Many law enforcement agencies already possess the data and technology necessary to transition from real-time response to trend analysis. Making the move will deliver significant operational and economic benefits.
Improving efficiency: The hidden benefits of existing data
Law enforcement agencies are adopting intelligent analytics to improve their real-time awareness. These alerts support fast and informed responses, which is good news for public safety. But the data captured by cameras and other sensors can also be put to work in a variety of different ways. When used for trend analysis, it can show what has quietly been happening in your district for months – and enable agencies to make predictions and plan for what might happen next.
Trends and data: The way to proactive intelligence
Trend analysis and pattern recognition support a proactive approach to public safety, as well as smart and effective long-term planning. Data-driven deployments can help officers with a wide range of tasks, such as crime prevention, suspect apprehension, and event policing. More efficient use of financial and human resources means budgets go further, with better and more measurable results.
The metadata generated by edge analytics – timestamped, location-tagged, searchable – is exactly the raw material trend analysis needs, so getting started doesn’t demand a heavy investment in new infrastructure.
The tools to visualize, aggregate, examine, and analyze what sensors are generating are probably already part of an existing ecosystem. Video Management Software (VMS), Evidence Management Software (EMS), and other technology platforms within a Real-Time Crime Center (RTCC) aggregate the metadata that edge analytics generate and display it as dashboards, heat maps, and pattern reports. There, operational leads, analysts, and command staff get actionable intelligence that enables them to be more proactive.
Real-world proof: Practical examples of trend analysis in action
Data supports decisions; it doesn’t make them. It’s a shoulder tap that guides the person to the right move. The operational impact of trend analysis on the individual agency workers extends across the full spectrum of public safety, allowing agencies to signal problems before they become emergencies.
- Example: Organized retail crime
Repeat offenders tend to follow predictable patterns. Organized retail theft groups, for example, typically target specific areas, rotating through locations in a systematic way. By aggregating license plate recognition (LPR) data from past incidents, law enforcement agencies can identify vehicles and scouting patterns associated with prior thefts, enabling more targeted, intelligence-led interception and prevention.
- Example: Crowd and event management
Large-scale events present unique challenges to urban infrastructure. By looking at data from previous events, departments can identify issues like an uptick in crime around the event area, and exactly where incidents like traffic bottlenecks typically occur. This allows for efficient allocation of law enforcement, better traffic light timings, and officer placement before the first attendee arrives.
- Example: Quality of life and public spaces
Issues like vandalism or recurring noise disturbances are often hard to address because they are not always reported, which makes it difficult to allocate resources strategically. Loitering analytics, occupancy analytics, and audio analytics can be used to gather data that creates a picture of when and where these issues peak. This allows for time-limited deployments that are based on documented patterns, ensuring resources are not wasted on repeated reactive patrols.
Audio analytics can also help agencies stay aware of sounds that could indicate more serious incidents. For example, they can detect screams, breaking glass, and other audio anomalies. Over time, that data can reveal where and when incidents occur most frequently.
Improving performance with cross-departmental data
There is another layer of advantages when departments draw on their historical data. When combined with data from other departments, fresh insights can emerge that benefit both parties. Interdepartmental collaboration can also open the door to shared investments and resource allocation that make the entire municipality work more economically and efficiently.
Example: Transportation and law enforcement
The transportation department uses its flow data to complement police cameras for a more complete picture of congestion across the city. When combined with police incident and response time data, it becomes possible to explore whether average officer response times fluctuate with predictable seasonal or event-related traffic congestion.
Example: Parks and law enforcement
Combining parks usage and maintenance data from parks-owned cameras with audio and loitering analytics from police cameras helps both departments manage their resources more efficiently. This data can help determine, for example, whether city investments in recreational spaces are correlated with reductions in nearby disturbances. The data also equips both departments with documented evidence to support budget decisions.
Change is coming: Why it’s best to be ready
Adopting data-led trend analysis and pattern recognition is a process that requires focused cross-departmental effort. However, not making the move has a cost that compounds quietly over time: slower response times, missed patterns, and resources deployed on instinct rather than evidence. The question many organizations are asking now is whether the potential gains are worth working through the hurdles. For successful organizations, the answer is “yes” – often followed by, “Where do we start?”
The “need to have” shortlist
Although every agency has its own challenges and requirements, they can safely adhere to some basic overarching principles.
- Flexibility: Sensors that can host evolving analytics enable you to adapt better than a single-purpose device.
- Interoperability: Open standards are what make cross-departmental data combination possible without expensive custom integration.
- Collaboration and shared infrastructure: Federated camera networks and interagency data agreements extend the dataset without duplicating investment.
The key to a future with trend analysis
Something all the above principles have in common: avoiding technological and departmental silos. Collaboration and openness in every sense is key. Fragmented systems don’t just slow down real-time response; they prevent the kind of rich analysis needed to reveal patterns and trends.
Open standards allow different departments to combine and connect their resources. They also ensure the most freedom when it comes to adopting new analytics, dashboards, and other tools in the future. And they enable broad collaboration on the supplier side of the equation – Axis, for example, partners with other industry-leading companies to be able to offer a wide range of solutions tailored to specific needs.
Delivering value: From choices today to improved performance tomorrow
Public safety agencies that are building strategic, high-quality data ecosystems today are the most likely to be able to show measurable improvements in performance and operational effectiveness tomorrow. When budgets are under pressure, it’s not difficult to work out who will be best positioned to secure the necessary resources in fiscal negotiations.
Realistic risk assessment: A clear view of what’s required
Agencies that are already thinking strategically about data infrastructure are also better positioned to end up with tools that support integrated solutions like RTCCs, Emergency Operations Centers (EOCs), and even the digital twins that will likely become mainstays of public safety in the coming years. Conversely, postponing strategy conversations may cost agencies dearly further down the line, when they have to find resources to replace obsolete or incompatible systems.
At the end of the day, the agencies gaining the most value from metadata and analytics are not necessarily creating new data. They’re asking better questions of the data they already have and using that information to make more informed decisions. For first steps, it can be as simple as that.
