** QUESTION **

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?

4.1 OPENING VIGNETTE: 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 enforce ment agencies including the Miami-Dade Police Department, whice mission is not only to protect the safety of Florida's largest county with 25 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 oy 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 oversee the impor- tance of tourism to the region's economy. So although few of the county's police officers would likely list economic development in their job descripcion, Dearly all grasp the vital link between safe streets and the region's tourist-driven prosperity, That connection is paramount for Lieutenant Amold Palmer, Currently supervising the Robbery investigations Section, and a former supervisor of the departient's Robbery Intervention Detail. This specialized team of detectives is focused on intensely policing the county's robbery hot spots and west repeat offenckers. He and the muccupy wod- est offices on the second floor of a modern booking concrete building set back from a pulin-lined street on the westem edge of Miami. In his 10 years in the unit out of 23 in toal on the force, Palmer has seen a lot of changes. It's noxjust in policing practices, like the way his team used to mark street crime hot spots with colored puslepins on a map. Policing with Less Palmer and the team have also seen the impact of a growing population, shifting demo- graphics, 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 govem ment, intensifying boder pressures have placed the department in a squeeze between rising demands and shrinking resources Palmer. who sees detectives sa front-line fighters against a ring tide of street Crime and the looming prospect of ever tightening resources, put it this way "Our basic chal lenge was how to cut street crime even as tighter resources have reduced the number of cups on the root Over the years, the team had been open to trying now tools, be most nocable of which was a program called analysis driver enforcement that used crime his tory data as the basis for positioning teams of detectives. We've evolved a lot since then in cur 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 sistem of investigates, one shared with the police of all major urtun areas, is in closing the hardest cases, where leads witnesses, video- any facts of evidence that can help sohe a case-are lacking. It's not surprising, explains Palmer, because the standard practices we used to serate leads, like taking to informas or to the community or to patrol officers, haven't changed much, fat 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 Chapter 4. Predictive Analytics Data Mining Process, Methods and Algorithms 217 blood as an inherently good thing, especially ghen their greater comfort with the new types of information from email, social media, and traffic cameras, to name a few that his team had acousso. Butas Palier recons, the problem came when the bandful of new detectes 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 experi- ence gap going forward." His ad hoxe efforts to come up with a solution led to blue sky speculation. What if new detectives on the squad couk 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 woulot 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 nation 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 keep ing the departments information technology (IT) personnel on the same page. That per so 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 ofenders 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 sale When early tests and demas proved encouraging with the model yielding accurate results when the details of solved Gises were fed into the team started gaining atten tion. The initiative received a critical boost when the robbery baru's unit max and captain voiced their support for the direction of the project. telling Palmer that you can make this work, run with it. But more important than the couragement, Palit explains, was their willingness to advocate for the project among the department's higher upe. I can't get it off the ground of the brass doesn't buy in," says Palmer. So their support was crucial." Success Brings Credibility Having been appointed the officials between IT and the robbery unit, Palmerset cut to strengthen the case for the lead modeling tool now officially cuilled Blue PALMS, for Threlictive Analytics Lead Modeling Software building up a series of successes His constituency was not only the department brass, but also the detectives whose sup port would be critical to its successful adoption as a robbery-saving lool. In his attempts to introduce Blue PAIMS, 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 Harly 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 operand, or MO. The systems statistical models compared these details to a database of historical data, looking for important correlations and sim Barties 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 218 Chapter 4 . Predictive Analytics I: Data Sining Proces. Methods and Algorithms detective the report, his "hunch suspect was listed in the top five. Soon after his arest he confessed, and Palmer had gained a solid convert. Though is a useful excise, l'altet realized that the true test west't in culin- Ing hunches but in breaking cases that had come to a dead end. Such was the situation in a cracking 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 Nue PALMS report Shown photographs of the top people on the suspect in 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 suc cess in getting his detectives on board. But if there's a part of his message that resonates even more with his detectes, it's the fact that Blue PALMS is designed not to change the basics of polking practices, but to enhance them by giving the second chance of cracking the case.Police work is at the core about human relations about talking to witnesses to victims, to the city and were bok 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 9% of the time, we're going to take * 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 Thul, in showing where his loyalty lies, be sees the detectives who've dosed these cold cases-not the program-s most deserving of the spotlight and that approach has gone over well. At his chief's request, Palmer is beginning to use his linison role as a platform for teaching 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-Dades Smarter Cases vision, Paimer sees Blue PALMS as an important tool to protect ourse of the county's greatest assets. "The threat to tourism posed by rising street crime was a big rea- son the unit was established," says Palmer. The fact that we're able to use analytics and intelligence to help us close more and keep more criminals off the street is good news for our citizens and our trist Industry