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Data Analysis: Uncover bike share usage patterns in US Cities

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Uncover bike share usage patterns in US Cities

Apr 27, 2018

Introduction

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day.

Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used.

In this project, I will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. I will compare the system usage between three large cities: Chicago, New York City, and Washington, DC.

This program is easily downloadable and executable via python command line.

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Data Analysis: Uncover bike share usage patterns in US Cities

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