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An Efficient Graph-based Method for Long-term Land-use Change Statistics

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  • Yipeng Zhang

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Yunbing Gao

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    College of Information and Electrical Engineering, China Agriculture University, Beijing 100083, China)

  • Bingbo Gao

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Yuchun Pan

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China
    Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China)

  • Mingyang Yan

    (Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agricultrue and Forestry Sciences, Beijing 100097, China
    National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
    College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China)

Abstract

Statistical analysis of land-use change plays an important role in sustainable land management and has received increasing attention from scholars and administrative departments. However, the statistical process involving spatial overlay analysis remains difficult and needs improvement to deal with mass land-use data. In this paper, we introduce a spatio-temporal flow network model to reveal the hidden relational information among spatio-temporal entities. Based on graph theory, the constant condition of saturated multi-commodity flow is derived. A new method based on a network partition technique of spatio-temporal flow network are proposed to optimize the transition statistical process. The effectiveness and efficiency of the proposed method is verified through experiments using land-use data in Hunan from 2009 to 2014. In the comparison among three different land-use change statistical methods, the proposed method exhibits remarkable superiority in efficiency.

Suggested Citation

  • Yipeng Zhang & Yunbing Gao & Bingbo Gao & Yuchun Pan & Mingyang Yan, 2015. "An Efficient Graph-based Method for Long-term Land-use Change Statistics," Sustainability, MDPI, vol. 8(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:8:y:2015:i:1:p:9-:d:61068
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    References listed on IDEAS

    as
    1. Rutherford, Gillian N. & Bebi, Peter & Edwards, Peter J. & Zimmermann, Niklaus E., 2008. "Assessing land-use statistics to model land cover change in a mountainous landscape in the European Alps," Ecological Modelling, Elsevier, vol. 212(3), pages 460-471.
    2. Huiran Han & Chengfeng Yang & Jinping Song, 2015. "Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    3. Richard D. McBride, 1998. "Advances in Solving the Multicommodity-Flow Problem," Interfaces, INFORMS, vol. 28(2), pages 32-41, April.
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