As the Austin Police Department continues to be staffed well below its budgeted headcount, a new staffing model unveiled Jan. 11 could be used to help city leaders determine how many officers the department needs and what their goal response times would be with those numbers.
The new model stems from a project funded by the Greater Austin Crime Commission and overseen by researchers at the University of New Haven and Texas State University. Their work gathered millions of data points from five years' worth of APD officer responses and calls for service into a machine learning model, which produced several recommendations for the department's patrol operations.
In a virtual briefing on the program, Police Chief Joseph Chacon hailed the "groundbreaking" model as the first of its kind in the U.S. and a tool that could shape ongoing conversations about the APD's staffing needs and the department's lagging priority response times.
"We pride ourselves as a police department in using data and evidence to make decisions, especially policy decisions that are going to make sense for our community," Chacon said. "This falls right in line with that and is going to be, I think, a very useful tool for our department and something that has that evidence behind it when we talk about, ‘This is the right number of officers that we need to be able to accomplish our goals.’”














