bikesharing

Overview of the statistical analysis

The purpose of this analysis bike-sharing program in Des Moines is a solid business proposal.

The bike trip analysis in this statistical analysis will provide insight into the following areas;

Show the length of time that bikes are checked out for all riders and genders
Show the number of bike trips for all riders and genders for each hour of each day of the week
Show the number of bike trips for each type of user and gender for each day of the week.

Resources

Data Sources: 201908-citibike-tripdata.csv

Code Files: NYC_Citibike_Challenge.ipynb

Software: Visual Studio Code 1.56.0, jupyter Notebook 6.3.0, Tableau 2019.2.2

Visualizations for the Trip Analysis

This section shows the various visaulization created for the purpose this analysis;

Checkout Times for Users Viz

The above image shows a line graph depicting the number of bikes and their trip duration by hour.

We can clearly see that most trips by users are mainly for short distance travels or trips less than one hour.

Checkout Times by Gender Viz

This image shows that male users are predominant in the bike sharing scheme. This also follows the pattern of the checkout time for users.

Trips by Weekday for Each Hour Viz

This heatmap indicates that the most popular time for bike sharing users is 5-6pm and 8am. This is most likely due to work commute hours.

Trips by Gender (Weekday per Hour) Viz

This heat map splits the user trips by weekday and by gender. The male gender Like we have seen in the earlier chat dominates the bike sharing.

User Trips by Gender by Weekday Viz

This heatmap shows that Subscriber usertype is the main usertype amongst the two user types. Here we see the majority of the male users are subscribers.

August Peak Hours

From the above bar graph, we can see that that the peak hours are between 5-6pm and 8am as indicated by the Trips by Weekday for Each Hour Viz heat map above.

Top Starting Location

This map shows the start location of the bike sharing users.

Top Ending Location

This map shows the end location of the bike sharing users.

Summary

Based on the above analysis we can arrive at the following conclusions;

Majority of the users are male riders.
The dominant user type are the subscribers.
Most trips are less than an hour.
Bike sharing is most likely utilized by commuters.

Theses points need to be factored when setting up a bike sharing program in Des Moines.

Further analysis can be done with the data at our disposal. We can plot a chat that shows trip duration by user type to understand the pattern of trip duration by these user types.

Alternatively, we can show a visualization that protrays the bike usage by birth year. This will showw the age bracket to target when deploying the bike sharing program.

Nnaemeka Enukorah

July 19th, 2021

NENUKORAH/bikesharing