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An approach for understanding human activity patterns with the motivations behind

A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations b...

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Hauptverfasser: Huang, Wei (VerfasserIn) , Li, Songnian (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2019
In: International journal of geographical information science
Year: 2018, Jahrgang: 33, Heft: 2, Pages: 385-407
ISSN:1365-8824
DOI:10.1080/13658816.2018.1530354
Online-Zugang:Verlag, Volltext: http://dx.doi.org/10.1080/13658816.2018.1530354
Verlag, Volltext: https://doi.org/10.1080/13658816.2018.1530354
Volltext
Verfasserangaben:Wei Huang and Songnian Li

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520 |a A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations behind behaviors. The aim of this study is to model and understand human activity patterns within urban areas using both spatiotemporal and cognitive psychology methods to measure both human behavior patterns and the underlying motivations . We first propose a framework that enables us to analyze the spatiotemporal patterns of urban human activities, infer the associated semantic patterns that represent the motivations driving human mobility choices and behaviors, and measure the similarity between human activities. We then construct a human activity network based on the similarity to depict human activity patterns. The framework is applied to a case study of Toronto, Canada, where geotagged tweets are used as a proxy for human activities to explore activity patterns. The analysis of the human activity network shows that 61% of tweeter users follow similar activity patterns. Our work provides a new tool for better understanding the way individuals interact with urban environments that could be applied to a variety of urban applications. 
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