Interpretable Machine Learning for Population Spatialization and Optimal Grid Scale Selection in Shanghai

Interpretable Machine Learning for Population Spatialization and Optimal Grid Scale Selection in Shanghai

Fine-scale population distribution information is crucial for applications in urban public safety, planning, and management. However, when using machine learning methods for population spatialization, issues such as data overfitting and limited interpretability need to be addressed. This study intro...

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Bibliographic Details
Main Authors: Yuan Cao, Hefeng Wang, Lanxuan Guo, Anbing Zhang, Xiaohu Wu
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Applied Sciences
Subjects:
population spatialization
XGBoost
SHAP
grid scale
feature variable
Online Access:https://www.mdpi.com/2076-3417/15/9/4755
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