%0 Journal Article
%T 中国客运量的空间统计分析<br>Spatial Statistical Analysis of China Passenger Capacity
%A 曹文彦
%A 乔舰
%J Statistics and Applications
%P 157-168
%@ 2325-226X
%D 2022
%I Hans Publishing
%R 10.12677/SA.2022.111018
%X 基于2012~2020年我国客运量以及相应年末常驻人口数量，本文对我国各省份客运量的空间关联性进行了空间统计分析。通过全域空间自相关分析以及局域空间自相关分析，明确了我国客运量的重心分布情况：我国客运量高值集聚区域(热点)集中分布于华东地区和中南地区，而客运量低值集聚区域(冷点)集中分布于西北地区和东北地区。通过客运量Moran指数的变化，分析了2020年新冠疫情对于客运量变化产生的影响；对两年之间的客运量关系以及客运量与年末常驻人口数量进行了双变量空间自相关分析，得到了相应的结论。<br />
Based on the passenger volume of China from 2012 to 2020 and the number of permanent residents at the end of the corresponding year, this paper conducts a spatial statistical analysis on the spatial correlation of passenger volume of each province in China. Through the global spatial autocorrelation analysis and local spatial autocorrelation analysis, the distribution of the center of gravity of passenger volume in China is clarified: the high value of passenger volume agglomeration area (hot spot) is concentrated in east China and central and southern China, while the low value of passenger volume agglomeration area (cold spot) is concentrated in northwest and northeast China. Through the change of passenger volume Moran index, the impact of COVID-19 on passenger volume change in 2020 is analyzed. The bivariate spatial autocorrelation analysis between passenger volume and resident population at the end of the two years is carried out and corresponding conclusions are obtained.
%K 空间权重矩阵，Moran指数，双变量自相关<br>Spatial Weight Matrix
%K Moran’s I
%K Bivariate Autocorrelation
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=48747