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Biblioteca prediction of interregional land use differences in Beijing: a Markov model

prediction of interregional land use differences in Beijing: a Markov model

prediction of interregional land use differences in Beijing: a Markov model

Resource information

Date of publication
Diciembre 2015
Resource Language
ISBN / Resource ID
AGRIS:US201500184045
Pages
4077-4090

This study combines statistical methods and a Markov model to analyze interregional differences in land use in Beijing since 2003 and to predict land use changes for 2015 and 2019. First, the paper proposes a new concept, land use flow, which counts the change in area from the beginning to the end of the period of interest, to analyze changing land use patterns using statistical records from 2003 to 2011. Second, based on land use data between 2003, 2007 and 2011, this paper applied a Markov model to the prediction of Beijing land use in 2015 and 2019. The results show that: (1) the area of arable land decreased significantly across all of Beijing, with the greatest decrease, 6,953 ha, occurring in Tongzhou. The amount of urban land increased significantly, particularly in eastern and southern Beijing, in areas such as Chaoyang, Tongzhou and Daxing. The amount of land use for orchards fluctuated depending on the distance from the city center; (2) Haidian experienced the greatest change in its ratio of land use flow to urban land (100 %), while Tongzhou had the greatest reduction in arable land (−67.6 %); (3) the annual rate of land use change for Beijing as a whole was 0.89 %. Fangshan had the highest rate of annual conversion to grassland (13.2 %), while the Daxing District had the highest rate of change to urban land (5.1 %). Shijingshan experienced the greatest rate of annual change to other uses due to its small base (135.38 %); and (4) the predictions suggest that urban land is increasing and arable land is decreasing in the 14 districts and counties of Beijing. Several conclusions can be drawn from these results. First, the concept of land use flow is useful for analyzing land use change and simplifies previous methods that were based on descriptions of the change orientation. Moreover, an understanding of the sub-flow of land use is improved by the linkage between land use change and the Markov model. Second, the predictions of land use in 2015 and 2019 suggest problems of urban sprawl and diminishing arable land in the patterns of land use that exist in Beijing and that will continue in the future. In terms of sustainability and Beijing’s goal of being a world city, the findings can help local authorities better understand and address a complex urban master plan, and develop improved land use management strategies that can better balance urban expansion and ecological conservation.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Huang, Yingchun
Nian, Peihao
Zhang, Wenxin

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