This article explores selective drug law enforcement practices in a single municipality, San Francisco, where racial disproportionality in drug arrest rates is among the highest in the United States. We situate this work in the vein of recent case-study examinations done in Seattle, Cleveland, and New York to help build a more nuanced picture of how the local geography of policing drugs produces racialized outcomes. Within this, we examine how historically embedded local politics shape the varied styles and structures of policing that result in racially discriminatory enforcement patterns. Our goal is to begin sketching out a robust framework of 'place' as an orientation for examining discretionary local policing practices, especially as they impact marginalized groups and communities of color.
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.
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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