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Analysing locations power in large-scale mobility data

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Abstract

The pervasiveness of smartphones has modeled our lives, the social norms, and structure that dictate human behavior now directly influence the way individuals interact with network services and demand resources or content. From this scenario, identifying key locations in cities is central in human mobility investigation as well as for societal problem comprehension. 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 gather from and disseminate over its neighborhood, respectively. Moreover, the independence power captures the capacity of 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.
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Dates and versions

hal-03128655 , version 1 (02-02-2021)

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Lucas Santos de Oliveira, Pedro O S Vaz-De-Melo, Aline Carneiro Viana. Analysing locations power in large-scale mobility data. 2021. ⟨hal-03128655⟩
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