Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las Vegas

Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las Vegas

Over the past two decades, research on residential segregation and environmental justice has evolved from spatial assimilation models to include class theory and social stratification. This study leverages recent advances in machine learning to examine how environmental, economic, and demographic fa...

Full description

Saved in:
Bibliographic Details
Main Authors: Jingyi Liu, Yuxuan Cai, Xiwei Shen
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Land
Subjects:
residential segregation
deep learning
machine learning
SHAP method
urban inequality
Online Access:https://www.mdpi.com/2073-445X/14/5/957
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2073-445X/14/5/957

Similar Items