In this module, I helped Kate in analyzing Bikesharing data for a potential business plan in Des Mouines. We used Citibike bikehsaring in New York City as an inspiration after our NYC trip. We analysed the bikesharing business in NYC. For this module, I used Tableau to make interesting data visualizations. I imported the csv file containing all the data into Tableau and then applied my Tableau skills on the given data. The purpose of providing insights on NYC Citibike bikesharing business was to figure out if it would be feasible to make a similar business in Des Moines.
- The below Tableau visualizations shows fow how long the bikes are checked out for all riders. We can see that the graph reaches its peak at around 5 minutes and the corresponding number of bikes is around 146,000.
- The below image gives a breakdown of checkout times according to gender. It can be clearly seen that the checkout time is significinaly higher for 'males' as compared to 'females' and 'unknown'.
- In the following visualization, I have graphed the number of bike trips by weekday for each hour of the day as a heatmap. It can be clearly seen from the darkness of the color that most people use bikes early in the morning around 8 am and in the evening around 5 pm. This time corresponds to office timings.
- The below heat map is similar to the previous one but this one categorizes the trips according to gender. It can be easily concluded that much more men than women use bikes to commute to and from their work places.
- Below is an image of a heatmap that shows the number of bike trips broken down by gender for each day of the week by each user type. From the image, we can tell that most people are subscribers. Secondly, most subscribers are males. When it comes to normal customers, both genders are quite evenly distributed.
- The following tree map image shows how some bikes require more maintenance than others. This will give a general idea regarding the maintenance of bikes.
- The below picture of the map shows the top starting locations for bikers to start their journey. It can be clearly seen that some locations are popular than the others. This can be due to higher population in general or more torists being present in those popular locations.
In conclusion, it will be a good idea to set up a similar business in Des Moines. There are many people who use bikes for commuting back and forth to their workplaces. So it is not just tourists who use bikes. Majority of the people who use bikes are the local people living and working in the city. As for the running costs, it can be seen from the treemap that not all bikes require the same amount of maintenance. Only bikes in popular locations which are used a lot will require more maintenance. In these same popular locations, the business can put more number of bikes over there as compared to other areas. We also saw from one of the Tableau visualizations that most bike users are subscribers who use bikes to commute back and forth to their workplaces. This means that the bikesharing business relies mostly on the local population.
There are two other visualizations that can be made to show a better picture. A map showing start station ID's can be incorporated. This will pinpoint the exact location of a particular bike station. Another visualization which can be used it to incorporate Station names on a map. This will help in allocating bike stations through the city by using the street names.