  Large-scale policing data is vital for detecting inequity in police behavior
and policing algorithms. However, one important type of policing data remains
largely unavailable within the United States: aggregated police deployment data
capturing which neighborhoods have the heaviest police presences. Here we show
that disparities in police deployment levels can be quantified by detecting
police vehicles in dashcam images of public street scenes. Using a dataset of
24,803,854 dashcam images from rideshare drivers in New York City, we find that
police vehicles can be detected with high accuracy (average precision 0.82, AUC
0.99) and identify 233,596 images which contain police vehicles. There is
substantial inequality across neighborhoods in police vehicle deployment
levels. The neighborhood with the highest deployment levels has almost 20 times
higher levels than the neighborhood with the lowest. Two strikingly different
types of areas experience high police vehicle deployments - 1) dense,
higher-income, commercial areas and 2) lower-income neighborhoods with higher
proportions of Black and Hispanic residents. We discuss the implications of
these disparities for policing equity and for algorithms trained on policing
data.
