This study examined satisfaction with the police at micro places, using data from citizen surveys conducted in 2001, 2009, and 2014 in one city.
The article illustrates t0he utility of this approach by comparing micro- and meso-level aggregations of policing attitudes, as well as by predicting views about the police from crime data at micro places. In each survey, respondents provided the nearest intersection to their address. Using that geocoded survey data, the study used inverse distance weighting to map a smooth surface of satisfaction with police over the entire city and compared the micro-level pattern of policing attitudes to survey data aggregated to the census tract. The study also used spatial and multi-level regression models to estimate the effect of local violent crimes on attitudes toward police, controlling for other individual and neighborhood characteristics. The findings demonstrate that there are no systematic biases for respondents refusing to answer the nearest intersection question. Hot spots of dissatisfaction with police did not conform to census tract boundaries, but rather aligned closely with hot spots of crime. Models that predicted satisfaction with police show that local counts of violent crime were a strong predictor of attitudes toward police, even above individual-level predictors of race and age. (publisher abstract modified)