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...
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MDPI AG
2025-04-01
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| author | Jingyi Liu Yuxuan Cai Xiwei Shen |
| author_facet | Jingyi Liu Yuxuan Cai Xiwei Shen |
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| description | 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 factors contribute to ethnic segregation, using Las Vegas as a case study with broader urban relevance. By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. To improve model transparency, the SHAP (SHapley Additive exPlanations) method was employed, identifying key variables influencing segregation outcomes. This interpretability framework helps clarify variable importance and interaction effects. The findings reveal that housing prices and poor environmental quality disproportionately affect minority populations, with distinct patterns across different ethnic groups, which may reinforce these groups’ spatial and economic marginalization. These effects contribute to persistent urban inequalities that manifest themselves in racial segregation and unequal environmental burdens. The methodology of this study is generalizable, offering a reproducible framework for future segregation studies in other cities and informing equitable urban planning and environmental policy. |
| format | Article |
| id | doaj-art-b95139aa48ad4ccf8d373976b0fd2a98 |
| institution | OA Journals |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-04-01 |
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| spelling | doaj-art-b95139aa48ad4ccf8d373976b0fd2a982025-08-20T02:33:54ZengMDPI AGLand2073-445X2025-04-0114595710.3390/land14050957Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las VegasJingyi Liu0Yuxuan Cai1Xiwei Shen2Faculty of Arts, Humanities and Arts, School of Design, University of Leeds, Leeds LS2 9JT, UKDivision of the Social Sciences, University of Chicago, Chicago, IL 60637, USASchool of Architecture, University of Nevada, Las Vegas, VA 89154, USAOver 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 factors contribute to ethnic segregation, using Las Vegas as a case study with broader urban relevance. By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. To improve model transparency, the SHAP (SHapley Additive exPlanations) method was employed, identifying key variables influencing segregation outcomes. This interpretability framework helps clarify variable importance and interaction effects. The findings reveal that housing prices and poor environmental quality disproportionately affect minority populations, with distinct patterns across different ethnic groups, which may reinforce these groups’ spatial and economic marginalization. These effects contribute to persistent urban inequalities that manifest themselves in racial segregation and unequal environmental burdens. The methodology of this study is generalizable, offering a reproducible framework for future segregation studies in other cities and informing equitable urban planning and environmental policy.https://www.mdpi.com/2073-445X/14/5/957residential segregationdeep learningmachine learningSHAP methodurban inequality |
| spellingShingle | Jingyi Liu Yuxuan Cai Xiwei Shen 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 Land residential segregation deep learning machine learning SHAP method urban inequality |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | residential segregation deep learning machine learning SHAP method urban inequality |
| url | https://www.mdpi.com/2073-445X/14/5/957 |
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