Using Mobile Phone Data to Examine Point-of-Interest Urban Mobility

Human mobility patterns have been investigated on a macroscale ranging from intra-city and intercity to intra-country based on mobile phone data. However, few studies have been conducted from a micro-view to characterize group-level human mobility behavior with respect to a point of interest (POI). In this paper, we intend to explore the differences in mobility patterns across those groups of community members at a specific POI. First, an appearance probability estimation algorithm is proposed to detect individual frequent locations for each user, and thereafter mobile users are classified into POI-related categories for further analysis of group-level mobility behavior. A hospital experiment is described based on a mobile phone dataset collected from Hangzhou City, China. An evaluation of this model illustrates the good performance of our scheme. Moreover, the mobility pattern analysis exhibits differences between groups with respect to frequent locations, radius of gyration, and population spatial distribution. The results of the radius of gyration distributions show that medical workers, out-patients, and passersby all follow an exponentially truncated power-law distribution, while in-patients present an exponential-law distribution.