Using GIS to Fight Drug Trafficking

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GIS has proven to be an immensely useful tool for police interdiction operations and crime prevention. One area, in particular, that has benefited from GIS is combating drug trafficking. This type of crime generally follows known or historical patters, where drug traffickers think of their operations as a cost or benefit. For them, they want to ship their products to a location at the least expense and with the least probability of the cargo being seized. For police and other agencies involved in drug interdiction, the opposite is true.

Examples of how police are able to use GIS is applying Bayesian modeling or statistical procedures that use proxies, such as past drug arrests, to estimate the likelihood a given location will produce likely drug activity.[1]  Inferential approaches, that use machine learning or data mining techniques, have also proven useful to investigate if given behaviors are “abnormal” for more legal types of activities. For instance, ships that may transport drugs could take less used or unlikely paths to drop off their cargo. An ontology approach, one that identifies accepted or less accepted behavior, could be utilized to inform drug enforcement agents that a ship identified on radar is more likely to contain illegal drugs.[2] Critical to drug interdiction is not so much always being in the right place at the right time but making decisions about resource allocation for units involved in interdiction. Specifically, one can never be sure what routes or ways a drug dealer make send their goods to market. For law enforcement, the decision is how many police officers or agents should be allocated to a given area.[3] GIS has been used, from commercial to open source, to make decisions about when and where to send agents using data such as the region’s geography and time and methods of transporting given drugs. Viewshed analysis has also been used to determine if a given likely area is more difficult to monitor or may require further resources at given times of the day.

Geographic pattern of illegal border crossings. Source: Rossmo, et al., 2008).

Geographic pattern of illegal border crossings. Source: Rossmo, et al., 2008).

References

[1] For an example of a statistical approach to drug interdiction using GIS, see:  Hunt, Eleazer D., Marty Sumner, Thomas J. Scholten, and James M. Frabutt. 2008. “Using GIS to Identify Drug Markets and Reduce Drug-Related Violence.” In Geography and Drug Addiction, edited by Yonette F. Thomas, Douglas Richardson, and Ivan Cheung, 395–413. Dordrecht: Springer Netherlands.

[2] For more on this developed software, see:  Vandecasteele, Arnaud, and Aldo Napoli. 2012. “Spatial Ontologies for Detecting Abnormal Maritime Behaviour.” In , 1–7. IEEE.

[3] For an example of interdiction using GIS based on many layers of geographic and historical data, see:  Rossmo, D Kim, Quint C Thurman, J D Jamieson, and Kristine Egan. 2008. “Geographic Patterns and Profiling of Illegal Crossings of the Southern U.S. Border.” Security Journal 21 (1-2): 29–57.

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