TY - GEN
T1 - Mobility profiling
T2 - 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019
AU - Amichi, Licia
AU - Viana, Aline C.
AU - Crovella, Mark
AU - Loureiro, Antonio F.
N1 - Publisher Copyright:
© 2019 held by the owner/author(s).
PY - 2019/12/9
Y1 - 2019/12/9
N2 - The prediction of individuals' dynamics has attracted significant community attention and has implication for many fields: e.g. epidemic spreading, urban planning, recommendation systems. Current prediction models, however, are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior. This paper contributes better understanding of this aspect and presents a new strategy for identifying exploration profiles of a population. Our strategy captures spatiotemporal properties of visits - i.e. a known or new location (spatial) as well as a recurrent and intermittent visit (temporal) - and classifies individuals as scouters (i.e., extreme explorers), routineers (i.e., extreme returners), or regulars (i.e., with a medium behavior). To the best of our knowledge, this is the first work profiling spatiotemporal exploration of individuals in a simple and easy-to-implement way, with the potential to benefit services relying on mobility prediction.
AB - The prediction of individuals' dynamics has attracted significant community attention and has implication for many fields: e.g. epidemic spreading, urban planning, recommendation systems. Current prediction models, however, are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior. This paper contributes better understanding of this aspect and presents a new strategy for identifying exploration profiles of a population. Our strategy captures spatiotemporal properties of visits - i.e. a known or new location (spatial) as well as a recurrent and intermittent visit (temporal) - and classifies individuals as scouters (i.e., extreme explorers), routineers (i.e., extreme returners), or regulars (i.e., with a medium behavior). To the best of our knowledge, this is the first work profiling spatiotemporal exploration of individuals in a simple and easy-to-implement way, with the potential to benefit services relying on mobility prediction.
UR - http://www.scopus.com/inward/record.url?scp=85077965511&partnerID=8YFLogxK
U2 - 10.1145/3360468.3366771
DO - 10.1145/3360468.3366771
M3 - Conference contribution
AN - SCOPUS:85077965511
T3 - CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019
SP - 9
EP - 11
BT - CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019
PB - Association for Computing Machinery, Inc
Y2 - 9 December 2019 through 12 December 2019
ER -