![]() Nonetheless, the number of subscribers vastly outstripped that of customers, in any month of the year. In terms of Year-on-Year (YoY) changes in Divvy rides, the 2017 growth rate in subscribers was half that observed in 2016, and about a third of that in 2015.īetween 20, usage of Divvy rides by both customers and subscribers was seasonal, typically increasing markedly in the warmer, summer months and steadily decreasing with the approaching winter. In contrast, the number of subscribers grew, albeit at a decreasing rate. This means that customers and subscribers comprised about a quarter and three quarters of all Divvy ride users, respectively.įrom 2014 to 2017, the number of Divvy ride customers steadily decreased. The current study identified some big data merits and challenges in the Chicago Divvy rides dataset and show-cased a number of big data analysis, visualization and mapping tools.īetween 20, about 3.5 Million customers and 9.7 Million subscribers accessed Divvy rides. Divvy stations from which users had travelled from and to.Time of access to Divvy rides (by day of week and by time of day) and.Number of Divvy rides and median trip duration, as well as year-on-year growth patterns.The Chicago Divvy rides users (customers and subscribers) showed two different usage patterns in terms of the: In 2017 alone, over 590 Divvy ride stations operated over 6,240 individual bikes. It found that over 13.5 Million trips were taken during that period. This study analysed the Chicago Divvy rides user transactional, big dataset collected between 2014 to 2017. By Dr John Gwinyai Nyakuengama (20 October 2018)Ĭhicago City, Divvy Bicycles (Divvy Rides) big dataset from 2014 -2017 Big data analytics, visualization and mapping Stata R RapidMiner Turbo Prep Tibco Spotfire Power Bi Google Maps
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