City Bike

Many cities offer bicycles people can rent for short trips and many people use it every day. As a result, by knowing when and where people use them, we may infer…

Data preparation

The following notebook describes how the data was obtained and preprocessed to build the examples introduced above.

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• Chicago
  • Data

The challenge

We know how people use bicycles. People, people… it is us. What do I know about myself I could use to explore the data and determines living and working areas of Chicago?

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• City Bike Challenge
  • The city
  • The data
  • The challenge
  • A few graph

The data can be seen in many ways.

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• City Bike Views
  • The data
  • About age
  • Non-linear correlations
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• Chicago
  • Data
  • Businesses

A solution

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• Ideas on City Bike Challenge
  • The data
  • First assumption

A solution including clustering

The first notebook only clusters on arrival time. The second one clusters on bith arrival and starting time.

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• Bike Pattern
  • The data
  • Normalize per week day (stop)
  • Clustering (stop)
  • Graph
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• Bike Pattern 2
  • The data
  • Normalize, aggregating and merging per start and stop time
  • Clustering (stop and start)
  • Graph

Other sources of data

Chicago has one of the richest open data portal. Here are some others sources of data:

• Taxi Trips - Dashboard

• Current Employee Names, Salaries, and Position Titles

• Building Permits

• Libraries - Locations, Hours and Contact Information

• Crimes - One year prior to present

• Fire Stations - Map

• Average Daily Traffic Counts

• Divvy Bicycle Stations - Historical - Dashboard

• Divvy Trips - Dashboard

• Business Licenses - Current Active