Abstract:

Grid population data can enable integrated analysis of population statistics with other spatial data on resources and the environment. Based on a Random Forest model and using nighttime lights, road network, surface water network, built-up area, slope, and DEM as control variables, the 2010 population data of the Pearl River Delta were distributed into 30 m grids. The estimation results were compared with three other public datasets. The importance of input variables was analyzed based on the results. The result shows that the accuracy of this simulation reached 83.32%, which is better than the WorldPop and the Population Grids of China datasets, and more close to the GPW dataset. Moreover, the 30 m resolution of our result furnishes detailed information of population density of the Pearl River Delta. According to the importance of covariates from the Random Forest model, strength of nighttime lights, distance to water, distance to built-up area, and density of roads are important factors in population distribution modeling in the Pearl River Delta. With the Random Forest model and multi-source data, high resolution population spatialization can be achieved. High spatial resolution grid data can provide important data source for high precision city management and policy making.

Key words: population spatialization, random forest, population distribution, impact factors, the Pearl River Delta