To analyze whether a bikesharing business model could be interesting in other areas, a dataset from New York City's citibike was used. The dataset contains rider and ride information for every single ride that occurred in the month of August of 2019. This dataset was used to extract valuable insights and determine what would be essential to successfully replicate this model elsewhere.

The results can be accessed via Tableau Public on this page.

In order to get an idea of how the userbase utilizes Citibike, it is essential to analyze the amount and standard duration of rides.

Further analysis of the rides is necessary. Breaking down the userbase by gender helps get a better idea of the userbase composition.

This heatmap displays the time and day of high demand.

This heatmap displays the same information as the previous one broken down by gender.

This heatmap shows the difference of activity between subscribers and non subscribers, broken down by gender.

This bar graph shows the peak usage hours of Citibike during the month of August.

This bubble chart displays the bikes with the highest amount of rides.

This heatmap identifies the Citibike activity hotspots.

Overall, it seems that the following are key points to successfully replicate this business model:

• Strategically place bikes near high traffic areas to ensure ease of use
• Allow people to have bikes to commute to and from work over the week
• Shift some of these bikes on weekend vs. the rest of the week for leisure activity
• Develop incentives to create a strong subscriber userbase
• Try to become more appealing to women, as they are currently a smaller part of the userbase
• Develop a maintenance plan for downtime
• Make sure bikes are 0-30 minutes away from hotspots

There are some useful visualizations which could be included in this project but not created +due to lack of data.

• A GPS trace of the most frequent routes could allow for strategic placement of bikes or service dropoffs in case of a malfunction.
• A graph displaying hotspots for the weekend compared to those of workdays would allow for optimal bike distribution.

All of these graphs would need to be used and approximately adapted to any other city in other to determine whether the market would be a good choice.