Since Hinkle et al. (2013) highlighted a statistical powerlessness problem in hot-spots policing experiments in midsized cities with moderate property crime rates, the current work demonstrated that this problem is less readily resolved than previously suspected, as It reviewed results from a predictive policing randomized control trial in a large city with property crime rates higher than Chicago or Los Angeles.
This study reports, for the first time, a graphical analysis indicating the marked car patrol intervention, practically effective at the 500′ by 500′ (mission) grid level, three grids per shift, likely had a district-wide impact on reducing reported property crime. In addition, it reviews results of a series of thought experiments exploring statistical power impacts of four modified experimental designs. Only one alternative design, with spatially up-scaled predictive policing mission areas and concomitantly higher property crime prevalence rates, produced acceptable statistical power levels. Implications follow for current theoretical confusion in community criminology about concentration effects and units of analysis, and how models organize impacts across those different units. Implications follow for practice amid ongoing concerns about whether predictive policing works and, if it did, how to gauge its impacts and social justice costs. This study brings to the fore important questions beyond “does predictive policing work?” Can we design predictive policing randomized experiments capable of showing statistical effectiveness? Furthermore, if we can, and if those studies include larger mission areas than the micro-scaled geographic grids used so far, how do we integrate social justice concerns into effectiveness metrics, given the broader segments of communities likely affected? (publisher abstract modified)
- Research in Brief: Predictive Policing: Understanding and Applying Analytical Techniques To Prevent and Combat Crime
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