TY - GEN
T1 - Understanding individuals' proclivity for novelty seeking
AU - Amichi, Licia
AU - Viana, Aline Carneiro
AU - Crovella, Mark
AU - Loureiro, Antonio A.F.
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/11/3
Y1 - 2020/11/3
N2 - Human mobility literature is limited in their ability to capture the novelty-seeking or the exploratory tendency of individuals. Mainly, the vast majority of mobility prediction models rely uniquely on the history of visited locations (as captured in the input dataset) to predict future visits. This hinders the prediction of new unseen places and reduces prediction accuracy. In this paper, we show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Further, we extract and analyze the mobility traits specific to each profile. We then investigate temporal and spatial patterns in each mobility profile and show the presence of recurrent visiting behavior of individuals even in their novelty-seeking moments. Our results unveil important novelty preferences of people, which are ignored by literature prediction models. Finally, we show that prediction accuracy is dramatically affected by exploration moments of individuals. We then discuss how our profiling methodology could be leveraged to improve prediction.
AB - Human mobility literature is limited in their ability to capture the novelty-seeking or the exploratory tendency of individuals. Mainly, the vast majority of mobility prediction models rely uniquely on the history of visited locations (as captured in the input dataset) to predict future visits. This hinders the prediction of new unseen places and reduces prediction accuracy. In this paper, we show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon - Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Further, we extract and analyze the mobility traits specific to each profile. We then investigate temporal and spatial patterns in each mobility profile and show the presence of recurrent visiting behavior of individuals even in their novelty-seeking moments. Our results unveil important novelty preferences of people, which are ignored by literature prediction models. Finally, we show that prediction accuracy is dramatically affected by exploration moments of individuals. We then discuss how our profiling methodology could be leveraged to improve prediction.
KW - Exploration
KW - Individual Mobility
KW - Mobility Profiling
UR - http://www.scopus.com/inward/record.url?scp=85097271603&partnerID=8YFLogxK
U2 - 10.1145/3397536.3422248
DO - 10.1145/3397536.3422248
M3 - Conference contribution
AN - SCOPUS:85097271603
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 314
EP - 324
BT - Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Trajcevski, Goce
A2 - Huang, Yan
A2 - Newsam, Shawn
A2 - Xiong, Li
PB - Association for Computing Machinery
T2 - 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
Y2 - 3 November 2020 through 6 November 2020
ER -