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Storyline

Storyline. An American businessman wants to open up a café and a nightclub in NYC which suits his business portfolio and finds a very promising blog of some students of the University of Liechtenstein.

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Storyline

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Presentation Transcript

  1. Storyline • An American businessman wants to open up a café and a nightclub in NYC which suits his business portfolio and finds a very promising blog of some students of the University of Liechtenstein. • After reading through the blog he is convinced that those students can help him in his business scenario and contacts them via E-Mail.

  2. Storyline • In order to put his business idea into practice and maximize his profits the businessman wants the students to identify what the optimal location for his café and nightclub is. • Eager to show their potential as Data Scientists the students look into the available data Sets of NYC and search for a solution using Big Data methodologies.

  3. Considered factors • How many people are around in a particular area in New York? • How many nightclubs or cafés or parks are in the area? • Is it a safe area?

  4. Underlying data • Exits and entries of the NYC Subway Turnstiles of each station • Amount of businesses within a radius of 200 meters of each station according to the Google Places API • Amount of311 request in relation to the NYPD in a circuit of 200 meters of each station. • Noise • Illegal Parking • Unlicensed selling • Camp of homeless people • Animal Abuse • Traffic issues

  5. Underlying data cont. • Possible drawbacks of the data • Only nightclubs, cafés and parks are considered which are registered on Google Places • 311 Requests do not cover emergency requests • Actual data • The data about the surrounded businesses is based on current data from the Google Places API • The data of the 311 Requests and exits/entries of the turnstiles are from the last month

  6. Potential Optimization Possibilities • Possible optimization of the results by introducing 2 new data sets • Location of schools and universities • Property prices of the individual areas • Analysis and identification of factors which influencecustomers to stop at a café / nightclub.

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