The final recommended subset of top-performing stations at which to display digital ads during the campaign is provided in a file named recommended_stations__YYmmdd_HHMMSS.parquet.gzip. This dataset contains the following columns

1. station_id
   1. station ID
   2. unique identifier for a single bike share station
2. name
   1. station name (street location)
3. physical_configuration
   1. whether station uses a kiosk (REGULAR), kiosk-less (*MAPFRAME) or supports e-bikes (ELECTRICBIKESTATION)
   2. stations with a kiosk provide steps to guide users through the bike check-out process
   3. stations without a kiosk require the user to check out bikes directly from one of the bike docks at the station, as shown on page 12 from here
4. capacity
   1. number of bicycle docks available at station
5. is_charging_station
   1. whether station supports electric bikes (True) or not (False)
6. credit
   1. methods of payment that are accepted at the station
7. Neighbourhood
   1. Toronto neighbourhood in which bike share station is located
   2. extracted by comparing station’s lat-lon to neighbourhood boundaries
8. Location
   1. downtown-related neighbourhood, which is one of
      1. Downtown
      2. West of Downtown
      3. East of Downtown
9. CTUID
   1. identifier for a census tract
10. is_active
    1. whether the station is active (True) or not (False)
    2. for this dataset, this is always set to True
11. departures_last_year
    1. departures during last full year (2022)
12. arrivals_last_year
    1. arrivals during last full year (2022)
13. departures_last_n_years
    1. departures during last n full years (2018 to 2022)
14. arrivals_last_n_years
    1. arrivals during last n full years (2018 to 2022)
15. is_downtown
    1. whether station is located in Downtown Toronto (True) or not (False)
16. rank_deps_last_year
    1. rank in terms of departures during last full year (2022)
17. rank_arrs_last_year
    1. rank in terms of arrivals during last full year (2022)
18. rank_deps_last_n_years
    1. rank in departures during last n full years (2018 to 2022)
19. rank_arrs_last_n_years
    1. rank in arrivals during last n full years (2018 to 2022)