| 夜光遥感支持下的城市人口核密度空间化及自相关分析 |
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| 引用本文： | 孙小芳.夜光遥感支持下的城市人口核密度空间化及自相关分析[J].地球信息科学,2020,22(11):2256-2266. |
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| 作者姓名： | 孙小芳 |
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| 作者单位： | 闽江学院海洋学院,福州 350108 |
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| 基金项目： | 福建省自然科学基金项目(2019J01769);闽江学院资金项目(MYK18017) |
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| 摘 要： | 基于福建省福州市鼓楼区街道社区人口统计数据、夜光遥感影像、Landsat8影像,融合核密度与回归方程,绘制30 m栅格空间分辨率的人口密度图并进行空间自相关分析。方法:① 采用核密度方法对69个社区人口计算生成人口密度分布图。建立786个居民小区点的人口密度与夜光遥感的常规QQ分位图,检测出人口密度存在较大的误差区域:五凤街道和洪山镇。② 建立人口密度与夜光遥感、Landsat8线性分解的不可渗透表面影像之间的二元二次回归方程,修正两个区域的人口密度误差。③ 采用高/低聚类分析、热点分析、聚类和异常值分析,得到鼓楼区人口高聚类属性,显示了鼓楼区最大商业圈区域与人口密度最大居民点区域,展示了人口聚类的空间局部差异性格局。结果:① 研究中所采用的人口空间化技术融合了2种空间化方法:核密度与回归方程。生成30 m栅格空间分辨率的人口密度图。② 鼓楼区人口密度均值分为3种类型:11 000、25 000和50 000人/ km2,人口密度分布近似正态分布。③ 当鼓楼区人口密度均值大于33 000人/ km2,不可渗透表面灰度值与人口密度相关性更强。反之,夜光亮度值与人口密度相关性更强。
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| 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 |
| 收稿时间： | 2020-06-06 |
Spatialization and Autocorrelation Analysis of Urban Population Kernel Density Supported by Nighttime Light Remote Sensing |
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| SUN Xiaofang.Spatialization and Autocorrelation Analysis of Urban Population Kernel Density Supported by Nighttime Light Remote Sensing[J].Geo-information Science,2020,22(11):2256-2266. |
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| Authors: | SUN Xiaofang |
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| Institution: | Department of Ocean,Minjiang College,Fuzhou 350108,China |
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| 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. |
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| Keywords: | Nighttime light remote sensing Population density Kernel density Regression equation Spatial autocorrelation Getis-Ord General G Getis-Ord Gi* Anselin local Moran I |
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