Abstract
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.
Highlights
IntroductionWith the development of China’s urbanization process from 1949 to 2015, the proportion of the urban population in China increased from approximately 10% to 57.35% [2]
Population data are one of the most direct indicators of human activity [1]
Population density population density of different types of residential houses should be estimated by least squares of different types of residential houses should be estimated by least squares regression through the regression through classification that is based on the assumption residential houses the classification that is the based on the assumption that residential houses ofthat the same type have theof same same type have the same population density population density [62]
Summary
With the development of China’s urbanization process from 1949 to 2015, the proportion of the urban population in China increased from approximately 10% to 57.35% [2]. The spatial distribution of the population influences the urbanization process and living environment [3,4], and the development plan of the regional public education system, medical facilities, and other services, which are related to people’s vital interests [5,6,7]. The spatial distribution of the population is affected by many factors, such as geographic location, land cover, convenience of road networks, water areas, and economic development [8,9]. Traditional research methods mainly fit spatial population distributions by studying the coupling relationship between regional population density and its influence factors. Liao Shunbao et al [10]
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