Study On Spatiotemporal Change And Driving Force Of Resident Income Of China From 2005 To 2015 Based On Night Light Remote Sensing Data

Resident income is one of the most direct indicators for refelecting living level,and was often used to check the consumption capacity and GDP's quality in a region.Since the reform and opening up,Chinese economy has been developing vigorously,and resident income improved overall.However,there exist obvious spatial heterogeneity because of the factors like nature,history and economy.Obtaining resident income data at finer scale and deeply studying its spatial pattern and driving force is crucial,which are helpful for making reasonable distribution policy,building nice pattern of resident income,and realizing the goal of being rich together.Most recent researches about resident income merely rely on statistical data;only can represent the difference of resident income at administrative districts limited by the scale of statistical data.They can only express the level of resident income at administrative division scale,can hardly explore the spatial heterogeneity inside a city.We build spatial regression models,and combine night light data and statistical data of resident income to generate spatial results of resident income from 2005 to 2015 at 1 km scale.On the basis of this,we explore the spatiotemporal change of resident income from following aspects:overall distribution characters;spatial distribution direction characters;recognition of urban spatial structure;the inequality of resident income.At last,we systematically analyze the driving force of natural,humane factors and station and routine of high speed railway.The main contents and conclusions are folowings:(1)Spatial regression of resident income.Resident income has obvious spatial relevance,so we use spatial regression models to map statistical data to grid.Results accuracy checking show that the errors of regression results are relatively small at province and city scale,about 15%;and after result-correcting,the relative errors are reduced to 10%.Spatialization results can reveal the gradual change of resident income from the core area to remote areas within a city,refelecting the spatial heterogeneity of resident income at a finer scale.(2)Spatiotemporal change of resident income.Using spatialization results of resident income,we find that the percentage of population of western side of Hu line remained 5.8%from 2005 to 2015,however,that of resident income increased from 5.12%to 7.63%during the period,which means that western side of Hu-line experience a rapid growth.As for spatial distribution,from country scale,resident income distributed more gatheringly,and the gravity center of resident income moved to the east at first and then to the west.At geographic partition scale,resident income gather to the partition's center cities.Spatialization results of resident income highlight the regions with strong city character,so we can conveniently and quickly recognize Chinese main cities agglomerations.Besides,we found Yangtze River Delta region has formed three groups:their center city is Hangzhou,Nanjing,and Shanghai.And Hangzhou has the smallest area,Nanjing is bigger,and Shanghai ranks the top.Chinese gene coefficient of resident income wree pretty high from 2005 to 2015,which indicated that difference of resident income are huge in China.Besides,we found that gene coefficient of western provinces generally higher that in middle or eastern provinces,and gene coefficient of western provinces are in a growing trend and that of middle or eastern provinces are in a falling trend.(3)Driving force of resident income.Based on bivariate Moran's I,we explore the driving force of natural and human factors.We found that the ranking of these 6 indactors is:GDP>population>rainfall>highway>railway>height.Meanwhile,we study the driving force of high speed railway.We found that city stations can bring a growth rate of 10%for resident income,and the number is about 7%for county stations,and the driving force delayed obviously.However,the growth rate of resident income for the route is only 2%more or less,which indicates that the driving force of route is smaller than the stations.The innovation points are followings:(1)Building the spatial regression models for resident income at grid scale by combining two kinds of night light data.(2)Exploring urban agglomeration's spatial structure at different levels by nuclear density and local contour lines.