New York City has released its taxi dataset to the public. Ana Sa explains how she used Python to determine areas of frequent pick-ups and drop-offs within a time frame and superimposed those hotspots atop a map of the subway system to identify taxi hotspots that fall within or outside of a particular radius of established subway stops. Ana dives into analysis conducted on a dataset comprising night hours with three major hotspots that fall a quarter mile outside of major subway routes. Drawing on these hotspots (and additional domain knowledge on nightlife in New York City), Ana proposes a bus route to accommodate these three hubs. But this kind of analysis is just the beginning of exploring open datasets and interacting with data generated as citizens interact with a city’s infrastructure and services.
Ana Sa is a graduate student at NYU working on combining sustainability with computation and resource reuse. Previously, Ana has investigated emissions trading schemes and green revolving loan funds, explored open transportation datasets in the context of networks, built an agent-based model to simulate how cells could communicate using a problem in graph theory, and interned at a cleantech incubator.
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