IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i12p2158-d120143.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/12/2158/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/12/2158/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    2. Eunbee Gil & Yongjin Ahn & Youngsang Kwon, 2020. "Tourist Attraction and Points of Interest (POIs) Using Search Engine Data: Case of Seoul," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    3. Patrycja Szarek-Iwaniuk & Agnieszka Dawidowicz & Adam Senetra, 2022. "Methodology for Precision Land Use Mapping towards Sustainable Urbanized Land Development," IJERPH, MDPI, vol. 19(6), pages 1-21, March.
    4. Jing Wu & Jingwen Li & Yue Ma, 2019. "Exploring the Relationship between Potential and Actual of Urban Waterfront Spaces in Wuhan Based on Social Networks," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    5. Patrycja Szarek-Iwaniuk, 2021. "A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    6. Lang, Wei & Long, Ying & Chen, Tingting, 2018. "Rediscovering Chinese cities through the lens of land-use patterns," Land Use Policy, Elsevier, vol. 79(C), pages 362-374.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2158-:d:120143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.