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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\u2019s 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\n            <jats:italic>attract, support, and independence powers<\/jats:italic>\n            . The\n            <jats:italic>attract power<\/jats:italic>\n            and\n            <jats:italic>support power<\/jats:italic>\n            measure how many visits a POI gathers from and disseminate over its neighborhood, respectively. Moreover, the\n            <jats:italic>independence power<\/jats:italic>\n            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\n            <jats:italic>support power<\/jats:italic>\n            and\n            <jats:italic>attract power<\/jats:italic>\n            ; (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\n            <jats:italic>independence power<\/jats:italic>\n            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.\n          <\/jats:p>","DOI":"10.1145\/3470770","type":"journal-article","created":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T04:05:52Z","timestamp":1630728352000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessing Large-Scale Power Relations among Locations from Mobility Data"],"prefix":"10.1145","volume":"16","author":[{"given":"Lucas Santos","family":"De Oliveira","sequence":"first","affiliation":[{"name":"State University of Southwestern of Bahia, Jequi\u00e9, Bahia, Brazil"}]},{"given":"Pedro O. 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