Application of Space-Time Scan Statistics to Describe Geographic and Temporal Clustering of Visible Drug Activity

Abstract

Knowledge of the geographic and temporal clustering of drug activity can inform where health and social services are needed and can provide insight on the potential impact of local policies on drug activity. This ecologic study assessed the spatial and temporal distribution of drug activity in Baltimore, Maryland, prior to and following the implementation of a large urban redevelopment project in East Baltimore, which began in 2003. Drug activity was measured by narcotic calls for service at the neighborhood level. A space-time scan statistic approach was used to identify statistically significant clusters of narcotic calls for service across space and time, using a discrete Poisson model. After adjusting for economic deprivation and housing vacancy, clusters of narcotic calls for service were identified among neighborhoods located in Southeast, Northeast, Northwest, and West Baltimore from 2001 to 2010. Clusters of narcotic calls for service were identified among neighborhoods located in East Baltimore from 2001 to 2003, indicating a decrease in narcotic calls thereafter. A large proportion of clusters occurred among neighborhoods located in North and Northeast Baltimore after 2003, which indicated a potential spike during this time frame. These findings suggest potential displacement of drug activity coinciding with the initiation of urban redevelopment in East Baltimore. Space-time scan statistics should be used in future research to describe the potential implications of local policies on drug activity.

Policing, Community Fragmentation, and Public Health: Observations from Baltimore

“…This study confirms previous qualitative studies reporting police violence in communities and places of high drug use and drug markets, poverty, and predominantly African-American. [24][25][26][27] It offers empiric analysis of community fragmentation and stress as a result of police violence. This study supports the hypothesis that policing results in community fragmentation.…”

The Spatial-Temporal Pattern of Policing Following a Drug Policy Reform: Triangulating Self-Reported Arrests With Official Crime Statistics

“…Although surveys of illicit drug users provide the most accurate assessment of their encounters with law enforcement, the costs and limitations of surveying such hard-to-reach populations (Rudolph et al, 2011) can be prohibitive in many settings (Spooner & Flaherty, 1993). Official crime statistics, by contrast, are routinely collected in many settings at the population level but likely oversamples highly marginalized subgroups of PWID (i.e., homeless) and under samples others (i.e., drug users that regularly migrate across settings) (Linton, Jennings, Latkin, Gomez, & Mehta, 2014). Nevertheless, our results are promising in that we observed a modest and positive spatial autocorrelation in the number of arrests reported by PWID and those reported by police.…”

Association of exposure to neighborhood drug activity, neurobehavioral traits, and marijuana use among at-risk African American females

“…However, it should be noted that at present there is no gold standard for measuring neighborhood drug activity. Previous research has used objective measures (e.g., geocoded drug arrest data) (see Jennings, Taylor, Salhi, Furr-Holden, & Ellen, 2012;Linton et al, 2014) as well as self-report measures, which typically consists of one or two items, to assess exposure to neighborhood drug activity (see Crum et al, 1996 andJennings et al, 2012).…”

“…One unique characteristic of economically disadvantaged African American neighborhoods that is conceptualized as a risk factor for substance use is visible drug market activity (Crum, Lillie-Blanton, & Anthony, 1996;Saxe et al, 2001;Wallace & Muroff, 2002;Wertz & Sayette, 2001). In lower socioeconomic (SES) neighborhoods with visible drug activity there are more opportunities to use drugs and drug sub-cultures likely reduce inhibitions about drug use, thereby increasing the likelihood of use (Wagner & Anthony, 2002;Linton, Jennings, Latkin, Gomez, & Mehta, 2014;Wertz & Sayette, 2001;Cochran, Grella, & Mays, 2012).…”