Research on the thermal characteristics and influencing factors of population night-time in Chinese megacities under multi-source big data
It is of great significance to master the dynamic changes and influencing factors of the temporal and spatial thermal dynamics of the urban population at night-time for the prosperi-ty of urban night-time economy and the promotion of urban management.However,the current authoritative census data is still faced with the bottleneck that it is difficult to track the night-time thermal changes of the urban population in real-time,but the continuous exploration of ur-ban multi-source big data in recent years provides a possibility to break through the above bottle-neck.Based on multi-source big data such as the Baidu population heat map,Luojia(LJ1-01)lu-minous remote sensing and urban Point of Interest(POI),and taking six megacities of Beijing,Shanghai,Guangzhou,Shenzhen,Chengdu and Wuhan as case areas,the population heat charac-teristics of these six cities during 20:00-20:30 were calculated by constructing a population heat model at night-time.Furthermore,the factors affecting the night-time heat characteristics of the population in the case area are analyzed using a geographical detector and geographical weight-ed regression method.The results show that:(1)the night-time heat distribution of the popula-tion in megacities is basically the same as the spatial distribution of main roads,which mainly presents three types:"core-edge"gradient decline,"one-core multi-center"contiguous distribu-tion and"group-type"scattered distribution;(2)Compared with the resident population,the night-time heat scale of urban population is smaller,accounting for only about 0.416‰ of the resident population on average,and nearly 47％of the population gathers in the medium heat ar-ea at night-time.(3)The mixing degree of urban land use,commercial activity and road accessi-bility are the main factors affecting the night-time heat of the urban population,but the influence degree varies in different cities and regions.The results of this study have practical significance for the formulation of night-time consumption policies and also provide reference for future ur-ban night-time construction.
population night-time heatBaidu population heat mapPoints of Interest(POI)GeodetectorGeographically weighted regression Model
NETL
NSTL
万方数据
