| 摘要: |
| 建筑是城市空间结构的主要构成要素，其在空间上的立体拓展，不但改变着城市下垫面，也影响着城市空间的能量平衡和空气流动，进而对城市热场的分布产生影响。文章以济南市中心城区为研究区域，基于Landsat 8遥感影像数据，首先使用大气校正法反演城市地表温度，以此表示城市热场，并基于建筑、绿地和水体构建多维城市空间结构指标体系；然后运用相关分析和双变量空间自相关方法，基于不同空间尺度，研究城市空间结构指标对城市热场的影响。结果表明：（1）城市热场与大多数空间结构指标的相关性较为显著，不同研究尺度下，两者的相关性及空间相关性略有差异，尺度越小，相关性越显著。（2）城市空间结构指标中建筑密度、建筑基底面积总和、容积率、占空比和建筑体积总和等五项指标与地表温度的正相关性最为显著，空间正相关性也最显著，表明城市地表温度既受到本区域该五项指标的重要影响，也受到周边区域该五项指标的重要影响。（3）地表温度与DEM标准差、建筑平均绝对高度、DEM平均值、建筑绝对高度标准差和户外活动面积比等五项指标的负相关性最为显著，研究区高程是影响城市地表温度的重要指标。建筑的立体分布及在平面上的延伸直接影响城市热场，优化城市空间结构可以在一定程度上缓解城市热岛效应。 |
| 关键词: 城市空间结构 城市热场 相关分析 双变量空间自相关 |
| DOI：10.7515/JEE212022 |
| CSTR：32259.14.JEE212022 |
| 分类号: |
| 基金项目:国家自然科学基金项目（51608309）；教育部人文社会科学研究规划基金项目（12YJA790019） |
| 英文基金项目:National Natural Science Foundation of China (51608309); Humanities and Social Science Foundation of Ministry of Education of the People's Republic of China (12YJA790019) |
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| The influence of urban spatial structure on urban thermal field—a case study of Jinan in summer |
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WANG Junning, SHAN Baoyan, LIU Yangyang, ZHANG Zhixuan
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School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
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| Abstract: |
| Background, aim, and scope Architecture is the main component of urban spatial structure. Its three-dimensional expansion in space not only changes the underlying surface of the city, but also affects the energy balance and air flow of the urban space, thereby affecting the urban thermal field effect. This study mainly discusses the correlations between the surface temperature of Jinan and the urban spatial structure indicators. The central urban area of Jinan in summer was considered as the research area, and buildings were considered as the primary research objects to construct the urban spatial structure indicator system. Materials and methods This study used the atmospheric correction method to inverse Landsat 8 remote sensing image data, and the retrieved surface temperature was used to represent the urban thermal field. A multi-dimensional urban spatial structure index system was constructed based on buildings, green spaces, and water bodies. Following this, correlation analysis and bivariate spatial autocorrelation methods were used to study the impact of spatial structure indicators on urban thermal fields at different spatial scales. Results The results showed that: (1) The correlations between the urban thermal field and many of the spatial structure indicators were significant. At different research scales, there were certain differences in their correlations and spatial correlations. (2) The five urban spatial structure indicators, including building density, the summation of the base area of the foundation, plot ratio, duty cycle, and the summation of the building volume, had the most significant positive correlations and spatial positive correlations with surface temperature. (3) Surface temperature had the most significant negative correlations with five indicators: the standard deviation and the average of the digital elevation model (DEM) value, the building absolute height value and its standard deviation, and the outdoor activity area ratio. Discussion These results suggest that the urban surface temperature was not only affected by the indicators in a region, but also by the five indicators in the surrounding area. The elevation of the study area was an important indicator that affected urban surface temperature. The correlation between the urban spatial structure and urban thermal field strongly depended on the scale of the selected spatial research unit. Their correlation was more evident at smaller research scales. The combined effect of the extension of the building on the plane and the extension of the three-dimensional space changed the urban thermal field. Conclusions Urban surface temperature was most affected by the five indicators of building density: the summation of the base area of the foundation, plot ratio, duty cycle, and the summation of the building volume in the area and the surrounding areas. The surface temperature increased with an increase in the values of these five indicators. The influence of elevation factors on the surface temperature was also extremely significant. The surface temperature was lower in areas with higher terrain and larger topography. This was more evident in smaller-scale spatial research units. Recommendations and perspectives The three-dimensional distribution characteristics of buildings and their extension on the plane would directly affect the urban thermal field, and optimizing the urban spatial structure can alleviate the urban thermal field effect to a certain extent. |
| Key words: urban spatial structure urban thermal field correlation analysis bivariate spatial autocorrelation |
