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Lowell, Massachusetts, Smart Policing Initiative: Reducing Property Crime in Targeted Hot Spots

NCJ Number
Brenda J. Bond; Lauren Hajjar; Arthur Ryan; Michael D. White
Date Published
March 2014
18 pages
This Smart Policing Initiative (SPI) Spotlight Report (March 2014) reviews the implementation and impact of the SPI model in the effort to reduce property crime in Lowell, Massachusetts.
Sectors of the city targeted for the SPI intervention experienced significant declines in property crime from the pre-intervention period to the intervention period. These declines ranged from 16 to 19 percent; however, targeted hot spots experienced larger drops in certain crime types. Crime data from all targeted hot spots indicate that the SPI-based intervention achieved substantial reductions in drug-related property crime. Crime data analysis conducted as part of the SPI determined that much of the pre-intervention increase in property crime was linked to drug offenders who committed property crimes to support their addictions. Lowell's SPI team addressed drug-related property crime through problem-oriented policing and the SARA model (Scanning, Analysis, Response, and Assessment). A Steering Committee composed of department staff and researchers experienced in advanced problem-solving guided this effort in order to avoid some of the traditional problems with SARA implementation. As part of the data analysis, the SPI team collaborated with the city's Health Department in examining the background and history of all individuals who died from a drug overdose from 2005 through 2008. Results confirmed the strong link between drug use and property crime. The SPI team identified 12 property crime hot spots across three sectors, most of which were near known drug markets. Police captains in each of the three sectors developed response plans that were discussed, modified, and monitored at the bi-weekly meeting of the Steering Committee. The Lowell SPI highlights the importance of accessing non-traditional data in extending the problem analysis process. It also shows the importance of near real-time monitoring of the problemsolving model.