NYC_Citibike_analysis

Overview of the analysis:

We want to visualize the August Citibike data to show its relationship with the trip-duration, gender, area, and popular using time. To approach this target, we first convert the data to specific type of datetime by utilizing Python. Secondly, we will use Tableau to create the graphs and a story for data visualization.

Story at Tableau:

(LINK GOES HERE)

Results:

Using the visualizations in the Tableau Story to describe the results of each visualization underneath the image.

<Fig.1 Time of Trip by Gender >

Women's total trips are around 588431times in August, 2019
Men's total trips are around 1530272 times in August, 2019
Unknown's total trips total are around 225521 times in August, 2019

<Fig.2 Checkout Times for Users>

The most users who used bike are less 1 hour.

<Fig.3 Checkout Times by Gender>

The most of users are male.

<Fig.4&5 Trips by Weekday for Each Hour >

The most popular stoptime is at 8-9am and 5-6pm Monday thru Friday and 11am to 6pm on Saturdays.
Thursdays and Fridays are super busy. The most popular time is 5pm on Tuesday and Friday.

<Fig.6 Trips by Gender (Weekday per Hour)>

<Fig.7 User Trips by Gender by Weekday >

<Fig.5 Popular Area >

The bikes are used frequently at some area.

Summary:

After analyzing the data, we notice the people who used bike mostly is male by gender. They use bike at 8-9am and 5-6pm during weekday. Thus, they probably use it as a transportation between work and home. Some of them use as subscribers. Additionally, according to the map, bikes are used frequently at some area. We can introduce number of bikes to let more people use them.