Within any type of system, the actors in the system inevitably compete over resources. With competition comes the possibility of conflict. To minimize such effects, actors often will partition the system into geographic territories. It is against the larger ecological backdrop of competition and conflict that we examine territory formation among urban street gangs. Although previous studies have examined the social and built environment where gangs form, and how the presence of a gang influences local levels of violence, we know little about how competitive interactions are tied to the formation and maintenance of gang territories. We use formal spatial Lotka–Volterra competition models to derive hypotheses about competition‐driven territory formation. By using data on 563 between‐gang shootings, involving 13 rival street gangs in the Hollenbeck Policing Division of Los Angeles, we show that violence strongly clusters along the boundaries between gangs in a way that is quantitatively predicted by the theory. The results suggest that even weak competitive interactions between gangs are sufficient to drive gang territory formation without recourse to other processes or assumptions.
This study explored how changes in neighborhood structural characteristics predicted variation in gang versus non-gang homicides in a policing division of the Los Angeles Police Department (LAPD). Longitudinal negative binomial models were examined to test the relationship between-neighborhood structural covariates with gang and non-gang homicides over a 35-year period. This study highlights the potential to estimate temporal effects not captured by cross-sectional analyses alone. The results underscore a unique feature that distinguishes gang homicides from other forms of non-gang violence, its tenacious clustering, and spatial dependence over time.
We report the results of a prospective, randomized study of the impact and cost-effectiveness of DNA evidence in investigating property crimes, mainly residential burglary. Biological evidence was collected at up to 500 crime scenes in five U.S. cities between 2005 and 2007, and cases were randomly assigned to the treatment and control groups in equal numbers. DNA processing was added to traditional investigation in the treatment group. A suspect was identified in 31% of treatment cases and 13% of control cases. A suspect was arrested in 22% of treatment cases and 10% of control cases. Across the five sites, each additional arrest-an arrest that would not have occurred without DNA processing-cost slightly more than US $14,000. In the most cost-effective sites, an additional arrest cost less than US$4,000. Expanding the use of DNA as an investigative tool has profound implications. Since DNA-led investigations are more costly than business-as-usual, substantial investments will be required to expand the capacity of crime laboratories, police, and prosecutors to use this investigative tool efficiently. In time, such a change may also impact the types of crimes of cases processed in the criminal justice system.
In this article we identify social communities among gang members in the Hollenbeck policing district in Los Angeles, based on sparse observations of a combination of social interactions and geographic locations of the individuals. This information, coming from LAPD Field Interview cards, is used to construct a similarity graph for the individuals. We use spectral clustering to identify clusters in the graph, corresponding to communities in Hollenbeck, and compare these with the LAPD's knowledge of the individuals' gang membership. We discuss different ways of encoding the geosocial information using a graph structure and the influence on the resulting clusterings. Finally we analyze the robustness of this technique with respect to noisy and incomplete data, thereby providing suggestions about the relative importance of quantity versus quality of collected data.
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