Jupyter Notebook for threshold detection and ecological zoning using the Priority Index framework

This repository contains the comprehensive Python source code and analytical framework developed for the study "A nonlinear threshold-based study on ecological zoning for water conservation in Guangdong Province, China". It provides a reproducible pipeline that integrates spatial analysis with advanced machine learning to delineate environmental management zones and interpret the non-linear drivers of water conversation.Methodological Workflow: The codebase is structured around two independent but complementary analytical phases:Spatial Zoning and Priority Index (PI): Processes multi-source environmental and socio-economic variables (e.g., Temperature, Precipitation, DEM, GDP, Population) to compute a spatially-weighted Priority Index. It couples these metrics with Gi* spatial statistics to classify regions into specific ecological management types.Machine Learning and SHAP Interpretation: Utilizes an Optuna-optimized XGBoost model to evaluate the complex, non-linear impacts of the driving factors. It includes extensive SHapley Additive exPlanations (SHAP) analyses to map spatial SHAP distributions, extract zero-SHAP thresholds, and visualize feature interactions.Repository Structure:config.py: Global configurations, dynamic file mappings, and spatial weights.spatial_utils.py: Core geospatial algorithms for raster alignment and PI computation.shap_utils.py: Utilities for SHAP calculation, polynomial fitting for thresholds, and spatial visualization.main_zoning.py: Execution script for the spatial zoning workflow.main_xgboost.py: Execution script for model training and SHAP interpretation.Usage Requirements: Researchers looking to reproduce or adapt this framework should place their aligned raster datasets (.tif), spatial grids (.shp), and tabular data (.csv) into the designated local ./data/inputs/ directory. For detailed setup instructions and dependencies, please refer to the included README.md file.