广州市地表热环境空间格局及其驱动因素研究  

Research on Spatial Pattern and Driving Factors of Surface Thermal Environment in Guangzhou

作　　者：王丽慧 聂子川 赵立华[1,2] WANG Lihui;NIE Zichuan;ZHAO Lihua(State Key Laboratory of Subtropical Building and Urban Science,South China University of Technology,Guangzhou 510640,China;Guangzhou Municipal Key Laboratory of Landscape Architecture,Guangzhou 510640,China)

机构地区：[1]华南理工大学亚热带建筑与城市科学全国重点实验室,广东广州510640 [2]广州市景观建筑重点实验室,广东广州510640

出　　处：《现代信息科技》2025年第7期161-167,共7页Modern Information Technology

基　　金：广东省自然科学基金面上项目(2024A1515011415)。

摘　　要：为探究城市热环境空间异质性及其与驱动因素的响应规律,以2022年广州热浪期地表温度(LST)为研究对象,运用普通最小二乘法(OLS)和地理加权回归模型(GWR),分析空间形态、土地利用和社会发展因素对热环境的影响。结果显示,GWR模型R^(2)为0.6,OLS模型R^(2)仅0.3,GWR拟合驱动因素与热环境关系方面表现更优。建筑密度、不透水表面占比、道路长度与LST正相关;数字高程模型(DEM)平均值、植被覆盖度与LST负相关。建筑高度均值与标准差、DEM标准差、夜间灯光指数和LST的相关性,在不同温度分区有差异。该成果可为优化城市布局、缓解热岛效应提供科学依据。In order to explore the spatial heterogeneity of urban thermal environment and its response to driving factors,the Land Surface Temperature(LST)during the heat wave in Guangzhou in 2022 is taken as the research object,and the Ordinary Least Squares(OLS)and Geographically Weighted Regression(GWR)model are used to analyze the influence of spatial form,land use and social development factors on the thermal environment.The results show that the R^(2) of the GWR model is 0.6,and the R^(2) of the OLS model is only 0.3.The relationship performance between the driving factors of the GWR fitting and the thermal environment is better.Building density,proportion of impervious surface and road length are positively correlated with LST.The average value of DEM and vegetation coverage are negatively correlated with LST.The correlation between building height mean and standard deviation,DEM standard deviation,Nighttime Light Index and LST is different in different temperature zones.The results can provide scientific basis for optimizing the urban layout and alleviating the heat island effect.

关 键 词：城市热环境 空间格局 驱动因素 地理加权回归模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TU984[自动化与计算机技术—计算机科学与技术]