Identifying multiscale spatio-temporal patterns in human mobility using manifold learning.

ABSTRACT: When, where and how people move is a fundamental part of how human societies organize around every-day needs as well as how people adapt to risks, such as economic scarcity or instability, and natural disasters. Our ability to characterize and predict the diversity of human mobility patterns has been greatly expanded by the availability of Call Detail Records (CDR) from mobile phone cellular networks. The size and richness of these datasets is at the same time a blessing and a curse: while there is great opportunity to extract useful information from these datasets, it remains a challenge to do so in a meaningful way. In particular, human mobility is multiscale, meaning a diversity of patterns of mobility occur simultaneously, which vary according to timing, magnitude and spatial extent. T

SUBMITTER: Watson JR 

PROVIDER: S-EPMC7924485 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature