The pervasiveness of smartphones has shaped our lives, social norms, and the structure that dictates human behavior. They now directly influence how individuals demand resources or interact with network services. From this scenario, identifying key locations in cities is fundamental for the investigation of human mobility and also for the understanding of social problems. In this context, we propose the first graph-based methodology in the literature to quantify the power of Point-of-Interests (POIs) over its vicinity by means of user mobility trajectories. 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 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 gathers from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of a POI to receive visitors independently from other POIs. We tested our methodology on well-known university campus mobility datasets and validated on Location-Based Social Networks (LBSNs) datasets from various cities around the world. Our findings show that in university campus: (i) buildings have low support power and attract power ; (ii) people tend to move over a few buildings and spend most of their time in the same building; and (iii) there is a slight dependence among buildings, even those with high independence power receive user visits from other buildings on campus. Globally, we reveal that (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same continent have similar independence patterns; and (iii) places with a high number of visitation and city central areas are the regions with the highest degree of independence.

中文翻译：

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从移动数据评估位置之间的大规模权力关系

智能手机的普及塑造了我们的生活、社会规范以及支配人类行为的结构。它们现在直接影响个人需求资源或与网络服务交互的方式。在这种情况下，确定城市中的关键位置对于研究人员流动性和理解社会问题至关重要。在这种情况下，我们提出了文献中第一个基于图的方法，通过用户移动轨迹来量化兴趣点（POI）在其附近的能力。与文献不同，我们在分析中考虑了人流，而不是相邻 POI 的数量或其在城市中的结构位置。因此，我们使用多流图模型对 POI 的访问进行建模，其中每个 POI 是一个节点，用户在 POI 之间的转换是加权的直接边。使用这个多流图模型，我们计算吸引、支持和独立的权力. 这吸引力量和支持力量分别衡量一个 POI 从其邻域收集和传播的访问量。此外，独立权力捕获 POI 独立于其他 POI 接待访客的能力。我们在著名的大学校园移动数据集上测试了我们的方法，并在来自世界各地城市的基于位置的社交网络 (LBSN) 数据集上进行了验证。我们的研究结果表明，在大学校园中：(i) 建筑物的低支持力量和吸引力量; (ii) 人们倾向于在几栋建筑物上移动，大部分时间都在同一栋建筑物中度过；(iii) 建筑物之间有轻微的依赖关系，即使是那些高独立权力接收来自校园内其他建筑物的用户访问。在全球范围内，我们揭示了 (i) 我们的指标捕捉到影响其附近访问次数的地点；(ii) 同一大陆的城市具有相似的独立模式；(iii) 人流量大的地方和城市中心地区是独立程度最高的地区。