Note: The racial categorization scheme mandated by California law requires law enforcement agencies to record stops of “Middle Eastern or South Asian” people separately from stops of “Asians” (that is, East Asians), and does not distinguish Middle Eastern from South Asian people. Because South Asians cannot be distinguished from non-Asian Middle Easterners in this dataset, stops of “Middle Eastern or South Asian” people are not counted as stops of “Asians” in the analyses on this page.
Richmond, CA 2023
NON-TRAFFIC STOPS
This section examines non-traffic stops, which are stops for which the recorded reason was anything other than “traffic violation.”
- 1. NON-TRAFFIC STOPS BY RACIAL GROUP
- 2. NON-TRAFFIC STOPS PER QUARTER
- 3. COMPARING NON-TRAFFIC STOP RATES
- 4. PERCENTAGE OF NON-TRAFFIC STOP FREQUENCY EXPLAINED BY NEIGHBORHOOD FACTORS
- 5. COMPARING SEARCH RATES AT NON-TRAFFIC STOPS
- 6. COMPARING SEARCH OUTCOMES AT NON-TRAFFIC STOPS
- 7. NON-TRAFFIC STOP REASONS BY RACIAL GROUP
- 8. NON-TRAFFIC STOP OUTCOMES BY RACIAL GROUP
- 9. NON-TRAFFIC STOPS BY WORK UNIT AND RACIAL GROUP
- 10. COMPOSITION OF OFFICER NON-TRAFFIC STOPS RELATIVE TO EACH OFFICER'S PATROL AREA
NON-TRAFFIC STOPS BY RACIAL GROUP
According to Richmond Police Department data on non-traffic stops recorded in 2022:
- 45% of all people officers stopped were Black, who made up 20% of the population of Richmond.
- 39% of all people officers stopped were Latinx, who made up 41% of the population of Richmond.
- 12% of all people officers stopped were White, who made up 18% of the population of Richmond.
More information
What does this show?
Each bar on the right shows the percentage of the total stops recorded in the report period that were of people in one racial group. The bars on the left in the same color (and the lighter colored background) show the percentage of the resident population that are people of the same racial group. Hovering over the bars on the right shows the number of stops that makes up that percentage.
How was this calculated?
We first took the average total recorded stops per year and calculated the percentage that were recorded of people of each racial group. Then we compared those percentages to the percentages of the resident population that are of each racial group. See the Data Notes tab for information on how we define racial groups.
We measure a non-traffic stop as a single record of a person being stopped by police, regardless of the number of officers involved (if multiple people are stopped at the same time, each person is counted as a separate stop). We encourage departments to define a non-traffic stop as any such incident. Some departments limit their data to stops that result in a citation, in which case we would not be able to analyze their records. Our guidance on collecting data has more information on how to report non-traffic stops.
We generally use all pedestrian stop data provided by departments, including incomplete years of data. However, certain analyses require complete years on data, so time periods may vary across charts.
We use local demographic data (from the Census Bureau’s American Community Survey 5-year estimates) as the most straightforward and complete representation of the local population. The use of Census data also allows us to perform standardized analyses across law enforcement agencies. We recognize that this measure of demographics may not capture the entire population of individuals with whom police interact. However, the analyses on this page can shed light on the role that local demographics may play in any observed disparities.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
NON-TRAFFIC STOPS PER QUARTER
More information
What does this show?
This chart shows the total number of non-traffic stops recorded during each quarter of the assessment period for all racial groups combined.
Disruptions related to the COVID 19 pandemic may have affected the volume of police-community interactions recorded in 2020 and 2021, although the extent of this impact likely varies by jurisdiction according to the local policies and restrictions implemented in response to the outbreak. It is worth noting whether or the extent to which racial disparities persisted over this time period, despite the reduction in overall police activity.
How was this calculated?
We measure a non-traffic stop as a single record of a person being stopped by police, regardless of the number of officers involved (if multiple people are stopped at the same time, each person is counted as a separate stop).
Data required for this analysis:
- Incident unique identifier
- Date of stop
COMPARING NON-TRAFFIC STOP RATES
After using a statistical technique called regression analysis to account for the influence of different crime rates, poverty levels, and percent of Black residents in neighborhoods:
- Officers recorded stopping Black people 3.6 times as often as White people.
- Officers recorded stopping Latinx people 1.3 times as often as White people.
We take into account the share of Black residents, crime rates, and poverty levels in a neighborhood because these factors affect the likelihood that a person of any racial group in a neighborhood will have police contact. For details on CPE’s regression analysis, see “More Information” below.
More information
What does this show?
This infographic displays findings from CPE’s regression analysis, a statistical technique that allows CPE to investigate differences in pedestrian stop rates by race, taking into account other socioeconomic factors that may affect policing strategies and deployment. Specifically, this regression tests how much more or less likely each racial group is than White pedestrians to be stopped in a neighborhood with an average poverty rate, crime rate, and percentage of Black residents – three factors commonly associated with increased rates of police contact. The results of this analysis show the size of racial disparities in pedestrian stops that remain even when the influence of poverty levels, crime rates, and the percentage of Black residents across neighborhoods are removed from the equation.
We take into account the share of Black residents in a neighborhood because this factor affects the likelihood that a person of any racial group in a neighborhood will have police contact. This relationship between police presence and the percent of Black residents in a neighborhood is, in part, a result of systemic racism and structural disadvantage (for example, a lack of community services can lead to more calls for police service). But police-driven factors, such as departmental policy or officer behavior, also contribute to increased police activity in neighborhoods with more Black residents. Our model cannot precisely distinguish the extent to which this increased police activity is due to reasons within a department’s control, or reasons outside a department’s control. By accounting for the neighborhood share of Black residents, the results of this analysis therefore may under-estimate the extent to which departmental factors contribute to observed disparities between Black and White people.
How was this calculated?
To represent neighborhoods, we use Census tracts — small geographic areas of approximately 4,000 residents — as defined by the Census Bureau. We use publicly available Census data to measure the percentage of Black residents in each neighborhood.
To measure serious crime rates, we count crimes in each neighborhood that are recorded by the department. Specifically, we count reports of Part 1 offenses. The FBI’s Uniform Crime Reporting Statistics defines Part 1 offenses as: murder and non-negligent homicide, rape (legacy and revised), robbery, aggravated assault, burglary, motor vehicle theft, larceny theft, and arson. Racial groups which made up less than 2% of all incidents, or which had fewer than 40 total incidents, were excluded from this analysis (see the Data Notes tab for information on how we define racial groups).
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
- Location of incident (i.e. street address, including zipcode or latitude/longitude)
- Crime unique identifier
- Crime date of incident
- Crime offense (NIBRS/UCR) classification or description
- Crime location (i.e. street address, including zipcode or latitude/longitude)
PERCENTAGE OF NON-TRAFFIC STOP FREQUENCY EXPLAINED BY NEIGHBORHOOD FACTORS
This chart displays findings from CPE’s regression analysis, a statistical technique that tests how much neighborhood poverty levels, crime rates, and share of Black residents—three common explanations for increased police contact—contribute to how often all recorded stops occur.
Statistical analysis showed that neighborhood crime rates, poverty, and share of Black residents explained 63% of the frequency of non-traffic stops, while 37% was not explained by these factors.
For details on CPE’s regression analysis, see “More Information” below.
More information
What does this show?
This chart displays findings from CPE’s regression analysis, a statistical technique that investigates how certain factors contribute to how often all stops occur. Specifically, it shows the results of testing how much neighborhood poverty levels, crime rates, and share of Black residents—three common explanations for increased police contact—are contributing to the frequency of stops overall.
The results of this analysis show that the frequency of stops is largely not explained by (or predicted by) these external factors. It is likely that factors within the control of the department, such as departmental policy and practice or officer behavior, play a part in determining when, where, and who is stopped.
For an explanation of why we measure the share of Black residents as a potential factor influencing stop frequency, see “More information” under the analysis above.
How was this calculated?
To represent neighborhoods, we use Census tracts — small geographic areas of approximately 4,000 residents — as defined by the Census Bureau. We use publicly available Census data to measure the percentage of Black residents in each neighborhood
To measure serious crime rates, we count crimes in each neighborhood that are recorded by the department. Specifically, we count reports of Part 1 offenses. The FBI’s Uniform Crime Reporting Statistics defines Part 1 offenses as: murder and non-negligent homicide, rape (legacy and revised), robbery, aggravated assault, burglary, motor vehicle theft, larceny theft, and arson. Racial groups which made up less than 2% of all pedestrian stops, or which had fewer than 40 total incidents, were excluded from this analysis (see the Data Notes tab for information on how we define racial groups).
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
- Location of incident (i.e. street address, including zipcode or latitude/longitude)
- Crime unique identifier
- Crime date of incident
- Crime offense (NIBRS/UCR) classification or description
- Crime location (i.e. street address, including zipcode or latitude/longitude)
COMPARING SEARCH RATES AT NON-TRAFFIC STOPS
According to Richmond Police Department data, once people were stopped:
- Officers recorded searching Latinx people 1.9 times as often as White people.
- Officers recorded searching Black people 1.6 times as often as White people.
More information
What does this show?
This visualization shows, out of the same number of stopped pedestrians, how many pedestrians in each racial group were then searched.
How was this calculated?
We first divided the number of stops that involved a search for each racial group by the number of stops of that racial group. We then multiplied that number by 1,000 to get the per 1,000 stops rate.
Police are typically required to search people they arrest. When the search reason is provided in the law enforcement agency’s data, these mandatory searches are excluded from this analysis because they are not necessarily based on an officer’s discretionary evaluation of whether they expect to find contraband. Otherwise, this analysis includes any search that was not recorded as mandatory, including both discretionary searches and those with no search reason provided.
See the Data Notes tab for information on how we define racial groups.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Driver's racial group
- Search(es) conducted
- Reason for search
COMPARING SEARCH OUTCOMES AT NON-TRAFFIC STOPS
One common explanation for racial disparities in stops and searches is that members of some racial groups may be more likely to have contraband. To assess this, this analysis examines how often officers recorded discovering contraband such as alcohol or stolen goods in searches of pedestrians in each racial group.
- The majority of all searches officers conducted did not result in any contraband found.
- Officers searched 19 Latinx people, 13 Black people, and 3 White people who did not have any contraband.
When discovery rates are lower for searches of people experiencing disparities, this may indicate they are being searched unproductively. Unproductive searches can indicate that officers’ suspicion of illegal activity or weapons possession is less likely to be accurate for members of this group, or that officers more frequently decide to search members of this group at a lower level of suspicion. When discovery rates are relatively similar across racial groups, it suggests that significant racial disparities in stop and search rates cannot be justified by differences in the outcomes of those searches.
More information
What does this show?
One common explanation for why members of some racial groups are stopped or searched at different rates is that they may be more likely to have contraband. To assess this, we looked at whether searches of pedestrians in different racial groups resulted in contraband being found at different rates. For each racial group, we separated all searches into the percentage that resulted in contraband found and the percentage that resulted in no contraband found.
The darker portion of each bar (on the bottom) shows the percentage of all searches of pedestrians of that racial group that ended with contraband found, while the lighter portion of the bar (at the top) shows the percentage where no contraband was found. Hovering over a bar shows the number of searches that makes up that percentage. Each bar at the top shows the total number of searches recorded for that racial group.
It is important to compare this chart to the stop rates for people, above, to identify which groups may be experiencing a stop rate that may be driving high totals of contraband found.
How was this calculated?
We took the total recorded searches of pedestrians of each racial group and calculated the percentage that did and did not reveal contraband.
Police are typically required to search people they arrest. When the search reason is provided in the law enforcement agency’s data, these mandatory searches are excluded from this analysis because they are not necessarily based on an officer’s discretionary evaluation of whether they expect to find contraband. Otherwise, this analysis includes any search that was not recorded as mandatory, including both discretionary searches and those with no search reason provided.
See the Data Notes tab for information on how we define racial groups.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
- Whether (a) search(es) was/were conducted
- Reason for search
NON-TRAFFIC STOP REASONS BY RACIAL GROUP
This analysis investigates which stop reasons are most often recorded for pedestrians of each racial group and overall.
- The most common reasons officers recorded for stops of White people were Suspicious Person / Behavior (80% of stops of White people) and K-12 Truancy Check (12%)
- The most common reasons officers recorded for stops of Black people were Suspicious Person / Behavior (75% of stops of Black people) and Warrant (9.4%)
Departments should investigate which stop reasons are more often recorded in stops of groups experiencing racial disparities, as well as what stop reasons are more often recorded overall. Stops that are not based on direct risks to public safety or evidence of criminal activity are less likely to be efficient or productive uses of investigative resources.
More information
What does this show?
Each colored bar shows the percentage of the total non-traffic stops of that racial group for which a stop reason was recorded. Hovering over a colored bar shows the number of stops that makes up that percentage. Each gray bar on the right shows the total number of non-traffic stops for which that stop reason was recorded. Any stop reason that was recorded in a high number of incidents will influence the racial makeup of non-traffic stops overall.
How was this calculated?
We took the total recorded stops of people of each racial group and calculated the percentage for which each reason was recorded. We then grouped these percentages according to stop reason.
We combine categories of reasons for easier interpretation. See the Data Notes for details on how these categories are created and how racial groups are defined.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Racial group of person stopped
- The reason for stop
NON-TRAFFIC STOP OUTCOMES BY RACIAL GROUP
This analysis investigates which stop outcomes are most often recorded for pedestrians of each racial group and overall. According to Richmond Police Department data, once people were stopped:
- Black people were arrested 1.3 times as often as White people. They were also 6 percentage points less likely to be released with a warning or no action taken.
Pedestrians who are more likely to be stopped despite not committing any crime or infraction are unnecessarily exposed to a greater risk of experiencing harmful stop outcomes including searches, arrests, and use of force. Reducing these kinds of low-level stops can help reduce racial disparities in policing and free up departmental resources for higher priority public safety needs.
More information
What does this show?
Each colored bar shows the percentage of all stops of pedestrians of that racial group for which that stop outcome was recorded. Hovering over a bar shows the number of stops that make up that percentage. Each gray bar shows the total number of stops for which that stop outcome was recorded.
Findings on recorded pedestrian stop outcomes should be interpreted in context with findings on racial disparities in recorded stop reasons and searches at pedestrian stops. Pedestrians of racial groups who are stopped more frequently are often also more likely to be stopped for reasons that tend to be less related to public safety, which may increase their likelihood of being released with a warning or no action taken. Pedestrians who are more likely to be stopped despite not committing any crime or infraction are subject to a greater burden of police contact, which increases the likelihood of a cascade of interrelated harms including arrest, criminalization, and even injury or death.
How was this calculated?
We took the total recorded stops of pedestrians in each racial group and calculated the percentages for which each enforcement outcome was recorded. We then grouped these percentages according to enforcement outcome.
We only count the most serious outcome for a given stop (e.g. if a person receives a citation and is also arrested, we only count the arrest). The order from most serious to least serious outcome is:
- Arrest
- Immigration Referral
- Psychiatric Hold
- Summons/Non-Custodial Arrest
- Citation
- Warning/No Action
We combine categories of outcomes for easier interpretation. See the Data Notes tab for details on how these categories are created and how racial groups are defined.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
- Stop outcome (e.g. arrest, citation, or warning/no outcome)
NON-TRAFFIC STOPS BY WORK UNIT AND RACIAL GROUP
- Every work unit recorded stopping Black people at a disproportionate rate relative to their share of the population. Black people made up 20% of the population of Richmond, but more than 25% of the people stopped by officers in every work unit.
- "Northern" and "Central" recorded the most stops overall.
More information
What does this show?
“Work unit” describes the work groups within a department. It can refer to the assignment of the officer making the stop (e.g. Detective Unit, Narcotics, Traffic, etc.), or the geographic areas where stops are made (i.e. precincts, districts, zones, etc.).
Each colored bar shows the percentage of stops recorded by each work unit of people of each racial group. The Multiple Work Units category, if used, represents stops involving officers from two or more work units.
Hovering over a colored bar shows the number of stops that make up that percentage. The grey bars on the right show the total number of stops recorded by each work unit. Any work unit that records a large number of stops, or records large racial disparities, will influence overall racial disparities in pedestrian stops. If disparities are present among most work units, or are severe in some work units, the different racial makeup of various neighborhoods is likely not the whole explanation for the observed disparity.
How was this calculated?
We took the total recorded stops and first separated them by the work unit that made the stop. We then calculated what percentage was recorded for pedestrians of each racial group.
The “Other Work Units” category, if used, combines the work units recording less than 2% of stops. See the Data Notes tab for information on how we define racial groups.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
- Officer department assignment or beat, precinct, district, or other designated police service zone
COMPOSITION OF OFFICER NON-TRAFFIC STOPS RELATIVE TO EACH OFFICER'S PATROL AREA
Proportion of Officers Who Stop Certain Racial Groups at Higher or Lower Rates than Expected by Population in their Patrol Area
Richmond Police Department provided data on non-traffic stops. However, the number of officers who conducted non-traffic stops during the assessment period was below CPE’s minimum sample size requirements for responsibly assessing the racial makeup of each officer’s stops. For details on how this analysis is calculated, see “More information” below.
More information
What does this show?
This chart compares the stop patterns of officers making pedestrian stops against the demographics of the areas they most commonly patrol and identifies the number of officers whose stop patterns are either reflective of their communities, or out of sync with their communities. It is a measure of disparity in enforcement, and does not necessarily reflect officer bias. For each officer, we calculate a “parity score” which compares the racial distribution of that officer’s stops to the racial demographics of the neighborhood in which the officer usually works. Then we group those scores into categories describing the rate at which the officer stops a given racial group (proportionate to neighborhood racial demographics, or higher, much higher, lower, or much lower). Hovering over a bar shows the percent of officers in each category.
“Very low” scores mean that officers made stops of a racial group much less frequently than the population size of residents of the same racial group in each officer’s patrol area would predict, while “low” scores indicate officers made stops less frequently than that group’s share of the local resident population would predict. “Proportionate” scores mean that officers made stops of a racial group at a rate approximately equal to the population size of residents of the same racial group in each officer’s patrol area; “high” scores mean that officers made stops more frequently than the population size of residents would predict; and “very high” scores indicate that officers made stops much more frequently than local demographics would predict.
How was this calculated?
The parity scores are created by first mapping the Census tracts. (Census tracts are small geographic areas of approximately 4,000 residents, as defined by the Census Bureau, where a given officer most frequently makes pedestrian stops that represent their patrol area.) The racial makeup of an officer’s stops within those tracts is compared to the racial makeup of the resident population in the tracts. The difference between the percentage of people an officer stops and that racial group’s share of the local population is the officer’s parity score for that group. Parity scores are calculated with anonymized data that ensures the identities of officers are not known to the researchers. See the Data Notes tab for information on how we define racial groups.
Data required for this analysis:
- Incident unique identifier
- Date of incident
- Pedestrian's racial group
- Location of incident (e.g. street address or latitude/longitude)
- Officer unique identifier or alternatively, officer race/ethnicity, gender, and date of birth or date of hire
