Geographical influences of an emerging network of gang rivalries

Hegemann, Rachel A.; Smith, Laura; Barbaro, Alethea; Bertozzi, Andrea L.; Reid, Shannon E.; Tita, George E.

doi:10.1016/j.physa.2011.05.040

Cited by 37 publications

(36 citation statements)

References 64 publications

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“…Further, criminal behavior has non-random structure and can often be framed in terms of routine activity theory [34,35]. In the case of gang violence, there is a strong spatial component [1,10,36]. One can extend the Estimate & Score Algorithm to include space.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…Recently methods have been proposed in the literature to mathematically model gang violence. The authors in [10] employ an agent-based model to investigate the geographic influences in the formation of the gang rivalry http://www.security-informatics.com/content/2/1/1 structure observed in Hollenbeck. These authors consider the long-term structure of the rivalry network embedded in space.…”

Section: Introductionmentioning

confidence: 99%

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An “Estimate & Score Algorithm” for simultaneous parameter estimation and reconstruction of incomplete data on social networks

2013

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Dynamic activity involving social networks often has distinctive temporal patterns that can be exploited in situations involving incomplete information. Gang rivalry networks, in particular, display a high degree of temporal clustering of activity associated with retaliatory behavior. A recent study of a Los Angeles gang network shows that known gang activity between rivals can be modeled as a self-exciting point process on an edge of the rivalry network. In real-life situations, data is incomplete and law-enforcement agencies may not know which gang is involved. However, even when gang activity is highly stochastic, localized excitations in parts of the known dataset can help identify gangs responsible for unsolved crimes. Previous work successfully incorporated the observed clustering in time of the data to identify gangs responsible for unsolved crimes. However, the authors assumed that the parameters of the model are known, when in reality they have to be estimated from the data itself. We propose an iterative method that simultaneously estimates the parameters in the underlying point process and assigns weights to the unknown events with a directly calculable score function. The results of the estimation, weights, error propagation, convergence and runtime are presented.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An “Estimate & Score Algorithm” for simultaneous parameter estimation and reconstruction of incomplete data on social networks

2013

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“…Spatially implicit models also do not take into consideration any constraints of mobility (Hubbell 2005;Turchin 1998). How far people move plays an important role in the generation of crime patterns (Brantingham and Tita 2008) and presumably plays and important role in the formation and maintenance of gang territories (Brantingham et al 2012;Hegemann et al 2011;Valasik and Tita 2018). Including mobility in the current model would require a spatially explicit approach.…”

Section: Limitationsmentioning

confidence: 99%

Competitive dominance, gang size and the directionality of gang violence

2019

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Intergroup violence is assumed to play a key role in establishing and maintaining gang competitive dominance. However, it is not clear how competitive ability, gang size and reciprocal violence interact. Does competitive dominance lead to larger gangs, or allow them to remain small? Does competitive dominance lead gangs to mount more attacks against rivals, or expose them to more attacks? We explore a model developed in theoretical ecology to understand communities arranged in strict competitive hierarchies. The model is extended to generate expectations about gang size distributions and the directionality of gang violence. Model expectations are explored with twenty-three years of data on gang homicides from Los Angeles. Gangs may mitigate competitive pressure by quickly finding gaps in the spatial coverage of superior competitors. Competitively superior gangs can be larger or smaller than competitively inferior gangs and a disproportionate source or target of directional violence, depending upon where exactly they fall in the competitive hierarchy. A model specifying the mechanism of competitive dominance is needed to correctly interpret gang size and violence patterns.

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“…The creation of street gang rivalries was studied via agentbased simulations in conjunction with data from the Hollenbeck policing division of the Los Angeles Police Department [119], home to many urban gangs. Each agent is part of an evolving rivalry network that includes past interactions be- tween gang members.…”

Section: Network Of Crime Gangs and Geographymentioning

confidence: 99%

Statistical physics of crime: A review

D’Orsogna

Perc

2015

Physics of Life Reviews

246

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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Geographical influences of an emerging network of gang rivalries

Cited by 37 publications

References 64 publications

An “Estimate & Score Algorithm” for simultaneous parameter estimation and reconstruction of incomplete data on social networks

An “Estimate & Score Algorithm” for simultaneous parameter estimation and reconstruction of incomplete data on social networks

Competitive dominance, gang size and the directionality of gang violence

Statistical physics of crime: A review

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