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 <ReactPlayer className="video__player" playing={true} muted={true} controls height="100%" url="https://fused-magic.s3.us-west-2.amazonaws.com/blog-assets/elizabeth_workbench.mp4" width="100%" />
 
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 As a data scientist at [Precisely PlaceIQ](https://www.precisely.com/product/precisely-placeiq/placeiq-movement), I get to spend much of my time using our vast portfolio of location data to develop these kinds of insights. Here, as a proxy for consumer activity, I used publicly available [NYC Taxi pickup data](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) to explore the similarity between different areas of New York City. I then used those similarity metrics to clarify the boundaries between different neighborhoods.
 
 The analysis highlighted similarities between relatively distant areas within the city based on the dynamics of Taxi pickup volume they experience at different points of the day. This project uses Taxi pickups as an example to showcase a methodology that could be expanded to larger regions with even richer population dynamics datasets.