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Examining the nonlinear and threshold effects of the 5Ds built environment to land values using interpretable machine learning models

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Journal of Geographical Sciences 2024年第12期34卷 2509-2533页

作者： Quang Cuong DOAN Khac Hung VU Thi Kieu Trang TRINH Thi Cam Ngoc BUI Department of Urban Planning and Design The University of Hong KongHong KongChina Department of Land Management VNU University of ScienceHanoiVietnam Vietnam National University HanoiVietnam Faculty of Land Administration Hanoi University of Natural Resources and EnvironmentHanoiVietnam

Previous studies have extensively explored the critical influence of the built environment on land values,but the non-linear relationship has yet to be fully *** study aims to uncover the non-linear relationship between land values and the five built environment dimensions using machine learning algorithms and Shapley Additive ex Planation(SHAP).The results highlight that the Gradient Boost Decision Tree (GBDT) outperforms e Xtreme Gradient Boosting (XGBoost),Ordinary Least Squares (OLS),and Multiscale Geographically Weighted Regression (MGWR) in land value estimation,exhibiting higher R^(2) and lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).The results illustrate that density and destination accessibility are the dominant factors,contributing 32.48%and37.38%to land value variation,*** observed that the top three factors affecting land values are the built-floor area ratio,the number of floors and the number of ***,the results revealed the non-linear relationship between the built environment and land values,suggesting that maintaining built environment features at optimal thresholds may increase land *** interaction effects may lead to bias in determining relationships between land values and the built *** study contributes to the literature by providing non-linear and threshold identification evidence in land value determinants,offering valuable insights for urban planners and real estate managers.

关键词： built environment land values housing price GBDT SHAP non-linear relationships machine learning

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