A team of researchers from Cornell Tech analyzed nearly 25 million dashboard camera photos from drivers for rideshare services — like Uber and Lyft — to identify where police cars are deployed in New York City. Their approach offers a new avenue to access info that the NYPD doesn’t release.
The authors found two major patterns of higher police deployment.
Looking at the citywide data as a whole, the highest deployment was in higher-income commercial areas, like Downtown Manhattan. And among residential areas, there was more deployment in low-income census tracts as well as in areas with higher Black and Latino populations, such the South Bronx and central Brooklyn.
The researchers also saw far more police vehicles in areas closer to police departments or Rikers Island, which they described as “unsurprising” in their paper. The study was released last month at the Association for Computing Machinery Conference on Fairness, Accountability, and Transparency in Chicago.
The Cornell Tech team used deep learning, an artificial intelligence technique that uses large sums of data to train a computer model to perform a task, in this analysis. After “teaching” the model how to identify police cars in a small group of example photos, it is able to identify the rest. In practice, deep learning requires fine-tuning to make sure it can perform a task accurately.
But for this model to predict whether the nearly 25 million photographs had police cars in them, the algorithms needed lots of examples — 9,449, to be exact.
The photos themselves came from Nexar, a dashcam company that sells to rideshare drivers. The company made the photographs available to researchers in 2020, and the images in question were taken throughout the five boroughs between March 4 and Nov. 15 of that year.
Matt Franchi, lead author of the study and a graduate student at Cornell Tech, spent six months fine-tuning the model to identify police cars from dashcam photos for the project. Only around 1…
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