PFPE map

Schrödinger’s crime hotspot

Attendance at the recent 2014 American Society of Criminology conference brought a chance to catch up with friends and observe a couple of splendid presentations (and quite a few awful ones). A couple of sessions in particular reaffirmed to me the gulf between some academic criminology and public policy. I watched as speakers attempted to parse in ever-increasing detail the boundaries of crime hotspots. Discussions continued around the efficacy of street blocks as potentially more accurate units of analysis compared to census block groups, and I could see the accuracy issue being a hot topic in predictive policing workshops. Hotspot boundary definition appeared to be the end in itself.

As my colleague Ralph B. Taylor has argued, “hot spots exist in the data world but not the real world” (Taylor, 2009) ¹. In this they are unlike land use parcels, behavior settings or street blocks – places that exist in the data and real worlds. He goes on to contend that “hot spots are amalgams of different types of locations” and we therefore have a construct validity problem. Simply because we see a cluster of events, does not mean we have a new entity (a crime hotspot) but rather a collection of events that exist as a cohesive entity only in the abstract world. To think otherwise is to commit a reification fallacy (Gould, 1981). When we move to the real world, and think about operationalizing a strategy to address our hotspots, things can unravel as it becomes clear that this collection of points exists for different reasons, each of which need addressing.

I thought of Ralph Taylor’s comments as I sat in the audience, and pondered the analogy between crime hotspots and Schrödinger’s cat. Erwin Schrödinger’s feline thought experiment was designed to explain why the Copenhagen interpretation of quantum superposition was flawed, because the cat exists in a paradoxical state of being neither alive nor dead. The hypothetical animal is placed in a steel box with a Geiger counter, a vial of poison, a hammer, and a small amount of radioactive substance, small enough to have only a 50/50 chance of being detected over the course of an hour. If the radioactive substance decay is detected by the counter, the hammer is triggered to smash the vial, release the poison and kill the cat. Only by looking in the box can the observer determine whether the cat is alive or dead. Prior to opening the box, the cat’s health is unknown and could be considered simultaneously alive and dead. The observer opens the box with the express intent of confirming the wellbeing of the cat. Before opening the box, the cat’s condition is unresolved and abstract. In the same way, crime hotspots are in a largely abstract state until we look at them from a particular viewpoint.

This brings me to two points that appeared rather lost on some of the conference speakers. First, crime mapping and the application of GIS to crime problems is not the end of the analysis – it is the start of it. Digital cartography is a necessary abstraction of the real world, and to think otherwise is to be oblivious to the classification, simplification and symbolization that takes place. It is through these processes that unrelated events can often be made apparently similar. The nighttime beating and robbery of a drug dealer will often be classified in the same manner as the punch a school child receives as they are relieved of their smart phone by a classmate. In a map of crime for the year, these events will likely be cartographically identical, but unlikely to be prevented in the future with the same response. Just because two crimes share geographic proximity, doesn’t mean they necessarily share a common cause (a point I’ve made elsewhere).

This brings me to a related second point. Crime hotspots (in the abstract world) are only made real when they are mapped for a purpose. When a police captain asks for a map of robbery hotspots, the captain is bringing a purpose to the analysis. He or she wants to deploy a surveillance team, task a crime prevention officer, or know where to assign more foot patrol officers. An academic, meanwhile, might want to seek underlying causal factors and understand why crime concentrates in certain areas. With the knowledge of the eventual purpose, a proficient analyst can create tailored hotspots that map to the parameters of the user’s needs. The maps would be different, but no less useful. Our captain brings a lens through which he or she interrogates the hotspot, and by looking at a map of crime hotspots they are made real and are understood. It is the captain, not the analyst, who opens the box.²

Both the captain and the academic bring to the analysis a predetermined purpose, and although different, the crime hotspots that each uncovers are equally valid. In opening the box, and staring at the map through the lens of a proposed application, crime hotspots are made real and understood. At this point, they can serve a purpose. But until then, they remain in an abstract state and their accuracy, or even their state as being alive or dead as viable entities, remains unknown. Like Schrödinger’s cat.

Works cited

Taylor, R. B. (2009). Hot spots do not exist, and other fundamental concerns about hot spots policing. In N. Frost, J. Freilich & T. Clear (Eds.) Contemporary Issues in Criminal Justice Policy: Policy Proposals from the American Society of Criminology Conference (pp. 271-278). Belmont, CA: Cengage/Wadsworth.

Gould, S. J. (1981). The Mismeasure of Man. New York: Norton.

Note

¹ And continues to discuss in greater detail in his forthcoming book, Taylor, R. B. (2015) Community Criminology. New York: New York University Press.

² In discussing this with John Eck he added the suggestion of a Schrödinger’s policy, where two policies sit in a box and are both in a state of existence, until someone looks at the data. Then only one policy becomes viable and exists.