关键词: 夜光遥感, 人口密度, 核密度, 回归方程, 空间自相关, 高/低聚类分析, 热点分析, 聚类和异常值分析

Abstract:

Based on the demographic data, nighttime light remote sensing images and Landsat8 images of streets and communities in Gulou district, Fuzhou city, Fujian province, combined with the kernel density and regression equation are integrated to draw a 30 m spatial resolution population density map and conduct spatial autocorrelation analysis. Firstly, the population density distribution map of 69 communities are calculated by kernel density method. Based on a quantile-quantile plot between the population density and nighttime light remote sensing of 786 residential community points, we find that the population density has a large error in wufeng street and hongshan town. Secondly, the binary quadratic regression equation is established to correct the population density error in these two regions. This equation expresses the relationship between population density, and the impervious surface image of Landsat 8 using linear unmixing and nighttime light remote sensing. Thirdly, Getis-Ord General G, Getis-Ord Gi*, and Anselin local Moran I are used to obtain the high clustering attributes of population in Gulou district to show the largest business circle area, the largest population density residential area in the city, and the local spatial pattern of population clustering. In this study, the population spatialization technique integrates two spatialization methods: kernel density and regression equation. The population density map with 30 m spatial resolution is generated finally. The mean population density of Gulou district is divided into three types: 11 000 people/km², 25 000 people/km², and 50000 people/km². The population density approximately obeys a normal distribution. When the mean population density of Gulou district is greater than 33 000 people/km², the correlation between the impervious surface gray value and population density is stronger. Otherwise, the correlation between nighttime light remote sensing image and population density is stronger.

Key words: Nighttime light remote sensing, Population density, Kernel density, Regression equation, Spatial autocorrelation, Getis-Ord General G, Getis-Ord Gi*, Anselin local Moran I