Chris Bousquet Grey

By Chris Bousquet • January 29, 2018

Complementing Harvard’s Map of the Month series, each week, Map Monday highlights a data visualization that enhances understanding of or helps resolve a critical civic issue.

People tend not to think that bad things will happen to them. This psychological proclivity towards optimism—logically termed “optimism bias”—is in many ways a beneficial feature of the human psyche, as most live better lives when they’re not constantly obsessing over the possibility of some calamity befalling them.

However, the optimism bias also has its disadvantages, as it may discourage people from preparing for emergencies. This was the case during Hurricane Sandy, during which 77 percent of New Yorkers reported that inland flooding was much higher than they expected. In New York City alone, the storm damaged 90,000 buildings, created $19 billion in damage, and killed nearly 50 people.

Beyond Floods seeks to help people get more realistic about flood risk and take the necessary precautions to mitigate damage. Built on a CARTO platform and using data from a huge collection of federal, city, and proprietary sources, Beyond Floods has assigned flood outlook scores to more than one million properties in 300 New York City neighborhoods, and has since expanded to other cities. And, in addition to these scores, Beyond Floods estimates flood insurance premiums for each property in an effort to help people kickstart their risk management strategies.

While governments have long sought to make flooding information available to residents, often this information is complicated and displayed in contrived or confusing formats. Beyond Floods sought to take the array of existing information and summarize it in a concise score on an intuitive platform. On the iOS-only application, users can search for their address or explore their city geographically in order to determine flood risks and premium costs. And, if they want more detailed information, users can access a report summary of risk information, though this requires a paid subscription.     

Take the example below in Brooklyn’s Cobble Hill Neighborhood. The address has a flood outlook score of 56, which puts it at about average in terms of predicted damage from a flood—with higher scores meaning a better outlook and lower scores indicating a worse outlook. The estimated annual flood insurance premium for the property is $1,721, which the owners would have to weigh against the potential cost of damage.

The map also shows that, predictably, addresses closer to the waterfront have lower flood outlook scores (displayed in red) than addresses further inland. Of course, these addresses also have higher flood insurance premiums.


By distributing this application or similar information, governments can encourage residents to take precautionary measures in the days leading up to a storm, as well as invest in things like flood insurance or retrofitting even when a storm is not imminent. With access to clear information on potential damage, residents will be more likely to take action.  

Beyond Floods is a prime example of the power of data visualization to share existing information in a much more accessible and unambiguous way. It is a reminder that human action often depends not only on the information shared with them, but the way in which that information is shared. Beyond Floods turns complex streams of data into a compelling call to action.