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Exploring the heterogeneity of human urban movements using geo-tagged tweets
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences. Tokyo Institute of Techology; Shenzhen University.ORCID iD: 0000-0001-9328-9584
Tokyo Institute of Techology.
Tokyo Institute of Techology.
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.ORCID iD: 0000-0002-2337-2486
2020 (English)In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, p. 1-22Article in journal (Refereed) Epub ahead of print
Abstract [en]

The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas?London and Tokyo - to explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a two-level agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types?metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space.

Place, publisher, year, edition, pages
Taylor & Francis, 2020. p. 1-22
Keywords [en]
Scaling structure, human movement, city hotspot, natural street, agent-based modeling
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:hig:diva-31618DOI: 10.1080/13658816.2020.1718153ISI: 000509026500001Scopus ID: 2-s2.0-85078403443OAI: oai:DiVA.org:hig-31618DiVA, id: diva2:1390884
Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2020-02-07Bibliographically approved

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Ma, DingJiang, Bin

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CiteExportLink to record
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Citation style
  • apa
  • harvard-cite-them-right
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • sv-SE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • de-DE
  • Other locale
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Output format
  • html
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  • asciidoc
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