How Patrolfinder Uses Data to Make Streets Safer

For many law enforcement leaders, it’s a familiar, nearly universal complaint — one that’s difficult to validate or refute with confidence.

Patrol officers may feel they’re covering their sectors thoroughly. Supervisors may trust their officers’ experience and instincts. But without clear, comprehensive data, no one can say with certainty where coverage is strong, sufficient or falling short.

For decades, supervisors have relied on a combination of institutional knowledge, officer discretion and limited analytics. While GPS tracking and computer-aided dispatch systems have improved visibility into where units are, they don’t provide a complete picture of where officers haven’t been — or how effectively patrol time is being used.

That gap is now driving a shift toward more data-driven approaches to patrol deployment. Inspired by and developed in partnership with working police chiefs, Patrolfinder was designed to measure, analyze and ultimately improve their presence in the communities they serve.

The Limits of Traditional Patrol

Traditional patrol models are built on experience. Officers familiarize themselves with their zones, develop routines and respond to calls for service as they arise. Supervisors rely on shift briefings and after-action discussions to communicate priorities. In some agencies, crime analysts provide hotspot data to guide deployment.

But even in well-run departments, gaps can persist.

Officers, like anyone else, tend to fall into patterns — relying on familiar routes and frequently traveled roads. Shift handoffs may not fully capture which areas received attention and which did not. And while GPS systems can show where a vehicle has been, they don’t highlight areas that may be consistently overlooked.

“GPS can tell you where you’ve been,” says Antonio Civitella, president and CEO of Schenectady, N.Y.-based Transfinder, the company behind the development of Patrolfinder. “But it can’t tell you where you have not been.”

The result is uneven coverage that can leave residential streets, parking lots and other lower-traffic areas underserved. These are often the very locations where opportunistic crimes — vehicle break-ins, thefts and burglaries — occur.

Without reliable data, agencies may also struggle to demonstrate patrol effectiveness to their communities or justify resource allocation when applying for grants or budget increases.

From School Buses to Police Cars

The push to address patrol management challenges didn’t originate in law enforcement. It began in student transportation.

Transfinder built its reputation developing routing and logistics software for school districts — helping map bus routes, improve efficiency and provide visibility to parents. The company’s expertise in managing movement and optimizing coverage laid the groundwork for what would become Patrolfinder.

The transition to policing began in 2016. Civitella’s high school classmate, Eric Clifford, was a member of the Schenectady Police Department and a candidate for chief. Clifford was familiar with Transfinder and wondered if the same technology could be applied to solve a law enforcement problem.

Clifford wanted to understand patrol coverage at a deeper level. Could Transfinder be adapted to tell supervisors not just where officers were going, but where they weren’t — and how coverage impacted crime rates?

That question sparked a multi-year development effort involving extensive engineering, testing and collaboration with law enforcement professionals. Early input from agency heads helped shape the platform’s functionality.

One key concept built into Patrolfinder’s DNA is “dosage,” derived from hotspot policing research and the work of criminologist Dr. David Koper. The principle is straightforward: spending a sustained amount of time in high-crime areas — as little as 10 to 15 minutes — can significantly reduce criminal activity.

“It’s not just about driving by,” Civitella explains. “It’s about being there, being visible and staying long enough to make a difference.”

Establishing a Baseline

Implementation of Patrolfinder’s patented platform takes a phased approach. Rather than immediately changing officer behavior, agencies begin by establishing a baseline.

In practice, that means installing the system in patrol vehicles and allowing officers to operate as usual for four to six weeks. During this period, the software collects data on movement patterns, coverage levels and time spent in specific areas.

Once enough data is gathered, analysts and command staff can begin to see where coverage may be lacking.

Interactive maps reveal which streets are frequently covered and which are not. Zones or sectors can be evaluated over different timeframes — 24 hours, 72 hours or longer — to assess consistency. Areas that receive little or no patrol attention become immediately visible.

From there, agencies can begin setting goals and adjusting deployment strategies.

In some cases, initial data shows that only a fraction of a patrol zone is being covered within a given timeframe. By identifying gaps and redistributing patrol efforts, departments can significantly increase coverage without adding personnel.

“Same cars, same officers,” Civitella says. “You’re just making them more efficient.”

Supervisors can also use real-time tools to direct patrol activity. If a specific area requires attention, a unit can be assigned directly through a map-based interface, allowing for quick adjustments based on emerging needs.

A Chief’s Perspective on Implementation

For Chief Brian Coons of the Cheektowaga (N.Y.) Police Department, the decision to adopt a data-driven patrol approach was initially driven by curiosity.

Like many law enforcement veterans, Coons believed his department already had strong coverage. Officers were assigned to districts, and it was generally assumed they were patrolling all areas within them.

The data told a different story.

“When we first looked at it, most of the cars were just on the main streets,” Coons says. “That’s where people tend to go.”

With that insight, the department began making adjustments — shifting patrol patterns to ensure more consistent coverage across residential areas and other less-traveled locations.

Coons says the impact was immediate. “In the first month, we increased coverage. And now we can actually measure it.”

The data and reporting have proven valuable in multiple ways. When residents raise concerns about a lack of police presence, the department can reference patrol data to provide a clear answer. When applying for grants, such as New York’s Gun Involved Violence Elimination (GIVE) program, the department can demonstrate how resources are being used.

Data has also enhanced operational decision-making. By analyzing call-for-service patterns and crime trends, the department can tailor patrol strategies by shift. Afternoon deployments may focus on traffic and retail areas, while overnight patrols emphasize residential neighborhoods where vehicle break-ins are more likely.

Integration with computer-aided dispatch systems further expands situational awareness, allowing officers to see calls and relevant information in real time.

“It gives us data for everything we do,” Coons says. “And it’s easy to use. The software does the work.”

Handling the ‘Big Brother’ Objection

As with any new technology, adopting a data-driven patrol system can raise concerns among officers. The most common objection is the perception of increased monitoring — a “Big Brother” effect that some fear could be used for disciplinary purposes.

Both Civitella and Coons emphasize that how the technology is introduced matters.

“There’s always going to be pushback with change,” Coons says. “But communication is key.”

In Cheektowaga, leadership made it clear that the system was not installed for punitive purposes. Supervisors were not expected to monitor officers continuously, and the data would be used to improve overall operations rather than single out individuals.

That approach helped build acceptance as officers began to see the benefits: Clearer patrol expectations, better alignment with crime patterns and improved outcomes all contributed to broader buy-in.

Younger officers, in particular, may be more comfortable with technology and more interested in measurable performance metrics. For others, understanding that similar tracking already exists through body cameras, license plate readers and other systems can help put concerns in perspective.

Budget considerations are another factor, but agencies are increasingly finding ways to offset costs through grants and demonstrated efficiencies.

“We charge by the license,” Civitella says. “It’s very affordable.”

The Future of Patrol Is Measurable

Law enforcement is steadily moving toward more evidence-based practices, and patrol operations are no exception.

Data-driven patrol strategies offer a way to move beyond assumptions — providing concrete insights into how resources are deployed and how those decisions impact public safety.

For agencies, the benefits extend beyond crime reduction. Improved transparency can strengthen community trust. Clear metrics can support funding requests and strategic planning. And more efficient use of existing personnel can help departments do more with limited resources.

Technology will continue to evolve, but Civitella says the underlying goal remains the same: Give law enforcement leaders the information they need to make better decisions, faster.

“We’re focused on making the data easier to understand and act on so chiefs can make decisions quickly.”

At its core, patrol is about presence. Where officers go — and how often — matters.

For agencies that have relied on instinct and experience alone, the addition of reliable data can be a powerful force multiplier. It doesn’t replace the judgment of seasoned officers, but it enhances it, filling in the gaps that intuition can miss.

And for the communities they serve, it helps answer a simple but important question: Are we being seen?

With the right tools and strategies in place, departments can ensure that the answer is yes — not just on the main roads, but on every street that depends on them.