective Look at My Professional Life to crimrxiv. When he first posted it on Academia.edu here was my response on how Mike (despite never having crossed paths) has influenced my career.


Hi Michael and thank you for sharing,

I’ve followed your work since a grad student at Albany. I initially got hooked on data viz based on Tufte’s book. When I looked for examples of criminologists discussing data viz you were the only one I found. That was sometime around 2010, so you had that chapter in the handbook of quantitative crim. You also had another article about drawing glyphs to illustrate life course transitions I was familiar with.

When I finished my classes at SUNY, I then worked at Troy as a crime analyst while finishing my dissertation. I doubt any of the coffee shops were the same from your time, but I did like walking over to Famous hotdogs for lunch every now and then.

Most of my work at the PD was making time series graphs and maps. No regression, so most of my stats training was not particularly useful. Even my mapping course I took focused on areal data analysis was not terribly relevant.

I tried to do similar projects to your glyph life-courses with interval censored crime data, but I was never really successful with that, they always ended up being too complicated with even moderately large crime datasets, see https://andrewpwheeler.com/2013/02/28/interval-graph-for-viz-temporal-overlap-in-crime-events/ and https://andrewpwheeler.com/2014/10/02/stacking-intervals/ for my attempts.

What was much more helpful was simply doing monitoring metrics over time, simple running means, and then I just inverted the PDF of the Poisson to give error bars, e.g. https://andrewpwheeler.com/2016/06/23/weekly-and-monthly-graphs-for-monitoring-crime-patterns-spss/. Then cases that were outside the error bands signified an anomalous pattern. In Troy there was an arrest of a single prolific person breaking into cars, and the trend went from a creeping 10 year high to a 10 year low instantly in those graphs.

So there again we have your work on the Poisson distribution and operations research in that JQC article. Also sometime in there I saw a comment you made on Andrew Gelman’s blog pointing to your work with error bands for BJS. Took that ‘fan chart’ idea later on and provided error bands for city level and USA level homicide trends, e.g. https://apwheele.github.io/MathPosts/FanChart_NewOrleans.html. Most of popular discussion of large scale crime trends is misguided over-interpreting short term noise in my opinion.

So all my degrees are in criminal justice, but I have been focusing more on linear programming over time borrowing from operations researchers as well, https://andrewpwheeler.com/2020/05/29/an-intro-to-linear-programming-for-criminologists/. I’ve found that taking outputs from a predictive model and then applying a decision analysis to specifically articulate strategies CJ agencies should take is much more fruitful than the typical way academic research is done.

Thank you again for sharing your story and best, Andy Wheeler

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