Guest post: Clustering and predicting NYC taxi activity

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This is a guest post by Deepak Subburam, a data scientist who works at Tessellate.

Screenshot from NYCTaxi.info

Greetings fellow Mathbabers! At Cathy’s invitation, I am writing here about NYCTaxi.info, a public service web app my co-founder and I have developed. It overlays on a Google map around you estimated taxi activity, as expected number of passenger pickups and dropoffs this current hour. We modeled these estimates from the recently released 2013 NYC taxi trips dataset comprising 173 million trips, the same dataset that Cathy’s post last week on deanonymization referenced. Our work will not help you stalk your favorite NYC celebrity, but guide your search for a taxi and maybe save some commute time. My writeup below shall take you through the four broad stages our work proceeded through: data extraction and cleaning , clustering, modeling, and visualization.

We extract three columns from the…

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