Detecting Spatial Patterns in Drug Abuse With GIS

Drug abuse has been shown to have clear spatial patterns that GIS has helped to demonstrate. Research has shown that both the physical presence of stores that can lead to high risk behavior, such as alcohol outlets, along with social factors including unemployment and the area being below the poverty line have very close correlation to high rates of drug abuse. However, the presence of treatment centers was shown to diminish such physical and social factors for high-risk neighborhoods.[1] Another study applying a logistic spatial regression looked at the effect of distance from a place of residence to the closest drug treatment facility and the number of facilities that a drug abuser could access. Findings also showed a mitigating effect that even made the worst affected individuals improve in rates of drug abuse.[2]

Maps showing Socioeconomic risk, physical risk, and total risk areas for drug abuse. From: Mendoza et. al, 2013).

Maps showing Socioeconomic risk, physical risk, and total risk areas for drug abuse. From: Mendoza et. al, 2013).

How Google Earth and Street View are helping to map drug abusers

While spatial patterns appear to be reflected in drug abuse, healthcare and other social service workers attempting to treat drug abusers have noted difficulties because many abusers are migratory. For example, in Tijuana, Mexico, large segments of the population are not from the area and are there temporarily. Using Google Earth and Street View has helped interviewers map where drug abusers are located, as migratory populations are often less aware of the street names, areas where they reside and what it might be called, and these areas are often not labeled.[3]

Drug abuse space-time clustering

Another approach uses narcotics related calls to healthcare and social services to know how drug abusers and their clustering might migrate in space and time. Using a discrete Poisson model, researchers indicated how the eastern part of Baltimore had a greater clustering of drug abusers between 2001-2003, while other parts of the city experienced greater increase after 2003, even as the east part diminished in the clustering of abuse-related calls.[4]


[1] For more on the role of the physical lay out of neighborhoods and social factors affecting drug abuse, see:  Mendoza, N. S., Conrow, L., Baldwin, A., & Booth, J. (2013). Using GIS to Describe Risk and Neighborhood-Level Factors Associatd with Substance Abuse Treatment Outcomes. Journal of Community Psychology, 41(7), 799–810.

[2] For more on these results, see: Kao, D., Torres, L. R., Guerrero, E. G., Mauldin, R. L., & Bordnick, P. S. (2014). Spatial accessibility of drug treatment facilities and the effects on locus of control, drug use, and service use among heroin-injecting Mexican American men. International Journal of Drug Policy, 25(3), 598–607.

[3] For more on how Google Earth and Street View are helping map drug abusers in Tijuana, see:  Beletsky, L., Arredondo, J., Werb, D., Vera, A., Abramovitz, D., Amon, J. J., Gaines, T. L. (2016). Utilization of Google enterprise tools to georeference survey data among hard-to-reach groups: strategic application in international settings. International Journal of Health Geographics, 15(1).

[4] For more on drug abuse space-time clustering, see:  Linton, S. L., Jennings, J. M., Latkin, C. A., Gomez, M. B., & Mehta, S. H. (2014). Application of Space-Time Scan Statistics to Describe Geographic and Temporal Clustering of Visible Drug Activity. Journal of Urban Health, 91(5), 940–956.


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