The document describes a study analyzing bike share usage patterns in relation to metro station locations. It used CSV data on bike share departures imported into Pandas to identify the most used stations. Stations in the top quarter were analyzed in relation to buffer zones around metro stations, finding that metro proximity influences bike share usage, especially during commuting hours. Challenges included formatting the data and some issues importing CSVs into ArcGIS. In conclusion, metro locations near bike share stations affect bike rental numbers, with most top stations located within a quarter mile of metro entrances.

1. Christopher Dively, Sujung Lee, Veronica Miniello
BICYCLE SHARE PROGRAM

2. OBJECTIVES & HYPOTHESIS
Source by
● To manage current capital bikeshare program
effectively
● To identify most used stations
<Hypothesis>
● Different demand depending on commute time
● The impact of Metro stations

3. STEPS AND CODES
•Import CSV into Pandas Dataframe
•Split date and time into two columns
•Select times that fall within AM and PM
commute hours
•Use groupby to create count column for
each station
•Use Pandas to_csv to create new CSV file
with necessary data, import into .GDB with
CopyRows_management, AddJoin_Management to
join to Bikeshare station
•Identify bikeshare stations with count in
the top quarter

4. •Add in Metro station data to analyze how
Metro location relates to the top
bikeshare stations used
•Use arcpy.Buffer_analysis to create 3
buffer zones around Metro Stations: 1
Mile, ½ Mile and ¼ Mile of a Metro
Station
•Search the buffer zones around Metro
Stations, finding how many bikeshare
stations intersect within the buffer.
•Using acrpy.GetCount_management, find
the count of stations in the Morning and
Afternoon within each buffer band
•Print the results.
STEPS AND CODES

5. ● Using Pandas to get the huge CSV into useable format
● Date and time for bikeshare departures in the same column, had to
split at the space, use a for-loop to create new “Time” column
● Creating the “Count” using “groupby()” and then “agg.(‘Count’)”
produced data that Arc parsed as a String.
● Attempted but failed to add a new column with the correct data type
to the attribute table
● Created “Count” column, populated it with “1s”, summed that
● Arcpy was stupidly tempermental about importing the CSVs
● Initially wanted to work with census tract data, but upon seeing
how messy that would be given the distribution of BikeShare
Stations, decided to analyze just Bikeshare and Metro
CHALLENGES

6. RESULTS

7. ● Metro locations near Bikeshare stations affects
the number of bikes rented.
● During the morning commute, 40% of the most used
Bikeshare stations were within .25 miles of a
Metro entrance, while 61% of the top afternoon
locations were within .25 miles of a Metro
entrance.
● All of the top 25% of most used Bikeshare stations
were within one mile of a Metro location.
CONCLUSION