The impact of human mobility data scales and processing on movement predictability

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 dataset...

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Autores principales:	Kamil Smolak, Katarzyna Siła-Nowicka, Jean-Charles Delvenne, Michał Wierzbiński, Witold Rohm
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1c0ae3b6ff9842388c89a1e4f9dddd01
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spelling	oai:doaj.org-article:1c0ae3b6ff9842388c89a1e4f9dddd012021-12-02T18:47:01ZThe impact of human mobility data scales and processing on movement predictability10.1038/s41598-021-94102-x2045-2322https://doaj.org/article/1c0ae3b6ff9842388c89a1e4f9dddd012021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94102-xhttps://doaj.org/toc/2045-2322Abstract 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.Kamil SmolakKatarzyna Siła-NowickaJean-Charles DelvenneMichał WierzbińskiWitold RohmNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kamil Smolak Katarzyna Siła-Nowicka Jean-Charles Delvenne Michał Wierzbiński Witold Rohm The impact of human mobility data scales and processing on movement predictability
description	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.
format	article
author	Kamil Smolak Katarzyna Siła-Nowicka Jean-Charles Delvenne Michał Wierzbiński Witold Rohm
author_facet	Kamil Smolak Katarzyna Siła-Nowicka Jean-Charles Delvenne Michał Wierzbiński Witold Rohm
author_sort	Kamil Smolak
title	The impact of human mobility data scales and processing on movement predictability
title_short	The impact of human mobility data scales and processing on movement predictability
title_full	The impact of human mobility data scales and processing on movement predictability
title_fullStr	The impact of human mobility data scales and processing on movement predictability
title_full_unstemmed	The impact of human mobility data scales and processing on movement predictability
title_sort	impact of human mobility data scales and processing on movement predictability
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1c0ae3b6ff9842388c89a1e4f9dddd01
work_keys_str_mv	AT kamilsmolak theimpactofhumanmobilitydatascalesandprocessingonmovementpredictability AT katarzynasiłanowicka theimpactofhumanmobilitydatascalesandprocessingonmovementpredictability AT jeancharlesdelvenne theimpactofhumanmobilitydatascalesandprocessingonmovementpredictability AT michałwierzbinski theimpactofhumanmobilitydatascalesandprocessingonmovementpredictability AT witoldrohm theimpactofhumanmobilitydatascalesandprocessingonmovementpredictability AT kamilsmolak impactofhumanmobilitydatascalesandprocessingonmovementpredictability AT katarzynasiłanowicka impactofhumanmobilitydatascalesandprocessingonmovementpredictability AT jeancharlesdelvenne impactofhumanmobilitydatascalesandprocessingonmovementpredictability AT michałwierzbinski impactofhumanmobilitydatascalesandprocessingonmovementpredictability AT witoldrohm impactofhumanmobilitydatascalesandprocessingonmovementpredictability
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The impact of human mobility data scales and processing on movement predictability

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