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It’s time for our annual look forward into the major trends that will shape Law Enforcement and public safety in 2022.
The coming year’s trends are going to be heavily influenced by the dual forces of demands for change, and by several technological breakthroughs that are already having a transformative effect on crime mitigation and public safety. These changes may begin in 2022, but they will have a long tail of development and impact over the coming decade.
Here are the most compelling Law Enforcement trends we see emerging in 2022 and beyond.
The first development on this list is arguably the most important and complex one. It involves the evolution of the Community Oriented policing model from a cliché that is often discussed but rarely holistically implemented, to a core tenet that integrates all aspects of policing into it.
Intelligence Led policing, with its emphasis on technology, CompStat, surveillance, and data visualization often lives in a separate silo from Community Led policing that emphasizes community engagement, environmental design, restorative justice, and quality of life crimes. These silos often exist within the same agencies, running programs that focus on these areas in parallel.
Many forward-thinking law enforcement leaders are waking up to the realization that by underpinning intelligence initiatives with the core tenet of community service, they can build highly successful public-private collaborations that result in better overall public safety outcomes.
The suggestion is not that law enforcement agencies necessarily need to be running social programs, but rather, that by establishing trust within their communities they will receive more cooperation, better data, and greater access to private security resources, which in turn will lead to more successful intelligence and crime mitigation programs.
For example, building value-driven holistic outreach programs can lead to collaborative efforts like camera sharing, crime tips, better youth outreach, and violence interruption. The result will be greater efficiency in the deployment of resources based on community sourced data, and the community will benefit from the force-multiplying effect of networking their security resources to create safer environments.
Reimagining the local police-public interface via environmental design for instance, can have a significant effect on how community members feel about their police force. Local police precincts should not be confused with corrections facilities. The precinct should be a welcoming place that community members feel comfortable entering to request assistance.
Hand in hand with physical design, training that emphasizes community safety and service, and focuses more on making people comfortable interacting with officers, rather than leaning into the traditional “shepherd vs. wolves” role, is likely to lead to smoother collaborations, and in turn, better intelligence.
While we need to acknowledge that the operational parameters and tactics of special operations units will necessarily differ from this approach (dealing with high-risk warrants, barricaded suspects, etc.) the overarching public safety model driven by community collaboration will be a driving force in law enforcement in the years to come.
Hand in hand with community collaboration, is the continued proliferation and unification of public safety and intelligence assets such as city owned and private cameras, gunfire sensors, CAD, AVL, body cameras, and various types of IoT sensors and alert mechanisms.
The wholesale move of security assets to cloud-based systems is aiding in this unification process, along with the death of closed-loop and proprietary systems. The technological future of public safety lies in open platforms that are able to integrate with a wide range of sources and fuse them into a common operating picture. Having a 360-degree view of public safety assets, especially during emergent situations or active investigations, is invaluable to creating better public safety outcomes.
The ability to draw from community owned cameras for instance, increases the speed of response and helps the allocation of resources, especially for agencies with limited resources. Automatically activating cameras around gunfire detections, receiving multi-media tips directly from community cell phones, or utilizing AI-driven weapons detection in public spaces can all have an extremely beneficial effect on public safety.
However, going back to point one, these technology-driven approaches to crime mitigation are infinitely more effective if couched within a community-oriented model. Greater trust between law enforcement agencies and the communities they serve yield more community involvement in the overall public safety ecosystem (private cameras, crime tips, targeted violence interruption, etc.) which in turn yield better outcomes.
The proliferation of cloud-based technologies has also drastically lowered barriers to entry for real-time crime centers. What was once only achievable by a few highly resourced agencies, is now available to almost any law enforcement agency that can afford a software license and some screens. Even a physical video wall can be installed using inexpensive, consumer grade screens.
The defragmentation of resources, as well as the ability to stand up a RTCC without ripping and replacing all their existing infrastructure is a step forward. Now, any agency can create a cloud based RTCC that can even be geographically decentralized (more on the topic of mobility later) to streamline operations.
The proliferation of these cloud-connected RTCCs is going to ramp up in a big way as more and more outdated on-premises technologies reach obsolescence. The great benefit of course will be significantly lower setup and recurring costs, and easy activations.
From a law enforcement perspective, holistic RTCCs will also reduce geographical blind-spots and enable faster response times. Enabling dispatchers, RTCC personnel, and even officers in transit to a call-for-service to have eyes on the scene, will aid in more informed decision making and better outcomes. For instance, proposed co-responder programs that pair mental health professionals with police officers will only be possible if dispatchers have a better idea of what the true nature of an (often vague or confusing) call for service is.
With the growth of RTCCs based on open platforms, we are also going to see wider geographical collaborations in the form of regional fusion centers. These fusion centers will enable better mutual aid, a more effective pooling of intelligence assets that span localities and jurisdictions, and better allocation and sharing of resources across agencies. Regional fusion centers are likely to grow in scale as more cities and counties decide to pool their resources in the interests of a larger public safety net.
While data driven approaches to law enforcement (like CompStat) have been around for a long time, they are evolving in sophistication, becoming a necessity that drives daily operations. The amount of data available to command staff is increasing exponentially- to take full advantage of this access, the development of better data modeling approaches and the application of the findings to daily operations is vital.
Unified databases drawing from various sources (CAD, sex-offender registries, ALPR hits, gunfire detections, AVL, etc.) that are cross-referenceable within an easy-to-operate visualization platform will be the new gold-standard for crime analysts and command staff.
Just as important as the ability to draw data, is the ability to derive meaningful correlations between large sets of disparate data. A basic example is a city map that displays ShotSpotter alerts. A high concentration of shots fired in a certain area within a discrete time period on the map can lead to the conclusion that more resources should be allocated to that region. However, that conclusion could be wrong, as it is equally possible that is the only area where ShotSpotter sensors are located. Instead, that data would need to be overlaid with the location of all ShotSpotter sensors, as well as geographically pinpointed references to gunfire from CAD calls within the same time period, to craft a true picture of gun violence in the city.
Building upon this example, the locations of specific ALPR hits, AVL breadcrumb trails from police units, and other data such as weather and time-of-day can all yield actionable conclusions.
Leading law enforcement agencies recognize that a data-driven approach that creates a feedback loop to optimize systems and practices is the way of the future. However, the major challenges to this approach are the volume and complexity of available data sets, and a skills deficit.
Let’s address both of those in the next two points.
Artificial intelligence is going to play an increasingly important role in law enforcement, especially due to its efficiencies at scale, as well as its ability to augment human intelligence. With the explosion of correlational data, AI models will be a necessity to parse, connect-the-dots, and find hidden connections on data sets too large for human analysts to evaluate.
Data visualizations, similar to the ShotSpotter example above, will be driven by AI applications that can rapidly interrogate the data and deliver to crime analysts and command staff the information they need to streamline operations and develop more effective public safety policies. Similarly, finding commonalities in CAD data and evidence reports can be rapidly accomplished by AI models.
AI can also create major efficiencies when paired with video applications. Newer AI models are able to effectively recognize a host of attributes within images and video such as colors, vehicles, objects like backpacks and weapons, and a range of motions. This enables them to be both extremely helpful when searching for evidence (rapidly finding a specific vehicle or individual within hundreds of hours of footage, or matching ballistics data across cases and regions) as well as in a real-time capacity (acting as a virtual sentry that identifies vehicles or individuals matching certain attributes across a city-wide video security network).
The real-time aspect of AI identification can have significant implications for violence reduction and rapid apprehensions. For example, weapons detection in or around schools, the detection and search for vehicles departing scenes where gunfire sensors are activated, or the detection and alert of unattended items like backpacks near major events, are all AI applications that can be automated and carried out at a scale that would otherwise require dozens of analysts watching hundreds of camera feeds simultaneously.
Longer term, AI can increasingly be applied in real-time to disparate video and sensor systems like weapons sensors, security cameras, and body cameras, pairing the visual data received with multiple databases to deliver real-time actionable intelligence to officers in the field.
In point five, we covered how AI can help create efficiencies that drive a data and systems driven approach to public safety. However, as part of the holistic community service model that we should consider both the reason for and foundation of intelligence led policing approaches, AI is merely a tool that enables civilian personnel and sworn officers to accomplish their missions successfully.
After years of draw-downs of civilian personnel, often due to limited resources, we are likely to see the addition of civilian personnel specializing in technology and analysis. Ironically, funding barriers have often created a greater demand for predictive policing models (to stretch resources more effectively) while also resulting in fewer personnel to facilitate those models. In the coming years, we’re likely to see more civilian analysts who are able to understand and support an intelligence-led approach (and the technologies that support it) and ask the right questions in order to interrogate public safety data more effectively.
Sworn officers should also be receiving updated training that emphasizes community engagement, de-escalation techniques, and the ability to recognize (and effectively and safely deal with) individuals experiencing mental health issues, to name a few areas.
To enhance community engagement and build trust, officers will also increasingly be drawn from the communities they police, giving them a more intimate knowledge of the residents and cultural underpinnings of the localities they are responsible for. The relationships they will be tasked with building within their communities will be the absolute foundation of the entire department’s intelligence led policing efforts- if trust is not built with the face of the police department, the community is far less likely to work with the department as a whole.
The increasing mobility of law enforcement technology will also play a major role in the life of a sworn officer, as they are increasingly equipped with advanced and integrated communications technology. Officers will have an unprecedented level of access to video and data on the move, giving them greater situational awareness. Departments will gradually move away from bulky in-vehicle MDTs to smartphones connected to cloud-based real-time intelligence systems. These decentralized, phone-based information terminals will allow officers in the field to participate in a real-time ecosystem that ties them more closely to the RTCC.
With these leaps in mobile intelligence, will also come leaps in accountability. IoT sensors and cameras will record more officer interactions in real-time, enabling command staff (and the general public when the situation calls for it) to gain objective access to events as they unfolded. While this may sometime be seen as an added burden by law enforcement professionals, it is also the answer to calls for greater transparency from the public at large.
While the profession of policing is always evolving, there has never been a better time for law enforcement leaders to seize the opportunity to enact major change. Leadership now has the mandate, public appetite, and technology to truly integrate intelligence and community focused practices into a holistic model of public safety that will effectively mitigate crime while keeping their communities and officers safe.
Creating this new multi-disciplinary framework for public safety will have its challenges, but the front-runners in our field who are able to meet this moment will reap the rewards in years to come.
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