Tag: Cold cases

Big Data Analytics: Crime Fighting

Case Study: Miami-Dade Police Department: New patterns offer breakthroughs for cold cases. 

Introduction:

Tourism is key to South Florida, bringing in $20B per year in a county of 2.5M people.  Robbery and the rise of other street crimes can hurt tourism and a 1/3 of the state’s sale tax revenue.  Thus, Lt. Arnold Palmer from the Robbery Investigation Police Department of Miami-Dade County teamed up with IT Services Bureau staff and IBM specialist to develop Blue PALMS (Predictive Analytics Lead Modeling Software), to help fight crime and protect the citizens and tourist to Miami-Dade County. When testing the tool it has achieved a 73% success rate when tested on 40 solved cases. The tool was developed because most crimes are usually committed by the same people who committed previous crimes.

 Key Problems:

  1. Cold cases needed to be solved and finally closed. Besides relying on old methods (mostly people skills and evidence gathering), patterns still could be missed, by even the most experienced officers.
  2. Other crimes like, robbery happen in predictable patterns (times of the day and location), which is explicit knowledge amongst the force. So, a tool shouldn’t tell them the location and the time of the next crime; the police need to know who did it, so a narrowed down list of who did it would help.
  3. The more experienced police officers are retiring, and their experience and knowledge leave with them. Thus, the tool that is developed must allow junior officers to ask the same questions of it and get the same answers as they would from asking those same questions to experienced officers.  Fortunately, the opportunity here is that newer officers come in with an embracing technology whenever they can, whereas veteran officers tread lightly when it comes to embracing technology.

Key Components to Success:

It comes to buy-in. Lt. Palmer had to nurture top-down support as well as buy-in from the bottom-up (ranks).  It was much harder to get buy-in from more experienced detectives, who feel that the introduction of tools like analytics, is a way to tell them to give up their long-standing practices and even replace them.  So, Lt. Palmer had sold Blue PALMS as “What’s worked best for us is proving [the value of Blue PALMS] one case at a time, and stressing that it’s a tool, that it’s a compliment to their skills and experience, not a substitute”.  Lt. Palmer got buy-in from a senior and well-respected officer, by helping him solve a case.  The senior officer had a suspect in mind, and after feeding in the data, the tool was able to predict 20 people that could have done it in an order of most likely.  The suspect was on the top five, and when apprehended, the suspect confessed.  Doing, this case by case has built the trust amongst veteran officers and thus eventually got their buy in.

 Similar organizations could benefit:

Other policing counties in Florida, who have similar data collection measures as Miami-Dade County Police Departments would be a quick win (a short-term plan) for tool adoption.  Eventually, other police departments in Florida and other states can start adopting the tool, after more successes have been defined and shared by fellow police officers.  Police officers have a brotherhood mentality and as acceptance of this tool grows. Eventually it will reach critical mass and adoption of the tool will come much more quickly than it does today.  Other places similar to police departments that could benefit from this tool is firefighters, other emergency responders, FBI, and CIA.

June 2020 Editorial Piece:

Please note, that the accuracy of this crime-fighting model is based on the data coming in. Currently, the data that is being fed into these systems are biased towards people of color and the Black community, even though crime rates are not dependent on race (Alexander, 2010; Kendi, 2019; Oluo, 2018). If the system that generated the input data is biased towards people of color and Black people, when used by machine learning, it will create a biased predictive model. Alexander (2010) and Kendi (2019) stated that historically some police departments tend to prioritize and surveillance communities of color more than white communities. Thus, officers would accidentally find more crime in communities of color than white communities (confirmation bias), which can then feed an unconscious bias in the police force about these communities (halo and horns effect). Another, point mentioned in both Kendi (2019) and Alexander (2010), is we may have laws in the books but they are not applied equally among all races, some laws and sentencing guidelines are harsher on people of color and the Black community. Therefore, we must rethink how we are using these types of tools and what data is being fed into the system, before using them as a black-box predictive system. Finally, I want to address the comment mentioned above “The tool was developed because most crimes are usually committed by the same people who committed previous crimes.” This issue speaks more about mass incarceration, private prisons, and school to prison pipeline issues (Alexander, 2010). Addressing these issues should be a priority, to not create racist algorithms, along with allowing returning citizens to have access to opportunities and fully restored citizen rights so that “crime” can be reduced. However, these issues alone are out of the scope of this blog post.

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