This study examines the environmental predictors that classify street robbery hot spots and control street segments in Indianapolis. Empirical controls were generated by matching each hot spot to a corresponding set of zero-crime control and low-crime control units. Then, units were evaluated based on the presence of crime generators and attractors, which were downloaded from open data sources and spatially joined to the street segments, and disorder indicators obtained via systematic social observation using Google Street View. The findings provide information about the influence environmental predictors have on the presence of street robbery hot spots, and whether the composition of hot spots significantly differs from that of similar places that experienced no crime or low counts of crime.
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