• Can we prevent homicide through network surveillance — and should we?

    This Saturday is the anniversary of the Sandy Hook School shootings.  The figure below (from Kieran Healy) shows that the US has a higher rate of homicides than other OECD countries. Since Sandy Hook, however, the US has done nothing of substance to address its homicide problem.

    assault-deaths-oecd-ts-all-new

    One reason that nothing has changed may be that the modest gun regulations proposed after Sandy Hook would likely have had only a modest effect on gun deaths.

    But in new research, Andrew V. Papachristos and Christopher Wildeman have shown that you can identify likely homicide victims in high crime neighborhoods through an analysis of their social network. This approach would not have predicted the deaths at Sandy Hook, because Adam Lanza wasn’t part of anyone’s network. Nevertheless, this research might be the foundation of an effective approach to preventing some types of homicides. But it frightens me.
    Papachristos and Wildeman studied a Chicago neighborhood of 82,000 people. The residents were already at high risk of being murdered (55 homicides per 100,000 / year, compared to 20/100,000 for the rest of the city). They narrowed the sample further to anyone in the neighborhood who was arrested between 2006 and 2011. They then used the arrest data to draw a social network graph by

    linking individuals together when they were arrested for the same crime. The basic underlying assumptions are that people who are arrested together (a) know each other and (b) engage in risky behaviors together, in this case, illegal behavior.

    They found that

    the victims in 41% (N = 103) of all gun homicides in the community are located in this co-offending network. These homicides occur in only 75 of the 1,732 sub-networks, which contain only 3,718 of the 82,000 individuals living in the neighborhood. In other words, 41% of all gun homicides occur in social networks consisting of approximately 4% of the population of the community.

    The co-offending network has one large component that connects 3601 people and a host of small networks that do not connect to the large component. Here is what the large component looks like.

    LargeComponent

    The red dots are homicide victims. The ties linking these victims are highlighted by coloring them yellow. Notice that even within this high risk group, murder victims are clustered. Papachristos and Wildeman argue that homicide is like a contagion spreading across the network.

    the socially closer one is to a homicide victim—the greater the influence on one’s own victimization. In this sense, homicide is socially contagious and associating with people engaged in risky behaviors—like carrying a firearm and engaging in criminal activities—increases the probability of victimization.

    A resident’s network proximity to other murder victims greatly increased the resident’s risk of being murdered.

    5year_avg_homicide_rates

    The bar on the left is your risk of being murdered if you live in the neighborhood. The third bar from the left is your risk if you live in the neighborhood and have been arrested in the last five years. The bar on the far right is your risk if you have been arrested and are closely linked to a murder victim. The victimization rate in this group is ten times that of the community members.

    Papachristos and Wildeman concluded that

    A social network approach suggests that victimization is not simply a function of [traits such as age, race, or gender], but of how people are connected, the structure of the overall network, the types of behaviors occurring in the network, and an individual’s position in the overall structure.

    I have some technical concerns about the paper, but let’s set them aside and discuss possible implications for homicide prevention. Papachristos argued in The Washington Post that the way to reduce homicide

    is not broad, sweeping policies, such as New York’s “stop and frisk” or mass arrests, but the opposite: highly targeted efforts to reach specific people in specific places, akin to providing clean needles to drug users to prevent the spread of HIV.

    Now, it is not immediately clear how you prevent the murders of these high risk persons. The people deeply embedded in the co-offending network surely know they are at risk of being shot. And we are not going to issue them body armor. However, having highly discriminating risk information would allow someone like Harold Pollack to target his violence prevention intervention to those at highest risk.

    It’s also possible that network predictions could be used by the police to conduct precisely targeted “stop and frisks.” Instead of indiscriminately stopping any young male person of color, the police might create a watchlist of persons whose location in a criminality network indicates that they are at high risk of being murdered.

    We should also think about how network criminology might develop in the future. Papachristos and Wildeman got strong results from arrest records, a surprisingly thin harvest of data. The social graph estimated from co-arrest data is doubtlessly only a weak proxy for the actual criminal network. However, perhaps adding other sources of data could lead to a graph that more closely mirrored the actual social network.

    For example, the Chicago police have an extensive system of wireless closed circuit surveillance cameras. High resolution cameras and facial recognition software could allow the police to record the everyday public contacts between neighborhood residents. Gang boundaries constrain residents’ movements, so perhaps gang memberships could be inferred from patterns of movement of residents. If so, co-membership in gangs would be observable and could be added to the network graph. Social network graphs estimated from richer data would likely mirror actual social networks better than co-arrests graphs. These enhanced graphs might predict victimization better than the co-arrest network; leading to yet more closely targeted police interventions.

    And then we need to ask: Do we want the police harvesting records and surveillance data to model our social networks? Even if it prevented a significant number of homicides?

    @Bill_Gardner 

    TIE authors have written a lot about guns. Here are 1, 2, 3, 4, 5, 6, 7, 8, 9… posts.

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    • Thanks for the sensible column. I believe that NO whether it prevents homicides or no the loss of freedom is so not worth it.

      • I disagree, most people freely give away their personal information with their online activities with nary a thought.

        I think profiling is a powerful and appropriate method of crime prevention. This seems like a natural progression. I’d rather have a sanctioned governmental entity collect and process the information instead of a civilian “road warrior”.

        I doubt that such a prevention technique is ready for broad use, but it’s certainly a step in the right direction.

        • “And then we need to ask: Do we want the police harvesting records and surveillance data to model our social networks? Even if it prevented a significant number of homicides?”

          That is a very good question. I would add to the discussion that Whites and Blacks are likely to answer it very differently. You can guess how.

          I would not generally trust police to use social network information wisely. You have the NYPD management essentially managing their operations to maximize the number of stop and frisk arrests. Talk about a process measure that is completely unlinked with any sort of good outcome.

          • This is why I’d support the use of algorithms, zero human interaction with the collection and calculation of data. Let the police use the data to allocate their resources efficiently (assuming the data isn’t skewed).

            Much like in other areas, incentives matter. If the focus is on total number of stops then I don’t think its reasonable to expect this system to do anything. However, if the emphasis is more on the rate of meaningful stops – then I think you can make some progress.

            I don’t have any insights on how you would implement such a system, but I don’t accept general human failure as a reason to not pursue improvements in crime rate.

    • How much of this elaborate analysis can be explained by the single phrase “people who use and deal drugs hang out with other people who use and deal drugs”?

      • Dangerman,
        Point well taken. The interesting thing about P & W’s work is not that street criminals tend to shoot each other. It’s that simply by looking at arrest records, you can get some idea about which street criminal is likely to get shot. To my knowledge, nobody had done that.

    • The question is, how many of the guns involved in gun violence are illegal? I can see that targeting inner city gang violence might work because those guns are more likely to be illegal, but most of the school shootings seem to be loners in the suburbs or rural areas with access to legal guns.

      And, of course, we, egged on by the media, get a lot more upset at innocent suburban kindergarteners getting sprayed with bullets than teens in places we avoid with skin a few shades darker. It’s so easy to rationalize that those teens were in gangs and up to no good anyway.

    • -Anyone who grew up in a neighborhood with a smattering of
      gangs and violence can make predictions about who is likely to get
      shot to death that are likely to be at least as accurate as formal
      methods relying on social network analysis. -We could immediately
      achieve a reduction in drug/gang related deaths, avoid the
      big-brother style instrusions *and* save scores of billions of
      dollars a year by ending our idiotic, Dickensian prohibition
      policies and immediately legalizing the sale of all drugs. Sadly,
      that’s fare less likely to appeal to the paternalistic left or the
      moralistic right, so we’ll get a massive, expensive, and intrusive
      non-solution instead. Great.