PSAP, Meet Big Data

The Public Safety Answering Point (PSAP) of the future is starting to take shape. We know that with Next Gen 9-1-1, emergency calls will no longer be just voice. Instead, they may include some combination of voice, texts, pictures, and video. Someday, as well, it is likely that video from public and private organizations will play a more central role in the PSAP, along with real-time video from patrol vehicles and body-worn cameras. Many other sensors and data sources will probably also be routed to PSAPs, such as LPR, gunshot detection, hazmat alerts, weather alerts, telematics, and even social media.

While these sources of Big Data hold a lot of promise, they will create major challenges too. The biggest challenge will be how to achieve situational awareness based on so much incoming information. 

Consider this possible future scenario at a large metropolitan PSAP: 

A gunshot detection sensor triggers an alert. This automatically brings up a video feed of the closest camera. The person manning the console sees an injured victim in the frame and what appear to be eyewitnesses, but no sign of the shooter. Minutes later, in a separate corner of the PSAP, a different call-taker (unaware of the gunshot alert) receives a 9-1-1 call from a citizen reporting a shooting. The citizen says that he took a cell phone video of the alleged shooter as he fled by car in the direction of the city center.

The ability to connect the dots between these seemingly disparate events and different pieces of information, in a timely way, could be key to catching the suspect. This ability to correlate different data sources is where Big Data technologies will play a role. 

For example, the PSAP could use voice analytics to detect the keyword “shooting” in the 9-1-1 call. This intelligence, along with the caller’s location, would then automatically match the 9-1-1 call to the gunshot alert, connecting the information from these two “separate incidents.” PSAP personnel could corroborate the vehicle/suspect description from the eyewitness accounts with the vehicle/license plate description from the 9-1-1 call/smartphone video. And, had there been text messages to 9-1-1 with more information, content analytics could similarly connect these to the original incident.

Think of how other sensors and inputs could connect to the future PSAP as well. By accessing surveillance video from stores along the suspect’s last known travel route, the call-taker might be able to pinpoint the suspect’s current location and relay that information to responding officers. She could also share the surveillance video and smartphone video with the officers. Once they get a hit on the vehicle using LPR, they would then be able to visually confirm the match using the multiple sources of video. Another Big Data challenge for future PSAPs will be managing all this information on the back end.

In addition to handling incidents, PSAPs are also the keepers of incident records. They maintain this information for investigations and prosecutions, liability protection, and quality assurance (QA). With more sources of data coming into the PSAP, incident reconstructions will become more complex. Today when an incident is investigated, or a call is QA-ed, it’s simply a matter of locating and assembling voice recordings, and maybe pulling the CAD records. But as larger quantities of Big Data become part of the incident record, PSAPs will want (and need) to aggregate all incoming data, including recordings of Next Gen 9-1-1 calls (voice, text, video) corresponding screen captures, mobile video, surveillance video, pictures, etc. for a complete evidentiary record.

It’s clear that the PSAP of the future will be a powerful information hub. The ESInet and public safety broadband will be the superhighways that deliver Big Data to PSAPs. But the key to unlocking the promise of this Big Data will lie in the PSAP’s ability to connect the dots, and get the right information to the right people at the right time.

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