Abstract
Research suggests that vacant homes are associated with a variety of negative outcomes for communities, including higher rates of some crimes. A few studies in this vein have examined the effects of particular types of vacancy, such as abandoned homes, empty occupiable residences, seasonal housing, and undeveloped lots. However, these works have focused on a single state or urban area. The present study sought to advance the understanding of vacancy’s relationship to acquisitive crime (burglary, robbery, and larceny) by including several vacancy rates (homeowner, rental, seasonal, and overall) as distinct predictors and by using a sample of large cities from across the United States. The analysis also controlled for social, demographic, and economic factors relevant to crime and vacancy. Results from negative binomial regression models indicated that the relationship between empty residences and crime varied depending upon the particular form of vacancy and upon the type of criminal offense.
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Notes
A-priori power calculations were based on robbery rates, since it occurs least frequently of the three offenses under consideration. The 2015 Uniform Crime Report (U.S. Department of Justice, 2016b) indicates a robbery rate of 255.2 for Group I cities (population > 250,000) and 137.8 for Group II cities (population 100,000 – 249,999). The mean of these two rates (196.5) was used in the power calculation. Jones and Pridemore (2016) reported that vacancy rates changed robbery rates by about 0.8%. Using these figures, a priori power calculations were performed using G*Power software version 3.1.9.2, Exp(B) = 1.008, power = .80, base rate = 0.002552, mean exposure 250,000, r2 other X = .40.
Selection of these dummy variables was guided by inspection of local Moran’s I and cluster maps produced in GeoDa 1.12 spatial analysis software, using an arc distance of 400 miles for the spatial weights so that every city had neighbors except for Anchorage, AK and Honolulu, HI. Locations included in the Mid-Atlantic dummy variable were all cities New York, New Jersey, Pennsylvania, Virginia, Maryland, and Washington, DC. Moran’s I values for all 12 of the final models were insignificant (p > .05), indicating a lack of spatial autocorrelation.
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