The New Open

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the New York taxicab dataset contains millions of GPS points locating where New Yorkers get picked up and dropped off in taxis. When you plot these locations as points on a map you see some very blurry areas and some very sharp areas. The blurry areas are a result of the GPS signal being blocked by tall buildings, because GPS signals are less precise when tall buildings are nearby. So, when you plot the points, they fall in ‘clouds’ near taller buildings because the accuracy of the location of these points is more “noisy”. The blurriness of the data-point clusters corresponds to the height of the buildings in that area. So in order to infer a three-dimensional condition, you only need two variables (latitude and longitude). These two variables can encode the three-dimensional shape of the city. As a data designer, if you look at the blurry areas as a data artefact, instead of as a mistake that needs to be eliminated, a third hidden variable is revealed to you. (View Highlight)