A new year always brings a time of reflection, both in our personal lives, and in our work. When the calls-for-service (CFS) project started years ago, one of the goals was to make better use of the massive amounts of data that comes into local 911 systems. Leaders in any agency will always have an idea of the types of incidents and issues they are likely to face in their areas. However, I was recently asked an intriguing question: What data findings have surprised people the most through the work on the CFS Analytics™ project?
One of the most often cited findings is in the area of response time. When most citizens think of response time the first thing that comes to mind is a critical, life threatening emergency. A shooting, cardiac arrest, or fire are examples of emergencies where every second counts. Mistakes or lapses to calls in one of these areas will bring significant negative attention to an agency. A deeper analysis of 911 data can reveal potential harbingers of risks to come, before someone is hurt.
Many citizens assume that living far off in the suburbs will cause a longer emergency response time to their address. This can be true, but CFS Analytics™ has also highlighted another phenomenon for agencies: downtown areas, usually close to major public safety buildings, often had the longest response times. One reason is that as units downtown become busy on calls, other units have to come from different areas to handle new issues.
The downtown areas can be significantly affected by changes in population or new construction. For example, a new apartment complex brings people and traffic to the area. Emergency services must meet the demand of the additional citizens, but they will also be affected by the additional traffic on major arteries as they go to other calls. The combination of units being busy, and others having to travel through new choke points to handle issues can quickly create a dangerous situation.
An agency that analyzes its data could quickly recognize upward trends in response time for a variety of different types of calls. With that information, the agency can position resources more effectively. One example might be a quick response vehicle placed in an area with no close fire station, which sees a spike in motor vehicle accidents. Or a police agency might choose to supply officers with Naloxone in areas close to identified opioid hotspots.
A user of CFS Analytics™ noted that the program does not make decisions for a leader. She commented that visualizing the data made the relevant issues quickly come to the surface, in ways that numbers alone could not.
In 2019, the CFS Analytics™ team will be proud to bring exciting new features to the table to help visualize your data, and to use it to make quick decisions in response.