Early Identification of Violent Criminal Gang Members

Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by law-enforcement to the problem in the case where the network is discovered overtime by virtue of new arrests - mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.Comment: SIGKDD 201

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New urbanism, crime and the suburbs: a review of the evidence

Sustainability now influences government policy in the UK, Australia and USA and planning policy currently advocates high density, mixed-use residential developments in 'walkable', permeable neighbourhoods, close to public transport, employment and amenities. This clearly demonstrates the growing popularity, influence and application of New Urbanist ideas.This paper reviews the criminological research relating to New Urbanism associated with the three key issues of permeability, rear laneway car parking and mixed-use development. These key issues are discussed from an environmental criminology perspective and challenge New Urbanist assumptions concerning crime. The paper proposes that crime prevention through environmental design (CPTED) and its crime risk assessment model represents a valuable tool for New Urbanists to utilise to reduce opportunities for crime and tackle fear of crime in the community. Recommendations for future research and collaboration are discussed

In and around: identifying predictors of theft within and near to major mass underground transit systems

This article identifies factors that encourage or reduce pick-pocketing at underground rail stations through a case study analysis of the London Underground. Negative binomial Poisson regression models found predictor variables of pick-pocketing selected from the internal characteristics of stations and features of their nearby surroundings. Factors that increased risk were those associated with greater congestion inside stations including lifts, waiting rooms and fewer platforms; and increased levels of accessibility near stations, more paths and roads. Features that reduced risk were those likely to encourage detection and guardianship; stations with more personal validators, staffing levels and shop rentals; and the presence of more domestic buildings nearby. Station type was also influential; those that were ‘attractors’ of crime and those frequently used by tourists were at greater risk. The findings suggest a transmission of theft risk between the internal settings of underground stations and their nearby surroundings

Burglary in London: insights from statistical heterogeneous spatial point processes

To obtain operational insights regarding the crime of burglary in London, we consider the estimation of the effects of covariates on the intensity of spatial point patterns. Inspired by localized properties of criminal behaviour, we propose a spatial extension to mixtures of generalized linear models from the mixture modelling literature. The Bayesian model proposed is a finite mixture of Poisson generalized linear models such that each location is probabilistically assigned to one of the groups. Each group is characterized by the regression coefficients, which we subsequently use to interpret the localized effects of the covariates. By using a blocks structure of the study region, our approach enables specifying spatial dependence between nearby locations. We estimate the proposed model by using Markov chain Monte Carlo methods and we provide a Python implementation

Existence of symmetric and asymmetric spikes for a crime hotspot model

Copyright @ 2014 Society for Industrial and Applied MathematicsWe study a crime hotspot model suggested by Short, Bertozzi, and Brantingham in [SIAM J. Appl. Dyn. Syst., 9 (2010), pp. 462--483]. The aim of this work is to establish rigorously the formation of hotspots in this model representing concentrations of criminal activity. More precisely, for the one-dimensional system, we rigorously prove the existence of steady states with multiple spikes of the following types: (i) multiple spikes of arbitrary number having the same amplitude (symmetric spikes), and (ii) multiple spikes having different amplitude for the case of one large and one small spike (asymmetric spikes). We use an approach based on Lyapunov--Schmidt reduction and extend it to the quasilinear crime hotspot model. Some novel results that allow us to carry out the Lyapunov--Schmidt reduction are (i) approximation of the quasilinear crime hotspot system on the large scale by the semilinear Schnakenberg model, and (ii) estimate of the spatial dependence of the second component on the small scale which is dominated by the quasilinear part of the system. The paper concludes with an extension to the anisotropic case

Crime and the NTE: multi-classification crime (MCC) hot spots in time and space

This paper examines crime hot spots near licensed premises in the night-time economy (NTE) to investigate whether hot spots of four different classification of crime and disorder co-occur in time and place, namely violence, disorder, drugs and criminal damage. It introduces the concept of multi-classification crime (MCC) hot spots; the presence of hot spots of more than one crime classification at the same place. Furthermore, it explores the temporal patterns of identified MCC hot spots, to determine if they exhibit distinct spatio-temporal patterns. Getis Ord (GI*) hot spot analysis was used to identify locations of statistically significant hot spots of each of the four crime and disorder classifications. Strong spatial correlations were found between licensed premises and each of the four crime and disorder classifications analysed. MCC hot spots were also identified near licensed premises. Temporal profiling of the MCC hot spots revealed all four crime types were simultaneously present in time and place, near licensed premises, on Friday through Sunday in the early hours of the morning around premise closing times. At other times, criminal damage and drugs hot spots were found to occur earlier in the evening, and disorder and violence at later time periods. Criminal damage and drug hot spots flared for shorter time periods, 2–3 h, whereas disorder and violence hot spots were present for several hours. There was a small spatial lag between Friday and Saturday, with offences occurring approximately 1 h later on Saturdays. The implications of these findings for hot spot policing are discussed

Art as a Means to Disrupt Routine Use of Space

This paper examines the publicly visible aspects of counter-terrorism activity in pedestrian spaces as mechanisms of disruption. We discuss the objectives of counter-terrorism in terms of disruption of routine for both hostile actors and general users of public spaces, categorising the desired effects as 1) triangulation of attention; 2) creation of unexpected performance; and 3) choreographing of crowd flow. We review the potential effects of these existing forms of disruption used in counter-terrorism. We then present a palette of art, advertising, architecture, and entertainment projects that offer examples of the same disruption effects of triangulation, performance and flow. We conclude by reviewing the existing support for public art in counter-terrorism policy, and build on the argument for art as an important alternative to authority. We suggest that while advocates of authority-based disruption might regard the playfulness of some art as a weakness, the unexpectedness it offers is perhaps a key strengt

Spatio-temporal crime hotspots and the ambient population

It is well known that, due to that inherent differences in their underlying causal mechanisms, different types of crime will have variable impacts on different groups of people. Furthermore, the locations of vulnerable groups of people are highly temporally dynamic. Hence an accurate estimate of the true population at risk in a given place and time is vital for reliable crime rate calculation and hotspot generation. However, the choice of denominator is fraught with difficulty because data describing popular movements, rather than simply residential location, are limited. This research will make use of new ‘crowd-sourced’ data in an attempt to create more accurate estimates of the population at risk for mobile crimes such as street robbery. Importantly, these data are both spatially and temporally referenced and can therefore be used to estimate crime rate significance in both space and time. Spatio-temporal cluster hunting techniques will be used to identify crime hotspots that are significant given the size of the ambient population in the area at the time