H. V. Jagadish in the Conversation: “Police departments, like everyone else, would like to be more effective while spending less. Given the tremendous attention to big data in recent years, and the value it has provided in fields ranging from astronomy to medicine, it should be no surprise that police departments are using data analysis to inform deployment of scarce resources. Enter the era of what is called “predictive policing.”
Some form of predictive policing is likely now in force in a city near you.Memphis was an early adopter. Cities from Minneapolis to Miami have embraced predictive policing. Time magazine named predictive policing (with particular reference to the city of Santa Cruz) one of the 50 best inventions of 2011. New York City Police Commissioner William Bratton recently said that predictive policing is “the wave of the future.”
The term “predictive policing” suggests that the police can anticipate a crime and be there to stop it before it happens and/or apprehend the culprits right away. As the Los Angeles Times points out, it depends on “sophisticated computer analysis of information about previous crimes, to predict where and when crimes will occur.”
At a very basic level, it’s easy for anyone to read a crime map and identify neighborhoods with higher crime rates. It’s also easy to recognize that burglars tend to target businesses at night, when they are unoccupied, and to target homes during the day, when residents are away at work. The challenge is to take a combination of dozens of such factors to determine where crimes are more likely to happen and who is more likely to commit them. Predictive policing algorithms are getting increasingly good at such analysis. Indeed, such was the premise of the movie Minority Report, in which the police can arrest and convict murderers before they commit their crime.
Predicting a crime with certainty is something that science fiction can have a field day with. But as a data scientist, I can assure you that in reality we can come nowhere close to certainty, even with advanced technology. To begin with, predictions can be only as good as the input data, and quite often these input data have errors.
But even with perfect, error-free input data and unbiased processing, ultimately what the algorithms are determining are correlations. Even if we have perfect knowledge of your troubled childhood, your socializing with gang members, your lack of steady employment, your wacko posts on social media and your recent gun purchases, all that the best algorithm can do is to say it is likely, but not certain, that you will commit a violent crime. After all, to believe such predictions as guaranteed is to deny free will….
What data can do is give us probabilities, rather than certainty. Good data coupled with good analysis can give us very good estimates of probability. If you sum probabilities over many instances, you can usually get a robust estimate of the total.
For example, data analysis can provide a probability that a particular house will be broken into on a particular day based on historical records for similar houses in that neighborhood on similar days. An insurance company may add this up over all days in a year to decide how much to charge for insuring that house….(More)”