By Public Works LLC

The City of Des Moines commissioned the consulting firm Public Works LLC to perform five basic functions: 1. Identify the current state as to how and what data is being collected by and within the Des Moines Police Department (DMPD) and how that data is applied to inform the practice and policies of law enforcement. 2. Identify promising (best) practices in the field of law enforcement data and show the ways that police departments are applying these practices to enhance how they collect, analyze, share, and act upon what they learn from data. 3. Conduct a needs assessment to identify gaps the DMPD faces between the current state and what could ideally be achieved by implementing promising practices in the field. 4. Identify opportunities to address those gaps and enhance what and how data is collected, analyzed, shared with the community, and acted upon. 5. Engage and learn from the community as to their perspectives and insights as to how and what law enforcement data is being collected, analyzed and shared. Public Works created a conceptual framework to research, examine, assess and organize the law enforcement data initiative we were tasked to develop. It centers upon the basic principle that data systems should achieve four core attributes – they should be accountable, analytic, transparent, and actionable. These four core data attributes serve as the architecture for the entire project, the framework for our research determining and describing the DMPD’s current state of data policy and practice, and our research in scoping out promising practices in the field of law enforcement data. This structure also guided how we determined needs, how we framed questions and gathered insights from the community and, finally, how we came to recommend action steps for the City of Des Moines to pursue in order to realize the ideal state in the field of law enforcement data policy and practice. Data Collection in Des Moines The goal of data collection is to record integral information on policing encounters and activities that enable the identification of trends, patterns, and outcomes leading to informed insights and action through policy and practice. The Des Moines Police Department currently collects data on: stops resulting in citations, arrest data, calls for service, use of force, offenders and victims of crimes. Data on Stops: The Des Moines Police Department does not currently collect data on stops that do not result in a citation, warning, or arrest. Data on Citations: Police officers issuing citations after a stop enter the citation data using the TraCS software that has been installed in their vehicles. A large part of the data is generated automatically from the cited individual’s driver’s license, but the driver’s license does not always include race and ethnicity data. Officers may manually enter that data based on observation, but the TraCS software does not require that the race and ethnicity data fields be collected. The Tyler New World System recently launched should alleviate the need for staff to manually enter data. Data on Arrests: When arrests are made in the field, an officer enters the incident into the Intergraph Field Reporting (IFR) Incident module, which is available in the police officer’s vehicle. Police Information Technicians use this information to generate an arrest record in the RMS. Data on Calls for Service: Calls for service to law enforcement agencies generally include calls to “911” for emergency assistance and calls to non-emergency numbers. Calls for service data are input into Hexagon CAD and imported to RMS I/LEADS. Calls for service data (CFS) input screens are set up for law enforcement, as well as for Fire/EMS calls. CFS data are collected by DMPD Public Safety Dispatchers by entering information into Hexagon CAD; they are then imported to Hexagon I/LEADS. Data on Use of Force: On January 1, 2019, the FBI began collecting use of force data from law enforcement agencies across the country that voluntarily participate. The data collection offers bigpicture insights, rather than information on specific incidents. The collection neither assesses nor reports whether officers followed their department’s policy or acted lawfully. The data includes any use of force that results in death, serious bodily injury, or discharge of a firearm by law enforcement. The Des Moines Police Department collects use of force data through web based IAPro/BlueTeam software programs, which enables input of complaints, use of force incidents, pursuits, and city-owned vehicle accidents. Reporting of Data: The Des Moines Police Department uses a Hexagon RMS custom-tailored data package for sending monthly crime and arrests data to the Iowa Department of Public Safety’s Uniform Crime Code Classification (UCR) program. At present, Des Moines is moving from UCR codes to National IncidentBased Reporting System (NIBRS) codes. Crime data are organized by incident, offense, victim, known offender, and arrestee. They are collected by the Des Moines Police Department RMS/I/LEADS Incident and Arrest modules by entering information into FBI UCR/NIBRS. Geographic Data: The Des Moines Police Department currently collects GIS coordinates, and zip code data for Calls for service incidents. The citation module in RMS is exclusively used by the Police Information Technicians to re-enter selected citation information from the PDF copy generated by the TraCS system, making it vulnerable to human error. When the Police Information Technicians enter the “Offense location,” the RMS system uses that information to automatically populate GeoX and GeoY coordinates. The Des Moines Police Department uses GIS data with its CrimeView system that links crime data with GIS information to map out where the crime took place. The Des Moines Police Department does not analyze the GIS data of Stops resulting in a citation, nor does it connect it to the rest of the Stop data collected. Not having such analysis makes it very challenging to produce any summary of analytic results by census track or zip code.

DesMoines, IA: City of DesMoines, 2022? 207p.

By Pradhi Aggarwal, Alec Brandon, Ariel Goldszmidt, Justin Holz, John A. List, Ian Muir, Gregory Sun, and Thomas Yu

Prior research on racial profiling has found that in encounters with law enforcement, minorities are punished more severely than white civilians. Less is known about the causes of these encounters and their implications for our understanding of racial profiling. Using high-frequency location data of rideshare drivers inFlorida (N = 222,838 individuals), we estimate the effect of driver race on citations and fines for speeding using 19.3 million location pings. Compared with a white driver traveling the same speed, we find that racial or ethnic minority drivers are 24 to 33% more likely to be cited for speeding and pay 23 to 34% more money in fines. We find no evidence that accident and reoffense rates explain these estimates, which suggests that animus against minorities underlies our results

Science, Volume 387, Issue 6741Mar 2025, 5p,

By Stephanie Seguino, Nancy Brooks, Pat Autilio

This study has three goals. The first it to analyze police behavior as regards race and traffic policing. The second is to evaluate police compliance with the law requiring the collection and reporting of traffic stop data. And the third is to evaluate the effectiveness of the legislation in generating robust data collection on race and traffic policing that is relatively user friendly for analysis by community stakeholders. With regard to the first goal, we examine the data for evidence of racial disparities in several areas: racial shares and magnitudes of stops as well as racial disparities in stop rates, reasons for stops, arrest rates, search rates, and contraband “hit” rates. We also examine trends to determine whether racial disparities fall over time, particularly in response to the legalization of cannabis in July 2018. Our study is based on more than 800,000 traffic stops and 79 Vermont law enforcement agencies. The study includes a number of agencies that had not reported their data in time for our earlier study (Seguino and Brooks 2017). In addition to providing a statewide overview of racial disparities, we compare policing patterns as well as racial disparities across agencies, and separately for municipal law enforcement agencies, sheriff’s departments, and the Vermont State Police. We report raw data on all agencies in our sample, and these results can be found in the appendix. We have been careful to include in the body of the report that follows agency-level statistics only for those agencies that have the minimum number of observations by race (typically, 10 or more). Our main findings are as follows: • The Black and Hispanic shares of stopped drivers exceed their shares of the estimated driving population. The data indicate Black drivers were over-stopped by between 3% to 81%, depending on the measure of the driving population used. Hispanic drivers were over-stopped by 26%. The shares of stops of all other racial groups are at or below their share of the driving population. These numbers represent a statewide average, and obscure wide variation at the agency level. We provide detailed agency-level data in the report, which show that approximately 45 agencies over-stopped Black drivers by more than 25%. • The stop rate per 1,000 residents is very high in Vermont (255 drivers stopped per 1,000 residents) compared to the national average of 86 per 1,000 residents. This overall average obscures notable racial disparities in stop rates. The statewide white stop rate per 1,000 white residents is 256 compared to 459 stops of Black drivers per 1,000 Black residents. The Black stop rate is about 80% higher than the white stop rate and matches the upper bound described above since one of our measures of the driving population of an area is its number of residents. • These averages also obscure the wide variation in stop rates per 1,000 residents. Of particular concern are agencies with large racial disparities in stop rates that also significantly over-stop relative to the national average. For example, in Bennington the overall stop rate per 1,000 residents is estimated to be 659 and Black drivers are over-stopped from 55% to 335% depending on the measure of driving population. • Black and Hispanic drivers were ticketed at a higher rate than white drivers, and Black drivers were also more likely to be given multiple tickets per stop. Our ability to report accurately on ticket rates is limited by data quality concerns as some agencies only report a single outcome per stop even when more than one outcome occurred, such as multiple tickets. • White drivers were more likely to be stopped for moving violations than Black drivers. Black drivers were more likely to experience a stop for vehicle equipment violations. We are concerned that this type of stop may be more investigatory and pretextual than moving violations. Stops that are investigatory/pretextual, based on suspicion of illegal activity rather than observable behavior or evidence, are more susceptible to officer racial bias than stops based on other reasons, such as a moving violation or suspicion of Driving While Impaired (DWI). Several experts have recommended banning this type of stop, which could help to reduce not only racial stop rate disparities but also search disparities. (A November 2019 ruling by the Oregon Supreme Court has banned this increasingly controversial policing practice). • The arrest rate of Black drivers is roughly 70% greater than that of white drivers. The Hispanic-white arrest rate disparity is even larger, with the arrest rate of Hispanic drivers 90% greater than the white arrest rate. Some agency-level disparities were much wider. In Brattleboro, Black drivers’ arrest rate is 400% greater than the white rate; in Colchester, 185% times greater. • Black drivers are about 3.5 times more likely to be searched subsequent to a stop than white drivers and Hispanic drivers are searched at a rate that is 3.9 times greater than that of white drivers. Asian drivers are less likely to be searched than white drivers. Again, some agencies exhibited much wider disparities than the state average. In Brattleboro, Black drivers are almost 9 times more likely to be searched than white drivers; in Shelburne, 4.4 times greater; in South Burlington; 3.9 times greater; in Vergennes, 3.8 times greater; in Burlington, 3.6 times greater; and in Rutland, 3.45 times greater. • Black, Hispanic, and Asian drivers were less likely to be found with contraband than white drivers. The lower hit rate (that is, the percentage of searches that yield contraband) of drivers of color is widely regarded as providing evidence the police rely on a lower bar of evidence to search drivers of color than white drivers, suggesting possible racial bias in the decision to search. In a second test (a logit analysis) for racial bias in searches, we find that the race of the driver continues to be strongly correlated with the officer’s decision to search a vehicle, even after controlling for other factors that may influence the officer’s decision to search a vehicle. • We find that searches based on reasonable suspicion (a lower threshold of evidence than probable cause) have lower hit rates for all racial groups. And, the gap between the (higher) white hit rates and (lower) hit rates for people of color increases. Just as with investigatory/pretextual stops, searches based on reasonable suspicion are more prone to racial bias. With regard to trends over time: • From 2015 to 2019, the number of traffic stops has increased for all racial groups. Sheriff’s Departments registered an 86.4% increase in traffic stops over this time period, compared to a statewide average for all agencies of 39.7%. • Racial disparities in the increase in number of traffic stops are notable. While stops of white drivers increased by 44.6% over this time period, Black stops increased 72.5%; Asian stops, 66.7%; and Hispanic stops, 120.3%. • The share of stops that are investigatory/pretextual, including vehicle equipment stops, increased for all racial groups, but increases were greatest for Black drivers—so much so that by 2019, about one third of all stops of Black drivers were included in this category, up from 23% in 2016. For Hispanics, the increases in the share of such stops was even greater, rising from 18.0% in 2015 to 27.5% in 2019. • Racial disparities in arrest rates have also widened since 2014. The widening gap is due to a decline in the white arrest rate from 2018 to 2019 rather than an increase in the Black arrest rate. • Search rates declined for all racial groups after cannabis legalization but by 2019, the Black search rate continued to be 3 times greater than the white rate. Legalization of cannabis, in other words, did not have a substantial impact on the Black-white search rate disparity. The Hispanic search rate disparity widened from 2018 to 2019 with Hispanic drivers 2.6 times more likely to be searched than white drivers by 2019. • Hit rates have decreased for searches that result in any outcome (warning, ticket, or arrest) but the arrest-worthy hit rate rose slightly from 20.3% to 24.9% from 2018 to 2019. As search rates have fallen, searches appear to be somewhat more productive with regard to those that lead to an arrest but are somewhat less productive overall. Increasing hit rates suggests greater efficiency in policing decisions regarding searches, and clearly, less negative impact on drivers for whom searches are often traumatic experiences. Regarding data quality, our main findings are: • Data quality has improved for some but not all agencies over time. There continues to be a lack of compliance with the legislation requiring race data collection during traffic stops. Missing data on all of the outcomes of a stop, when stops have more than one outcome, date and time of stop, and stops IDs also hinders analysis. • Particularly worrisome is the large number of stops missing race of driver, the main concern of traffic stop data collection. One way to put into perspective the quantity of missing data is to compare the share of stops missing race of driver to the percentage of stops that are of BIPOC drivers. Given the low percentages of people of color in Vermont, even a small amount of missing race data can distort results. For more than a dozen agencies, the percentage of stops missing race of driver is at least double the percentage of stops that are reported to be of BIPOC drivers. At a minimum, this leads to low quality data and the accuracy of results from those agencies. It also violates the spirit of the legislation requiring race data collection. • The legislation has not been sufficiently precise or comprehensive in delineating the data to be collected. Police chiefs have interpreted the meaning of various components of the legislation differently, and thus do not follow a uniform method of reporting data. Some categories of data that would be useful—and are already collected—were not stipulated in the legislation. Law enforcement agencies have as a result declined to share those data. These findings suggest the need to revise the legislation on traffic stop race data collection in order to insure complete data that is uniformly submitted so that it can be analyzed without excessive difficulty.    

Burlington, VT: University of Vermont, 2021. 159p.

By Robin Joy

Act 193 mandates that law enforcement agencies collect data on roadside stops for the purpose of evaluating racial disparities. The Act dictates agency data collection and any related conversation centers on agency behavior. The Act and the data collected do not focus on or reflect the stories told by Black, Indigenous and People of Color (BIPOC) as related to their contacts with law enforcement agencies. Because of Vermont’s rural nature, small populations, and policing strategies, we conclude that traffic stop and race data are not sufficient to inform policy makers and stakeholders. Rigorous qualitative research focused on the experiences of the BIPOC community which detects patterns and trends can distinguish structural issues within the criminal justice system. Agency data should be used as a supplement to that research. The purpose of the study was to test different methods of assessing racial disparities in traffic stops for their applicability for all Vermont law enforcement agencies. In short, we found that this was not possible. This report reviews the methodologies tested and the findings. On Measuring Disparities 1. We tested three peer reviewed methods for benchmarking the driving population: Commuting Hour populations, Resident Driver populations, and Crash Data benchmarking. All three failed in Vermont because of the state’s rural nature and small populations. The low volume of people of color makes it difficult for consistent analysis. It is not possible for one benchmarking standard to be applied to all law enforcement agencies in the state. 2. We can recommend the “Veil of Darkness” analysis as an effort to examine racial disparities. However, that analysis essentially measures one work shift in a police department. In some departments that may just be a single officer. 3. Post-stop outcome measures may be useful, however, without more information on the stop (such as the violation for which the person was ticketed/arrested and other circumstances surrounding the stop) it is of limited value. Further, because so few people are searched or arrested it is hard to draw a conclusion from the data. 4. Stop data will now include information as to how often the same person is stopped by a department. Specifically, the year, make, model, and color of the car and the town/state of residence and the state of the plate will be available. This will help illustrate the stories community members have spoken about in protests, legislative hearings, and news articles – stories of people who feel they are being continuously targeted. For example, using these additional data fields, researchers can identify a 30-year-old Asian female from Montpelier driving a 2008 White Honda CRV who has been stopped four times in one month for various reasons

Montpelier, VT: Crime Research Group, 2021, 27p. 

By Samuel Langton, Stijn Ruiter, Tim Verlaan

This paper describes the scale and composition of emergency demand for police services in Detroit, United States. The contribution is made in replication and extension of analyses reported elsewhere in the United States. Findings indicate that police spend a considerable proportion of time performing a social service function. Just 51% of the total deployed time responding to 911 calls is consumed by crime incidents. The remainder is spent on quality of life (16%), traffic (15%), health (7%), community (5%), and proactive (4%) duties. A small number of incidents consume a disproportionately large amount of police officer time. Emergency demand is concentrated in time and space, and can differ between types of demand. The findings further highlight the potential implications of radically reforming police forces in the United States. The data and code used here are openly available for reproduction, reuse, and scrutiny.

POLICE PRACTICE AND RESEARCH 2023, VOL. 24, NO. 5, 523–538