The Blog

Predictive Analytics Prevent Crime

This post was published on the now-closed HuffPost Contributor platform. Contributors control their own work and posted freely to our site. If you need to flag this entry as abusive, send us an email.

2015-12-09-1449684402-5905649-AAEAAQAAAAAAAAUKAAAAJGMxNzA0NTE0LWUyMDUtNDQyYi04Yzc2LTU0ZWM2MTRhNWMxZg558x320.jpg

An Anticipatory Organization incorporates the future into their present day practices. They have an eye toward what is coming and make concrete plans in the present to prepare for what lies ahead. Of course, this model doesn't just apply to businesses. Anticipating the future has benefits for everyone. A new product developed by Hitachi takes note of this mass relevance. Hitachi's Visualization Predictive Crime Analytics offers police departments the chance to begin anticipating the future.

For most of their history, police departments have followed the healthcare model we will refer to as the "break/fix" model. Something goes wrong, and you call a doctor or the police to fix it. You get sick or your house is broken into, for instance. The doctor or the police are always arriving after the fact to do damage control. But what if the police could get to the scene of the crime before it happened? Better yet, prevent the crime from happening in the first place!

As a recent article on Hitachi's Predictive Crime Analytics notes, it sounds a lot like Minority Report. Hitachi, however, didn't need psychics to develop this technology. The latest advances in machine learning offer police departments a new look at the future of crime.

So how does it work? Rather than rely on predictive factors based on the idiosyncrasies of experiences within a given police department, Predictive Crime Analytics analyzes multiple overlapping data sets to determine correlations between crime and factors such as weather patterns, public transit conditions, activity on social media, as well as numerous other factors. Not all of these factors might be considered relevant (or would even be visible) by officers, but the capacity of Predictive Crime Analytics offers the chance to test them out in a timely manner -- something officers couldn't do on their own.

As a result, police departments would have at their disposal a whole new breadth of data by which to anticipate the future. The most obvious advantage to this is the number of lives that could be saved. While the "break/fix" model in health care has the option of the cure, the "break/fix" model in policing can't do anything for those whose lives are taken during an act of crime. This can't be overstated.

At the same time, Predictive Crime Analytics saves money in multiple ways. Many of us have seen The Wire by now. Police departments often don't have the resources they need to do their job. Cuts are common, often falling on officers who are already overworked and underpaid. Cuts lead to unprotected officers on duty without the tools they need to do their job. The number of officers on duty can also be cut, leaving towns and neighborhoods vulnerable to crime.

As an alternative to this, Predictive Crime Analytics would help these departments work smarter, not harder. While resources might still be scarce, departments could now use what they have most effectively. Resources can be concentrated on areas where crimes are predicted to happen. This means more officers in the right areas with the right equipment ready to prevent crime.

Additionally, through Predictive Crime Analytics businesses save money. Why? Because the police can prevent the robbery before it happens. Damage to storefronts are costly and in some cases can inhibit business. Loss of inventory can ruin a small business.

It's clear that everyone has a stake in police departments acting as an Anticipatory Organization. Foresight and anticipation are the tools toward making a safer community as much as they are about developing your business. Without a culture of anticipation, however, we risk not only falling victim to disruption but also, as Hitachi has made possible, crime. The application of this model has yet to be exhausted in its possibilities.