Estimating ambient population density using physical features from GIS and machine learning: a study based on Japanese neighborhood

Abstract

This research aimed to develop an alternative method for estimating ambient population density at the neighborhood scale by utilizing a simple and universally available dataset. Physical environment data from GIS & OSM databases, basic statistics, and population density data from Mobile Spatial Statistics were combined to train tree-based Machine Learning models. The experiment resulted in an XGBoost model using 16 features capable of estimating ambient population density across three classes of outcome with 75.9% accuracy. The trained models were analyzed and visualized using SHAP and Partial Dependence Plot techniques to reveal feature importance and their threshold values. The study concludes that physical features can be effectively used as predictors of ambient population density and highlights areas for further investigation.

Keywords

• neighborhood planning
• machine learning
• correlation analysis
• ambient population
• gis