Visual Analysis Of Bike-sharing Data

In the past ten or twenty years,the bike-sharing service has emerged as a green and environment friendly,a new way of transportation in many big cities.Bike-sharing system is an important part of public transport system,which is a supplement to urban public transport system,which helps to solve the problem of “last mile” of urban traffic,and has great significance for alleviating traffic pressure,realizing energy saving and emission reduction.However,in the face of massive and complex bike-sharing data,we need to analyze useful patterns,and traditional data analysis may be difficult to meet the needs.This paper designed an interactive visual analysis system,based on the data of Hangzhou bike-sharing system and Divvy bike-sharing system.The system provides rich interactive functions,using a variety of visual graphics to help users analyze the implicit in the bike-sharing system.On the aspect of data analysis,this paper preprocesses data first and analyzes the data through statistical analysis.The similarity matrix is built according to the distance between bike-sharing stations and rent-and-return records.The Affinity Propagation clustering algorithm to cluster the bike-sharing stations.On the aspect of visualization,the system is implemented on the basis of Web technology,and the user can observe the dynamic loading of the bike-sharing system data intuitively.According to the characteristics of bike-sharing data,this paper selects a suitable visual coding method to visualize.This paper applies spiral graphics,polar coordinate thermodynamic charts,calendar ring diagrams and other visible figures display and analysis the bike-sharing data.This system supports multiple interactive functions,which is convenient for users to better understand and analyze bike-sharing data.Through the visual interface,users can also analyze the similarities and differences of the rules of stations in different clusters.In this paper,user composition of public bicycle data,visual analysis of time and space patterns,and the clustering results of bike-sharing stations are helpful to help users fully understand the rules of bike-sharing data.