Section: New Results

Identifying how places impact each other by means of user mobility

Participants : Lucas Santos de Oliveira [EMBRACE] , Pedro Olmo Stancioli [Federal U. of Minas Gerais] , Aline Carneiro Viana.

The way in which city neighborhoods become popular and how people trajectory impacts the number of visitation is a fundamental area of study in traditional urban studies literature. Many works address this problem by means of user mobility prediction and POI recommendation. In a different approach, other works address the human mobility in terms of social influence which refers to the case when individuals change their behaviors persuaded by others. Nevertheless, fewer works measure influence of POI based on human mobility data.

Different from previous literature, in this work, we are interested in understanding how the neighborhood POI affect each other by means of human mobility using location-based social networks (LBSNs) data source. Key location identification in cities is a central in human mobility investigation as well as for societal problem comprehension. In this context, we propose a methodology to quantify the power of point-of-interests (POIs) in their vicinity, in terms of impact and independence – the first work in the literature (to the best of our knowledge). Different from literature, we consider the flow of people in our analysis, instead of the number of neighbor POIs or their structural locations in the city. Thus, we first modeled POI's visits using the multiflow graph model where each POI is a node and the transitions of users among POIs are a weighted direct edge. Using this multiflow graph model, we compute the attract, support and independence powers. The attract power and support power measure how many visits a POI gather from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of POI to receive visitors independently from other POIs. Using a dataset describing the mobility of individuals in the Dartmouth College campus, we identify a slight dependence among buildings as well as the tendency of people to be mostly stationary in few buildings with short transit periods among them.

This work was published in ACM MobiWac 2019 [14] and an extended version is being prepared. Lucas is doing an internship in our team from Nov. 2019 to Jan. 2020.