This article is based on research conducted as a part of the CA POST Command College. It is a futures study of a particular emerging issue of relevance to law enforcement. Its purpose is not to predict the future; rather, to project a variety of possible scenarios useful for planning and action in anticipation of the emerging landscape facing policing organizations.
The article was created using the futures forecasting process of Command College and its outcomes. Managing the future means influencing it — creating, constraining and adapting to emerging trends and events in a way that optimizes the opportunities and minimizes the threats of relevance to the profession.
By Captain Matt Lethin
What if your officers regularly did something that had little to no effect on crime, took an enormous amount of their free time and harmed police-community relationships? And even worse, your agency, like most others, worked tirelessly to maintain staffing levels so your officers had enough free time to do it day after day?
Proactive policing is defined as “self-initiated activities that are not otherwise committed to calls-for-service or other administrative activities,” [1] and it has long been an industry staple. When done through enforcement, it can manifest through effective and proven strategies like problem-oriented policing (POP), community-oriented policing (COP), intelligence-led policing (ILP), hotspot patrols and focused deterrence. [2] Recently, during the COVID-19 pandemic and “defund the police” movement, proactive enforcement declined. Since then, agencies have labored to return to proactivity, believing proactive enforcement reduces crime and improves police-community relationships.
For most departments, proactive policing means discretionary enforcement, where officers can decide when, where, how and with whom to conduct enforcement. While leadership has long championed evidence-based and data-informed strategies (like POP, COP, hotspot policing, etc.), they seldom provide direction, review or follow-up to ensure alignment between policy and practice. [1] Officers are usually unaware of or ignore these strategies in favor of intuition and discretion. [3] Mounting evidence has confirmed that such proactive discretionary enforcement ineffectively reduces crime and harms police legitimacy and community connection. [2,4,5]
As we look toward the future, police must do better. Acknowledging the challenges in shifting culture from discretion-based to proven strategies, agencies should turn to a technology-driven approach to guide officers’ efforts. By embracing tools such as surveillance cameras, automated license plate readers (ALPRs), drones, gunshot detection, facial recognition, predictive policing algorithms, real-time information centers (RTICs) and artificial intelligence, police can identify and channel their activities to address crime without jeopardizing community trust. Our profession needs to adopt a new, smarter policing model. For many raised in a discretion-based proactive culture (the author included), it may be difficult to accept the dangers of past strategies and the potential benefits of the proposed model. A story from the future illustrates these dynamics.
The dangers of discretionary proactive enforcement
Officer Rodriguez has been an officer for five years when she experiences a turning point in her career. As a seasoned patrol officer, Rodriguez is extremely proactive, regularly conducting traffic and pedestrian stops, high-visibility passing checks and foot patrols in her free time. But her efforts don’t always achieve her desired results.
One evening, after she pulls over a vehicle for a minor traffic violation, the situation escalates. The driver, a young man from a neighborhood where residents have long felt unfairly targeted by police, is frustrated by what he perceived as an unjust stop. Tensions rise, bystanders gather, and within minutes what should have been a routine stop becomes a confrontation that spirals out of control. The experience causes Rodriguez to question the impact of her proactive enforcement.
Around that time, her department introduces widespread ALPR cameras, a real-time information center and predictive policing tools. Rodriguez is intrigued. Instead of relying solely on intuition and experience, she now has real-time data on where and when crimes are occurring and are likely to occur in the future. One day, the predictive policing tool flags a series of car thefts in a specific geofenced area, and due to an ALPR activation, an RTIC operator radios her about a car previously flagged in the system as likely involved in similar crimes. Rodriguez responds to the neighborhood and soon spots the suspicious vehicle, makes a traffic stop and arrests an individual wanted for multiple thefts. It is a breakthrough case in a crime spree that frustrated the department for weeks, and it notably reduces crime in the neighborhood.
For Rodriguez, this experience changes her paradigm: She realizes proactive policing is more effective when directed by technology. With the right tools, she can make a bigger impact by working smarter, not harder, while also increasing trust and connection with the community.
This story highlights the challenges and concerns with discretionary proactivity. While officers like Rodriguez mean well, drawing on discretion alone often leads to unintentional negative outcomes. [6] Studies have shown that discretionary enforcement’s effectiveness in reducing crime is debatable. [4,7] While at best it may reduce crime short-term, it often fails to address the underlying patterns of criminal activity. At its worst, it fails to impact overall crime, allows inefficient use of officer time, opens the door for both explicit and implicit bias and “may reduce the legitimacy of the police, especially among communities of color.” [5] Recent legislation in states like California, Oregon and Washington to restrict officer proactivity stems from these concerns. [8] Additionally, although officers believe they know when and where to look for criminals, studies have shown they do not. [9] In all, officers often lack awareness of where to be proactive, and when they do so, they have little to no impact on crime, and it negatively impacts police-community relationships.
There is a better way forward.
Why surveillance technology is the future for officer proactivity
Surveillance technologies like those that helped Officer Rodriguez offer a promising path forward. While additional research is needed, existing studies offer important lessons. Surveillance cameras and ALPRs reduce crime, sometimes by as much as 80%. [10,11] RTIC-assisted investigations are 66% more likely to be solved and can reduce overall crime by as much as 15%. [12,13] Predictive policing systems offer efficiency and data-sharing improvements that improve case closure rates and reduce crime by as much as 26%. [14,15]
In recognition of such benefits, agencies have already modified how they deploy resources, with one California department staffing RTIC operators instead of additional officers to combat motor vehicle theft, with improved results. [16] Numerous agencies have already deployed predictive policing systems, sharing information with officers about where crime is likely to occur, achieving dramatic reductions in cities like Los Angeles and Newark, New Jersey. [15] And since 2008 the NYPD has used its Domain Awareness System, a mixture of sensors, cameras and predictive policing analytics, to deliver real-time information to officers in the field, leading to more efficient resource allocation (with cost savings of $50 million a year) and correlating with a 6% crime reduction. [17]
Ultimately, surveillance technologies have significantly reduced crime. By incorporating historical data, algorithms and artificial intelligence, they can help direct officers where they are most needed, allowing for more effective policing — as long as they are balanced with strong, evidence-based, community-informed policy.
Community trust is essential. Public opinion on police use of technology is divided, with concerns about privacy, bias and misuse being most prevalent. [18] To overcome these challenges, police departments must prioritize community partnership, transparency and accountability.
Agencies should actively involve community members to discuss how to use surveillance technologies. Whether through standing groups like police commissions, oversight committees and advisory boards or ad-hoc groups formed solely to provide feedback and recommendations, police must invite the public into these discussions and invest in their education to ensure informed decision-making. By including the public in decision-making processes — such as setting policies on facial recognition or appropriate data sources for predictive policing systems — police departments can ensure these tools align with community values and do not perpetuate systematic historical biases.
Clear guidelines around police use of surveillance technology and the security of associated data are critical. The public must be assured that personal information will be stored, protected and used responsibly. Establishing independently audited, transparent and regularly reviewed processes will ensure community awareness and enable their trust and support for continued use. Data on technology use — successes, failures and other takeaways — must be shared openly, and there must be strong oversight and accountability mechanisms to ensure police usage aligns with community direction. [19] When agencies establish robust safeguards to ensure engagement, transparency and accountability, they lay the foundation for community support.
Recommendations for future policing
As we look to the future, proactive enforcement should be shaped by thoughtful integration of surveillance technology, community partnerships and strategic officer deployment. Here are key steps to guide the transition, drawing from focus groups and expert interviews:
1. Drastically reduce discretionary proactive enforcement
Acknowledging there will be exceptions, agencies should discourage enforcement solely based on discretion and instead encourage proven evidence-based and data-driven strategies to reduce crime and improve police legitimacy.
2. Implement surveillance technology systems
Adopt and operationalize technologies like surveillance cameras, ALPRs, drones, RTICs and predictive policing systems to direct officer activity more effectively and efficiently. While initial costs may be problematic, research has shown this technology leads to cost savings. [14]
3. Invite and build community partnerships
Engage community stakeholders in developing and overseeing the use of surveillance technology to foster trust, ensure transparency and align law enforcement practices with public values (such as accountability, privacy and effectiveness). Ensure comprehensive policies are developed and followed.
4. Change officer recruitment and development
Prioritize recruiting officers with technological skills and invest in ongoing training to keep pace with technology advancements. Insist that police academies and state commissions adopt technology-based curricula.
5. Increase civilian staff
Employ civilians to manage and oversee the technology, allowing sworn officers to focus on core policing duties, in a more cost-effective way to manage growing technological demands and costs.
6. Explore regional partnerships
Partner with other police departments (and even private companies) to share data, resources, etc., to expand the reach of surveillance technologies and enhance system effectiveness.
A new era of smarter policing
The story of Officer Rodriguez is just one example of how surveillance technology can reshape proactive policing. By moving away from discretionary enforcement and embracing a technology-initiated approach, agencies can direct officers more effectively to capitalize on their expertise, intuition and professional discretion. In adopting this strategy, agencies should regularly evaluate progress and share learnings with the community and their officers — to improve this strategy and raise awareness for the present and future. This strategy will reduce crime, build stronger community relationships and help restore trust in law enforcement.
References
- Wooditch A. The benefits of patrol officers using unallocated time for everyday crime prevention. J Quant Criminol. 2021.
- Koper CS, Wu X, Lum C. Calibrating police activity across hot spot and non-hot spot areas. Police Q. 2021.
- Famega CN, Frank J, Mazerolle L. Managing police patrol time: The role of supervisor directives. Justice Q. 2005;22(4):540-569.
- Wu X, Lum C. The practice of proactive traffic stops. Policing. 2020.
- Lum C, Koper CS, Wu X, et al. Examining the empirical realities of proactive policing through systematic observations and computer-aided dispatch data. Police Q. 2020.
- Petersen K, Weisburd D, Fay S, et al. Police stops to reduce crime: A systematic review and meta-analysis. Campbell Syst Rev. 2023;19(3):e1302.
- McCann S. Low-level traffic stops are ineffective – and sometimes deadly. Why are they still happening? Vera. 2023.
- Machado E. California bill would end low-level traffic stops. ABC 10. 2024.
- Ratcliffe JH, McCullagh MJ. Chasing ghosts? Police perception of high crime areas. Br J Criminol. 2001;41(2):330-345.
- Piza EL, Welsh BC, Farrington DP, Thomas AL. CCTV surveillance for crime prevention: A 40-year systematic review with meta-analysis. City Univ New York. 2019.
- Illinois State Police. Automated license plate readers being installed in metro east to combat and solve violent crime. Illinois State Police. 2023.
- Guerette RT, Przeszlowski K. Does the rapid deployment of information to police improve crime solvability? A quasi-experimental impact evaluation of real-time crime center (RTCC) technologies on violent crime incident outcomes. Justice Q. 2023.
- Hollywood JS, McKay KN, Woods D, Agniel D. Real-time crime centers in Chicago. Rand. 2019.
- Mastrobuoni G. Crime is terribly revealing: Information technology and police productivity. Rev Econ Stud. 2020.
- Norga A. 4 benefits and 4 drawbacks of predictive policing. Liberties. 2021.
- Cortez A. Personal communication. April 2024.
- Levine ES, Tisch J, Tasso A, Joy M. The New York City Police Department’s domain awareness system. Informs J Appl Anal. 2017;47(1):1-14.
- Ezzeddine Y, Bayerl PS, Gibson H. Safety, privacy or both: Evaluating citizens’ perspectives around artificial intelligence use by police forces. Policing Soc. 2023.
- Radiya-Dixit E. A sociotechnical audit: Assessing police use of facial recognition. Minderoo Centre for Technology & Democracy. 2022.
About the author
Matt Lethin is a captain with the San Mateo (California) Police Department in the San Francisco Bay Area. He has 24 years of policing experience, beginning his career with the Marin County Sheriff’s Office before joining SMPD. In addition to policing, he is an adjunct professor of criminal justice for the College of San Mateo.