Imagine a city where emergency services do not just react to crises but anticipate them. Where crime hotspots are identified before they escalate, and resources are deployed with laser-like precision. This is not science fiction; it is the burgeoning reality of data analytics and artificial intelligence in public safety. We are moving beyond the traditional reactive model, where sirens blare after an incident, to a proactive approach powered by the insights hidden within vast datasets. This transformation promises not only to enhance efficiency, but if we do it right, it will also foster greater transparency and build stronger community trust.
Public safety agencies are experiencing a data deluge. From 911 call logs and incident reports to CCTV footage and sensor networks, the sheer volume can be overwhelming. But within this deluge lies a goldmine of information. This data, when properly analyzed, can paint a vivid picture and reveal hidden connections and potential risks.
So, how do data and technology coalesce to advance these capabilities? Some key applications include:
Predictive Policing: AI models can analyze historical crime data and call volumes to predict potential crime hotspots and recommend optimal officer deployment strategies. Crime is both geospatially and temporally related, which makes this type of AI application simple to design and implement…albeit a little too simple. It is crucial to acknowledge the ethical considerations and potential biases inherent in such systems and incorporate mechanisms to both expose and minimize them.
Incident Categorization: Current CAD systems often classify calls for service with a single, simplistic categorization, failing to capture the nuances of many incidents. AI can analyze CAD data, including narratives, to automatically tag calls with multiple relevant attributes. For example, a domestic incident call might also involve child endangerment, substance abuse, or mental health issues, and AI can help identify and tag these elements. This provides dispatchers and responders with a more complete understanding of the situation, leading to better resource allocation and response strategies.
Efficient Reporting: AI, using Natural Language Processing (NLP) and Large Language Models (LLM), can automatically generate incident and investigation progress reports from unstructured data like officer notes, video transcripts, or audio recordings. This reduces the time officers and investigators spend on paperwork, increasing their availability for fieldwork. However, there is risk of AI hallucinations making their way into official reporting. Systems like these rely on culture shifts within agencies to be successful, as they require strong adherence to policies and consistent quality assurance.
Enhanced Analysis: It is no secret that the majority of major or violent crimes in any jurisdiction are committed by a very small percentage of the population. Offenders, like most humans, follow consistent patterns of behavior that can be surfaced and attributed. AI can analyze various crime data points to recognize patterns and potential links between crimes, helping investigators identify potential serial offenders or organized crime activities.
The wave of innovation in public safety is only beginning, and these examples are just a brief sampling of things to come. As we ride this wave, it is important to note that analytics and AI are not about replacing human judgment; they are about augmenting it. The insights gleaned from data can inform decision-making, but it is the experienced officers and other first responders who make the final calls. We must remember that data is a tool, not a substitute for human intuition and empathy. Nor should the tool define our processes, rather, they must operate within the confines of well thought out guidelines and operational procedures.
Of course, there are challenges. Data privacy and potential bias are the most notable ones, but ensuring there is transparency, accountability, appropriate training, and community involvement are all equally as important. The future of public safety is data-driven, but it is also human-centered. By harnessing the power of analytics and AI responsibly and transparently, we can create safer, more resilient communities for all, and foster greater trust between public safety agencies and the communities they serve.
Michael is a retired police captain and former commander of the New York City Police Department’s Strategic Technology Division, where he led the development and implementation of all public safety technology platforms. Having formerly served in patrol, crime analysis, intelligence, and counterterrorism roles, Michael leveraged this subject matter expertise to establish an IT transformation team that revolutionized the NYPD’s applications, systems, and infrastructure, bringing the Department into 21st century policing. Currently, Michael leads the public safety business for 22nd Century Technologies, bringing his two decades of experience enabling transformational change in public safety by improving agencies’ utilization of technology and evolving their operational culture. Michael holds an MBA from the University of Arizona, a Masters in Computer Information Systems from Boston University, and a Bachelors in Sociology from the University at Albany. He is also a frequent speaker on public safety technology and a published author of situational awareness and data analytic research.