Police Use of Force

I’ve made the following county-level graphs of fatal police shooting rates by race using data from the Census and compiled from the data I’ve now made available on figshare:

Bradford, John Hamilton (2017): Fatal Police Shootings (2013-2016) & County Level Data. figshare.

Below is the graph. I. Loading Data 1. A function to retrieve state abbreviations getState.abb <- function(x){ ##remove punctuation and twim white space, convert to lower case x <- tolower(trimws(gsub("[[:punct:]]", "", x))) x[which(x == "washington dc")] <- "district of columbia" states.

I’ve just published new data sets to figshare with accompaying code to RPubs. Below is the project description with links. This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016. Five of the files use only the Washington Post data covering years 2015 and 2016, whereas the other five files utilize fatal police shootings of civilians for years 2013-2016, combining the Lott & Moody (2016) data for years 2013-2015 and Washington Post data for 2016.

This is a set of csv files including a number of variables including fatal police shootings and other crime and socioeconomic covariates at different levels of geographical aggregation from 2013-2016.

A new article has just been published in the Journal of Criminal Justice, “The prevalence of fatal police shootings by U.S. police, 2015–2016: Patterns and answers from a new data set” by Shane, Lawton, and Swenson (2017) . The authors make some good points that I mostly agree with, but I cannot make sense of their finding that the mean rate of black civilians killed by police (per 100,000) is lower than that of whites.

Figure 1. SVG

Figure 2. SVG

Above are some new county-level heat maps of police shooting data, compiled from the Lott & Moody (2016) data for years 2013-2015, and the Washington Post for 2016. Below the graphs, the R code is provided. R Code 1. Load the Data. (I plan to upload these data sets to figshare shortly.) library(readr) library(tidyr) library(dplyr) vars <- fst::read.fst(path = "countyVars.csv") df_wapoLott <- dplyr::left_join(vars, readr::read_csv("killed_lott_wapo.