Bikesharing

Overview

Bikesharing would be a lucrative addition to the city of Des Moines, Iowa-Both, financially, economically, and virtuously. An investor, exploring how bikesharing works, is interested in a business proposal. Utilizing high-level data visualizations with Tableau software, bikesharing Data will be imported, related, and styled into visual analyses. These illustrations will be used to present prospectuses in 3 separated means of presentation: Worksheets, Dashboards, and Stories.

Data Environment:

Tableau

Link to Tableau Story:

CitiBike: Manhattan, NY

Results

User Trips by Gender by weekday :

• For Non-Subscribed customers the UNKNOWN-gender population has the highest trips through all 7 days of the week
• For Subscribers, the MALE gender, has the highest trip through out all 7 days of the week, followed by the FEMALE gender.

Trips by Gender (weekday per Hour)

• MALE and FEMALE trips by weekday per hour roughly show the same patterns of trip through Sunday to Saturday during the same hours.

• The UNKNOWN-gender customer base is more concentrated on the weekends through the hours of 10am-8pm

  

Trips by weekday per hour

• Thursday from 5pm -8 pm, is the most popular schedule time for trips
• The most popular trips during the week, Monday - Friday are scheduled:
• 6am - 10am , then 4pm-8pm; Hours alienating direct sunlight.
• There’s a sharp increase in trips during the ending days of the work week, Thursday - Friday
• The weekends, Saturday and Sunday, show similar trip by hour patterns of 8am to 8pm, with Saturday being slightly more favorable.

Checkout times by Gender:

• The most popular trip duration is between 4-6 hrs, for MALE and FEMALE
• The UNKNOWN gender population has not central trip duration, They equal ride whenever.

Checkout times for users

• The highest trip durations are between 4-7 hrs
• There’s a decrease in checkout times within the first hour-What is the intended purpose of these rides that the duration within the first hour decrease instead of following the infilatedtrend for the rest of the chart?

Bike Utilization

• Can be used to reference needed repairs
• The darker the Bike ID the more frequent the trips

Top Starting and Ending Locations

• The pattern is the same for top starting and ending locations being concentrated around the Bay area and the inner city

Summary: Create a story in Tableau and write a report that describes the key outcomes of the NYC CItibike analysis

Some key outcomes derived from Citibike analysis is

• The MALE-gender makes up the greater population of the CitiBike customer base.
• The MALE-gender makes up the greater population of the subscriber base
• Most of the trips are started and ended along the Bay Area
• Customers like to ride, for an average of 4-6 hrs
• The peak days of the week to ride is Thursday -Saturday
• The UNKNOWN gender data is hard to target without being able to identify them.

Tableau allows Data Analysts professionals to create visualizations that are assets and are visually appealing and easy for a non-technical audience to understand. The CitiBike proposal can now be comprehended with ease just by interpreting the visualization and reading key notes. In additional study, visualizations depicting the following studies would really add an attractive kick for the business proposal

• The volume of Top start location by weekday vs the Top volume of ending locations by weekday. The study would allow allocation of resources where most needed. And will encourage planning of shipping logistic to cover supply and demand.
• Subscribers' parameters by month: the number of rides by user id, and by gender, to be able to predict likeness of non-subscriber customer conversion to subscriber and to develop possible promotional efforts to influence subscriptions.