Congratulations on your new job! As the new lead analyst for the New York Citi Bike Program, you are now responsible for overseeing the largest bike sharing program in the United States. In your new role, you will be expected to generate regular reports for city officials looking to publicize and improve the city program.
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.
However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have a number of questions on the program, so your first task on the job is to build a set of data reports to provide the answers.
Your task in this assignment is to aggregate the data found in the Citi Bike Trip History Logs to build a data dashboard, story, or report. You may work with a timespan of your choosing. Optionally, you may merge multiple datasets from different periods. The following are some questions you may wish to tackle, especially if you are working with merged datasets. Do not limit yourself to these questions; they are suggestions for a starting point. Be creative!
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How many trips have been recorded total during the chosen period?
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By what percentage has total ridership grown?
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How has the proportion of short-term customers and annual subscribers changed?
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What are the peak hours in which bikes are used during summer months?
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What are the peak hours in which bikes are used during winter months?
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Today, what are the top 10 stations in the city for starting a journey? (Based on data, why do you hypothesize these are the top locations?)
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Today, what are the top 10 stations in the city for ending a journey? (Based on data, why?)
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Today, what are the bottom 10 stations in the city for starting a journey? (Based on data, why?)
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Today, what are the bottom 10 stations in the city for ending a journey (Based on data, why?)
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Today, what is the gender breakdown of active participants (Male v. Female)?
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How effective has gender outreach been in increasing female ridership over the timespan?
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How does the average trip duration change by age?
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What is the average distance in miles that a bike is ridden?
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Which bikes (by ID) are most likely due for repair or inspection in the timespan?
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How variable is the utilization by bike ID?
Additionally, city officials would like to see the following visualizations:
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A static map that plots all bike stations with a visual indication of the most popular locations to start and end a journey with zip code data overlaid on top.
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If you're working with a merged dataset: a dynamic map that shows how each station's popularity changes over time (by month and year) -- with commentary pointing to any interesting events that may be behind these phenomena.
Lastly, as a chronic over-achiever:
- Find at least two unexpected phenomena in the data and provide a visualization and analysis to document their presence.