A new taxi company hired an advertising agency to advertise their services on screens at the Time Square in New York, NY. The marketing company was tasked to identify five best screens for their client. In order to reach maximum number of the potential clients for the new taxi company the criterion they decided to use was the average number of taxi pickups in a close proximity to an advertising screen.

The marketing company found two public datasets that they are going to use:

1. The list of screens at the Time Square (Links to an external site.)Links to an external site.:

https://catalog.data.gov/dataset/times-square-screens-a08d4w

The illustration above (which was created by importing a given dataset to Google Maps) visualizes the locations of the screens.

2. Public taxi ridership data (for NYC) (Links to an external site.)Links to an external site.: (Links to an external site.)Links to an external site.

http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml

Large datasets like this one are usually consist of a) data dictionary, a table that lists all the fields in the dataset; b) and the actual dataset in a variety of formats (Excel compatible comma separated values (.cvs), XML or JSON).

Directions

Familiarize yourself with both datasets. Note, that the second dataset files are very large (up to 1 GB). The ridership data is also given in separate files grouped by taxi companies (e.g. Yellow). Pick a dataset related to any company that services the Time Square area.

With the dataset structures (field names, or dictionaries) in mind, use Word to design a flow chart of the algorithm to automate the process of identifying the top five screens that would be seen most often by the taxi riders. Note that you dont need to provide code, and you dont need to calculate top screens, just provide the algorithms that would perform that task.