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自行车共享分析

自行车共享分析

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Arts and Entertainment,Cycling Classification

数据结构 ? 60.06M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    Context 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 for short trips, typically 30 minutes or less. With the latest 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. The data consists of BikeShare information for three large cities in the US - New York City, Chicago, and Washington, DC. Content Sample Data
    • 'tripduration', '839'
    • 'starttime', '1/1/2016 00:09:55'
    • 'stoptime', '1/1/2016 00:23:54'
    • 'start station id', '532'
    • 'start station name', 'S 5 Pl & S 4 St'
    • 'start station latitude', '40.710451'
    • 'start station longitude', '-73.960876'
    • 'end station id', '401'
    • 'end station name', 'Allen St & Rivington St'
    • 'end station latitude', '40.72019576'
    • 'end station longitude', '-73.98997825'
    • 'bikeid', '17109'
    • 'usertype', 'Customer'
    • 'birth year', ''
    • 'gender', '0'
    Acknowledgements Thanks to Udacity for the data. Inspiration This data set can be a very good inspirations for not only new users but experienced veterans who wants to explore more insights.
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