Law enforcement is now a data-driven profession, forcing agencies to provide statistically-defensible articulation for how many patrol officers are needed to maintain or reach specific goals such as emergency response times or backup unit availability.
This is quickly becoming a hot-button issue, and we are witnessing a collision between politics and demand. If demand dictates more officers, is the area then being over-policed? There is a struggle between officer safety and budgets, and sometimes there simply is no more money to spend. Some officers working the street may feel their safety is being disregarded and they are being taken for granted while others may think everything is great. That’s the byproduct of an imbalanced workload. Then there’s the issue of minimum staffing. An ambiguous number that becomes cemented in the agency culture. We can all agree that there needs to be a certain number of officers on duty, but what is the basis for that number? Is it by shift? Is it by the hour, so the minimum can be met by different shifts?
Even if an agency had all of the officers they’re allocated, there could still be an imbalance in the workload. Officers could struggle to keep up during certain hours while at other hours officers don’t run call-to-call. That’s the byproduct of an inefficient schedule. It’s nearly impossible to consider all of these factors and come up with a patrol schedule and deployment plan that feels perfect for everyone. So, how do we find a defensible and agreeable plan to move forward? What is the best way?
The answer lies in the first paragraph of this blog post…. a DATA-DRIVEN solution. Upfront, everyone needs to know that they won’t get exactly what they want. This means the community, the officers, the command staff, the purse-string holders, and anyone else who thinks they know the right answer.
It’s hard to argue with numbers and when those numbers are shown to everyone, they become fact. It’s important to be transparent in how these numbers were derived. Once, during my years as an analyst, there was a neighborhood that felt neglected by the police department. They claimed they never saw the police patrol their neighborhood, even as they had high crime and drive-by shootings every weekend. Wow, I thought, we’ve really dropped the ball with these guys. Guess I better see what they’re talking about. I did my analyst thing, pulled the data, cleaned the data, and gathered additional data just to make sure I didn’t miss anything. Where was this high crime they spoke of? Drive-by shootings, surely we’d have records? The data was succinct. Low crime, and no drive-by shootings. Did they have crime, sure, but it was minimal in comparison to other neighborhoods. We sympathized with their perception that they were neglected by the police. But, when we showed them (with DATA) that we really need to spend our time in the neighborhoods that had higher crime and we couldn’t justify pulling cops from high crime areas where they are needed more, they actually agreed! They didn’t come away with what they wanted but were satisfied with the reason why.
The same solution applies to patrol staffing. There’s a plethora of data available to do this, specifically within CAD. The key is to know exactly which elements you need, how to scrutinize and clean that data, how to organize it, and most importantly how to apply it.
Interested in learning more about the most efficient way available to deploy our patrol officers? Click here