Abstract
Criminal justice data are hardly ever linear, normal, and/or independent, but most statistical techniques rely on the assumptions of linearity, normality and/or independence. Moreover, since the data are often entered by hand, they are prone to error. Plotting the data permits the analyst to determine the extent to which the assumptions are valid and to catch obvious errors in data entry. Moreover, while standard statistical techniques are useful in testing hypotheses, visualization allows the data to tell its story and thus is useful in generating hypotheses. This chapter provides some examples of how visualizing criminal justice data contributes to a fuller understanding of the processes that produced the data.
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Notes
- 1.
Regarding data entry, one of my favorite quotes is from an economist in the 1920s (Stamp 1929: 258): “The Government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But what you must never forget is that every one of these figures comes in the first place from the chowty dar [village watchman]. who just puts down what he damn pleases.”
- 2.
Freedman (1985: 308) notes that “investigators often talk about ‘modeling the data.’ This is almost perverse: surely the object is to model the phenomenon, and the data are interesting only because they contain information about that phenomenon. Whatever it is that most social scientists are doing when they construct regression models, discovering natural laws does not seem to be uppermost in their minds.”
- 3.
As the president of the American Statistical Association recently (Morton, 2009) said: “[D]on’t trust complicated models as far as you can throw them – protect yourself and examine those data every which way. Are your assumptions correct? What are those pesky outliers up to? Let the data speak for themselves, rather than being masked and manipulated by complex methods.”
- 4.
Those as fascinated as I am with the great variety of data visualization techniques should visit the website http://addictedtor.free.fr/graphiques/thumbs.php?sort$=$votes, which also includes the source code for the plots (in the computer language R). I thank Andrew Gelman for pointing out this treasure. Antony Unwin noted (personal communication) that Martin Theus developed Mondrian, an interactive graphics package that can be used for most data visualization purposes.
- 5.
Visualization is the key ingredient in map-based analyses as well. It is not covered in this chapter; Ratcliffe (Chap. 2) describes how it can be used to find space-time patterns in crime data.
- 6.
A similar strategy was used in cleaning FBI-collected arrest data (Maltz and Weiss 2007).
- 7.
If the field contains five characters, as it might for the crime of larceny in large cities, then the missing value might be 99999.
- 8.
The Crime Index is the sum of the crimes of murder, forcible rape, robbery, aggravated assault, burglary, larceny, and vehicle theft.
- 9.
It turns out that, for many of the years included in the study, the only police department in Cook County Illinois reporting arrest statistics to the FBI was the Chicago Police Department.
- 10.
Sometimes it takes another pair of eyes to see what may be obvious. I thank Howard Wainer for serving in that capacity.
- 11.
These files have been prepared by Fox (2008) for over a decade. They aggregate all of the annual SHR reports from 1976 to the most current year, and can be downloaded from the National Archive of Criminal Justice Data (http://www.icpsr.umich.edu/nacjd). The most recent version contains data from 1976 through 2005.
- 12.
See Maltz (1998) for how the smoothing was accomplished.
- 13.
The peak values are indicated on the figures. Note that the scales of the male-offender and female-offender figures are not the same.
- 14.
Again I thank Howard Wainer for pointing this out.
- 15.
A researcher might posit a log-linear or a polynomial (i.e., quadratic or cubic) relationship, but even these are linear in some sense. Assuming that something is related to (for instance) the log of family income, plus age squared, plus education level cubed is also a linear assumption: the model implicitly assumes that the factors are additive, because statistical software can handle that kind of relationship more easily.
- 16.
Gelman (2004) shows how “(a) exploratory and graphical methods can be especially effective when used in conjunction with models, and (b) model-based inference can be especially effective when checked graphically.”
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Acknowledgements
Over the years my research has been supported by the National Institute of Justice, the Bureau of Justice Statistics, the Ohio Office of Criminal Justice Services, the California Department of Corrections and Rehabilitation, and the American Statistical Association, which I acknowledge with gratitude. I am indebted to Andrew Gelman and Howard Wainer for their comments on earlier versions.
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Maltz, M.D. (2010). Look Before You Analyze: Visualizing Data in Criminal Justice. In: Piquero, A., Weisburd, D. (eds) Handbook of Quantitative Criminology. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77650-7_3
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