The purpose of this project is to analyze Citi bike data from New York City to showcase different visualizations and answer questions on bike sharing. We will be using Python to clean the data and Tableau, a powerful interactive data analytics and visualization tool that provides easy-to-understand dashboards, to illustrate different charts. We will then use our analysis to propose a bike-sharing program in cities.
Tableau
Python 3.20
As we can see from the chart below, almost all bikes are checked out for less than one hour and the duration for most of the bike rides is around 5 minutes.
This next chart visualizes the checkout times for each gender. The orange line represents male riders, the blue line represents female riders, and the red line represents unknown genders. We can see that a majority of bikes are checked out by male riders.
Most people use Citi bikes during commuting hours on weekdays. The morning commute is between 6 am - 9 am and the afternoon commute is between 4 pm – 7 pm. On the weekends, the number of rides increases at midday and stays consistent throughout the day.
Both male and female riders have similar trends. Most tend to use Citi bikes during commuting hours on the weekdays and throughout the day on the weekends. There doesn't seem to be a trend for the unknown variable due to the lack of data.
This heatmap shows the number of bike trips broken down by gender for each day of the week by each Usertype. We can see that most users are subscribers and that male subscribers represent the greatest rider demographic.
The visualization below illustrates the top bike stations in the city for starting a journey. We see that the top starting locations are clustered in the downtown area of New York with the larger symbols on the map representing the more popular locations.
From the chart below, we can see that the afternoon commuting hours have a higher number of Citi bike usage during the month of August. Most people use the Citi bikes between 5 pm – 6 pm.
From the visualizations created in Tableau, we can conclude that:
Recommended Visualizations: