Lakewood, CA 2024

PEDESTRIAN STOPS

This section examines pedestrian stops, which are stops for which the recorded reason was anything other than “traffic violation.”

Note: The racial categorization scheme mandated by California’s Racial and Identity Profiling Act (RIPA) 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.

Under RIPA, all California law enforcement agencies are required to report data on all stops to the California Attorney General. In June 2022, the Los Angeles County Office of the Inspector General issued a report detailing a significant underreporting of stops by LASD in its RIPA reporting in 2018 and 2019. Though CPE has received no indication that the issues leading to this underreporting were resolved, Compton saw a marked increase in recorded stops in 2022 and 2023, which could indicate better adherence to RIPA protocol. CPE ran all the analyses presented here excluding pre-2022 data to see if underreporting impacted our findings, but found that the results were not significantly impacted.

PEDESTRIAN STOPS BY RACIAL GROUP

According to LEA NAME data on pedestrian stops recorded between 20XX and 20XX:

XX% of all pedestrians officers stopped were Black, who made up XX% of the population of JURISDICTION.
XX% of all pedestrians officers stopped were Latinx, who made up XX% of the population of JURISDICTION.
XX% of all pedestrians officers stopped were Asian, who made up XX% of the population of JURISDICTION.
XX% of all pedestrians officers stopped were White, who made up XX% of the population of JURISDICTION.
XX% of all pedestrians officers stopped were Other, who made up XX% of the population of JURISDICTION.

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 pedestrian 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 pedestrian 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 pedestrian 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.

Because Lancaster Station deputies patrol an area that includes multiple towns and unincorporated areas, CPE used Census tracts to determine the resident racial demographics of the area patrolled. Census tracts are small geographic areas of approximately 4,000 residents each, as defined by the Census Bureau. To calculate these demographics, CPE requested and received a map from the Los Angeles County Sheriff’s Department of the area for which Lancaster Station is responsible. CPE then determined which Census tracts were in this area. Because the jurisdiction area doesn’t perfectly align with Census tracts, CPE used a 5% inclusion threshold – that is, any tract with at least 5% of the its area within Lancaster Station’s patrol area was included in the calculation of the resident demographics

Data required for this analysis:

PEDESTRIAN STOPS BY YEAR

The total number of pedestrian stops recorded each year with complete data ranged from a high of XX in 20XX to a low of XX in 20XX.

More information

What does this show?

This chart shows the total number of pedestrian stops recorded during each year 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?

Data required for this analysis:

COMPARING PEDESTRIAN STOP RATES

Pedestrian Stop Rates After Accounting for Neighborhood Crime Rates, Poverty Levels, and Share of Black Residents

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:

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 PEDESTRIAN STOP FREQUENCY EXPLAINED BY NEIGHBORHOOD FACTORS

Percentage of Stop Frequency Explained by Differences in Neighborhood Crime Rates, Poverty Levels, and Share of Black Residents

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 XX% of the frequency of pedestrian stops, while XX% 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 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 PEDESTRIAN STOPS

According to LEA NAME data, once pedestrians were stopped:

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:

COMPARING SEARCH OUTCOMES AT PEDESTRIAN 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 people in each racial group.

When discovery rates are lower for searches of pedestrians 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.

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.

See the Data Notes tab for information on how we define racial groups.

Data required for this analysis:

PEDESTRIAN STOP REASONS BY RACIAL GROUP

This analysis investigates which stop reasons are most often recorded for people of each racial group and overall.

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. 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:

PEDESTRIAN 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 LEA NAME data:

32% of all pedestrian stops resulted in a warning or no action taken, which suggests significant opportunities to reduce stops that are not based on any direct risks to public safety or evidence of criminal activity.
Black pedestrians were arrested at similar rates to White pedestrian. They were also released with a warning or no action taken at similar rates to White pedestrians who were stopped.
Latinx pedestrians were arrested at similar rates to White pedestrian. They were also released with a warning or no action taken at similar rates to White pedestrians who were stopped.

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.

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:

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.