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. 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. 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. Therefore, this study proposes a new concept of 3D space-filling degree (3DSFD), which is applicable to evaluate the city’s 3D spatial structure. However, a few relevant studies focus on the differences between employment population and night population distribution in relation to urban 3D 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. The three-dimensional (3D) spatial structure within cities can reveal more information about land development than the two-dimensional spatial structure.
0 kommentar(er)
