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dc.contributor.authorBrum-Bastos, Vanessa S.
dc.contributor.authorLong, Jed A.
dc.contributor.authorDemsar, Urska
dc.identifier.citationBrum-Bastos , V S , Long , J A & Demsar , U 2018 , ' Weather effects on human mobility : a study using multi-channel sequence analysis ' , Computers, Environment and Urban Systems , vol. In press .
dc.identifier.otherORCID: /0000-0001-7791-2807/work/48516827
dc.identifier.otherORCID: /0000-0002-5865-0204/work/67167731
dc.descriptionThe authors would like to acknowledge SWB (Science Without Borders) - CAPES (Coordination for the Improvement of Higher Education Personnel) (BEX 13438/13-1) for the financial support during the development of this study.en
dc.description.abstractWidespread availability of geospatial data on movement and context presents opportunities for applying new methods to investigate the interactions between humans and weather conditions. Understanding the influence of weather on human behaviour is of interest for diverse applications, such as urban planning and traffic engineering. The effect of weather on movement behaviour can be explored through Context-Aware Movement Analysis (CAMA), which integrates movement geometry with its context. More specifically, we use multi-channel sequence analysis (MCSA) to represent a person’s movement as a multi-dimensional sequence of states, describing either the type of movement or the state of the environment throughout time. Similar movement patterns can then be identified by comparing and aligning mobility sequences. In this paper we apply CAMA and MCSA to explore weather effects on human movement patterns. Data from a GPS tracking study in a Scottish town of Dunfermline are linked to weather data and converted into multi-channel sequences which are clustered into groups of similar behaviours under specific weather typologies. Our findings show that the CAMA + MCSA method can successfully identify the response of commuters to variations in environmental conditions. We also discuss our findings on how travel modes and time spent at different places are affected by meteorological conditions, mainly wind, but also rainfall, daylight duration, temperature, comfort and relative humidity.
dc.relation.ispartofComputers, Environment and Urban Systemsen
dc.subjectContext-aware movement analysisen
dc.subjectContext-aware similarityen
dc.subjectHuman mobilityen
dc.subjectHuman movementen
dc.subjectMulti-channel sequence analysisen
dc.subjectG Geography (General)en
dc.subjectGE Environmental Sciencesen
dc.subjectH Social Sciences (General)en
dc.titleWeather effects on human mobility : a study using multi-channel sequence analysisen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Geography & Sustainable Developmenten
dc.contributor.institutionUniversity of St Andrews. Bell-Edwards Geographic Data Instituteen
dc.description.statusPeer revieweden

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