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bike-share-optimization

Data Visualization for bike share data in Los Angeles Submission for Capital One Software Engineering Summit.

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Contents

Stack outline

Front End

Back End

Strategy

1. Visualize the data:

  • Popular Destinations vs. Time of Year
  • Yearly Heat Map
  • Seasonal Heat Map
    • If I was to look where the most popular stations were in order to be guarenteed a bike in Los Angeles, I would go to the places in high demand first. Furthermore, can assess in a seasonal mindset.

2. Heat Map:

Filter out the NaN, empty string, null, and 0 values from the data

Inputs: Starting/Ending Station Latitude, Starting/Ending Station Longitude,

Output: Heat Map made in python, exported as an HTML file

Strategy:

  1. Filter data that didn't incorporate lat's or long's
  2. Filter data down to seasonal values

Data Visualizations

1. Heat Map

  • **Heat Map for Yearly and Seasonal Data

2. Popular Passes

  • **What types of passes are used yearly and seasonal

3. Starting Stations

  • **Top 10 Starting Stations Listed
    • Bar Graph: Top 10 Starting Station with Station ID and # of visits listed
    • Pin Points: Pin Pointed the 10 Stations on Google Maps (If marker is clicked, shows popularity # followed by Station ID)

4. Ending Stations

  • **Top 10 Starting Stations Listed
    • Bar Graph: Top 10 Ending Station with Station ID and # of visits listed
    • Pin Points: Pin Pointed the 10 Stations on Google Maps (If marker is clicked, shows popularity # followed by Station ID)

5. Time and Distance

  • ** Visually appealing way of showing average time and distance on a yearly and seasonal basis to viewers
    • Full Interaction

Creativity

Animate: Add an animation to your visualization.

  • First data visualization graph expands from the bottom up.
  • Other data visualization maps are interactive.