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The Land Gini Coefficient and Its Application for Land Use Structure Analysis in China

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  • Xinqi Zheng
  • Tian Xia
  • Xin Yang
  • Tao Yuan
  • Yecui Hu

Abstract

We introduce the Gini coefficient to assess the rationality of land use structure. The rapid transformation of land use in China provides a typical case for land use structure analysis. In this study, a land Gini coefficient (LGC) analysis tool was developed. The land use structure rationality was analyzed and evaluated based on statistical data for China between 1996 and 2008. The results show: (1)The LGC of three major land use types–farmland, built-up land and unused land–was smaller when the four economic districts were considered as assessment units instead of the provinces. Therefore, the LGC is spatially dependent; if the calculation unit expands, then the LGC decreases, and this relationship does not change with time. Additionally, land use activities in different provinces of a single district differed greatly. (2) At the national level, the LGC of the three main land use types indicated that during the 13 years analyzed, the farmland and unused land were evenly distributed across China. However, the built-up land distribution was relatively or absolutely unequal and highlights the rapid urbanization in China. (3) Trends in the distribution of the three major land use types are very different. At the national level, when using a district as the calculation unit, the LGC of the three main land use types increased, and their distribution became increasingly concentrated. However, when a province was used as the calculation unit, the LGC of the farmland increased, while the LGC of the built-up and unused land decreased. These findings indicate that the distribution of the farmland became increasingly concentrated, while the built-up land and unused land became increasingly uniform. (4) The LGC analysis method of land use structure based on geographic information systems (GIS) is flexible and convenient.

Suggested Citation

  • Xinqi Zheng & Tian Xia & Xin Yang & Tao Yuan & Yecui Hu, 2013. "The Land Gini Coefficient and Its Application for Land Use Structure Analysis in China," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
  • Handle: RePEc:plo:pone00:0076165
    DOI: 10.1371/journal.pone.0076165
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    References listed on IDEAS

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    3. Peter Narh & Cosmas Kombat Lambini & Matthew Sabbi & Van Dien Pham & Trung Thanh Nguyen, 2016. "Land Sector Reforms in Ghana, Kenya and Vietnam: A Comparative Analysis of Their Effectiveness," Land, MDPI, vol. 5(2), pages 1-17, March.
    4. Ferrara, Carlotta & Carlucci, Margherita & Grigoriadis, Efstathios & Corona, Piermaria & Salvati, Luca, 2017. "A comprehensive insight into the geography of forest cover in Italy: Exploring the importance of socioeconomic local contexts," Forest Policy and Economics, Elsevier, vol. 75(C), pages 12-22.
    5. Yuanyuan Yang & Shuwen Zhang & Dongyan Wang & Jiuchun Yang & Xiaoshi Xing, 2014. "Spatiotemporal Changes of Farming-Pastoral Ecotone in Northern China, 1954–2005: A Case Study in Zhenlai County, Jilin Province," Sustainability, MDPI, vol. 7(1), pages 1-22, December.
    6. Jain, Charu & Saxena, Disha & Sen, Somnath & Sanan, Deepak, 2023. "Women’s land ownership in India: Evidence from digital land records," Land Use Policy, Elsevier, vol. 133(C).
    7. Jiale Liang & Sipei Pan & Wanxu Chen & Jiangfeng Li & Ting Zhou, 2021. "Cultivated Land Fragmentation and Its Influencing Factors Detection: A Case Study in Huaihe River Basin, China," IJERPH, MDPI, vol. 19(1), pages 1-25, December.
    8. Luca Salvati & Ilaria Tombolini & Roberta Gemmiti & Margherita Carlucci & Sofia Bajocco & Luigi Perini & Agostino Ferrara & Andrea Colantoni, 2017. "Complexity in action: Untangling latent relationships between land quality, economic structures and socio-spatial patterns in Italy," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.

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