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Author (up) Humasak Tommy Argo Simanjuntak; Fabio Ciravegna pdf 
  Title Semantic Understanding of Human Mobility Lifestyle to support Crisis Management Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages 421-434  
  Keywords lifestyle patterns; mobility patterns; semantic annotations; semantic mobility  
  Abstract In this paper, we propose a method for understanding the semantics of mobility (mainly related to lifestyle) patterns based on stay point detection from tracking data. The method identifies the context (trip purpose and visited point of interest) of tracking data by using large-scale data collection infrastructure. We evaluate our method with a tracking dataset in Birmingham (European project SETA) generated by 534 users from September 2017 to September 2018. To this end, we compare insights from the tracking data with check-in mobility in social media. The results show that both data capture rich human lifestyle features related to the visited point of interest. Our study provides solid evidence that lifestyle patterns from tracking and social media data can indeed be useful for understanding and gauging the level of disruption after a crisis, as it is possible to check the deviation of habits from normal conditions and post-crisis.  
  Address The University of Sheffield, United Kingdom  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T6- Geospatial Technologies and Geographic Information Science for Crisis Management (GIS) Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes; Approved no  
  Call Number Serial 1731  
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