地理学报

地理学报 ›› 2021, Vol. 76 ›› Issue (10): 2439-2458.doi: 10.11821/dlxb202110008

• 城市与区域发展 • 上一篇    下一篇

基于隐性—韧性—显性的武汉城市资源环境承载力空间特征

吴浩1,2(), 江志猛1,2, 林安琪1,2, 朱文超1,2, 王伟1,2   

  1. 1.华中师范大学城市与环境科学学院,武汉 430079
    2.地理过程分析与模拟湖北省重点实验室,武汉 430079
  • 收稿日期:2021-02-18 修回日期:2021-08-31 出版日期:2021-10-25 发布日期:2021-12-25
  • 作者简介:吴浩(1977-), 男, 湖北黄冈人, 博士, 教授, 研究领域为地理信息科学理论与应用技术。E-mail: haowu@mail.ccnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(42071358);国家自然科学基金项目(41671406);湖北省自然资源科技项目(ZRZY2020KJ01);中央高校基本科研业务费项目(CCNU20TS035)

Analyzing spatial characteristics of urban resource and environment carrying capacity based on Covert-Resilient-Overt:A case study of Wuhan city

WU Hao1,2(), JIANG Zhimeng1,2, LIN Anqi1,2, ZHU Wenchao1,2, WANG Wei1,2   

  1. 1. College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
    2. Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
  • Received:2021-02-18 Revised:2021-08-31 Published:2021-10-25 Online:2021-12-25
  • Supported by:
    National Natural Science Foundation of China(42071358);National Natural Science Foundation of China(41671406);Natural Resource Science Project of Hubei Province(ZRZY2020KJ01);Fundamental Research Funds for the Central Universities(CCNU20TS035)

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摘要：

资源环境承载力是衡量城市国土空间发展状况的晴雨表,深入揭示资源环境承载力系统要素的相互作用机理对城市国土空间格局优化具有重大意义。本文在双评价指南的基础上引入夜光/大气遥感、兴趣点、交通态势、社交媒体等多源时空数据,建立了基于隐性—韧性—显性的城市资源环境承载力空间特征分析理论框架,提出了空间自相关和分形几何相结合的承载力空间特征挖掘方法。以武汉市为例开展研究,结果表明：① 武汉市资源环境承载力呈显著空间异质性,隐性承载力指数高值区分布于城市外围生态环境优越的区域,韧性承载力指数高值区集中于综合风险应对能力较强的中心城区,显性承载力指数高值区位于各项基础功能均较为健全的城市次中心一带;② 武汉市资源环境承载力存在正向空间聚集性,并呈显著半径向心分形特征,隐性承载力指数高值区呈放射状向四周扩散,韧性承载力指数高值区呈向心状聚集在城市核心圈层,显性承载力指数高值区分布于则介于二者之间。本文构建的城市资源环境承载力空间特征分析与挖掘方法体系,通过引入多源时空数据弥补了传统数据现势性的不足,完善了城市资源环境承载力研究的理论和技术范式,可为新时期城市资源环境承载力研究提供一种新思路。

关键词: 城市资源环境承载力, 空间特征, 城市韧性, 空间自相关, 分形几何, 武汉市

Abstract:

The resources and environment carrying capacity (RECC) is a barometer of the development of urban territory space, so exploring the spatial characteristics of RECC is greatly significant for optimizing the territory spatial pattern. In this context, this paper uses new urban geographic information data, such as night-time lights and atmospheric remote sensing, points of interest, historical traffic situation and social media, on the basis of relevant procedures and guidelines. Furthermore, a theoretical framework based on the covert-resilient-overt analysis of the spatial characteristics of urban RECC is proposed, and a method combining spatial autocorrelation and fractal geometry is developed for spatial characteristics mining of RECC. Taking Wuhan as an example to carry out research, the results show that: (1) The distribution of RECC in Wuhan has significant spatial heterogeneity. Areas with higher covert carrying capacity are concentrated in the Huangpi District and Xinzhou District with superior ecological environment in the northern periphery of the city, areas with high resilient carrying capacity are concentrated in the central urban area with strong comprehensive risk response capabilities, and areas with high overt carrying capacity are located in the sub-center of the city with sound basic functions. (2) The RECC in Wuhan has positive spatial aggregation, and it shows significant radius-centripetal fractal and circle characteristics. The areas of high covert carrying capacity spread radially around, and the areas with high resilient carrying capacity are concentrated in the core of the city in a centripetal shape, the areas with high overt carrying capacity are between the areas with high values of covert and resilient carrying capacity. In conclusion, the spatial characteristics analysis and mining method system of urban RECC constructed in this paper make up for the shortcomings of the low dynamics of traditional data by introducing multi-source spatial temporal data, and improves the theoretical and technical paradigm of urban RECC research. The research provides a new way of thinking for the carrying capacity of urban resources and environment.

Key words: urban resource and environmental carrying capacity, spatial feature, urban resilience, spatial autocorrelation, fractal geometry, Wuhan city