My paper (joint work with Jasmine Silver, Rob Worden, and Sarah McLean), Mapping attitudes towards the police at micro places, has been published in the most recent issue of the Journal of Quantitative Criminology. Here is the abstract:

Objectives: We examine satisfaction with the police at micro places using data from citizen surveys conducted in 2001, 2009 and 2014 in one city. We illustrate the 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.

Methods: In each survey, respondents provided the nearest intersection to their address. Using that geocoded survey data, we use inverse distance weighting to map a smooth surface of satisfaction with police over the entire city and compare the micro-level pattern of policing attitudes to survey data aggregated to the census tract. We also use spatial and multi-level regression models to estimate the effect of local violent crimes on attitudes towards police, controlling for other individual and neighborhood level characteristics.

Results: We demonstrate that there are no systematic biases for respondents refusing to answer the nearest intersection question. We show that hot spots of dissatisfaction with police do not conform to census tract boundaries, but rather align closely with hot spots of crime. Models predicting satisfaction with police show that local counts of violent crime are a strong predictor of attitudes towards police, even above individual level predictors of race and age.

Conclusions: Asking survey respondents to provide the nearest intersection to where they live is a simple approach to mapping attitudes towards police at micro places. This approach provides advantages beyond those of using traditional neighborhood boundaries. Specifically, it provides more precise locations police may target interventions, as well as illuminates an important predictor (i.e., nearby violent crimes) of policing attitudes.

And this was one of my favorites to make maps. We show how to take surveys and create analogs of hot spot maps of negative sentiment towards police. We do this via asking individuals to list their closest intersection (to still give some anonymity), and then create inverse distance weighted maps of negative attitudes towards police.

We also find in this work that nearby crimes are the biggest factor in predicting negative sentiment towards police. This hints that past results aggregating attitudes to neighborhoods is inappropriate, and that police reducing crime is likely to have the best margin in terms of making people more happy with the police in general.

As always, feel free to reach out for a copy of the paper if you cannot access JQC. (Or you could go a view the pre-print.)