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Social Science Computer Review
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Use of a "Microecologic Technique" to Study Crime Around Substance Abuse Treatment Centers

Susan J. Boyd

University of Maryland School of Medicine Baltimore Veterans Affairs Medical Center, sboyd{at}psych.umaryland.edu

Kevin M. Armstrong

University of Maryland Baltimore County, kevin{at}gcsllc.com

Li Juan Fang

University of Maryland School of Medicine, t lfang{at}psych.umaryland.edu

Deborah R. Medoff

University of Maryland School of Medicine, dmedoff{at}psych.umaryland.edu

Lisa B. Dixon

University of Maryland School of Medicine Baltimore Veterans Affairs Medical Center, ldixon{at}psych.umaryland.edu

David A. Gorelick

National Institute on Drug Abuse, National Institutes of Health, DGORELIC @intra.nida.nih.gov

Whether substance abuse treatment centers affect neighborhood crime is hotly debated. Empirical evidence on this issue is lacking because of the difficulty of distinguishing the crime effect of treatment centers in high-crime areas, the inability to make before-and-after comparisons for clinics founded before computerized crime data, and the need for appropriate control sites. The authors present an innovative method (without an actual data analysis) to overcome these challenges. Clinic addresses and crime data are geocoded by street address. Crimes are counted within concentric-circular, 25-meter "buffers" around the clinics. Regression analyses are used to calculate the "crime slope" (ß) among the buffers. A negative ß indicates more crimes closer to the site. A similar process is used to evaluate crimes around control sites: convenience stores, hospitals, and residential points. This innovative technique provides valid empirical evidence on crime around substance abuse treatment centers.

Key Words: substance abuse • treatment • crime mapping • GIS • geocoding • buffers

Social Science Computer Review, Vol. 25, No. 2, 163-173 (2007)
DOI: 10.1177/0894439307298928
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