成都平原郫都区乡村聚落人居环境质量要素对其人口密度空间数据的影响OA

This study examines how human settlement environment quality shapes the spatial distribution of population density across 50 village-level units in Pidu District on the Chengdu Plain.Grounded in the"Production-Living-Ecological"(PLE)space framework,it builds a comprehensive index system capturing key dimensions of rural habitat quality.Pidu is a pertinent case:the Chengdu Plain is both a critical agricultural base and a rapidly urbanizing region,making it ideal for exploring how production conditions,living amenities,and ecological endowments jointly influence where people live.Using GIS,the authors compute spatial population density as the dependent variable.The analytical pipeline integrates TensorFlow for modeling,Gradient Boosting Decision Trees(GBDT)for prediction,and SHapley Additive exPlanations(SHAP)for interpretation,yielding a powerful,interpretable model of complex non-linear relationships.Data collection combines statistical yearbooks,remote sensing imagery,and field surveys.GBDT is chosen for its ability to handle high-dimensional,non-linear interactions and resist overfitting—traits crucial for mixed environmental and socio-economic features.Model training employs parameter tuning and cross-validation to optimize performance.SHAP then provides a transparent decomposition of predictions into feature-level contributions,delivering both global and local interpretability and improving on traditional importance measures by ensuring consistency and additivity.Model validation shows high predictive accuracy.On the production and ecological fronts,three variables stand out with positive associations to population density:per capita arable land area,annual gross production value,and average annual precipitation.Villages with more farmland per person,stronger economic output,and ample rainfall tend to host denser populations.In the Chengdu Plain's agricultural context,higher precipitation directly supports yields,reinforcing local production capacity and sustaining settlement concentrations.Conversely,several variables display negative relationships with density:per capita disposable income,NDVI,and the coverage of water bodies and forests.The income result likely reflects a mobility transition dynamic:higher household incomes expand choices and can catalyze out-migration toward urban opportunities,reducing rural density in origin villages.Elevated NDVI and extensive forest/water coverage may denote areas prioritized for ecological conservation or constrained by natural features,limiting buildable land and infrastructure expansion.These patterns underscore a planning trade-off:stronger ecological protection can coincide with lower settlement concentration,necessitating careful spatial zoning to balance conservation with habitation needs.Notably,most indicators in the"living"subsystem.Exhibit minimal influence on density at this analytic scale.This suggests that,within Pidu,production capacity and first-order ecological conditions are the dominant drivers of where people cluster,outweighing finer-grained differences in residential comfort.The finding challenges the common assumption that living-environment upgrades alone can strongly reshape rural population patterns;instead,it emphasizes the primacy of economic opportunity and foundational environmental suitability.Policy implications are substantial.Methodologically,the study demonstrates how combining machine learning with explainable AI can illuminate the determinants of rural population distribution in a way that is both accurate and decision-relevant.Practically,the positive ties between production indicators and density point to strategies that secure agricultural productivity,support local industry,and leverage rainfall-driven advantages.Meanwhile,the negative links with ecological indicators caution planners to calibrate development intensity in ecologically sensitive zones,using conservation-aware layouts and targeted service provision to avoid undermining environmental goals.The study's contribution is both substantive and methodological.Substantively,it clarifies the socio-environmental levers of rural population density on the Chengdu Plain in the wake of Rural Revitalization Strategy,showing production and ecological baselines as decisive.Methodologically,it offers a transparent,data-driven workflow that enhances trust and actionability.Future work could generalize across regions,incorporate panel data to trace temporal dynamics,and test policy shocks or infrastructure upgrades to observe causal shifts.Together,these steps would deepen understanding of how habitat quality and social behavior co-evolve in rural China,informing sustainable,balanced development around metropolitan hinterlands like Pidu.