NYC Stop And Frisk

The 3 figures on this page introduce an ongoing project to support research and public discussion about controversial Stop-And-Frisk policing tactics. The first figure maps all recorded stops collected by the NYPD from 2006-2015. Dot size corresponds to the number of stops in a location and color indicates race. The left of the figure shows recorded stops on a map of New York City. For example, the key highlights the distribution of stops at Times Square 42nd Street Station. The right or the space-time view extends stops on the map horizontally over time. Together, both views reveal phenomena such as the intensity and uneven racial distribution of stops as well as the dramatic fall in stops after the program was ruled unconstitutional on August, 12th 2013.

  • Shapiro, B.R. & Pearman II, Francis A. (2017). Using the Interaction Geography Slicer to Visualize New York City Stop & Frisk. In Proceedings of the IEEE VIS 2017 Arts Program, VISAP’17. Phoenix, AZ. PDF

  • This ongoing project is made possible by the National Science Foundation.

This 2nd figure shows New York City felonies recorded by the NYPD at the same scales and dot conventions as stops shown in the 1st figure. The key on the map highlights the distribution of Felonies at Macy’s Herald Square. The right or space-time view shows how felonies have remained relatively consistent from 2006-2015 in comparison to stops shown in Figure 1. This provides a vivid contrast to those who believe felonies have risen dramatically since stop and frisk was ruled unconstitutional in New York City. This does not mean that stop and frisk is having no effect on reported felonies (e.g., felonies have decreased slightly over these 10 years). However, it supports existing research showing that stop and frisk policing practices may not influence crime in a direct or cause-and-effect way.

This 3rd figure highlights stops and murders along Broadway Street, one of the oldest North-South thoroughfares in Manhattan, New York City. The figure is primarily meant to inspire discussion about questions such as how policing activity responds to violent crime differently in different neighborhoods and to further illustrate ways to use a dynamic visualization tool called the Interaction Geography Slicer or IGS to view, interact with, and study large scale data sets over space and time.