Using publically available information from a bike-sharing program in New York, data was analyzed and presented in Tableau to evaluate the feasibility of implementing a similar program in Des Moines, IA.
The average bike-sharing ride is approximately 10 minutes long, and the majority of riders are men. Over the course of a week, ongoing subscribers of the bike-sharing service use the bikes more frequently than casual consumers, with the greatest number of trips being taken by men on weekdays. Among casual bike-sharing users, men use the bikes slightly more than women, with both genders taking the most trips on the weekend.
A possible explanation for this data is that those who subscribe to the bike-sharing service use it primarily for commuting to/from work, whereas casual users are more likely to use it for leisure or fun trips over the weekend.
Further supporting this hypothesis, Mon-Fri, the greatest number of rides are taken between 7-9am and 5-7pm. Sat-Sun, most rides are taken between 9am-7pm. When split by gender we can see that more men ride the bikes during these times than women, yet the concentration of rides during the aforementioned time frames is the same across genders.
The starting and ending locations for the greatest number of bike trips are similar, though not exactly the same. Those familiar with the area would likely be able to interpret what those locations have in common (are they tourist attractions? Areas with a large concentration of businesses or housing?). That information could be applied to proposed future locations to place bike-sharing stations in the areas of highest demand.
A large factor in determining the feasibility of a bike-sharing program would be the specific local environment and population. Bike friendly cities would be more likely to be profitable, whereas rural areas would not. Before making any decisions, visualizations should be created to look at the proposed location in Des Moines, IA. Including map layers with the terrain, streets/highways/routes, and points of interest on it would help narrow down possible locations where bike stations could be placed. Additional visualizations including map layers of building footprints and population density would also help determine if there would be a large enough number of people commuting a reasonable distance to workplaces that would warrant a bike-sharing station.
View Tableau Public files here.