While police departments may believe that this technology could be “the wave of the future,” and some research has shown that it substantially reduces crime, it may also threaten civil liberties. Nor does it address the underlying problems of crime-ridden areas–it punishes those neighborhoods rather than building community trust. These critiques of predictive policing reflect the ongoing debate around how data and algorithms should be used in our society, and how humans transfer their biases onto ostensibly unbiased software. But how do you illustrate the hazards of these opaque tools?

One way to do so is to build your own.

A new project published by The New Inquiry inverts common assumptions about the efficacy of predictive policing, pointing out how the logic that underlies these data-driven models may be inherently flawed. The interactive, called White Collar Crime Risk Zones, uses similar tactics to the predictive policing models that are used by many cities today–but focuses on white collar crime. Zoom in on New York City, and the maps shows that the highest risk areas for white collar crime are in the financial district and Midtown in Manhattan. Zoom out, and you begin to see criminal hot spots in the wealthy Connecticut towns of Greenwich and Stamford, home to many of the nation’s most prestigious hedge funds. “Unlike typical predictive policing apps, which criminalize poverty, White Collar Crime Risk Zones criminalizes wealth,” the team writes on The New Inquiry‘s website.

Intended as a critique of predictive policing, the map was created by NYU professor and New Inquiry editor Sam Lavigne, Buzzfeed data scientist Bryan Clifton, and New Inquiry co-publisher and New Inc. researcher Francis Tseng. After researching the methods used by companies that build predictive policing tools like HunchLab (used by Miami, St. Louis, and New York City) and PredPol (used by Chicago and Los Angeles), both of which focus on predicting street crimes like burglary and assault, the three began to build their own version for white collar crime.

“It was important for us to develop our predictive policing application in the way that they do theirs because we believe that makes our critique stronger,” Tseng says.

Using data from the Financial Industry Regulatory Authority, an independent regulatory body that keeps records of when companies break the rules and are forced to pay a fine (even if they don’t go to court), Lavigne, Clifton, and Tseng were able to build a map of where white collar crimes occurred over the past 50 years. Then, they searched for publicly available data sets that overlapped closely with these white collar crime locations. One was the number of investment advisors on a given block. One was the number of liquor licenses. And the last was the number of nonprofit organizations in the area.