The following tweet is posted on open source media:
@Johndoe-5150 – I’ve loaded my AR-15. On my way to @cardshark’s house to retrieve my bitcoin winnings. I will kill you and anyone else who gets in my way!
How do law enforcement agencies respond to this threat? Would they even be aware of it? Do they have the tools to identify the suspect and the potential victim? Could they stop the suspect without inadvertently stopping innocent people in the process? Could it be done in less time than it takes for the suspect to drive to the victim’s house?
As daunting as the prospect might be to stop this possible crime, the information required by law enforcement to develop actionable intelligence to address this threat already exists. It exists in the form of big data. The challenge for most police departments will be in their ability to access and analyze the information in real time.
Big Data
Society continues to become fully immersed in the digital information age, with more than 90 percent of people in the developed world having mobile-broadband subscriptions.1 These people are creating 2.5 exabytes of data every day from things such as tweets, photos, purchase histories, blogs, and mobile devices.2 To put this into perspective, the data created every day are equivalent to 125,000 years of DVD-quality video.3 Additionally, Kevin Kelly predicts that by 2020, the global marketplace will be manufacturing 54 billion trackable sensors each year.4 These sensors are already being embedded in cars, used to monitor public and private spaces, and carried by people in the form of wearables. Wearable technologies track various aspects of a person’s activities, including location, movement, heart rate, and sleep. Wearables are currently a $700 million industry and experts predict the market will continue to grow quickly.5
Unfortunately, criminals are also users of these systems. The good news is data can become the digital fingerprint that law enforcement can use to identify criminals. These data include information that identifies individual’s past criminal activity and plans to commit future crimes. All this information is uploaded, usually without a second thought, by the criminal, and then stored digitally in large interconnected databases located around the globe. This is called big data: data that is generally too big and moves too fast for the processing capacity of conventional databases.6
According to Rick Graham, retired chief of detectives from the Jacksonville, Florida, Sheriff’s Office, the problem is no longer a lack of actionable intelligence but an overwhelming surplus of data.7 It will be up to each individual law enforcement agency and its ability to access big data in real time to enable big data to be a force multiplier in crime suppression efforts. When accessing big data, departments must be aware of potential flaws and biases within the data.8 Personal privacy and police transparency are two critical issues that also must be addressed by law enforcement looking to utilize big data initiatives.
Big Brother
The public is demanding any nontransparent policing policies, along with biased and ineffective procedures and tactics, be eliminated from U.S. law enforcement. These were amongst the most significant topics contained in the United States Department of Justice Civil Rights Division Reform Reports on the cities of Ferguson, Missouri and Baltimore, Maryland.9 Additionally, as noted in the aftermath of the San Bernardino terrorist attack, corporations, the courts, and many members of the public were averse to forcing Apple to provide the FBI with access to the data stored on a smart phone carried by the dead suspect, even though the phone was owned by the County of San Bernardino.10
In spite of privacy concerns, big data is already being used in numerous cities to predict where crime might occur and to help deploy officers using software such as PredPol.11 According to University of California, Davis, Law Professor Elizabeth Joh, the Supreme Court allows predictive analysis to be considered as evidence as long as it is not the only justification to bring charges against someone.12 The caution for law enforcement agencies is to ensure their officers do not utilize big data as the sole means to detain someone. Rather, officers should utilize big data as a means to supplement their reasonable suspicion through specific and articulable facts leading them to be more successful in identifying people who are committing criminal activity. This practice will help ensure officers are not targeting certain groups or violating people’s Fourth Amendment rights as had occurred under the stop-and-frisk strategies utilized by some departments. Former New York City Police Department (NYPD) Commissioner Bratton acknowledged that under the stop-and-frisk strategy police became imprecise exercising their powers as evidenced by a period of 700,000 stops, which resulted in an arrest rate of less than 10 percent.13 Additionally, in cities such as Baltimore, the Department of Justice found these types of tactics led the police department to intrude disproportionately upon the lives of African Americans in its enforcement activities.14 The responsible use of big data can uphold personal privacy and maintain government accountability, allowing the police to be not only more precise in their crime suppression efforts but also more impactful.
Research by Goel, Perelman, Shroff, and Sklansky indicates that big data can be used to help police better identify people who are in criminal possession of a weapon, thus leading to more effective stop-and-frisk protocols that protect against discrimination while preserving the use of individualized suspicion.15 Big data also has the potential to improve officer safety and decision-making by providing officers who are responding to emergency calls with useful intelligence to help them develop a plan to best address the situation.16 In addition, the consistent analysis and dispatching of these data accurately create a system of increased accountability, while demonstrating that law enforcement bias isn’t against anyone’s ethnicity or race, but against those who commit crimes. Finally, big data does not have to mean big brother as most of the information law enforcement agencies will be analyzing is already accessible to them. If used well, it can also produce significant results to enhance public safety.
Not a Big Deal
The access and analysis of big data currently exist, at most if not all agencies, in the form of computer-aided dispatch (CAD) systems; law enforcement records management systems (RMS); automated license plate readers (ALPRs); gunshot locaters, such as ShotSpotter; public and private surveillance camera systems; court records; and open source information, for example, social media. These analytical tools are the future of crime suppression. At most departments, the analysis is done by crime analysts during the investigative process after a crime has occurred—not before. Real-time access and analysis of big data allow law enforcement to more precisely and effectively solve crimes without intruding on the lives of innocent people. A recent successful example of this was noted by Thomas Davenport who wrote how NYPD officers were able to use information from their Domain Awareness System to locate two guns and arrest three suspects after they were alerted to a shooting by ShotSpotter.17
Big data is credited with helping to decrease LAPD’s violent crime rate by 21 percent and their property crime rate by 12 percent.18 Additionally, through the monitoring of open source systems, law enforcement can prevent crime before it even happens. A recent example of this occurred when deputies from the Lake County, Illinois, Sheriff’s Office arrested a documented gang member for the unlawful possession of weapon by a felon after he posted a video of himself with the gun to social media.19 In Florida, officers were tipped off by online viewers of a woman who was live streaming herself while driving drunk on the video application Periscope.20 Police were able to stop and arrest the woman before she injured herself or someone else. The access and analysis of social media have allowed law enforcement to arrest suspects before they injure or kill someone without having to unintentionally intrude on the lives of uninvolved community members. To develop a system for these type of successes, agencies should consider establishing real-time crime centers (RTCCs) so law enforcement can employ these tools in real time.
Real-Time Crime Centers
So, the question for many is what does the use of big data tangibly look like at an average police department that doesn’t have a hundred-million-dollar budget? The answer is an RTCC, which can provide the real-time, actionable intelligence, which is gained from big data, to the officers in the field as they are responding to the calls.
The NYPD is credited with opening the first RTCC in the United States in 2005 as a result of the 9/11 terrorist attacks with a cost of over $49 million.21 The Fresno Police Department, an agency with more than 700 officers, was able to open its RTCC in 2015. Fresno’s center utilizes real-time access to big data to determine potential threat levels associated with calls for service. Though not complete, Fresno Police Department was able to open its center for approximately $600,000, which included some significant private donations.22 Though much less expensive than NYPD’s center, Fresno’s center is still out of reach for many mid-sized police departments; however, options exist for smaller agencies to consider. Some jurisdictions are combining forces to create regional RTCCs; one example is New Jersey’s Corr-Stat, which represents more than 80 cities.23 Smaller cities, such as the Hartford, Connecticut, Police Department, are also opening RTCCs. In just the first few months the Hartford center was open, it was credited with assisting the department in hundreds of criminal cases, some of which resulted in arrests.24
The RTCC can be staffed by simply a dispatcher, analyst, or officer who is assigned a workstation with the ability to communicate directly with officers in the field. The staff member should also have access to the following:
- public and private video surveillance feeds
- local records information (such as RMS)
- arrest records
- national crime databases
- ALPR data
- open source information (such as social media)
The goal of personnel assigned to the RTCC should be to effectively manage all of this information in real time and then translate it into critical intelligence for immediate distribution to field personnel for a more proactive response.25
The development of an RTCC starts with a vision, which should be led from the top of the agency. Buy-in from elected officials, a city manager or chief administrative officer, department directors, and the public is also critical to a successful implementation. Additionally, significant preparation must be undertaken first, which includes learning from others. This should consist of visits to other RTCCs where committee members can see the various technologies in action, allowing them to determine what would work for their individual department. When the Albuquerque, New Mexico, Police Department (APD) set up its RTCC, the process began with a steering committee that included the center manager, IT manager, strategic support manager, 9-1-1 communications manager, violent crimes lieutenant, operations lieutenant, and special investigations commander.26 Within the first three months that APD’s RTCC was open, it was credited with solving hundreds of crimes and keeping responding officers safer.27
For an RTCC to be most effective, the members of the RTCC should be allowed to directly communicate with the personnel responding to calls for service or handling follow-up investigations. One of the goals of Albuquerque’s RTCC is to provide critical information to field personnel prior to them arriving on scene, allowing responding officers to make better and safer decisions.28 Though the staffing for the RTCC does not have to be robust, it should be staffed with department members (sworn, civilian, or combined sworn and civilian) who know how to access the available data and provide a useful analysis of it. This can be a single crime analyst, dispatcher, investigator, or skilled patrol officer, but it should be a permanently scheduled assignment to avoid the staff member being reassigned due to other staffing needs.
Departments should not allow cost to dissuade them from pursuing an RTCC. As was previously discussed, many of the systems utilized by RTCCs are systems already in use in most departments. This includes CAD and RMS, analytic software, and ALPR systems. Funding sources like grants and asset forfeiture can provide the financing for hardware such as computers, cameras, and computer monitors. Additionally, agencies can seek assistance from other city departments when it comes to infrastructure costs related to things such as fiber optics and wireless networks.
Conclusion
Now, back to the tweet from @johndoe-5150.
Inside the River City Police Department’s RTCC, which is a single workstation located in the department’s 12-person dispatch center, Alan, a young millennial officer with a robust understanding of open source media and the various law enforcement data bases, sits under the glow of a half-dozen monitors. The monitors depict live video feeds from key areas around the city, ALPR that are placed at the intersections of the city’s major egress and ingress routes, open source social media feeds, and active 9-1-1 call information. Alan notices the tweet from @johndoe-5150, which appeared on the department’s Twitter page after someone retweeted it. He views the @johndoe-5150 open profile and finds a match on the subject in a local database. Alan also identifies the subject’s vehicle and license plate. He promptly places an alert for the vehicle’s license plate number into the department’s ALPR system. Alan is then able to find photos the suspect posted of himself on social media holding an illegal assault rifle. Alan puts out a BOLO (be on the lookout) to all in-service units, which includes a photograph of the suspect.
The fixed-point ALPR placed at State Route 60 and First Street, the main ingress point for the city, picks up the suspect’s vehicle that Alan had entered only moments earlier. It indicates the suspect is driving south into the city. Having been previously notified of the potential threat, officers safely locate the vehicle and conduct a high-risk stop detaining @johndoe-5150 without incident and within 10 minutes of the suspect’s tweet. The subsequent investigation determined that the suspect was in possession of a loaded illegal assault weapon. He later confessed to the detectives to being on his way to the victim’s house. The victim turned out to be a 13-year-old child that had beat the suspect in a virtual poker game taking all his bitcoins.
That’s the beauty of an RTCC: its staffed with qualified personnel whose only job is to detect, analyze, and communicate.
Notes:
1International Telecommunication Union, ICT Facts and Figures 2016 (Geneva, Switzerland: ITU, June 2016).
2Ben Walker, “Every Day Big Data Statistics: 2.5 Quintillion Bytes of Data Created Daily,” Vcloud News, April 5, 2015.
3Margaret Rouse, “What Is Exabyte (EB)? – Definition from WhatIs.com,” com, April 2005.
4Kevin Kelly, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future (NY, NY, Viking, 2016), 267.
5Lindsey Patterson, “The Current and Future State of Wearables: Trends, Attitudes and Total Domination,” Wearable Tech World, February 16, 2016.
6Edd Wilder-James, “Defining Big Data,” Forbes, June 27, 2014.
7Rick Graham, “How Social Media Tools Can Help PDs Overcome Data Overload,” PoliceOne.com, December 1, 2015.
8William Isaac and Kristian Lum, “Predictive Policing Violates More Than It Protects: Column,” USA Today, December 02, 2016.
9U.S. Department of Justice Civil Rights Division, Investigation of the Ferguson Police Department (Washington, D.C.: U.S. Department of Justice, March 4, 2015); U.S. Department of Justice Civil Rights Division, Investigation of the Baltimore City Police Department, (Washington, D.C.: U.S. Department of Justice, August 10, 2016).
10James Queally, Richard Winton, Dave Paresh, and Brain Bennett, “Apple vs. FBI Is Epic Fight Over Privacy and National Security,” Los Angeles Times, February 18, 2016.
11Ellen Huet, “Server and Protect: Predictive Policing Firm PredPol Promises to Map Crime Before It Happens,” Forbes, February 11, 2015.
12John Villasenor, “Big Data and Its Threat to the Fourth Amendment,” Big Data Made Simple, June 24, 2014.
13Vincent J. Bove, “Precision Policing: Respecting Our Citizens’ Dignity,” Epoch Times, August 18, 2016.
14U.S. Department of Justice Civil Rights Division, Investigation of the Baltimore City Police Department, 62.
15Sharad Goal, Maya Perelman, Ravi Shroff, and David Alan Sklansky, “Combating Police Discrimination in the Age of Big Data,” SSRN, May 13, 2016.
16Louis F. Quijas, “The Time Is Right for Big Data Solutions,” Police Magazine, September 1, 2016.
17Thomas Davenport, “How Big Data Is Helping the NYPD Solve Crimes Faster,” Fortune, July 17, 2016.
18Judy Selby, “Big Data to Prevent Crime; Cyber Stop & Frisk Risk?” Big Data Made Simple, June 19, 2015.
19Jim Newton, “Cops: Gang Member Posted Facebook Video Displaying Gun after Traffic Stop,” Lake County News-Sun, June 3, 2016.
20Sarah Begley, “Woman Broadcasts Her Own Drunk Driving on Periscope,” Time, October 12, 2015.
21Joe Kemp, “Inside Look at NYPD’s $49M Nerve Center for Fighting Crime,” New York Daily News, September 6, 2011.
22Justin Jouvenal, “The New Way Police Are Surveilling You: Calculating Your Threat ‘Score,’” Washington Post, January 10, 2016.
23 Kathleen Hickey, “New Jersey Opens Regional Real Time Crime Center,” GCN, February 11, 2015.
24Bruce Brown, “Police Departments in Smaller Cities Getting Real-Time Street Surveillance Centers,” Digital Trends, May 24, 2016.
25Mike Fox, “How Real-Time Crime Center Technologies Are Force Multipliers,” Police One, April 14, 2014.
26Clint Hubbard, “Setting Up the Albuquerque Police Department’s Real Time Crime Center” (presentation, 37th Law Enforcement Information Management [LEIM] Annual Education Conference and Technology Exposition, Scottsdale, AZ, May 23, 2013).
27Angela Brauer, “APD Plans to Add on to Real Time Crime Center,” KOAT Action News, April 11, 2013.
28“Albuquerque Police Department, “APD Real Time Crime Center 911 Emergency Call,” YouTube Video, 4:42, posted by Fire and Police Videos, March 29, 2013.
