What is your story?
We wanted to dig into the possibility that police divisions may be profiling certain races as stop-and-frisk suspects more often than they should. This hypothesis is based on some of the occurrences that happened in the last year that suggests police officers may be unfairly targeting African-Americans. As data journalists, we know this is a hot topic in the media and wanted to provide context on what we’re seeing in the Los Angeles region.
How does the selected data support your story?
Taking the top 11 police divisions that had the most number of stop-and-frisks allowed us to break down the contribution of each race as a % within the total number of encounters. This data showed us that there is definitely the possibility that certain police divisions are targeting African Americans more often than other races considering the racial makeup of their assigned neighborhoods. We need an additional set of data to confirm this, which would be the racial breakdown, by neighborhood, of residents over the age of 14 (a gauge for possible stop-and-frisk suspects). We would then compare our data with the racial breakdown data to see if police in certain divisions are unfairly targeting African-Americans (the % of African Americans in their stop-and-frisk encounters is higher than the % of African American residents in those neighborhoods above the age of 14). If this comparison did indeed show that certain divisions were particularly targeting African Americans, then they could be easy targets for division-wide training programs to help them recognize their biases.
What data did you omit, and why?
There are 25 police divisions that we omitted in order to just pick the ones that had the most number of stop-and-frisk encounters. Instead of just picking the top 10, we chose to also include the division with the 11th most encounters since it had a very large % of African Americans involved. As for the other data metrics, we felt that the police officers’ division and the race of the suspect were the most critical dimensions to evaluate based on our intended investigation.
How does the representation support your story?
The representation shows that there are a number of divisions with a very large % of stop-and-frisk encounters involving African American suspects. The data representation provides us with a clear starting point of divisions to investigate to see if they are targeting a higher % of African Americans than there are in their assigned neighborhoods.
What visual metaphor(s) did you use and why?
We want to present this data through a geographical representation as well so that our audience (readers of our journal) can easily identify what the data shows for their neighborhood. This is most relevant to our audience since it is a broader group that would be concerned about their location. We would want to also use the concept of small multiples to have a separate geographical representation for each race.