The team here at PredPol has decided to tell you a little about how we define predictive policing and our philosophy around it. We hope you come away with a better understanding of PredPol’s use of data to make cities safer, while at the same time preserving the civil rights and privacy of the residents of those cities.
PredPol has a narrow definition of predictive policing. For us and our customers, it is the practice of identifying the locations where specific crimes are most likely to occur, then proactively patrolling those areas to prevent those crimes from occurring. Put simply, it’s about using data to reduce victimization.
Reducing victimization is meaningless, however, if it means people have to give up their civil rights or privacy protections along the way. That’s why we have consciously worked to ensure that the data and algorithms we use are as unbiased, objective and transparent as possible.
Let’s start with the source of our data. We use only records management systems (RMS) incident data from our law enforcement partners. RMS incident data is collected and managed according to guidelines prepared by the US Department of Justice. The records in the RMS are based on incident reports filed by victims of crimes. These are collected by a patrol officer, then reviewed and vetted by a supervisor before being entered in the system. It is the most accurate and objective data to which we have access.
The kind of data we use is also important. We make our predictions based on victimization information, i.e. crimes that have been reported to police. This information is anonymized; no personally identifiable information is ever collected or used. The only data items we collect and use are:
- Incident type (residential burglary, robbery, assault, etc.)
- Incident location (address or latitude/longitude)
- Incident date and time (or a time range if the exact time is not known)
- Case ID, docket number or other unique identifier.
Notice, that we’re not collecting anything else about the victim and nothing at all about an alleged perpetrator. We do not use demographic, economic, or any kind of potential “profiling” information about the location or neighborhood where the incident took place. The what-where-when information from the RMS is all our model needs and nothing more. These three data points are the most effective predictors of future crime activity by time and location.
Some people have expressed concerns around the idea of using algorithms in public safety. We believe in transparency and open debate, so we actually publish the algorithm we use. We did this several years ago in a peer-reviewed article in the Journal of the American Statistical Association. You can read it here:
It’s worthwhile talking about algorithms themselves for a moment. Algorithms are simply a set of rules to process information and arrive at an answer. Essentially, any information that is processed by a computer is following the steps described by an algorithm.
So why use an algorithm to recommend patrol locations for police officers? For starters, the more data you have, harder it is for humans to analyze it. A crime analyst looking at several years of auto theft data, for example, may be trying to find patterns in thousands of records - a tedious task and one prone to error. The PredPol algorithm can instantly scan that data to find patterns by time of day, day of week, and season for any location in the city.
We are also subject to underlying biases and habits in how we see the world, in ways we don’t even recognize. Bias works against good policing in a couple of ways. First of all, the perception that officers are unfairly targeting certain neighborhoods can justifiably undermine trust between the community and police. It’s also an inefficient use of patrol resources. You want your officers to be patrolling the areas where crimes are most likely to occur – using an objective set of transparent criteria – rather than where they think crimes are going to occur.
Our model does not simply guide patrol operations into areas that officers may perceive have the most crime, but instead objectively guides them to the areas where victimization is most likely to occur during their shift.
We know that no model is perfect and we know that there is no magic wand that can eliminate victimization. But we’re proud of the work we do here at PredPol. Our goal from the beginning has been to help law enforcement agencies reduce rates of victimization by identifying and patrolling those areas where victimization is most likely to occur. At the same time, we believe that protecting the privacy and civil rights of the residents of our communities is as important as protecting them from crime.
The data we use, the technologies we apply, and the transparency we embrace all support this mission. We have found that our work has reduced victimization in the communities that have deployed it, and we are proud of the fact that we seek to be a community partner. It’s important to us that we continue to be as transparent as possible, and we appreciate your taking the time to learn a little bit more about what we do.