All posts in category scholarly

Music and distractions in the workplace

I was recently re-reading Zen and the Art of Motorcycle Maintenance, and it re-reminded me of why I do not like to listen to music in the workplace. The thesis in Pirsig’s book (in regards to listening to music) is simple; you can’t concentrate entirely on the task at hand if you have music distracting you. So those who value their work tend to not have idle distractions like music playing (and be all engrossed in their work).

I have worked in various shared workspaces (cubicles and shared offices) for quite a while now, and I do have a knack for going off into space and ignoring all of the background noise around me. But I still do not like listening to music, even though I have learned to cope with the situation. At this point I prefer the open office workspace, as there at least is no illusion of privacy. When I worked at a cubicle someone coming behind me and scaring me was basically a daily thing.

Scott Adams, the artist of the Dilbert comic, had a recent blog post saying that music is the lesser evil compared to constant distractions via the internet (email, facebook, twitter, etc.) This I can understand as well, and sometimes I turn off the wi-fi to try to get work done without distraction. I don’t see how turning on music helps, but given its prevalence it may just be differences between myself and other people. I should probably turn off the wi-fi for all but an hour in the morning and an hour in the afternoon everyday, but I’m pretty addicted to the internet at this point.

It partly depends on the task I am currently working on though how easily I am distracted. Sometimes I can get really engrossed in a particular problem and become obsessed with it to the point you could probably set the office on fire and I wouldn’t notice. For example this programming problem dominated my thoughts for around two days, and I ended up thinking of the general solution while I did not have access to the computer (while I was waiting for my car to get inspected). Most of the time though I can only give that type of concentration for an hour or two a day though, and the rest of the time I am working in a state of easy distraction.

Background music I don’t like, and other ambient noises I can manage to drown out, but background TV drives me crazy. My family was watching videos (on TV and tablets) the other day while I was reading Zen and ironically I became angry, because I was really into the book and wanted to give it my full concentration. I know people who watch TV in bed to go to sleep, and it is giving me a headache just thinking about it while I am writing this blog post.

I highly recommend both Zen and the Art of Motorcycle Maintenance and Scott Adam’s blog. I’m glad I revisited Zen, as it is an excellent philosophical book on the logic of science that did not make much of an impression on me as an undergrad, but I have a much better grasp of it after having my PhD and reading some other philosophy texts (like Popper).

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by Andy Wheeler on August 26, 2015  •  Permalink
Posted in Personal Productivity, scholarly, writing
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Posted by Andy Wheeler on August 26, 2015

https://andrewpwheeler.com/2015/08/26/music-and-distractions-in-the-workplace/

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.

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by Andy Wheeler on July 3, 2015  •  Permalink
Posted in Papers, scholarly
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Posted by Andy Wheeler on July 3, 2015

https://andrewpwheeler.com/2015/07/03/new-working-paper-what-we-can-learn-from-small-units-of-analysis/

Tables and Graphs paper rejection/update – and on the use of personal pronouns in scientific writing

My paper, Tables and Graphs for Monitoring Temporal Crime Patterns was recently rejected from Policing: An International Journal of Police Strategies & Management. I’ve subsequently updated the SSRN draft based on feedback from the review, and here I post the reviews and my responses to those reviews (in the text file).

One of the main critiques by both reviewers was that the paper was too informal, mainly because of the use of "I" in the paper. I use personal pronouns in writing intentionally, despite typical conventions in scientific writing, so I figured a blog post about why I do this is in order. I’ve been criticized for it on other occasions as well, but this is the first time it was listed as a main reason to reject an article of mine.

My main motivation comes from Michael Billig’s book Learn to Write Badly: How to Succeed in the Social Sciences (see a prior blog post I wrote on the contents). In a nut-shell, when you use personal pronouns it is clear that you, the author, are doing something. When you rewrite the sentence to avoid personal pronouns, you often obfuscate who the actor is in a particular sentence.

For an example of Billig’s point that personal pronouns can be more informative, I state in the paper:

I will refer to this metric as a Poisson z-score.

I could rewrite this sentence as:

This metric will be referred to as a Poisson z-score.

But that is ambiguous as to its source. Did someone else coin this phrase, and I am borrowing it? No – it is a phrase I made up, and using the personal pronoun clearly articulates that fact.

Pretty much all of the examples where I eliminated first person in the updated draft were of the nature,

In this article I discuss the use of percent change in tables.

which I subsequently changed to:

This article discusses the use of percent changes as a metric in tables.

Formal I suppose, but insipid. All rewriting the sentence to avoid the first person pronoun does is make the article seem like a sentient being, as well as forces me to use the passive tense. I don’t see how the latter is better in any way, shape, or form – yet this is one of the main reasons my paper is rejected above. The use of "we" in academic articles seems to be more common, but using "we" when there is only one author is just silly. So I will continue to use "I" when I am the only author.

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by Andy Wheeler on June 24, 2015  •  Permalink
Posted in Crime Analysis, scholarly
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Posted by Andy Wheeler on June 24, 2015

https://andrewpwheeler.com/2015/06/24/tables-and-graphs-paper-rejectionupdate-and-on-the-use-of-personal-pronouns-in-scientific-writing/

Favorite maps and graphs in historical criminology

I was reading Charles Booth’s Life and Labour of the People in London (available entirely at Google books) and stumbled across this gem of a connected dot plot (between pages 18-19, maybe it came as a fold out in the book?)

(You will also get a surprise of the hand of the scanner in the page prior!) This reminded me I wanted to make a collection of my favorite historical examples of maps and graphs for criminology and criminal justice. If you read through Calvin Schmid’s Handbook of Graphical Presentation (available for free at the internet archive) it was a royal pain to create such statistical graphics by hand before computers. It makes you appreciate the effort all that much more, and many of the good ones will rival the quality of any graphic you can make on the computer.

Calvin Schmid himself has some of my favorite example maps. See for instance this gem from Urban Crime Areas: Part II (American Sociological Review, 1960):

The most obvious source of great historical maps in criminology though is from Shaw and McKay’s Juvenile Delinquency in Urban Areas. It was filled with incredible graphs and maps throughout. Here are just a few examples. (These shots are taken from the second edition in 1969, but they are all from the first part of the book, so were likely in the 1942 edition):

Dot maps

Aggregated to grid cells

The concentric zonal model

And they even have some binned scatterplots to ease in calculating linear regression equations

Going back further, Friendly in A.-M. Guerry’s moral statistics of France: Challenges for multivariable spatial analysis has some examples of Guerry’s maps and graphs. Besides choropleth maps, Guerry has one of the first examples of a ranked bumps chart (as later coined by Edward Tufte) of the relative rankings of the counts of crime at different ages (1833):

I don’t have access to any of Quetelet’s historical maps, but Cook and Wainer in A century and a half of moral statistics in the United Kingdom: Variations on Joseph Fletcher’s thematic maps have examples of Joseph Fletcher’s choropleth maps (as of 1849):

Going to more recent mapping examples, the Brantingham’s most notable I suspect is their crime pattern nodes and paths diagram, but my favorites are the ascii glyph contour maps in Crime seen through a cone of resolution (1976):

The earliest example of a journey-to-crime map I am aware of is Capone and Nichols Urban structure and criminal mobility (1976) (I wouldn’t be surprised though if there are earlier examples)

Besides maps, one other famous criminology graphic that came to mind was the age-crime curve. This is from Age and the Explanation of Crime (Hirschi and Gottfredson, 1983) (pdf here). This I presume was made with the computer – although I imagine it was still a pain in the butt to do it in 1983 compared to now! Andresen et al.’s reader Classics in Environmental Criminology in the Quetelet chapter has an age crime curve recreated in it (1842), but I will see if I can find an original scan of the image.

Edit: Was able to find an online scan of Quetelet’s original work in French. This has a fitted sine curve as one of the figures, but if you check out the tables he has binned arrest rates (page 65).

Quetelet_AgeCrimeCurve

I will admit I have not read Wolfgang’s work, but I imagine he had graphs of the empirical cumulative distribution of crime offenses somewhere in Delinquency in a Birth Cohort. But William Spelman has many great examples of them for both people and places. Here is one superimposing the two from Criminal Careers of Public Places (1995):

Michael Maltz has spent much work on advocating for visual presentation as well. Here is an example from his chapter, Look Before You Analyze: Visualizing Data in Criminal Justice (pdf here) of a 2.5d kernel density estimate. Maltz discussed this in an earlier publication, Visualizing Homicide: A Research Note (1998), but the image from the book chapter is nicer.

Here is an album with all of the images in this post. I will continue to update this post and album with more maps and graphs from historical work in criminology as I find them. I have a few examples in mind — I plan on adding a multivariate scatterplot in Don Newman’s Defensible Space, and I think Sampson’s work in Great American City deserves to be mentioned as well, because he follows in much of the same tradition as Shaw and McKay and presents many simple maps and graphs to illustrate the patterns. I would also like to find the earliest network sociogram of crime relationships. Maltz’s book chapter has a few examples, and Papachristo’s historical work on Al Capone should be mentioned as well (I thought I remembered some nicer network graphs though in Papachristos’s book chapter in the Morselli reader).

Let me know if there are any that I am missing or that you think should be added to the list!

3 Comments
by Andy Wheeler on June 12, 2015  •  Permalink
Posted in Crime Mapping, Data Visualization, scholarly
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Posted by Andy Wheeler on June 12, 2015

https://andrewpwheeler.com/2015/06/12/favorite-maps-and-graphs-in-historical-criminology/

Randomness in ranking officers

I was recently re-reading the article The management of violence by police patrol officers (Bayley & Garofalo, 1989) (noted as BG from here on). In this article BG had NYPD officers (in three precincts) each give a list of their top 3 officers in terms based on minimizing violence. The idea was to have officers give self-assessments to the researcher, and then the researcher try to tease out differences between the good officers and a sample of other officers in police-citizen encounters.

BG’s results stated that the rankings were quite variable, that a single officer very rarely had over 8 votes, and that they chose the cut-off at 4 votes to categorize them as a good officer. Variability in the rankings does not strike me as odd, but these results are so variable I suspected they were totally random, and taking the top vote officers was simply chasing the noise in this example.

So what I did was make a quick simulation. BG stated that most of the shifts in each precinct had around 25 officers (and they tended to only rate officers they worked with.) So I simulated a random process where 25 officers randomly pick 3 of the other officers, replicating the process 10,000 times (SPSS code at the end of the post). This is the exact same situation Wilkinson (2006) talks about in Revising the Pareto chart, and here is the graph he suggests. The bars represent the 1st and 99th percentiles of the simulation, and the dot represents the modal category. So in 99% of the simulations the top ranked officer has between 5 and 10 votes. This would suggest in these circumstances you would need more than 10 votes to be considered non-random.

The idea is that while getting 10 votes at random for any one person would be rare, we aren’t only looking at one person, we are looking at a bunch of people. It is an example of the extreme value fallacy.

Here is the SPSS code to replicate the simulation.

***************************************************************************.
*This code simulates randomly ranking individuals.
SET SEED 10.
INPUT PROGRAM.
LOOP #n = 1 TO 1e4.
  LOOP #i = 1 TO 25.
    COMPUTE Run = #n.
    COMPUTE Off = #i.
    END CASE.
  END LOOP.
END LOOP.
END FILE.
END INPUT PROGRAM.
DATASET NAME Sim.
*Now for every officer, choosing 3 out of 25 by random (without replacement).
SPSSINC TRANS RESULT = V1 TO V3
  /FORMULA "random.sample(range(1,26),3)".
FORMATS V1 TO V3 (F2.0).
*Creating a set of 25 dummies.
VECTOR OffD(25,F1.0).
COMPUTE OffD(V1) = 1.
COMPUTE OffD(V2) = 1.
COMPUTE OffD(V3) = 1.
RECODE OffD1 TO OffD25 (SYSMIS = 0).
*Aggregating and then reshaping.
DATASET DECLARE AggResults.
AGGREGATE OUTFILE='AggResults'
  /BREAK Run
  /OffD1 TO OffD25 = SUM(OffD1 TO OffD25).
DATASET ACTIVATE AggResults.
VARSTOCASES /MAKE OffVote FROM OffD1 TO OffD25 /INDEX OffNum.
*Now compute the ordering.
SORT CASES BY Run (A) OffVote (D).
COMPUTE Const = 1.
SPLIT FILE BY Run.
CREATE Ord = CSUM(Const).
SPLIT FILE OFF.
MATCH FILES FILE = * /DROP Const.
*Quantile graph (for entire simulation).
FORMATS Ord (F2.0) OffVote (F2.0).
GGRAPH
  /GRAPHDATASET NAME="graphdataset" VARIABLES=Ord PTILE(OffVote,99)[name="Ptile99"] 
                                    PTILE(OffVote,1)[name="Ptile01"] MODE(OffVote)[name="Mod"]
  /GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
  SOURCE: s=userSource(id("graphdataset"))
  DATA: Ord=col(source(s), name("Ord"), unit.category())
  DATA: Ptile01=col(source(s), name("Ptile01"))
  DATA: Ptile99=col(source(s), name("Ptile99"))
  DATA: Mod=col(source(s), name("Mod"))
  DATA: OffVote=col(source(s), name("OffVote"))
  DATA: Run=col(source(s), name("Run"), unit.category())
  GUIDE: axis(dim(1), label("Ranking"))
  GUIDE: axis(dim(2), label("Number of Votes"), delta(1))
  ELEMENT: interval(position(region.spread.range(Ord*(Ptile01+Ptile99))), color.interior(color.lightgrey))
  ELEMENT: point(position(Ord*Med), color.interior(color.grey), size(size."8"), shape(shape.circle))
END GPL.
***************************************************************************.
3 Comments
by Andy Wheeler on May 12, 2015  •  Permalink
Posted in scholarly
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Posted by Andy Wheeler on May 12, 2015

https://andrewpwheeler.com/2015/05/12/randomness-in-ranking-officers/

New paper: The Effect of 311 Calls for Service on Crime in D.C. At Micro Places

I have a new pre-print posted, The Effect of 311 Calls for Service on Crime in D.C. At Micro Places, at SSRN. Here is the abstract:

Broken windows theory has been both confirmed and refuted with several different measures of physical disorder. Small experiments tend to confirm the priming effects of physical disorder on minor deviant acts, but measures based on order maintenance policing and surveys are much more mixed. Here I use 311 calls for service as a proxy for physical disorder, as it is a simple alternative compared to neighborhood audits or community surveys. For street segments and intersections in Washington D.C., I show that 311 calls for service based on detritus (e.g. garbage on the street) and infrastructure complaints (e.g. potholes in sidewalks) have a positive but very small effect on Part 1 crimes while controlling for unobserved neighborhood effects. This suggests that 311 calls for service can potentially be a reliable indicator of physical disorder where available. The findings partially confirm the broken windows hypothesis, but reducing physical disorder is unlikely to result in appreciable declines in crime.

And here are some maps of the crimes and calls per service per the regular grid I use as the neighborhood boundaries (because everything is better with some pretty maps!):

As always, if you have feedback I am all ears. This is what I signed up to present at ASC this fall, and is based on work in my dissertation.

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by Andy Wheeler on April 30, 2015  •  Permalink
Posted in Crime Mapping, scholarly
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Posted by Andy Wheeler on April 30, 2015

https://andrewpwheeler.com/2015/04/30/new-paper-the-effect-of-311-calls-for-service-on-crime-in-d-c-at-micro-places/

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.

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by Andy Wheeler on April 8, 2015  •  Permalink
Posted in Crime Mapping, Mapping, Papers, scholarly
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Posted by Andy Wheeler on April 8, 2015

https://andrewpwheeler.com/2015/04/08/cartography-and-gis-special-issue-on-crime-mapping/

New paper: Replicating Group-Based Trajectory Models of Crime at Micro-Places in Albany, NY

I posted a pre-print of a paper myself, Rob Worden and Sarah McLean have finished, Replicating Group-Based Trajectory Models of Crime at Micro-Places in Albany, NY. This is part of the work of the Finn Institute in collaboration with the Albany police department, and the goal of the project was to identify micro places (street segments and intersections) that showed long term patterns of being high crime places.

The structured abstract is below:

Objectives: Replicate two previous studies of temporal crime trends at the street block level. We replicate the general approach of group-based trajectory modelling of crimes at micro-places originally taken by Weisburd, Bushway, Lum and Yan (2004) and replicated by Curman, Andresen, and Brantingham (2014). We examine patterns in a city of a different character (Albany, NY) than those previously examined (Seattle and Vancouver) and so contribute to the generalizability of previous findings.

Methods: Crimes between 2000 through 2013 were used to identify different trajectory groups at street segments and intersections. Zero-inflated Poisson regression models are used to identify the trajectories. Pin maps, Ripley’s K and neighbor transition matrices are used to show the spatial patterning of the trajectory groups.

Results: The trajectory solution with eight classes is selected based on several model selection criteria. The trajectory of each those groups follow the overall citywide decline, and are only separated by the mean level of crime. Spatial analysis shows that higher crime trajectory groups are more likely to be nearby one another, potentially suggesting a diffusion process.

Conclusions: Our work adds additional support to that of others who have found tight coupling of crime at micro-places. We find that the clustering of trajectories identified a set of street units that disproportionately contributed to the total level of crime citywide in Albany, consistent with previous research. However, the temporal trends over time in Albany differed from those exhibited in previous work in Seattle but were consistent with patterns in Vancouver.

And here is one of the figures, a drawing of the individual trajectory groupings over the 14 year period. As always, if you have any comments on the paper feel free to shoot me an email.

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by Andy Wheeler on March 27, 2015  •  Permalink
Posted in Criminal Justice, Papers, scholarly
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Posted by Andy Wheeler on March 27, 2015

https://andrewpwheeler.com/2015/03/27/new-paper-replicating-group-based-trajectory-models-of-crime-at-micro-places-in-albany-ny/

Dissertation Defense

The date is set, Friday, February 27, 2015 at 10:00 a.m. in Draper Hall, Room 105. As always, if you feel like sitting in the mail room and flipping through it, it is there! (My crappy picture – I do not have smart phone.)

But if not, here is a pdf copy of the dissertation. If anyone is interested, here are my hacks to get LaTex to conform to SUNY Albany’s dissertation guidelines.

The title is What we can learn from small units of analysis, and here is my abstract:

The dissertation is aimed at advancing knowledge of the correlates of crime at small geographic units of analysis. I begin by detailing what motivates examining crime at small places, and focus on how aggregation creates confounds that limit causal inference. Local and spatial effects are confounded when using aggregate units, so to the extent the researcher wishes to distinguish between these two types of effects it should guide what unit of analysis is chosen. To illustrate these differences, I examine local, spatial and contextual effects for bars, broken windows and crime using publicly available data from Washington, D.C.

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by Andy Wheeler on February 13, 2015  •  Permalink
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Posted by Andy Wheeler on February 13, 2015

https://andrewpwheeler.com/2015/02/13/dissertation-defense/

New paper: Tables and graphs for monitoring temporal crime patterns

I’ve uploaded a new pre-print, Tables and graphs for monitoring temporal crime patterns. The paper basically has three parts, which I will briefly recap here:

  • percent change is a bad metric
  • there are data viz. principles to constructing nicer tables
  • graphs >> tables for monitoring trends

Percent change encourages chasing the noise

It is tacitly understood that percent change when the baseline is small can fluctuate wildly – but how about when the baseline average is higher? If the average of crime was around 100 what would you guess would be a significant swing in terms of percent change? Using simulations I estimate for a 1 in 100 false positive rate you need an over 40% increase (yikes)! I’ve seen people make a big deal about much smaller changes with much smaller baseline averages.

I propose an alternative metric based on the Poisson distribution,

2*( SQRT(Post) - SQRT(Pre) )

This approximately follows a normal distribution if the data is Poisson distributed. I show with actual crime data it behaves pretty well, and using a value of 3 to flag significant values has a pretty reasonable rate of flags when monitoring weekly time series for five different crimes.

Tables are visualizations too!

Instead of recapping all the points I make in this section, I will just show an example. The top table is from an award winning statistical report by the IACA. The latter is my remake.

Graphs >> Tables

I understand tables are necessary for reporting of statistics to accounting agencies, but they are not as effective as graphs to monitor changes in time series. Here is an example, a seasonal chart of burglaries per month. The light grey lines are years from 04 through 2013. I highlight some outlier years in the chart as well. It is easy to see whether new data is an outlier compared to old data in these charts.

I have another example of monitoring weekly statistics in the paper, and with some smoothing in the chart you can easily see some interesting crime waves that you would never comprehend by looking at a single number in a table.

As always, if you have comments on the paper I am all ears.

1 Comment
by Andy Wheeler on January 19, 2015  •  Permalink
Posted in Crime Analysis, Data Visualization, Papers, scholarly
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Posted by Andy Wheeler on January 19, 2015

https://andrewpwheeler.com/2015/01/19/new-paper-tables-and-graphs-for-monitoring-temporal-crime-patterns/

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