Introduction | Environment Setup | Data Loading and Spatial Integration | Loading Spatial Data: | Exploratory Mapping: | Data Integration: | Thematic Mapping of Disease Incidence | Quantile Mapping: | Multiple Indicators: | Interpretation | Machine Learning Modelling | Model Training: | Prediction: | Model Comparison: | Model Evaluation | Performance Metrics: | Cross-Validation: | Model Prediction Visualisation | Prediction Trends Across Observations | Reshaping Predictions: | Trend Visualisation: | Observed vs Predicted Comparison | Observed vs Predicted with Correlation | Cross-Validation of Predictive Models | Step 1: Setup | Step 2: Random Forest Cross-Validation | Step 3: XGBoost Cross-Validation | Step 4: Support Vector Regression (SVR) Cross-Validation | Spatial Maps of Model Predictions | Step 1: Join Shapefiles and Incidence Data | Step 2: Add Predicted Values | Step 3: Visualize Individual Model Maps | Step 4: Compare Models Side by Side | Residual Analysis | Step 1: Compute Residuals | Step 2: Compare Residual Distributions | Step 3: Map Residuals Spatially | Spatial Visualization of Model Residuals | Step 1: Add residuals to the spatial data | Step 2: Set static plotting mode | Step 3: Create individual residual maps | Step 4: Combine residual maps in a grid | Barplot and Spatial maps for RMSE and MAE | Step 1: Compute evaluation metrics | Step 2: Barplots of RMSE, MAE, and R² | Step 3: Annotate residual maps with performance metrics | Spatial Autocorrelation Analysis | Global Spatial Autocorrelation: