The Long Beach Post recently gained access to a detailed data log of every person the Long Beach Police Department stopped or detained over the span of 2019.
Already, this unprecedented data has shown Black drivers are stopped at a rate that is disproportionate to Long Beach’s population. And the data also shows that when non-White drivers are stopped, they face more scrutiny than White drivers, being required to get out of their cars more often and being more frequently asked for consent to be searched.
The release of this data comes as part of landmark anti-racial profiling legislation passed in California in 2015 called the Racial and Identity Profiling Act, known as RIPA.
RIPA mandates nearly all law enforcement agencies collect information and certain demographic data on stops of people who were arrested, detained or searched, including consensual searches.
Under RIPA, the Long Beach Police Department was required in 2019 to start collecting the race, gender, age, LGBTQ+ status, disability status and other data points for stops and individuals they encountered, which that year totaled 40,523.
The Post obtained this massive trove of more than 1.6 million data points through a public records request to the California Department of Justice and the Long Beach Police Department.
Since receiving the raw data, the Post newsroom staff has worked hundreds of hours interpreting the data, validating information with mathematicians and statisticians, interviewing elected leaders and policing experts, and confirming information with the LBPD.
To process and visualize this data, our newsroom utilized Workbench, a powerful open-source data platform from Columbia University’s Journalism School that allowed us to clean, sort, pivot and group large amounts of data to look for correlations and trends in how Long Beach police interact with the public.
Today’s reporting is the first in a series as the Post newsroom more deeply examines LBPD’s RIPA data, looking into patterns and trends, such as who is stopped for truancy or for failing to pay bus fare, how race may play a part in officers’ decisions, disparate outcomes for those of different races, ethnicities, genders, or age by analyzing who is more likely to be arrested or cited by officers.
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