Evaluation of the matching degree of the public medical service level based on DMSP/OLS nighttime light data

The accurate allocation of public service resources within urban areas is an important task in the development of cities. Therefore, it is of great significance to study the matching between urban permanent population and public service resources. However, there is little analysis on the estimation of small-scale population and the level of public services. This paper takes Guangzhou as the research area, and the public medical resources as the research object. Based on the DMSP-OLS (Defense Meteorological Satellite Program- Operational Line scan System)night light data, this paper combines land use data, statistical data and spatial regression model of population estimation to deal with grid processing of permanent residents in Guangzhou. Then the accuracy test and the result correction were carried out to get the grid result of the permanent population in the whole city. And we use the improved Huff model, traffic network data and public medical service capabilities to calculate the allocation of public medical resources. Using it and the number of permanent population of grid cell as the calculated variable, we calculate the allocation of public medical resources and the matching degree of the permanent population in the grid unit, that is the matching degree of the public medical service level, and get more detailed information inside the regions. The results show that, regression analysis of permanent population by grid unit, and calculation of the matching degree of the level of public medical services, can make a more detailed understanding of the internal situation in the region, can make up for the low resolution of traditional demographic data and the high standard of public service differentiation. And a more detailed display of the distribution of the population distribution "one main area, multiple core" and the level matching of the circle distribution of matching level of public medical services.