ovinueza / Tableau_CitiBikesAnalytics

Tableau analysis for Jersey City Citi-Bikes program

Home Page:https://public.tableau.com/views/CitiBikeAnalytics_15728476963910/CitiBikeAnalysis?:retry=yes&:display_count=y&:origin=viz_share_link

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Tableau Citi Bikes Analytics

Click here to see the result on Tableau Public

Background

CitiBike is the largest bike sharing program in the United States. Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the Citi Bike Data webpage.

The thought behind this project is to visualize and analyzed the data provided by Citi Bike in and effort to provide analytics and recommendations to officials that oversee this program.

Task

Decide one major metropolitan area, either NY City or Jersey City. Choose a period of time. (Records go back to 2013) Collect and combine multiple data sources into one to create and Extract. Create a tableau Story and explain any undercover trends

Findings

After creating and analyzing our visualizations for Jersey City, there were many phenomena to be uncovered.

The first being that riders are mostly Male representing 72% of the total riders, with female ridership declining, from August 2017 to August 2018, by 5,000 riders.

The trips for the most used bikes are often started on or around the same stations, this may indicate that we need to increase our supply of bicycles in these areas. Adding more bikes to these stations will allow more users to have a bike available as well as preventing the same bikes from enduring heavy usage which will increase our repair fees, or worse, the risk of user injury.

Another phenomenon that was uncovered from our visualizations, is that most of our riders are between the ages of 45 and 54. However, there are some red flags raised as it appears that we do not confirm age in any way and that the user can say they were born in any year. This was noticed when we had multiple users over 100 years old riding bikes. This is extremely unlikely and caused that data to be filtered. It may be wise to start having users validate their age so we do not have invalid data or even have underage children lying about their age to ride our bikes.

The final phenomenon that I want to recognize is that the most popular time of the year for ridesharing is the summer. This makes sense as climate is more favorable and enjoyable in the summer months.