Crime and Policing Data Collection

Missing Data and Examining Law Enforcement Behavior and Policy

I. Introduction.

A Hispanic boy in LA sees his father taken away to jail. Multiply that by twenty-seven thousand—every night (Million Dollar Hoods). Extracting largely from Black, Latinx, and other communities, this mass incarceration is one example highlighting that discriminatory policing behavior and law enforcement policy are critical social issues. However, the data that researchers studying these issues have access to is extremely limited. There currently does not exist a database for reported crime incidents, arrest charges, and calls for service across all US cities containing metadata such as addresses and demographics, however, a research team at UCLA led by Professors Emily Weisburst and Felipe Goncalves is dedicated to collecting this missing data. In this article, I introduce the idea and explanation on how US cities’ policing data (crime incidents, arrest charges, and calls for service) is missing, and how the assembling of this data can be used for actionable research by revealing discriminatory policing behaviors and law enforcement policies.

II. Review of Existing/Missing Data.

The most-used and primary US crime databases are the FBI’s Uniform Crime Reporting Program (UCR) and the National Incident-Based Reporting System (NIBRS), which collect crime incident metadata such as date/time, locations, and descriptions (“National Incident-Based”). However, the location data lacks specific addresses, meaning intra-city analysis is unfeasible. Additionally, the NIBRS does not have any data on 911 calls for service, which is important because police reports can be biased in their assessment of crime occurrence. Thus, alternatively tracking crimes through dispatches, which are initiated via callers themselves, may reduce bias in report records (Smith). Still, examining the FBI’s Crime Data Explorer database reveals that the extent of NIBRS data is severely limited. In 2022, only around seventy-one percent of police agencies in the US report their data to the FBI, with those in Illinois, Louisiana, West Virginia, and Florida all below a forty percent reporting rate (Federal Bureau).

There are a myriad of reasons for this missing data. Technology researcher Mimi Onuoha states that missing data occurs when those who have the resources often lack proper incentives or it requires extensive effort to collect the data (Onuoha). For example, the FBI also collects information on case clearance rates, and Rachel Harmon at University of Virginia Law indicates that this leads to conflicting incentives, revealing how agencies might not submit data, fearing the data may portray potential inefficacies (1135). There also exist logistical barriers; some cities’ records systems might not be compatible with the FBI (e.g. records in physical copies). These examples demonstrate Onuoha’s ideas, showing how the FBI’s deficiency in data, from specific addresses to dispatch calls, can be attributed to misaligned incentives and high effort on behalf of the local departments, resulting in a lower coverage of US cities.

Narrowing down to individual police departments, many barriers still prevent the collection of missing policing data. The primary method of gathering data from police departments is through freedom of information (FOI)/public records requests. However, FOI laws vary across state/county jurisdictions; for example, Virginia only allows requests to in-state residents (“Virginia Law”), so outside research teams need to contact in-state residents to request data. Even once a request is processed, departments may, for example, only permit record-viewing in person, not have a system that allows for large extractions (common for smaller population cities), or charge substantial prices (Smith). These examples again illustrate Onuoha’s ideas on missing data, as police departments could dedicate resources to collecting precise data and facilitating FOI requests, yet lack the incentive (evidenced by large quotes). Additionally, Onuoha’s point on how the nonexistence of data can benefit certain groups manifests here, as departments may want to prevent their actions and reputations from being evaluated by outside media with such data.

While institutional sources contribute significantly to missing policing data, many barriers on the victim level also exist. Many incidents do not even get reported to police; according to the US Department of Justice, around 46 percent of violent victimizations (rape, robbery, assault) were not reported, for reasons that may include fear of the offender getting punished or the belief in police inefficacy (“Criminal Victimization”). This shows the onset of missing data at the level of the individual victim, not even reaching departments to be gleaned through FOI requests. A remedial approach presents itself in the Hate Crime Map, which allows victims to anonymously submit their experiences of hate-based assault and crime (“Mapping Hate”). This anonymity could alleviate the burden for victims to report to the police, tackling the missing data issue at the source, and allowing researchers access to a more transparent crime data landscape.

III. Discussion.

From the previous discussion, it is clear that precise policing data (crime incidents, arrest charges, and calls for service) on US cities is missing, requiring change on the national, state, local, and individual levels to remedy. But to what extent can the collection and mapping of this data be used for actionable research by revealing certain law enforcement behaviors and exposing discriminatory policies?

Fortunately, we have evidence of the effect assembling and mapping comprehensive policing data has. Referring back to the mass incarceration issue in Los Angeles, the Million Dollar Hoods (MDH) project at UCLA employs the mapping of data gleaned from LAPD FOI requests to conduct research reports, revealing various age, race, and gender disparities in arrests. Through these, they document the costs of mass incarceration on disadvantaged populations, such as Black, Latinx, and Indigenous communities, and advocate for new public investments (Million Dollar Hoods). MDH demonstrates how arrest data mapping in particular, albeit on a single-city level, revealed discriminatory law enforcement behaviors in Los Angeles, making the social issue of mass incarceration more transparent to the public.

By going broader and incorporating other policing data on calls for service and incident reports, the applications to policy analysis are plentiful. The UCLA research team led by Professors Weisburst and Goncalves, which aims to form the most comprehensive data repository from hundreds of cities across the US on the aforementioned missing crime data, has used data presently collected to research topics such as the impact of law enforcement policies. One recent paper is “Immigration Enforcement and Public Safety,” which studies the effect of the US Secure Communities program and its increasing law enforcement on unauthorized immigrants, many of whom are Hispanic. With previously missing crime data, researchers were able to provide evidence that the program reduced the likelihood that Hispanic victims report crimes to the police, which in turn also increased the total victimization of Hispanics (Goncalves et al.). Again, this is just one example highlighting the power this policing data has, revealing the latent and unintended discriminatory effects of policies such as the Secure Communities program, and calling attention to the issues, such as causes for reduced victim reporting rates, within US policing institutions.

Moving forward, the UCLA team has many potential research uses as new policing and crime data continues to be collected. One key advantage that collecting data on many cities provides is that each city has unique qualities that may factor into certain law enforcement trends, e.g. coastal vs. landlocked, distributions of ethnic groups, natural segregation lines (rivers, hills, etc.), population sizes, or distinct current events. For example, some cities have housing shortages, and recovering missing crime data can help reveal potential causations between implemented housing policy and crime rates. Also, by gathering data from traditionally overlooked rural areas or cities near urban centers (i.e. “sister cities”), research into how policy in one area affects neighboring regions is possible (Smith). Some cities have large income disparities, which have been shown to be highly associated with increased crime risk (Sugiharti et al.), so additional research could come from pairing income data with geographical crime data. These examples all illustrate the innumerable sociological applications through recovering and mapping crime, arrest, and dispatch data, as this data could be used to aid in research and assess the impact of many policies on crime rates or policing trends in many unique environments and expose certain discriminatory practices in the law enforcement system.

IV. Conclusion.

In this article, I introduced the idea that policing data (crime incidents, arrests, and calls for service) for US cities is missing, with barriers from misaligned incentives to logistical issues on national and local levels preventing its complete collection. Additionally, applications of this missing data towards assessing the crime-related impact of policy or exposing discriminatory trends in policing were discussed. This crime data analysis has many implications. For example, as crime tracking and predictive policing technologies such as Compstat get introduced, understanding crime and policing behavior across various environments is important to maintain the benefits this technology has in reducing crime (Police Executive) while accounting for discriminatory practices expected from hegemonic data or algorithms. Looking at crime data could also bridge research gaps in gang behavior, of which the only comprehensive dataset currently is in Chicago (Bruhn). Through this missing policing data, the actionable sociological applications are endless.

V. Works Cited

• Bruhn, Jesse. Competition in the Black Market: Estimating the Causal Effect of Gangs in Chicago, 30 Apr. 2021.
• Buna, M. “Carceral Capitalism: A Conversation with Jackie Wang.” Los Angeles Review of Books, 13 May 2018, lareviewofbooks.org/article/carceral-capitalism-conversation-jackie-wang/.
• “Criminal Victimization, 2021.” Bureau of Justice Statistics, Sept. 2022, bjs.ojp.gov/content/pub/pdf/cv21.pdf.
• Federal Bureau of Investigation Crime Data Explorer, cde.ucr.cjis.gov/LATEST/webapp/#/pages/home. Accessed 11 Mar. 2024.
• Goncalves, Felipe, and Steve Mello. “A Few Bad Apples? Racial Bias in Policing.” American Economic Review, vol. 111, no. 5, May 2021, pp. 1406–1441, https://doi.org/10.1257/aer.20181607.
• Goncalves, Felipe, et al. “Immigration Enforcement and Public Safety.” National Bureau of Economic Research, Feb. 2024, https://doi.org/10.3386/w32109.
• Harmon, Rachel. “Why do we (still) lack data on policing.” Marquette Law Review, 2013, pp. 1119–1146.
• “Mapping Hate Crimes in the US #OneHateCrimeIsTooMany.” UCLA American Indian Studies Center, www.hatecrimemap.com/. Accessed 11 Mar 2024.
• Million Dollar Hoods, milliondollarhoods.pre.ss.ucla.edu/. Accessed 11 Mar. 2024.
• “National Incident-Based Reporting System (NIBRS).” FBI, www.fbi.gov/how-we-can-help-you/more-fbi-services-and-information/ucr/nibrs. Accessed 12 Mar. 2024.
• Onuoha, Mimi. “Mimionuoha/Missing-Datasets: An Overview and Exploration of the Concept of Missing Datasets.” GitHub, github.com/MimiOnuoha/missing-datasets. Accessed 11 Mar. 2024.
• Police Executive Research Forum. “COMPSTAT: ITS ORIGINS, EVOLUTION, AND FUTURE IN LAW ENFORCEMENT AGENCIES.” Bureau of Justice Assistance, 2013, bja.ojp.gov/sites/g/files/xyckuh186/files/Publications/PERF-Compstat.pdf.
• Smith, Benjamin. Interview. Mar. 2024.
• Sugiharti, Lilik, et al. “The nexus between crime rates, poverty, and income inequality: A case study of indonesia.” Economies, vol. 11, no. 2, 13 Feb. 2023, p. 62, https://doi.org/10.3390/economies11020062.
• “Virginia Law.” Virginia Freedom of Information Act, law.lis.virginia.gov/vacodepopularnames/virginia-freedom-of-information-act/. Accessed 11 Mar. 2024.
• Weisburst, Emily. Interview. Dec. 2023.