Research On Fine-scale Population Spatialization Method And Application Considering Urban-rural Differences

Fine-scale population data plays an important role in urban planning,disaster management,estimation of the disease burden,epidemiological modelling,resource allocation,poverty mapping,and environmental impact assessment.It is an important data foundation for the construction of new-type urbanization and smart city,and it is of great significance to the fine management of cities.Because of the low temporalspatial resolution,and the difficulty in multi-source data fusion,it is difficult for demographic data based on administrative cells to describe fine-grained information about population distribution and to meet current application requirements.Transferring the demographic data from administrative units to regular grids through effective population spatialization methods could improve the spatial resolution of population data,which helps reflect the details of the differences in population distribution.With the development of the technology for big data processing and storing,the geospatial big data recording people’s location could reflect the information about population distribution,which provides new data sources and ideas for the spatialization of demographic data.Because of the problems existing in current research,such as insufficient consideration of the urban-rural differences in population distribution,low utilization of big data,the low spatial resolution of population simulation,and unclear application approaches of girded population data,based on freely available remote sensing data with high resolution and fine-grained POI(Point of Interest)data,this paper constructs a fine-scale population spatialization method with high resolution,high accuracy,high usability,and sustainable renewal based on the consideration of urban-rural differences.Taking eastern Hubei as an example,this paper performs an empirical study and obtains the gridded population data with the resolution of 30 m in eastern Hubei in 2020.The accuracy of the result has been verified.This paper constructs the evaluation method for the spatial distribution of public service facilities based on fine-scale population spatialization,which has been used in the evaluation of the spatial distribution of basic educational facilities in Wuhan in 2020.The main conclusions are as follows:(1)The gridded population data with the resolution of 30 m in eastern Hubei in2020 could describe the differences in population distribution within an administrative unit and a residential area in a detailed way.The result shows that the urban population is higher than the rural population,and the grids with a high population are mainly concentrated in central Wuhan.(2)There are obviously individual differences in the allocation level of basic education facilities in Wuhan in 2020,and most facilities are at the medium-high level.The allocation level of basic education facilities in the overall space is unbalanced,showing a spatial pattern of high in the center and low in the periphery.(3)The fine-scale population spatialization method considering urban-rural differences has superiority in both spatial resolution and precision.The consideration of urban-rural differences improves the ability to reflect the differences in population distribution in detail.The high availability of the data sources supports the method in high usability and sustainable renewal.(4)Fine-grained POI data provides favorable conditions for depicting the urbanrural differences in population distribution.POI data has great potential for the largescale spatialization of demographic data.(5)Gridded population data could be applied to the construction of individuallevel indicators.The evaluation method for the spatial distribution of public service facilities based on fine-scale population data could not only reflect the overall spatial pattern of public service facilities from the macroscale but also describe the individual differences from the microscale.