This project focused on data collection, validation and visualization. First I collected data from CitiBike NYC, specfically the August 2019 trip data. Then using Jupyter Notebook, I created a Pandas dataframe from the original file and converted the 'Trip Duration' data to a suitable Datetime Format. The clean data was written into a CSV file and used in Tableau to analyze and create multiple visualizations.

• Source Code: NYC_CitiBike_Challenge_Code
• Source Data: 201908-citibike-tripdata
• Technology: Tableau Public, Pandas

• Deliverable 1: Change Trip Duration to a Datetime Format
• Deliverable 2: Create Visualizations for the Trip Analysis
• Deliverable 3: Create a Story and Report for the Final Presentation

Looking at the NYC data, there is strong evidence that the CitiBike model is suitable for Des Moines, Iowa.

Checkout Times for Users confirms most trips last five minutes. The data also shows there are several hundred trips during the month of August that last an hour or longer.

Looking at Checkout Times by Gender, males average shorter rides more frequently, but females also checkout bikes often and occassionally for longer durations. With the Des Moines population growing, there is a large market in need for the bike share program.

Creating a heatmap, Peak Riding Hours, shows there are peaks in bike check-outs around morning and evening rush hour. With the Des Moines economic growth and strong insurance business center there is evidence that employees need a traffic friendly afternative to driving to work.

Trips by Gender per Weekday breaks the previous heatmap down by Gender. The data shows there is an increase in rides during rush hour for all groups, however males are still the most frequent users.

The data shows most users are in fact subscribers. Additionally we see there are more male subscribers than female. Offering perks for subscribers and incentives for new accounts may boost growth.

Top Starting Location demonstrates bikes are most frequently checked out at popular business and tourist locations. Identifying similar locations will be profitable in Des Moines.

Bike Utilization is an efficient way to see the proportion of bikes utilized. Des Moines is 88 sq mi (land), significantly smaller than New York City. That means Des Moines is potentially more accessible by bike.

To view the complete visual story checkout the Tableau CitiBike Dashboard. Des Moines appears to be a strong economic city, with a booming business center. It's smaller square mileage is accessible by bike and even has tourist appeal with waterfront trails to bikeride.