The Central Texas Public Safety Commission turns twenty-five this year. Since 1997, we’ve supported Central Texas first responders and promoted public safety planning.
Our silver anniversary begins with a groundbreaking police staffing project announced today by Police Chief Joseph Chacon. Austin is the first city to use machine learning to model police staffing. The Central Texas Public Safety Commission funded the research by the University of New Haven and Texas State University.
The research report and summary are available online.
The patrol model analyzed more than six million officer responses to nearly two million calls for service. Machine learning suggests responding to urgent calls for service within 6 minutes and 30 seconds for the best public safety outcomes.
The police department is authorized 774 patrol positions, but has a lower staffing level due to vacancies and leave. The model projects an additional 108 authorized patrol positions are needed to meet the target response time.
The research also included a Citywide Survey of Police Services, which asked residents about fear of crime victimization, perceived effectiveness of the Austin Police Department, perceived procedural justice, and police officer use of time. The next phase of the research, which models administration and specialized units is already underway.
What does this mean? The Austin Police Department now has a groundbreaking evidence-based patrol response model. No more guessing about staffing levels. No more cops-per-thousand. No more taxpayer-funded staffing studies.
This morning, the Central Texas Public Safety Commission Board of Directors unanimously adopted a resolution urging the Austin City Council to solve the police staffing crisis by:
- Implementing patrol model recommendations
- Increasing training capacity
- Reducing attrition
If city leaders and the community work together, police staffing is a problem we can fix.
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