Remy Cazabet, Pierre Borgnat, Pablo jensen
In ESANN 2017
Bicycle Sharing Systems are now ubiquitous in large cities around the world. In most of these systems, journeys’ data can be ex- tracted, providing rich information to better understand it. Recent works have used network based-machine learning, and in particular space-corrected node clustering, to analyse such datasets. In this paper, we show that spatial-null models used in previous methods have a systematic bias, and we propose a degree-contrained null-model to improve the results. We finally apply the proposed method on the BSS of a city.