Inspiration

We wanted to visualize Citibike data as on-screen movement in order to track crowdedness of certain areas of Manhattan.

What it does

It visually displays predicted travelled paths for citibikes.

How we built it

Bike station data is taken from CitiBike's database. The database includes every bike transaction at different stations. We tracked bikes by bike ID and used Google Maps' direction API to predict a travelled path. An animated dot represents every bike taken or docked.

Challenges we ran into

Cleaning up the citibike data was very time consuming. Additionally, predicting path points based on google api directions was very tedious.

Accomplishments that we're proud of

Manipulating the data to create a visual representation in a google maps object.

What we learned

How to use google maps api, strengthened knowledge in JS, Python, and pandas.

What's next for CitiDots

We want to improve our graph sectors, making them live according to our active bike data.

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