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
• Strategy
• Bonuses

Stack outline

Front End

• Deployed on Github Pages
• jQuery + AJAX
• Google Maps API
• Chart.Js
• Canvas.Js
• amCharts.Js

Back End

• Parsed using Python
  • CSV
  • Pandas
  • Collections

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.