Prior to construction of the Hoover Dam, the Colorado River had an average flow of about 21,000 cubic feet per second, peaking at about 100,000 cubic feet per second after the snow melt in the Rocky Mountains. This was powerful enough to carve the Grand Canyon over a few million years, but beyond that, the Colorado River had very little utility. Today, the power of the river is fully harnessed. The dam’s hydroelectric plant generates about 4 billion kilowatt hours of electrical power for use in Arizona, Nevada, and California.1 It also irrigates 5.5 million acres of agricultural land that provides 15 percent of U.S. crops.2
Today’s law enforcement community is in a similar set of circumstances. As public safety broadband communications continue to evolve, a vast amount of data is being generated that has the potential to dramatically enhance emergency response by improving situational awareness and to support smart policing and predictive policing initiatives by introducing more actionable intelligence into the decision-making process.
However, using all of these data sources depends on the ability to harness data effectively. This challenge will be exacerbated in the coming years by data streams generated by sensors and Internet of Things (IoT) devices, which will increase the amount of data available to the law enforcement community by orders of magnitude.
A Big Problem
According to the Security Industry Alarm Coalition, alarm companies generate roughly 23 million notifications each year—and nearly 100 percent of them are unverified. This represents 10 percent of the total volume of 911 calls made—and some communities report that alarms account for 20 percent of total 911 calls. This is a big problem for law enforcement agencies. Some have decided not to respond to unverified alarms. For instance, in Sandy Springs, Georgia, alarm companies are actually prevented by local ordinance from passing unverified alarm calls to the city’s 911 system.3
However, most law enforcement agencies still respond to unverified alarms, and, when they do, they place officers at unnecessary risk when the call turns out to be a false alarm. Worse, the allocated resources are unable to respond to legitimate emergencies, which often increases response times. When lives are on the line and every second matters, that’s a problematic outcome.
The solution being developed by the alarm industry is still in a nascent stage, but it shows promise. It leverages the Automated Secure Alarm Protocol (ASAP), which originally was designed to transmit data generated by alarm systems more quickly—in seconds rather than minutes. More important, by verifying the existence of an actual emergency, an alarm company can reduce the sheer number of false alarms being sent for dispatch. Further, improvements to officer safety are expected, as sensors provide additional information about the incident even before responders arrive on scene.
Early trials of ASAP have been promising. The Houston Police Department reportedly used the protocol to eliminate about 140,000 alarm calls, which projects to an annual cost savings of about $1 million—mostly by reducing the staff needed to answer non-911 phone lines, which typically receive alarm calls.4
Data Harnessing
The long-term aspects of resolving sensors’ data challenges will address data harnessing. Public safety agencies don’t want to comb through a flood of raw data to make emergency response decisions. What they want instead is a much smaller volume of highly contextual data.
That context ultimately will be delivered by improved data analytics and artificial intelligence (AI) solutions. While data analytics and AI are nothing new, they continue to evolve rapidly, which is good news for the law enforcement sector. As public safety broadband communications continue to gain traction, a tremendous amount of data will be generated by existing systems that will need context. These systems include the following:
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- Fixed, mobile, and body-worn cameras
- Automatic license plate readers (ALPR)
- Geofencing
- Gunshot location/identification sensors
- AMBER alerts, which now can be integrated into highway display signage
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Such data, when effectively leveraged, can enhance situational awareness and lead to a profoundly positive emergency response outcome, as demonstrated by an incident that took place in Virginia. On August 26, 2015, Trooper Pamela Neff, at the time an 11-year veteran of the Virginia State Police, was positioned in her patrol car at the Interstate 81/Interstate 66 interchange in Middletown, Virginia. Five hours earlier, Vester Lee Flanagan II, aka Bryce Williams, fatally shot WDBJ-TV reporter Alison Parker and cameraman Adam Ward as they conducted a live, on-air interview in Moneta, Virginia, about 170 miles away. Flanagan’s alleged motive was revenge stemming from a dispute with station management.
As Flanagan made his escape, the state police had transmitted the license plate number of his rental car to troopers in the field. Trooper Neff typed the number into her ALPR system and received a hit—according to information captured by her vehicle’s video camera system, Flanagan’s vehicle had passed hers heading eastbound on I-66 just three minutes earlier.
Trooper Neff immediately began her pursuit, catching up to Flanagan about four minutes later. Six minutes after that, Flanagan’s car veered into an embankment and troopers found him with a self-inflicted gunshot wound. He died a couple of hours later. Neff’s quick action, aided by her agency’s ALPR program, stopped a murderer intent on inflicting more carnage in his tracks, undoubtedly saving the lives of additional victims.5
Another Big Problem
The problem with the scenario of ALPR saving the day is that the ALPR system is just one of many that are in play, all generating vital data—if they can be harnessed. The amount of available data is already daunting. The challenge exacerbates by orders of magnitude when one considers the data generated by IoT devices and sensors, which today total in the billions but eventually will reach into the trillions—and that day isn’t far away.
The world is driven by data, and with 90 percent of the world’s data generated in just the last two years, leveraging data effectively will continue to be an increasingly important factor in bringing greater intelligence to law enforcement response. The ability to do so will hinge on the utility of the data, because data with no utility has no value.
IoT innovation is helping to leverage data to improve response times while providing law enforcement with more situational awareness than they would gain from a lone 911 call.
For example, a 911 emergency occurring across town likely will involve officers having to maneuver through traffic, thus delaying their response time. However, thanks to IoT technologies, traffic lights be controlled to help fast-track travel time, and crowd-sourced traffic information will reduce response times. Responders are further protected by wearable biometrics and body-worn cameras that enable an officer and his or her incident commander to monitor everything from the officer’s heart rate to a first-person perspective during those stressful situations.
But not every data opportunity is futuristic—numerous systems exist today that work to keep officers safe in the line of duty. One example is automatic vehicle location (AVL), which helps dispatchers monitor the location of officers and provide backup from nearby personnel.
For example, in December 2005, a 22-year veteran of the Pennsylvania State Police was engaged in a high-speed pursuit on an interstate highway outside Pittsburgh, Pennsylvania. The suspect crashed his car and then engaged the trooper in a violent struggle, killing him with his service revolver. A few minutes later, the trooper’s body was found by a local police officer who just happened to see the flashing lights of the trooper’s patrol car.6 Apparently, things evolved so quickly that the trooper didn’t have time to radio dispatchers with his status and location. If an AVL solution had been in place or other sensors were available in his patrol car, PSAP telecommunicators would have been alerted that the trooper was in trouble and a local police officer was nearby, able to provide critical backup.
Yet, there is a certain point where the torrent of unfiltered data stemming from public safety broadband communications can prove to be too much for field responders to comprehend, much less use to make sound and rapid emergency response judgements. These rich data need to be electronically and automatically harnessed—human intervention only slows things down.
The ability to leverage data effectively involves more than data-harnessing technologies such as data analytics and AI. It also requires procedural changes. An important one concerns breaking down the informational silos that traditionally have existed in public safety. Today’s emergency response ecosystem extends far beyond the traditional elements of law enforcement, fire and rescue, and emergency medical services to secondary and tertiary elements such as public works; public utilities; and departments of transportation, forestry, and conservation. The ability of all elements in the ecosystem to share data seamlessly is critical to maximizing the intelligence that can be applied to emergency response decision-making.
One area of data’s role in intelligent emergency response represents a key goal: predictive policing, which analyzes data in myriad of ways to prevent crime from occurring in the first place.
At the heart of predictive policing is the ability to contextualize data using filters—such as crime mapping, geospatial analysis, historical data mining, and social media analysis—to identify when and where crime is likely to occur and then marshal the appropriate resources in those places.
One of the earliest—and best—examples occurred in Richmond, Virginia, nearly two decades ago. The city experienced a huge increase in random gunfire every New Year’s Eve. In 2003, using predictive policing tactics, the city identified the locations where random gunfire would most likely occur and stationed police officers in those locations. The result was a 47 percent decrease in random gunfire incidents and a 246 percent increase in weapons seized.7
It should be clear that harnessing data is the gateway to a rich future of dramatically improved emergency response outcomes, which in turn will result in exponentially more lives and property saved and, just as important, safer police officers. The work needed to effectively harness the treasure trove of data that will be available to law enforcement agencies in the future needs to begin now.
Every agency can start this process by becoming familiar with the plethora of data streams that are and will be available; identifying opportunities to share data with stakeholders across the community; researching potential data-analytics and AI solutions that could be leveraged; and breaking down the silos that prevent highly contextual and actionable data from being shared seamlessly by all elements of today’s emergency response ecosystem.
Data is the oil of the 21st century—it’s great to hit a gusher, but if you can’t harness it, it has little to no value.
Notes:
1 Bureau of Reclamation, “Hoover Dam,” August 1, 2018.
2 “The Colorado River,” Quoc and River, November 1, 2019.
3 Ben Brasch, “Police Won’t Respond? Sandy Springs Law Shows Tension with Alarm Groups,” Atlanta-Journal Constitution, April 19, 2019.
4 APCO International, “Houston’s New Alarm Response Program Projects Annual Savings of $1 Million–$2 Million,” press release, July 6, 2011.
5 Alix Bryan and Jon Burkett, “How Virginia State Police Captured Suspected Journalist Killer Vester Flanagan,” NBC 6 News, Richmond, August 26, 2015.
6 Officer Down Memorial Page, “Corporal Joseph Raymond Pokorny, Jr.”
7 Beth Pearsall, “Predictive Policing: The Future of Law Enforcement,” NIJ Journal, no. 266 (May 2010): 16–19, 17.
Please cite as:
John Chiaramonte, “The Opportunities and Challenges of Intelligent Emergency Response,” Police Chief Online (March 2020).
