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 Brittany C. Cunningham, Vincent Bauer, Kira Cincotta, Jessica Dockstader, Benjamin Carleton, Bridgette Bryson, Daniel S. Lawrence

Officer safety is of critical importance in an era of increased risk for law enforcement officers (hereinafter “officers”). Officers respond to some of the most unpredictable, traumatic, and violent encounters of any profession. Although much of an officer’s workday entails repetitive interactions, some calls for service or self-initiated contacts by officers may escalate into dangerous encounters. For officers to adequately mitigate the risks they may encounter while responding to calls for service, they must be well informed regarding the types of risks they face, the situations that may pose greater risk, and the strategies that will mitigate these risks.

Although previous empirical work on officer safety has yielded many important insights, to our knowledge, no prior work has applied machine learning models to produce risk assessments to promote officer safety. This project explored the potential for machine learning to identify high-risk incidents to officers using only the information available to dispatchers. A risk assessment model that could successfully flag high-risk incidents at dispatch would be immensely useful to law enforcement agencies, making it possible for officers to be better informed about potential risk factors before arriving on scene. Such a model would also be useful to agencies as they decide how to allocate scarce resources, such as deciding which calls should receive single- or dual-officer vehicles, where to send alternative response teams, and whether to deploy specialized units.

Readers should be aware that the model reflects the data upon which it is built. Biases in reporting and collecting officer injuries, as well as in how officers respond to calls for service, will be mirrored in the model’s risk assessments. While we have gone to great lengths to build the model using objective factors, these biases could sometimes lead the model to identify a situation as high risk when in fact that situation reflects low risk to officers. Concerns about the potential for bias in machine learning are important to evaluate, and these techniques offer opportunities for objective empirical examination of divisive topics to minimize the bias that is already present in the real world.

Calls for service and Law Enforcement Officers Killed and Assaulted (LEOKA) data were merged from each of the four agencies, revealing the following findings:

Overall, the machine learning model performed well, correctly identifying officer injuries about half of the time. Given the rarity of officer injuries within the four agencies, being able to identify half of such rare situations is notable.

The model was also able to identify the factors that were the most important in predicting risk to officer safety and the types of incidents that posed the highest risk to officer safety. The results demonstrate that such a model can identify officer injuries from data on call characteristics; thus, whether such a model could be built into the dispatch process should be explored so that officers would be informed about potential risk factors before arriving at the location of a call.

The model highlighted factors and calls for service types that posed greater risks to officer safety.

The results of the machine learning model, along with the results from the officer interviews and surveys, also highlighted an often-overlooked aspect of police operations that is critically important to officer safety: dispatch.

Beyond producing statistical models, this project also collaborated with participating agencies to explore officer perspectives on safety and identify promising practices and recommendations to reduce risks to officers.

This project provides several practical benefits for improving officer safety. These benefits include the following:

Quantifying concepts that until now have been only informally or qualitatively understood (e.g., the relative risks of different calls for service types).

Comparing officer perceptions about injury risk to the quantitative data and identifying where gaps in understanding exist.

Highlighting the important relationship between dispatch and patrol, as well as the implications that this relationship has for officer safety.

Helping agencies assess the efficacy of their trainings and policies that directly affect officer safety.

Providing guidance on the information agencies collect and make available to dispatchers.

Supporting agencies to improve the amount and quality of risk and injury data agencies collect and use.

We hope that by providing agencies with a foundational knowledge of risks to officer safety, agencies will have a basis for modifying policy, training, and operations, leading to the implementation of strategies, processes, and procedures to keep officers and the communities they serve safe.

Arlington, VA: CNA Corporation, 2024. 52p.

By Erin Hammers Forstag, Rapporteur; Division of Behavioral and Social Sciences and Education; National Academies of Sciences, Engineering, and Medicine

Predictive policing strategies are approaches that use data to attempt to predict either individuals who are likely to commit crime or places where crime is likely to be committed, to enable crime prevention. To explore law enforcement's use of person-based and place-based predictive policing strategies, the National Academies of Sciences, Engineering, and Medicine held a two-day public workshop on June 24 and 25, 2024.

National Academies of Sciences, Engineering, and Medicine, 2024. 14 pages