1. Au revoir
Congratulations on completing the course! Let's recap some of the insights we gained from our final visualization.
2. The most popular route
By sorting on the total number of rides, we see that the most popular route is from de la Commune / McGill to Marché Atwater, with over 2400 rides. Due to the lack of morning and afternoon "rush hours", this appears to not be a commuter route.
3. Prominent stations
By opening up the filter view on the station variables, we see that Marche Atwater shows up frequently as a start station in the top 100 routes. If we filter on only routes that originate from here, we see that none of these routes are have commuter rush hours. Identifying stations with this type of behavior can be helpful in modeling bike demand.
4. Commuter vs. non-commuter
Sorting on the weekday / weekend difference in mean hourly rides helps call out routes that appear to be used more for commuting. Notice that some routes have morning and afternoon peaks, but some routes are only hitting peak usage in just the morning or just the afternoon. This is an important insight to take into account in further analysis.
5. Commuter routes are short
A very interesting insight that you may have noticed when looking at the display is that top 100 routes that have commuter rush hour behavior are genarally very short. In fact, using 10 minutes as a cutoff, we can see that nearly all routes longer than 10 minutes don't exhibit this rush hour behavior.
A very interesting insight that you may have noticed when looking at the display is that top 100 routes that have commuter rush hour behavior are genarally very short. In fact, using 10 minutes as a cutoff, we can see that nearly all routes longer than 10 minutes don't exhibit this rush hour behavior.
6. More displays
In this case study, we were only able to look at a small number of targeted visualizations. There are many other possible ways to explore the data, both in summary and in detail, that I would encourage you to investigate on your own.
7. Resources
There are many other aspects of TrelliscopeJS that we were not able to cover in this course that you can learn about by looking at the documentation or following my blog, where I periodically will be posting examples. Some of these aspects include different methods to deploy and share your displays, displays that call web services to render panels, and other supported plotting libraries.
8. Congratulations!
Congratulations once again!