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Dynamic Land-Use Map Based on Twitter Data

Author

Listed:
  • Yuyun

    (Graduate School of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
    School of Management Informatics and Computer (STMIK Handayani Makassar), Makassar 90231, Indonesia)

  • Fritz Akhmad Nuzir

    (Department of Architecture, Faculty of Engineering, Bandar Lampung University, Bandar Lampung 35142, Indonesia)

  • Bart Julien Dewancker

    (Department of Architecture, Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

Abstract

Location-based social media allows people to communicate and share information on a popular landmark. With millions of data records generated, it provides new knowledge about a city. The identification of land use intends to uncover accurate positions for future urban development planning. The purpose of this research is to investigate the use of social networking check-in data as a source of information to characterize dynamic urban land use. The data from this study were obtained from the social media application i.e., Twitter. Three kinds of data that are prioritized in this research are check-ins (specific location), timestamps, and a user’s status text or post activities. In this study, we propose a grid-based aggregation method to divide the urban area. Two different approaches are compared—rank and clustering methods to group the place’s activities. Then we utilize time distribution frequency to attain the land-use function. In this case, Makassar City, Indonesia, has been selected as the case study. An analysis shows that the check-in activity and the method we proposed can be used to group the actual land-use types.

Suggested Citation

  • Yuyun & Fritz Akhmad Nuzir & Bart Julien Dewancker, 2017. "Dynamic Land-Use Map Based on Twitter Data," Sustainability, MDPI, vol. 9(12), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2158-:d:120143
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    References listed on IDEAS

    as
    1. Yandong Wang & Teng Wang & Ming-Hsiang Tsou & Hao Li & Wei Jiang & Fengqin Guo, 2016. "Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
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    Citations

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    Cited by:

    1. Xinxin Zhou & Yuan Ding & Changbin Wu & Jing Huang & Chendi Hu, 2019. "Measuring the Spatial Allocation Rationality of Service Facilities of Residential Areas Based on Internet Map and Location-Based Service Data," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    2. Fan Yang & Fan Ding & Xu Qu & Bin Ran, 2019. "Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data," Sustainability, MDPI, vol. 11(11), pages 1-14, June.

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