See what the White Collar Crime Early Warning System (WCCEWS) thinks of your neighborhood

George Zimmerman profiled Trayvon Martin by making assumptions about his appearance and clothing, negative meanings intended to criminalize and then justify Zimmerman killing Martin, while claiming self-defense. Remember, Zimmerman not only had a gun, but clearly revealed his motivations and assumptions in the 911 call.

Did not Trayvon have the right to self-defense? The point is that the profiling and policing by Zimmerman is a cultural algorithm, a logic embedded in cultural formations, programming that is related to algorithms used in determining where "crime" will occur. The crimes are generally property and survival crimes related to health issues, addictions, and mental health crisis.

"White Collar Crime Risk Zones" flips this model on its head and our assumptions about what is a crime and who commits crimes. So, instead of profiling for hoodies or other markers of "urban identity," what if we would profile for white-collar crimes by noticing boat shoes and suits, tuxedoes, pencil skirts, stilettos, or loafers with tassels, or a nice timepiece, in other words, business and elite professional attire as an indicator of possible criminal activity and behavior.

"White Collar Crime Risk Zones uses machine learning to predict where financial crimes are mostly likely to occur across the US. To learn about our methodology, read our white paper."

In "Predicting Financial Crime: Augmenting the Predictive Policing Arsenal," researchers Brian Clifton, Sam Lavigne, and Francis Tseng explain that "Financial crime is a rampant but hidden threat. In spite of this, predictive policing systems disproportionately target "street crime" rather than white collar crime. This paper presents the White Collar Crime Early Warning System (WCCEWS), a white collar crime predictive model that uses random forest classifiers to identify high risk zones for incidents of financial crime."

You can type in your home city or zip code and figure out what areas of town to avoid.