Bike Sharing Atlas: Visual Analysis of Bike-Sharing Networks

Abstract

In this paper, we introduce an interactive visualization system, bikesharingatlas.org, that supports the explorative data analysis of more than 468 bike-sharing networks worldwide. The system leverages a multi-coordinated view approach and innovative interaction techniques can help, for instance, to expose capacity bottlenecks, commuting patterns, and other network characteristics. Our broader goal is to illustrate how visual analysis can be used for exploring distributed, heterogeneous data from smart cities. Based on our collaboration with different target users, we present usage scenarios that show the potential of our approach to understanding bike-sharing and urban commuting behaviors. 1. Motivation The majority of the world's population is living in urban areas, and this proportion continues to grow. An increase in efficiency is needed for cities to function, and sustainable infrastructures will be essential to accommodate larger numbers of people. As digitization has become an integral part of our life, massive amounts of data from a variety of sources are generated continuously in cities worldwide. Leveraging this data intelligently offers great potential towards smarter and more efficient cities. Although having these immense datasets at our fingertips, we often lag behind in supporting people to intelligently leverage the huge amount of data that is produced daily. A city planner might want to identify and better understand commuting patterns in order to develop more robust and cohesive transportation infrastructures. A sociologist wants to study local effects of job density and residential segregation on society, or wants to perform other data-intensive tasks like crosscountry comparisons of urbanization. However, currently most of the data comes in machine-readable form only and hence is hard to access for people without sophisticated computational and statistical skills. In this paper, our goal is to illustrate how interactive visualization can help to open the data that is produced in smart cities to a wider audience. We believe that interactive visualization can help us to engage users, and to interactively explore and understand collected data from smart city sensors, in order to make life more comfortable, safer and sustainable. Towards this goal, we take public bike-sharing systems as an example and show how visualization can help to better leverage the data produced by these systems. Bike-sharing systems have been established as permanent components in urban passenger transport since 1996 [1]. Being increasingly digitized, these systems nowadays produce data that can reveal interesting insights, not only into patterns of bicycle usage, but also underlying spatio-temporal dynamics of a city, as Froehlich et al. [2] and Wood et al. [3] pointed out. As a first step, we recorded continually, over a period of 17 months, distributed data from several hundred bike-sharing networks worldwide. We aggregated the data, combined it with other data sources and derived characteristic network metrics.