Bicycle data was the most relate-able data to us, since we also have a bicycle sharing system. It was interesting to see that there are data on the trips that were made, as well as Time interval

What it does

Our little tool analyze, based on the region shown on the map of NYC, the most popular paths that were taken by different type of users. In other words, you can view the most taken trips dependent on the type of member (Casual vs Regular) and the area displayed.

How We built it

We used python (pandas) to create the queries and Dataframes and lists of information that is the most relevant to present. These queries are then called by the frontend which is made out of python (Dash Plotly). We used MapBox to generate the map

Challenges I ran into

  • Dash Plotly is not callback friendly for web presentation...(it is very hard to get the view of the object that is clicked, hovered, etc)
  • Python. (We have rarely coded in python). (We are rock/rigidity/structure/datatypes lovers. cough*Java*cough)
  • No idea about Data Visualization
  • Git (why are there pyc files being commited every second??)

Accomplishments that I'm proud of

  • We have overcame our fear of pandas (and pythons)
  • We were able to calculate the latitude/longitude/zoom based on callback of what the user is doing to the map

What I learned

  • Learnt new frameworks
  • Hands-on data visualization
  • Git

What's next for DataDriveTransport

Rewrite in REACT-vis.

Essential information to put

  • List the category you are in : Transportation
  • List the datasets you used from us : 2014-04 - Citi Bike trip data.csv
  • List sources for any external datasets used: N/A
  • List your team members: Julian McCarthy and Kevin Lu-Trinh
  • Team: Hyper REACTive

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