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张考,黄春华,王志远,等. 基于优化地理模拟的长沙市城镇开发边界划定[J]. 科学技术与工程, 2021, 21(24): 10461-10471.
Zhang Kao,Huang Chunhua,Wang Zhiyuan,et al.Delimitation of Urban Development Boundary in Changsha City Based on Optimal Geographical Simulation[J].Science Technology and Engineering,2021,21(24):10461-10471.
基于优化地理模拟的长沙市城镇开发边界划定
Delimitation of Urban Development Boundary in Changsha City Based on Optimal Geographical Simulation
投稿时间:2021-03-07  修订日期:2021-06-07
DOI:
中文关键词:  城镇开发边界  地理探测器  MCCA模型  用地变化  驱动机制  情景模拟  用地适宜性评价  长沙市
英文关键词:Urban development boundary  geographic detector  MCCA model  land use change  driving mechanism  scenario simulation  land use suitability evaluation  Changsha City
基金项目:国家自然科学基金面上项目(51876087);国家自然科学基金重点项目(U1867221);湖南省教育厅课题(18C0418);湖南省教育厅科学研究项目(19B480)
              
作者单位
张考 南华大学建筑学院
黄春华 南华大学建筑学院
王志远 南华大学建筑学院
伍随意 南华大学土木工程学院
丁志鹏 南华大学建筑学院
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中文摘要:
      城镇开发边界划定方法的合理性、科学性研究对未来国家国土空间优化发展具有重要意义。然而目前国内城镇开发边界划定方法的研究缺乏对建设用地驱动因子的模拟和探讨。本研究通过1990-2018年四期土地利用数据,利用土地利用转移矩阵、地理探测器,深入探讨长沙市建设用地变化的成因,并从建设用地驱动机制视角下优化MCCA模型,预测多情景下的长沙市2030年的土地利用结构变化,最终结合城镇用地适宜性评价划定多情景下的城镇开发边界。结果表明:1)优化后的地理模拟比未优化的模拟结果相比,KAPPA系数提高了1.44%,总体精度提高了3.00%,相对熵降低了1.11%;2)长沙市刚性城镇开发边界面积为7028.20 km2,占全域土地总量的59.31%,主要分布在长沙市中心城区;3)常规情景和一体化情景下长沙市2030年弹性城镇开发边界面积分别为1021.23 km2和1158.60 km2,且其边界差异主要分布在长沙县。基于建设用地驱动机制优化后的模拟将有助于真实的预测未来土地利用变化,为城镇开发边界的科学划定与土地决策提供技术支撑。
英文摘要:
      The rationality and scientific delimitations of urban development boundaries are of great significance to the optimization of national land space. However, current methods for delineating urban development boundary lacks discussion of the driving factors of construction land simulation. In this study, land-use transfer matrix and geographic detector were used to explore the causes of changes in construction land of Changsha based on the land use datasets from 1990 to 2018; and from the perspective of selection of driving forces, the MCCA model was optimized; and then the urban development boundary of Changsha City in 2030 for multiple scenarios was delineated based on the evaluation of urban land suitability. The results show that: 1) Compared with the non-optimized simulation results, the Kappa coefficient of optimized simulation increased by 1.44%, overall accuracy increased by 3.00%, and relative entropy decreased by 1.11%; 2) Changsha’s rigid urban development boundary area is 7028.20 km2, accounting for 59.31% of the total land area, and mainly distributed in the downtown area of Changsha; 3) Under the conventional and integrated scenarios, the flexible urban development boundary areas of Changsha in 2030 are 1021.23 km2 and 1158.60 km2, respectively. The differences among different simulations are mainly distributed in Changsha County. The optimized simulation methods based on the driving mechanism of construction land will benefit to predict future land use and provide technical support for the scientific delineation of urban development boundaries and land decision-making.
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