Table of Contents
The Long Beach RIPA project demonstrates the inefficiencies and racial profiling in the Long Beach Police Department's (LBPD) police actions through analysis of LBPD's Racial & Identity Profiling Act (RIPA) data. The data shows that LBPD spends more time and money racially profiling folks in North and Central Long Beach than they spend actually protecting the community. Most of this profiling is conducted via traffic stops. These stops do not make the community safer, and they mean the LBPD spends less time responding to calls for service.
Note: The code does not include lines relating to importing of the data. We pull tables from our private PostgreSQL database using credentials accessed through a separate script before running any of the code below. The database is accessible only by our Research & Data Analysis team.
To get a local copy up and running follow these simple example steps.
We completed the data cleaning, analysis, and visualization using the following software.
We used several R packages to analyze data and perform different functions, including the following.
- data.table
- dplyr
- sf
- tidyr
- usethis
- highcharter
- leaflet
list.of.packages <- c("usethis","dplyr","data.table", "sf", tidyr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
devtools::install_github("r-lib/usethis")
library(usethis)
library(dplyr)
library(data.table)
library(sf)
library(tidyr)
library(highcharter)
library(leaflet)
Our analysis focused on stops that occured in 2019, as this was the most recent data available that was not impacted by the COVID-19 pandemic. You can access our full methodology here.
Police Stop Data
- City of Long Beach, Long Beach Police Department, 2019, Police Stop Data (RIPA). Retrieved from https://www.longbeach.gov/police/about-the-lbpd/lbpd-stop-data/.
Population Estimates by Race
- U.S. Census Bureau, 2016-2020, American Community Survey, 5-Year Estimates. Tables DP05, B04006, B02018. Retrieved from https://data.census.gov/cedsci/.
- U.S. Census Bureau, 2020, TIGER/Line Shapefiles, Census Tracts. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.html#list-tab-790442341.
- City of Long Beach, 2022, City Council Districts. Retrieved from https://longbeach.opendatasoft.com/explore/dataset/colb-council-districts/information/
High-Injury Corridors and Intersections
- City of Long Beach, Safe Streets Long Beach: A Vision Zero Action Plan (July 2020), 2013-2017, Top 20 High-Injury Pedestrian/Bicycle and Motor Vehicle/Motorcycle Corridors and Intersections. Retrieved from https://www.longbeach.gov/globalassets/go-active-lb/media-library/documents/programs/safe-streets-lb-action-plan---final.
As with all data, the findings and trends seen in this analysis are dependent on the quality and limitations of the data used. We strongly encourage readers to consider the limitations of RIPA data when interpreting findings. RIPA data are based on officers’ reports, meaning the information attached to each stop is solely based on officer disclosure and perceptions of what occurred during the stop. This also includes the officers' perception of the characteristics of the person(s) being stopped, including their age, gender, and race. For a full discussion of limitations, please see our Methodology document
We would like to express our deepest appreciation to the Long Beach People’s Budget Coalition for their tremendous help and guidance in this research project. Over the years, the Coalition has been at the forefront of efforts to reimagine and redefine public safety, by trying to eradicate racial profiling and eliminate threats to human life posed by law enforcement.
Our research project was done with the explicit aim of providing the Coalition with a comprehensive insight into local policing practices. This effort marks the first step in a long journey to meaningfully engage the community in finding alternatives to traditional policing approaches. By shifting the focus towards investing in upstream services and fostering thriving communities, we collectively aspire to create a safer and more equitable future for all residents of Long Beach.
The following individuals from Catalyst California contributed to the framing and writing of the report:
- Jacky Guerrero
- Kianna Ruff
- Chauncee Smith
The following individuals from Catalyst California contributed to the data analysis and visualizations that show in up in the report:
For policy-related inquiries:
- Jacky Guerrero -jguerrero[at]catalystcalifiornia.org
For data-related inquiries:
- Elycia Mulholland Graves - egraves[at]catalystcalifornia.org
- Jennifer Zhang - jzhang[at]catalystcalifornia.org
A world where systems are designed for justice and support equitable access to resources and opportunities for all Californians to thrive.
Catalyst California advocates for racial justice by building power and transforming public systems. We partner with communities of color, conduct innovative research, develop policies for actionable change, and shift money and power back into our communities.
Click here to view Catalyst California's Projects on GitHub
Distributed under the MIT License. See LICENSE.txt for more information.