Relationship between Urban Three-Dimensional Spatial Structure and Population Distribution: A Case Study of Kunming's Main Urban District, China

作     者：Wang, Yang Yue, Xiaoli Li, Cansong Wang, Min Zhang, Hong'ou Su, Yongxian 

作者机构：Yunnan Normal Univ Fac Geog Kunming 650500 Yunnan Peoples R China Guangdong Acad Sci Guangzhou Inst Geog Guangzhou 510070 Peoples R China Guangdong Univ Technol Sch Architecture & Urban Planning Guangzhou 510090 Peoples R China 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2022年第14卷第15期

页      面：3757-3757页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Natural Science Foundation of China GDAS Special Project of Science and Technology Development [2020GDASYL20200104001, 2020GDASYL-20200102002] Key Program of the National Natural Science Foundation of China Special Construction Project of Guangdong-Hong Kong-Macao Greater Bay Area Strategic Research Institute [2021GDASYL-20210401001] 

主　　题：three-dimensional spatial structure three-dimensional land use 3D space filling degree population distribution spatial regression model Kunming 

摘      要：The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure. Studying the relationship between the urban 3D spatial structure and the population distribution is a crucial aspect of the relationship between people and land within cities. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D spatial structure. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city s 3D spatial structure. We took 439 blocks in Kunming s Main Urban District as a sample and analyzed the 3D spatial structure based on geographic information data at the scale of a single building. The characteristics and differences of the daytime and night population distribution in Kunming s Main Urban District were identified using cell phone signaling big data. Accordingly, a cross-sectional dataset of the relationship between the city s 3D spatial structure and the population distribution was constructed, with the 3D space-filling degree of the block as the dependent variable, two indicators of population distribution (daytime and night population density) as the explanatory variables, and seven indicators of distance from the city center, and building, road, and functional place densities, proportion of undevelopable land area, housing prices, and land use type as the control variables. We used spatial regression models to explore the significance, strength, and direction of the relationship between urban 3D spatial structure and population distribution. We found that the spatial error model (SEM) was the most effective. The results show that only night population distribution is significantly and positively related to 3DSFD. Every 1% increase in night population density in a block will increase the value of 3DSFD by 2.8307%. The night population distribu