卢宾宾, 田小溪, 秦思娴, 史祎琳, 李建松. 基于地理加权建模技术的武汉市土地资源承载力评价研究[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220778
引用本文: 卢宾宾, 田小溪, 秦思娴, 史祎琳, 李建松. 基于地理加权建模技术的武汉市土地资源承载力评价研究[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220778
LU Binbin, TIAN Xiaoxi, QIN Sixian, SHI Yilin, LI Jiansong. Urban Land Resources Carrying Capacity Evaluation of Wuhan with Geographically Weighted Techniques[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220778
Citation: LU Binbin, TIAN Xiaoxi, QIN Sixian, SHI Yilin, LI Jiansong. Urban Land Resources Carrying Capacity Evaluation of Wuhan with Geographically Weighted Techniques[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220778

基于地理加权建模技术的武汉市土地资源承载力评价研究

Urban Land Resources Carrying Capacity Evaluation of Wuhan with Geographically Weighted Techniques

  • 摘要: 在我国全面推进生态文明建设的背景下,资源环境承载力监测评价研究具有重要的理论和实践意义。而随着我国城镇化进程的高速推进,城市土地资源逐步成为城市发展的核心资源要素,逐步吸引了众多学者的关注。但在以往研究中,评价分析方法多以宏观的全局统计为主,难以满足城市尺度下进行精细尺度分析评价的需求,也无法体现评价指标及其关系所呈现的空间异质性或非平稳性特征。本文以武汉市土地资源承载力评价为例,采用地理加权汇总统计、地理加权主成分分析和地理加权回归分析等基础地理加权建模技术实现了集成评价、影响要素和成因分析,更加合理地阐释了武汉市建成区土地承载压力分布及其影响要素。结果证明,地理加权建模技术框架能够实现城市土地资源承载力精细化评价,从全新的视角实现单项指标分析与综合评价。而随着地理加权建模技术的拓展与演化,分析层次更加丰富,为更多场景与领域的应用提供可行的技术方案。

     

    Abstract: Objective: With the rapid urbanization occurring in China, urban land resource has become a key part in urban development, and attracted more and more attentions. However, global methods were traditionally adopted for its carrying capacity evaluation and analysis, which largely ignored the spatial heterogeneities or non-stationarities in the data relationships. Method: In this study, we proposed a technical framework to evaluate and analyze the urban land carrying capacity with geographically weighted (GW) techniques from local perspective. We exemplified this framework with a case study in Wuhan. In details, we adopted GW summary statistics, GW principal component analysis and GW regression to conduct the evaluation and analysis. . Results: Results show that: the comprehensive pressure of land carrying capacity shows a decaying trend from the city center to the outside areas, to which population density and floor area ratio contribute the most. Moreover, different factors present spatially varying influences on the comprehensive pressure of land carrying capacity. Conclusion: The proposed framework with GW techniques works well on evaluating and analyzing urban land carrying capacity from local perspective, and its evolution is still ongoing with more local techniques involved, which makes it be applicable in more cases and scenarios .

     

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