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Data extraction system for hot bike-sharing spots in an intermediate city

Abstract:

The amount of available data from different sensors, things, and systems increases every day, and bike sharing systems are no exception. Analysis of these data can be divided into geospatial and temporal. The former refers to the position of objects and the latter to their behavior over time. In the context of dock based bike sharing systems, this work develops a system for visualizing number of transactions of each station through heat maps. First step is data gathering through web scraping for generating time series of docks behavior. Then, time series are processed to obtain relevant information about use of this service. System uses absolute value of transactions as preferred metric. Finally, a web application allows users to automatically generate heat map of an specific day. These methods allow the identification of hot spots in terms of stations or docks. So far there are more than 220 days of sampling. System provides end users information about bike-sharing operation, since no official data are available.