Abstract
Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies.
Highlights
Precise GNSS data were less predictable in their raw form
The variability of mobility measures is related to the problem of statistical bias arising from the spatial aggregation of point-based measures and was identified almost 90 years a go[22] and is known as a modifiable areal unit problem (MAUP)
High-resolution GNSS data were used to investigate the relationship between data resolution and predictability. In these cases, the impact of this relationship was shown only for the time-bin approach using grid-based aggregation and at a limited set of scales, from a hundred to a few hundred metres spatially and from 5 min up to 2 h temporally. We fill this gap by thoroughly studying the effect of spatio-temporal aggregation methods for the time-bin and the place approaches across a range of scales, spanning from fine spatio-temporal resolutions of ten metres and 5 min up to the data maximum extent
Summary
Precise GNSS data were less predictable in their raw form. On the other hand, when the temporal resolution of the data decreased, the predictability was falling. A large portion of the literature, majorly originating from a physics-based point of view on empirical analyses of human mobility, describes human movement trajectories as scale-free[25,26,27] This recent study shows that human mobility is characterised by nested containers at different spatial levels. High-resolution GNSS data were used to investigate the relationship between data resolution and predictability In these cases, the impact of this relationship was shown only for the time-bin approach using grid-based aggregation and at a limited set of scales, from a hundred to a few hundred metres spatially and from 5 min up to 2 h temporally. We evaluate these methods at the mentioned levels of temporal resolution
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
