While that may evoke the 2002 Tom Cruise science-fiction movie "Minority Report," in which criminals in the year 2052 are arrested based on foreknowledge of crimes they haven't yet committed, there's a big difference. The preemptive arrests of "Minority Report" result from the predictions of psychics. The preemptive government that New York City is pioneering is based on real data.
Performance metrics and digital warehouses make up the building blocks of preemptive government. One of the practices that first attracted me to Mayor Michael Bloomberg's administration was the city's comprehensive use of metrics. New York City measures just about everything, from 311 call trends to tree plantings to detailed, agency-specific indicators. The mayor's City Hall bullpen even contains a large screen that scrolls through many of these metrics all day, allowing officials and visitors alike to constantly monitor performance. Hundreds of indicators help direct mayoral focus to areas in need of attention.
These metrics have their limits: They tend to measure activity more than outcomes, they tend to look back at data rather than forward and they usually concentrate on a single agency's actions. But these metrics provide a critical first step toward a new and broader effort that utilizes cross-agency analytics to preempt problems.
For example, last April 24 a New York City family died in a fire that started in a building that had been illegally subdivided to hold too many families, a situation that can create unsafe conditions. We immediately called the buildings department to see if there had been prior reports of an illegal subdivision at that location. They said there had been, but that they had tens of thousands of such complaints across the city and had been working through the queue as quickly as they could.
Given the city's finite resources, what if the goal were not just to inspect for illegal conversions but rather to predict where they are likely to be found and aggressively solve the attendant problems? And, beyond that, what if the city could predict at which of these locations a fire is most likely to occur? This information would allow the city to strategically target its resources to inspect and vacate the most dangerous dwellings.
To pursue this goal, the city launched a pilot project, led by Chief Policy Advisor John Feinblatt and guided by Mike Flowers, a talented lawyer and data analyst, that focused on fire risk and used metrics to identify the most dangerous of the illegally subdivided properties. This clarity allowed immediate action by a joint response team of the buildings and fire departments that issued vacate orders to preemptively address fire-prone living conditions.
The effort used the following metrics to identify the properties most at risk for fire:
—Owners in financial distress (including those with foreclosures and/or tax liens).
—Multiple illegal-conversion complaints.
—Multiple-family dwellings built before 1938, the year a significant building-code revision took place.
—Low-income/high-immigrant/low-employment neighborhoods.
The special team consulted easily available data such as agency reports, real-estate filings, and finance and tax information. The analysts found that dwellings with all four risk factors were over 40 times more likely to have a fire. Further screening allowed the team to target 225 dwellings over a two-week period, in which building and fire inspectors worked together to secure a near-100-percent enforcement rate. The city is currently looking at additional metrics that could refine the list even further.
The project showed that, by intelligently moving from a focus on activity to a focus on cross-agency data to achieve specific outcomes, resources can be much more effectively targeted. Targeting all illegal conversions captures many properties, but fails to focus resources on those most at risk for fires. By instead targeting the properties most at risk, the city can more successfully preempt disasters like the tragic April 24th fire.
And, as with most comprehensive review efforts, the study's insights led to other discoveries. For example, what about the illegally subdivided buildings that had a low risk of fire? In a place like New York City with a severe housing shortage, perhaps zoning or other regulatory changes should be made to allow such conversions. Sometimes illegality results not from risk factors but from outdated regulations that should be updated to reflect changing realities.
There are many other examples of preemptive government unfolding across New York City. Deputy Mayor Linda Gibbs, for example, is aggressively using analytics to improve the efficacy of social-service interventions. And Finance Commissioner David Frankel is using analytics to go after unpaid or underpaid taxes. In addition the city is beginning to use 311-system reports to identify areas in need of preemptive attention.
Public officials will always have more work than they can handle, and hiring more people to respond is neither affordable nor often necessary. Local and state governments sit on mountains of valuable data. As they increasingly learn to use insights gathered from this data productively, breakthroughs in service delivery will occur with increasing frequency. It's true that useful information will almost always be located in different city agencies and in varying data systems, but with careful analysis and the help of newer software tools, these barriers can be removed. Solving problems before they happen will no longer be the sole province of science fiction.
