Near-Repeat Patterns in Philadelphia Shootings

Ratcliffe, Jerry H.; Rengert, George F.

doi:10.1057/palgrave.sj.8350068

Cited by 236 publications

(278 citation statements)

References 23 publications

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“…Some individuals will fall into a baseline group after victimization, while others might be more at risk after being victimized (Lauritsen and Quinet 1995). Similarly, Ratcliffe and Rengert (2008) analyze shootings in Philadelphia and find an elevated risk of near-repeat shootings occurring within 2 weeks and within one city block of previous incidents. Our model would suggest that the near-repeat effect observed by Ratcliffe and Rengert stems from the area around a previous shooting event transitioning to an excited state characterized by a higher rate of shooting events.…”

Section: Discussionmentioning

confidence: 99%

“…From the moving-window method, we see that in this simulation run 11.3% of simulated events were subject to an exact repeat within 724 days, as compared to 10.8% in the Long Beach data tend to occur more closely in time as well, like exact-repeats, whereas those that are far apart seem to exhibit no temporal correlation. These previous studies use Monte Carlo algorithms to find the likelihood of the observed patterns happening if there were no correlation between the spatial and temporal distributions Ratcliffe and Rengert 2008), determining that this is highly unlikely. In this section, we instead test explicitly for near-repeat event dependence by extending our finite-window counting method used above for exact-repeats to the case of near-repeat events in our Long Beach data.…”

Section: Near-repeatsmentioning

confidence: 99%

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Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Short¹,

D’Orsogna²,

et al. 2009

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We develop a mathematical framework aimed at analyzing repeat and nearrepeat effects in crime data. Parsing burglary data from Long Beach, CA according to different counting methods, we determine the probability distribution functions for the time interval s between repeat offenses. We then compare these observed distributions to theoretically derived distributions in which the repeat effects are due solely to persistent risk heterogeneity. We find that risk heterogeneity alone cannot explain the observed distributions, while a form of event dependence (boosts) can. Using this information, we model repeat victimization as a series of random events, the likelihood of which changes each time an offense occurs. We are able to estimate typical time scales for repeat burglary events in Long Beach by fitting our data to this model. Computer simulations of this model using these observed parameters agree with the empirical data.

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Section: Discussionmentioning

confidence: 99%

Section: Near-repeatsmentioning

confidence: 99%

Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Short¹,

D’Orsogna²,

et al. 2009

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“…Potential crime targets such as homes, automobiles, or persons, depending on crime type, are continuously distributed in space, and each location x ¼ ðx; yÞ is characterized by a risk of victimization, defined as a field Aðx; tÞ, representing general environmental cues about the feasibility of committing a successful crime (12)(13)(14)(15) and/or specific knowledge offenders possess about target or victim vulnerability in the area (16)(17)(18). While Aðx; tÞ is easiest to conceptualize in reference to stationary targets (such as homes in the case of burglary), it may also be used to represent the risk of attacks on mobile victims at any given spatial location (19,20). Our model and its results are therefore independent of crime type.…”

Section: Modelmentioning

confidence: 99%

“…Finally, D, a diffusion coefficient, and η ∈ ½0; 1 control the rate of diffusive spread of crime risk within the local environment, describing a so-called "near repeat" phenomenon whereby targets within several hundred meters of an initial crime are more likely to be victimized than by chance (Fig. 1B) (19,21,24).…”

Section: Modelmentioning

confidence: 99%

Dissipation and displacement of hotspots in reaction-diffusion models of crime

Short

Brantingham²,

Bertozzi³

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

200

140

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The mechanisms driving the nucleation, spread, and dissipation of crime hotspots are poorly understood. As a consequence, the ability of law enforcement agencies to use mapped crime patterns to design crime prevention strategies is severely hampered. We also lack robust expectations about how different policing interventions should impact crime. Here we present a mathematical framework based on reaction-diffusion partial differential equations for studying the dynamics of crime hotspots. The system of equations is based on empirical evidence for how offenders move and mix with potential victims or targets. Analysis shows that crime hotspots form when the enhanced risk of repeat crimes diffuses locally, but not so far as to bind distant crime together. Crime hotspots may form as either supercritical or subcritical bifurcations, the latter the result of large spikes in crime that override linearly stable, uniform crime distributions. Our mathematical methods show that subcritical crime hotspots may be permanently eradicated with police suppression, whereas supercritical hotspots are displaced following a characteristic spatial pattern. Our results thus provide a mechanistic explanation for recent failures to observe crime displacement in experimental field tests of hotspot policing.crime pattern formation | hotspot policing | mathematical modeling | nonlinearity | partial differential equations C rime is a ubiquitous feature of all modern cities, but not all neighborhoods are affected equally. In fact, serious crimes ranging from residential burglary to homicide are strongly patterned in time and space, forming crime "hotspots" (1-3). Studies show that policing actions directed at crime hotspots do lead to real reductions in offending and calls to the police for service (4, 5), while displacement of crime to adjacent settings may be less common than once thought (6-8). However, further gains in crime reduction are dependent upon gaining a quantitative understanding of the mechanisms that drive the emergence, spread, and dissipation of crime hotspots. Reaction-diffusion models, in which activators and inhibitors move, mix, and interact, provide a useful framework in which to investigate the formation of crime patterns and the impact of alternative policing strategies on crime hotspot stability. In this context, motivated offenders (activators) search their environment for suitable targets or victims (activators), which may also be mobile, following simple behavioral routines (9, 10). If an offender encounters a target in the absence of an effective security measure (inhibitor), then he is free to exploit that target. The immediate presence of security such as law enforcement is sufficient to deter that crime. Here we show that largescale spatial crime patterns, including the formation of stationary crime hotspots, are strongly dependent upon the local diffusion of risk, driven by offender mobility in the environment, coupled with the phenomena of repeat and near repeat victimization.

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“…While this pioneered the concept, the popular term to emerge seems to be near repeats (Morgan 2001), referring to similar crimes a short time and distance away. For example, neighbouring and nearby households are more likely to be victimized after a break-in (Townsley et al 2003, Johnson et al 2007a, while Ratcliffe and Rengert (2008) found repeat shootings in Philadelphia more likely within a city block and two weeks. Thus repeats and near repeats together offer insight into why crime concentrates spatially and underpin the algorithms of predictive policing (Groff and LaVigne 2001;Chainey et al 2008;Short et al 2009;Johnson 2010;Pease and Tseloni 2014).…”

Section: Introductionmentioning

confidence: 99%

Crime concentration theory

Farrell

2015

Crime Prev Community Saf

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Near-Repeat Patterns in Philadelphia Shootings

Cited by 236 publications

References 23 publications

Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Measuring and Modeling Repeat and Near-Repeat Burglary Effects

Dissipation and displacement of hotspots in reaction-diffusion models of crime

Crime concentration theory

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