Population Spatialization based on Random Forest Model and Multi-source Geospatial big data

Abstract

Population spatialization research is an important approach to achieve fine-grained management of urban space and coordinated development of rural resources and the environment. By converting administrative-level population data into a finer grid scale, it allows for in-depth analysis of the spatial distribution characteristics of population density and geographic heterogeneity within a region. Currently, in China, a population census is conducted every ten years, with the township as the smallest statistical unit. However, due to advancements in computer science and geography, the level of precision in data can no longer meet the requirements of modern geographical research. Population spatialization, based on national population statistics, utilizes techniques such as multi-source data fusion and data mining to decompose large-scale population data into corresponding grid-based data, enabling more accurate spatial representation of national population statistics and facilitating the understanding of population distribution patterns. This study used administrative boundary data for 88 counties in Guizhou Province in 2021, county-level population data from the 2021 China County Statistical Yearbook, and diverse geospatial data from Guizhou in 2017. Nine spatial variables that impact the spatial distribution of the study area's population, such as points of interest and nighttime light indices, were extracted. A random forest method was used to construct a population spatialization model and simulate population distribution.