Modeling the Underlying Dynamics of the Spread of Crime

McMillon, David; Simon, Carl P.; Morenoff, Jeffrey D.

doi:10.1371/journal.pone.0088923

Cited by 62 publications

(33 citation statements)

References 40 publications

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“…Further research on this model include the introduction of spatial disorder, methods for police suppression to dynamically adapt to evolving crime patterns or to choose from different deployment strategies and more rigorous analysis [28,[59][60][61][62]. Other mathematical work on the spread of crime in society include dynamical systems that include competition between citizens, criminals and guards [63], the effects of socio-economic classes, changes in police efficiency and/or resources assigned to them [64], the effects of imprisonment and recidivism [65] and the possibility of communities defending themselves from criminals [66]. Viewed as a whole, this body of work may prove useful in developing better and more cost-effective crime mitigation methods and to allow for the optimization of containment and suppression resources.…”

Section: Crime Hotspotsmentioning

confidence: 99%

Statistical physics of crime: A review

D’Orsogna

Perc

2015

Physics of Life Reviews

251

129

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Criminality is a big challenge at several different levels. This is particularly evident -even for microcriminality -in urban areas (in Europe and North America the percent of population living in urban areas is around 85%). It is considered by sociologists among the most important indexes affecting the (perception of the) quality of life in a given place.Starting from the seminal paper by G. Becker [3], the study of crime and criminality from the point of view of economics has been developed in several directions. And the role of mathematical, statistical, and physical models has been steadily increasing. Thus, the paper [6] appears to be extremely timely and useful.Generally speaking, models are often more descriptive than predictive, in the sense that it is not expected that they predict e.g. the number of burglaries or car thefts that will occur in a given district over a given period of time. Nevertheless, they can be instrumental in describing the mechanisms by which it can be foreseen that a concentration of crimes can appear in particular zones (hot spots), or the "contagion" that criminal behaviour can have on particular classes of individuals. This description can in turn suggest how to contrast the phenomena.Therefore, modelling the diffusion of criminal (or simply unlawful) behaviour in urban areas can be a tool that administrations and police authorities can use in order to choose optimal strategies to combat crime. And this is particularly important in a horizon of budget cuts that impose the best use of the existing (scarce) resources, optimization of strategies, logistics etc.Another feature of the models is the fact that they allow to perform simulations to mimic the response of the system to changes of parameters, of external inputs or constraints. Of course "for complex phenomena as criminality (

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Section: Crime Hotspotsmentioning

confidence: 99%

Statistical physics of crime: A review

D’Orsogna

Perc

2015

Physics of Life Reviews

251

129

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“…David and his coauthors (D. McMillon, Simon, & Morenoff, ) derived a tipping point that separates a community's convergence to a low‐crime equilibrium from a high‐crime equilibrium in terms of several policy levers, including incarceration rates of first‐time versus repeat offenders, the rate of prisoner reentry/rehabilitation, and the rate at which people turn to criminal behavior in the first place. The systems analysis indicates that reducing deviant behavior and increasing achievement for at‐risk students in schools is the most powerful point of intervention to combat the school‐to‐prison pipeline from a holistic, systems perspective.…”

Section: A Community‐based Initiative: the Proctor Institutementioning

confidence: 99%

School–University–Community Collaboration: Building Bridges at the Water's Edge

McMillon¹

2016

J Adolescent & Adult Lit

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Students learn best when their out‐of‐school literacy experiences are incorporated into daily classroom instruction. Teachers who participate in community‐based initiatives, develop relationships and acquire knowledge and skills needed for this type of instructional practice. They get to know students, families, and community cultural brokers who can share experiential knowledge that influences literacy teaching and learning. When teachers become a part of the community, they are also able to prepare students to successfully negotiate cultural borders, improve academically, and serve their own communities. This commentary is a call to action for teachers to understand the benefits of community‐based initiatives and become active participants.

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“…Similar to previous models (McMillon et al, 2014), we assume that crimes are generated by the interaction of criminals and noncriminals, on which criminals perform a criminal action, with rate constant α. Crimes are prevented by law enforcement at a rate proportional to the density of crimes and law enforcement with ARTICLE PALGRAVE COMMUNICATIONS | DOI: 10.1057DOI: 10.…”

Section: Modelmentioning

confidence: 99%

“…Crime has been modelled using mathematical models like ordinary differential equation models (McMillon et al, 2014), partial differential equation models (Baudains et al, 2013c;D'Orsogna and Perc, 2014) and spatial and agent-based models (Short et al, 2010;Baudains et al, 2013c;Davies et al, 2013;Perc et al, 2013). Our approach is to use differential equation models that capture the competitive dynamics between law enforcement and criminals and apply them to large-scale data on crime in cities worldwide.…”

Section: Introductionmentioning

confidence: 99%

Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities

2015

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While cities have been the engine for innovation and growth for many millennia, they have also endured disproportionately more crime than smaller cities. Similarly to other urban sociological quantities, such as income, gross domestic product (GDP) and number of granted patents, it has been observed that crime scales super-linearly with city size. The default assumption is that super-linear scaling of crime, like other urban attributes, derives from agglomerative effects (that is, increasing returns from potentially more productive connections among criminals). However, crime initiation appears to be generated linearly with the population of a city, and the number of law enforcement officials scales sublinearly with city population. We hypothesize that the observed scaling exponent for net crime in a city is the result of competing dynamics between criminals and law enforcement, each with different scaling exponents, and where criminals win in the numbers game. We propose a simple dynamical model able to accommodate these empirical observations, as well as the potential multiple scaling regimes emerging from the competitive dynamics between crime and law enforcement. Our model is also general enough to be able to correctly account for crime in universities, where university crime does not scale super-linearly, but linearly with enrolment size.

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Modeling the Underlying Dynamics of the Spread of Crime

Cited by 62 publications

References 40 publications

Statistical physics of crime: A review

Statistical physics of crime: A review

School–University–Community Collaboration: Building Bridges at the Water's Edge

Competitive dynamics between criminals and law enforcement explains the super-linear scaling of crime in cities

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