Reference no: EM133515910

Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime

Predictive analytics and data mining have become an integral part of many law enforcement agencies including the Miami-Dade Police Department, whose mission is not only to protect the safety of Florida's largest county with 2.5 million citizens (making it the seventh largest in the United States), but also to provide a safe and inviting climate for the millions of tourists that come from around the world to enjoy the county's natural beauty, warm climate, and stunning beaches. With tourists spending nearly US$20 billion every year and generating nearly a third of Florida's sales taxes, it's hard to overstate the importance of tourism to the region's economy. So although few of the county's police officers would likely list economic development in their job description, nearly all grasp the vital link between safe streets and the region's tourist-driven prosperity. That connection is paramount for Lieutenant Arnold Palmer, currently supervising the Robbery Investigations Section, and a former supervisor of the department's Robbery Intervention Detail. This specialized team of detectives is focused on intensely policing the county's robbery hot spots and worst repeat offenders. He and the team occupy modest offices on the second floor of a modern-looking concrete building, set back from a palm-lined street on the western edge of Miami. In his 10 years in the unit, out of 23 in total on the force, Palmer has seen a lot of changes. It's not just in policing practices, like the way his team used to mark street crime hot spots with colored pushpins on a map.

Policing with Less

Palmer and the team have also seen the impact of a growing population, shifting demographics, and a changing economy on the streets they patrol. Like any good police force, they've continually adapted their methods and practices to meet a policing challenge that has grown in scope and complexity. But like nearly all branches of the county's government, intensifying budget pressures have placed the department in a squeeze between rising demands and shrinking resources. Palmer, who sees detectives as front-line fighters against a rising tide of street crime and the looming prospect of ever-tightening resources, put it this way: "Our basic challenge was how to cut street crime even as tighter resources have reduced the number of cops on the street." Over the years, the team had been open to trying new tools, the most notable of which was a program called "analysis-driven enforcement" that used crime history data as the basis for positioning teams of detectives. "We've evolved a lot since then in our ability to predict where robberies are likely to occur, both through the use of analysis and our own collective experience."

New Thinking on Cold Cases

The more confounding challenge for Palmer and his team of investigators, one shared with the police of all major urban areas, is in closing the hardest cases, where leads, witnesses, video-any facts or evidence that can help solve a case-are lacking. It's not surprising, explains Palmer, because "the standard practices we used to generate leads, like talking to informants or to the community or to patrol officers, haven't changed much, if at all," says Palmer. "That kind of an approach works okay, but it relies a lot on the experience our detectives carry in their head. When the detectives retire or move on, that experience goes with them." Palmer's conundrum was that turnover, due to the retirement of many of his most experienced detectives, was on an upward trend. True, he saw the infusion of young blood as an inherently good thing, especially given their greater comfort with the new types of information-from e-mails, social media, and traffic cameras, to name a few-that his team had access to. But as Palmer recounts, the problem came when the handful of new detectives coming into the unit turned to look for guidance from the senior officers "and it's just not there. We knew at that point we needed a different way to fill the experience gap going forward." His ad hoc efforts to come up with a solution led to blue-sky speculation. What if new detectives on the squad could pose the same questions to a computer database as they would to a veteran detective? That speculation planted a seed in Palmer's mind that wouldn't go away.

The Big Picture Starts Small

What was taking shape within the robbery unit demonstrated how big ideas can come from small places. But more important, it showed that for these ideas to reach fruition, the "right" conditions need to be in alignment at the right time. On a leadership level, that means a driving figure in the organization who knows what it takes to nurture top-down support as well as crucial bottom-up buy-in from the ranks, while at the same time keeping the department's information technology (IT) personnel on the same page. That person was Palmer. At the organizational level, the robbery unit served as a particularly good launching point for lead modeling because of the prevalence of repeat offenders among perpetrators. Ultimately, the department's ability to unleash the broader transformative potential of lead modeling would hinge in large part on the team's ability to deliver results on a smaller scale. When early tests and demos proved encouraging-with the model yielding accurate results when the details of solved cases were fed into it-the team started gaining attention. The initiative received a critical boost when the robbery bureau's unit major and captain voiced their support for the direction of the project, telling Palmer that "if you can make this work, run with it." But more important than the encouragement, Palmer explains, was their willingness to advocate for the project among the department's higher-ups. "I can't get it off the ground if the brass doesn't buy in," says Palmer. "So their support was crucial."

Success Brings Credibility

Having been appointed the official liaison between IT and the robbery unit, Palmer set out to strengthen the case for the lead-modeling tool-now officially called Blue PALMS, for Predictive Analytics Lead Modeling Software-by building up a series of successes. His constituency was not only the department brass, but also the detectives whose support would be critical to its successful adoption as a robbery-solving tool. In his attempts to introduce Blue PALMS, resistance was predictably stronger among veteran detectives, who saw no reason to give up their long-standing practices. Palmer knew that dictates or coercion wouldn't win their hearts and minds. He would need to build a beachhead of credibility. Palmer found that opportunity in one of his best and most experienced detectives. Early in a robbery investigation, the detective indicated to Palmer that he had a strong hunch who the perpetrator was and wanted, in essence, to test the Blue PALMS system. So at the detective's request, the department analyst fed key details of the crime into the system, including the modus operandi, or MO. The system's statistical models compared these details to a database of historical data, looking for important correlations and similarities in the crime's signature. The report that came out of the process included a list of 20 suspects ranked in order of match strength, or likelihood. When the analyst handed the detective the report, his "hunch" suspect was listed in the top five. Soon after his arrest, he confessed, and Palmer had gained a solid convert. Though it was a useful exercise, Palmer realized that the true test wasn't in confirming hunches but in breaking cases that had come to a dead end. Such was the situation in a carjacking that had, in Palmer's words, "no witnesses, no video and no crime scene-nothing to go on." When the senior detective on the stalled case went on leave after three months, the junior detective to whom it was assigned requested a Blue PALMS report. Shown photographs of the top people on the suspect list, the victim made a positive identification of the suspect leading to the successful conclusion of the case. That suspect was number one on the list.

Just the Facts

The success that Blue PALMS continues to build has been a major factor in Palmer's success in getting his detectives on board. But if there's a part of his message that resonates even more with his detectives, it's the fact that Blue PALMS is designed not to change the basics of policing practices, but to enhance them by giving them a second chance of cracking the case. "Police work is at the core about human relations-about talking to witnesses, to victims, to the community-and we're not out to change that," says Palmer. "Our aim is to give investigators factual insights from information we already have that might make a difference, so even if we're successful 5% of the time, we're going to take a lot of offenders off the street." The growing list of cold cases solved has helped Palmer in his efforts to reinforce the merits of Blue PALMS. But, in showing where his loyalty lies, he sees the detectives who've closed these cold cases-not the program-as most deserving of the spotlight, and that approach has gone over well. At his chief's request, Palmer is beginning to use his liaison role as a platform for reaching out to other areas in the Miami-Dade Police Department.

Safer Streets for a Smarter City

When he speaks of the impact of tourism, a thread that runs through Miami-Dade's Smarter Cities vision, Palmer sees Blue PALMS as an important tool to protect one of the county's greatest assets. "The threat to tourism posed by rising street crime was a big reason the unit was established," says Palmer. "The fact that we're able to use analytics and intelligence to help us close more cases and keep more criminals off the street is good news for our citizens and our tourist industry."

Questions

1. Why do law enforcement agencies and departments like Miami-Dade Police Department embrace advanced analytics and data mining?

2. What are the top challenges for law enforcement agencies and departments like Miami-Dade Police Department? Can you think of other challenges (not mentioned in this case) that can benefit from data mining?

3. What are the sources of data that law enforcement agencies and departments like Miami-Dade Police Department use for their predictive modeling and data mining projects?

4. What type of analytics do law enforcement agencies and departments like Miami-Dade Police Department use to fight crime?

5. What does "the big picture starts small" mean in this case? Explain.