Probabilistic Crime Solving

In our Big Data age, policing may shift its focus away from catching criminals to stopping crime from happening. That might sound like Hollywood “Minority Report” fantasy but not to researchers hoping to leverage data to identify future crime areas. Consider as an illustration a research project sponsored by Rutgers Center on Public Security. According to Government Technology, Rutgers professors have obtained a two-year $500,000 grant to conduct “risk terrain modeling” research in U.S. cities. Working with police forces in Arlington, Texas, Chicago, Colorado Springs, Colorado, Glendale, Arizona, Kansas City, Missouri, and Newark, New Jersey, the team will analyze an area’s history of crime with data on “local behavioral and physical characteristics” to identify locations with the greatest crime risk. As Professor Joel Caplan explains, data analysis “paints a picture of those underlying features of the environment that are attractive for certain types of illegal behavior, and in doing so, we’re able to assign probabilities of crime occurring.” Criminals tend to shift criminal activity to different locations to evade detection. The hope is to detect the criminals’ next move before they get there. Mapping techniques will systematize what is now just a matter of instinct or guess work, explain researchers.

Will reactive policing give way to predictive policing? Will police departments someday staff officers outside probabilistic targets to prevent criminals from ever acting on criminal designs? The data inputs and algorithms are crucial to the success of any Big Data endeavor. Before diving head long, we ought to ask about the provenance of the “local behavioral and physical characteristics” data. Will researchers be given access to live feeds from CCTV cameras and data broker dossiers? Will they be mining public and private sector databases along the lines of fusion centers? Because these projects involve state actors who are neither bound by the federal Privacy Act of 1974 nor federal restrictions on the collection of personal data, do state privacy laws limit the sorts of data that can be collected, analyzed, and shared? Does the Fourth Amendment have a role in such predictive policing? Is this project just the beginning of a system in which citizens receive criminal score risk assessments? The time is certainly ripe to talk more seriously about “technological due process” and the “right to quantitative privacy” for the surveillance age.

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1 Response

  1. Ipso Facto says:

    Santayana– “Those who cannot remember the past are condemned to repeat it”

    “Risk terrain modeling” reminds me of other buzzwords like “defensible spaces.”

    I’m reminded because I’ve read it all before (and I’m only in my 40’s). Analyses of crime versus terrain (built or otherwise), and crime versus all sorts of factors, have been done to death since the 1960’s, as a few minutes in the library will confirm.

    The grant to Rutgers is pure pork because the Rutgers work has only a miniscule probability of discovering anything novel or interesting. I would guess that the situation is worse than wasteful– the grant is probably part of a campaign to conceal, rather than reveal, the essentials about crime, which is that people, not places, commit crime, so the strongest predictors of criminal activity are personal characteristics.

    “Cluster analysis” shows that people can be grouped by certain variables, that the resulting groups have noticeably different mean and std-dev propensity to commit various sorts of crime (e.g.g., drunkenness, violence, larceny, fraud), and that some interventions are useful (e.g., limiting access to alcohol) and some are not (e.g., moving adults from “bad” neighborhoods to “good” ones– when you do that, you simply relocate the crime along with the people, because people–not neighborhoods–commit crime).

    If “predictive policing” means “concentrating police effort in places where people with a high propensity to commit crime are found,” then I’m for it. If it means “spying on everyone all the time” then I’m against it.