Should I Stay or Should I Go?

Hermoso, Ramón; Dunkel, Jürgen; Rückauf, Florian

doi:10.1145/3242102.3242140

Cited by 3 publications

(2 citation statements)

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“…Social norms and structure dictating human behavior (e.g., mobility, interests) are now directly influencing the way individuals interact with the network services and demand resources or content. Many works address these opportunities by means of user mobility prediction and Point-of-Interest (POI) recommendation [52,17,29]. However, another fundamental area of study in traditional urban literature is the investigation of the way in which city neighborhoods become popular and how the movement of citizens impacts the number of visitations in POIs [18,17,11,20].…”

Section: Introductionmentioning

confidence: 99%

Assessing Large-Scale Power Relations among Locations from Mobility Data

Oliveira

Melo

Viana

2021

ACM Trans. Knowl. Discov. Data

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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.

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Section: Introductionmentioning

confidence: 99%

Assessing Large-Scale Power Relations among Locations from Mobility Data

Oliveira

Melo

Viana

2021

ACM Trans. Knowl. Discov. Data

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“…The rise of mobile technologies and collective sensing in the last decade has contributed to the generation of large datasets that describe activity dynamics in cities, which created new opportunities for research [7,24]. Many works address these opportunities by means of user mobility prediction and Point-of-Interest (POI) recommendation [6,14].…”

Section: Introductionmentioning

confidence: 99%

Measuring Power Relations Among Locations From Mobility Data

Oliveira

Melo

Viana

2019

Proceedings of the 17th ACM International Symposium on Mobility Management and Wireless Access

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Key location identification in cities is 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.

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Towards MANET-based Recommender Systems for Open Facilities

Dunkel

Hermoso

2021

Appl Intell

Self Cite

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Nowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics.

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Should I Stay or Should I Go?

Cited by 3 publications

References 10 publications

Assessing Large-Scale Power Relations among Locations from Mobility Data

Assessing Large-Scale Power Relations among Locations from Mobility Data

Measuring Power Relations Among Locations From Mobility Data

Towards MANET-based Recommender Systems for Open Facilities

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