Category Archives: Policing strategies

Harm-focused policing

On 28th January, 2015 I gave the Police Foundation‘s Ideas in American Policing lecture on the topic of harm-focused policing. This brief blog provides some background details to the talk. Please note that for a number of reasons (including photograph copyright) I am not distributing copies of the PowerPoint slides.

Harm-focused policing weighs the social harms of criminality and disorder with data from beyond crime and antisocial behavior, in order to focus police priorities and resources in furtherance of both crime and harm reduction.

Example information and data sources could include drug overdose information that could help triage drug markets for interdiction, traffic fatality data to guide police patrol responses, and community impact assessments to prioritize violent street gangs. For a summary of the core of the presentation and a grey-scale version of some of the graphics, please see:

Ratcliffe, J. H. (2015). Towards an index for harm-focused policing. Policing: A Journal of Policy and Practice, 9(2), 164-182.

You can visit the journal site and access the paper here (or here) and watch my annotated video of the lecture below (you might want to make it full screen so you can read the slides).

During the presentation, I had a couple of quotes. Here are the quotes and their sources.

“to establish priorities for strategic criminal intelligence gathering and subsequent analysis based on notions of the social harm caused by different sorts of criminal activity”. The source for this is page 262 of Ratcliffe, J. H. & Sheptycki, J. (2009) Setting the strategic agenda. In J. H. Ratcliffe (ed.) Strategic Thinking in Criminal Intelligence (2nd edition) Sydney: Federation Press.

“Weighting crimes on the basis of sentencing guidelines can be justified on good democratic grounds as reflecting the will of the people. …… it remains far closer to the will of the people than any theoretical or even empirical system of weighting that academics might develop.” The source for this is Sherman, L. W. (2013). Targeting, testing and tracking police services: The rise of evidence-based policing, 1975-2025. In M. Tonry (Ed.), Crime and Justice in America, 1975-2025. Chicago: University of Chicago Press. Page 47.


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.


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.

What we have learned from Philadelphia foot patrols

With the recent publication of our comparison of foot patrol versus car patrol, it ‘s worth a quick review of all that we learned from the Philadelphia Foot Patrol Experiment. Especially as the paper Liz Groff took the lead on is available for free from the publishers until the end of May.

The original foot patrol experiment paper described our randomized controlled field experiment which saw the Philadelphia Police Department place 240 officers on 60 violent crime hotspots (randomly selected from a list of 120) for the long hot summer of 2009. And it was hot walking the streets of Philadelphia in a ballistic vest – we all remember the fieldwork and empathizing with the officers who did it all summer!

At the end of the experiment, 90 violent crimes had been prevented, resulting in a net reduction of 53 violent offenses after some displacement. This was a 23 percent reduction in violent crime as a result of foot patrol in carefully-targeted areas – a unique finding for policing.

How was this achieved? We found that pedestrian stops increased by 64 percent in the foot patrol areas, probably increasing the likelihood that offenders would be stopped, and subsequently reducing their enthusiasm for carrying a firearm. We learned some other things that summer:

  1. There was no community backlash within the foot patrol areas. To the contrary, members of the local community were really upset when their foot patrol officers were eventually removed, and they let the PPD know about it in no uncertain terms.
  2. The image of foot patrol as a punishment posting changed to a degree within the PPD. Good commanders became convinced that foot patrol was a practical tactic in high crime areas, and some patrols remained in place after the experiment.
  3. The fool patrol officers got a real feel for their foot patrol areas, developing community and criminal intelligence in the months they spent on foot.
  4. The foot patrol officers engaged in more pedestrian stops than their vehicle-bound colleagues, and they also dealt with many more disorder incidents – an activity that is always an issue in the summer in Philadelphia. They dealt with fewer serious crime incidents, yet were undoubtedly responsible for the decline in violent crime.
  5. Importantly, the foot patrol officers engaged is a different type of police work than their colleagues in cars. Less response-driven, they engaged in more order maintenance and community-related activities. They did not replace the activities of the cars, but rather work in a complementary fashion, being co-producers of community safety with their colleagues. Even if they sometimes wandered a little.

Unfortunately, we also learned – in a subsequent Criminology article headed up by two enterprising graduate students, Evan Sorg and Cory Haberman – that the gains achieved during the foot patrol experiment did not last. The effects dissipated as soon as the foot patrol officers were removed, and in fact some effects were starting to wear off as the foot patrol experiment continued into the late summer.

My colleague Jen Wood took the lead on the qualitative component so important to understanding the nuance of the foot patrol experiment. We learned that officers negotiated order based on geography, people and space, and varied their strategies and tactics based on their knowledge of the people and the environment.

The experiment was generously awarded with a research award from the IACP and from the American Society of Criminology’s Division of Experimental Criminology, but more importantly it helped people recognize the Philadelphia Police Department as an innovative department willing to try new things, take risks, and learn. And while it involved a lot of researchers, they nearly all volunteered their time on top of their normal duties. Temple University and the College of Liberal Arts generously helped out with some fieldwork costs, indicative of their desire and ongoing commitment to moving the city forward; But the experiment – which learned so much – did not cost the city taxpayers a single cent.