A population spatialization method based on the integration of feature selection and an improved random forest model
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an improved random forest model for accurate population spatialization, outperforming existing methods. The enhanced model improves urban planning and resource allocation by providing more precise population distribution data.
Area Of Science
- Geographic Information Science
- Spatial Analysis
- Machine Learning Applications
Background
- Accurate population spatial distribution is crucial for urban planning, resource allocation, and emergency response.
- Traditional random forest (RF) models struggle with unbalanced population data, limiting prediction accuracy.
- Existing spatialization methods require improvement to handle complex population distribution characteristics.
Purpose Of The Study
- To develop an improved population spatialization model integrating feature selection and a refined random forest algorithm.
- To enhance the accuracy and reliability of population distribution mapping for urban planning and resource management.
- To address the limitations of standard random forest models in handling imbalanced spatial population data.
Main Methods
- Employed recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) for feature selection.
- Constructed random forest models (MIC-RF, RFECV-RF, MDA-RF) using selected feature subsets.
- Integrated K-means++ clustering and bootstrap sampling with random forest to create an improved model for imbalanced datasets.
- Generated a 500m resolution spatial population distribution dataset for the Southern Sichuan Economic Zone.
Main Results
- Feature selection methods significantly improved model accuracy compared to using all factors, with MDA-RF achieving the lowest MAPE (0.174) and highest R2 (0.913).
- The improved random forest model, utilizing K-means++ clustering and bootstrap sampling on the MDA-selected subset, further increased prediction accuracy by 1.7% over MDA-RF.
- The proposed method demonstrated superior accuracy compared to the WorldPop dataset, with significantly lower Mean Relative Error (MRE) and Root Mean Square Error (RMSE).
Conclusions
- Feature selection, particularly using the MDA method, is effective in optimizing input data for population spatialization models.
- The integration of K-means++ clustering and bootstrap sampling with random forest effectively addresses data imbalance, enhancing prediction accuracy.
- The proposed population spatialization model offers a more accurate and reliable approach for mapping population distribution, benefiting urban planning and emergency management.
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