Homepage Inria website
  • Inria login
  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

  • Legal notice
  • Cookie management
  • Personal data
  • Cookies

Section: New Results

Identifying how places impact each other by means of user mobility

Participants : Lucas Santos, Pedro Olmo [UFMG (Brazil) - Dept. of Computer Science] , 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. In other words, how important is this POI for its neighborhood? We proposed thus a framework to measure POI influence by means of LBSN data. First, we modeled the problem using mobility graph approach where each POI is a node and the transitions of users among POI is a weighted vertex. Also, we treat the users' check-in records among POI as a measure of uncertainty, and their strength can be measured by entropy, which enabled to measure direct influence. Second, using same graph, we propose another influence measure taking account the POI importance for its one-hop vicinity in terms of incoming human transition. In addition, this mobility graph can be viewed as a collaborative filtering. We use this collaborative filter for compute the G-causality and evaluate if the transitions among POI has a causal relation and consequently, the influence among POI. Moreover, to the best of our knowledge, we are the first study which investigated POI influence by means of human mobility using LBSN data source.

This work is being prepared for a submission to an international conference.