Experimental study of population density using an optimized random forest model

Random forest model is the mainstream research method used to accurately describe the distribution law and impact mechanism of regional population.We took Shijiazhuang as the research area,with comprehensive zoning based on endowments as the modeling unit,conducted stratified sampling on a hectare grid cell,and systematically carried out incremental selection experiments of population density impact factors,optimizing the population density random forest model throughout the process(zonal modeling,stratified sampling,factor selection,weighted output).The results are as follows:(1)Zonal modeling addresses the issue of confusion in population distribution laws caused by a single model.Sampling on a grid cell not only ensures the quality of training data by avoiding the modifiable areal unit problem(MAUP)but also attempts to mitigate the adverse effects of the ecological fallacy.Stratified sampling ensures the stability of population density label values(target variable)in the training sample.(2)Zonal selection experiments on population density impact factors help identify suitable combinations of factors,leading to a significant improvement in the goodness of fit(R^(2))of the zonal models.(3)Weighted combination output of the population density prediction dataset substantially enhances the model's robustness.(4)The population density dataset exhibits multi-scale superposition characteristics.On a large scale,the population density in plains is higher than that in mountainous areas,while on a small scale,urban areas have higher density compared to rural areas.The optimization scheme for the population density random forest model that we propose offers a unified technical framework for uncovering local population distribution law and the impact mechanisms.