Scooters in Austin
Data Analysis and Visualization Boot Camp 2019-2020 JSON& Team (2) (T/Th)
If you've been out and about on campus or in downtown Austin, you can't help but notice that scooters are all over the place. We decided to examine distance and duration of scooter rides and where people complain about scooters.
In our first project we merged data sets to create visualizations comparing complaints to rides, to see if there were some neighborhoods that were being the squeaky wheels or if there really was a scooter problem in certain areas. As we had hypothesized, scooters that find their way into further out neighborhoods are less likely to be picked up and thus take longer to be ridden back into town, are much more likely to result in a complaint.
In our previous presentation, we called out some limitations and remaining questions that we had, and have demonstrated some of those here in Project 2.
In this project we used mySQL, FastAPI, Uvicorn, Plotly and Cloud Run for this web app.
Our Project is a dashboard for reviewing complaints, rides, and certain trends that we found interesting. The routes we decided to demonstrate for you all are:
• Longest rides
• Rides that went nowhere
• Explore a random ride that someone took in Austin
• What is the red zone, or neighborhoods you just shouldn't leave scooters in?
• What are the percent of complaints, rides in, rides out, and other stats about specific zip codes?
We hope you'll find this data to be as interesting as we did, regardless of whether you consider yourself a part of the shared mobility revolution or not.