TY - JOUR
T1 - In Silico Human Mobility Data Science
T2 - Leveraging Massive Simulated Mobility Data (Vision Paper)
AU - Züfle, Andreas
AU - Pfoser, Dieter
AU - Wenk, Carola
AU - Crooks, Andrew
AU - Kavak, Hamdi
AU - Anderson, Taylor
AU - Kim, Joon Seok
AU - Holt, Nathan
AU - Diantonio, Andrew
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/7/3
Y1 - 2024/7/3
N2 - Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform "in silico"experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.
AB - Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform "in silico"experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.
KW - Spatial simulation
KW - in silico
KW - location-based social network data
KW - mobility data science
KW - trajectory data
UR - http://www.scopus.com/inward/record.url?scp=85198022257&partnerID=8YFLogxK
U2 - 10.1145/3672557
DO - 10.1145/3672557
M3 - Article
AN - SCOPUS:85198022257
SN - 2374-0353
VL - 10
JO - ACM Transactions on Spatial Algorithms and Systems
JF - ACM Transactions on Spatial Algorithms and Systems
IS - 2
M1 - 13
ER -