spatial-epi-101

An Introduction to Spatial Epidemiology using R

3D Visualization of Nördlinger Ries

Welcome to the Course

This course provides a comprehensive introduction to the principles and practices of spatial epidemiology. You will learn how to leverage the power of R to analyze, visualize, and interpret spatial health data, gaining critical skills for public health research and practice.

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Course Modules

Module 1: Mastering R for Spatial Data

Build a rock-solid foundation in handling, manipulating, and visualizing vector and raster data using the essential sf, terra, and tmap packages.

Module 2: Advanced Spatial Data Operations

Learn the core data wrangling techniques of spatial analysis, including spatial joins, buffering, and extracting raster values for defined zones.

Module 3: Remote Sensing for Epidemiology

Process satellite imagery to derive key environmental variables like NDVI and NDWI, and understand their application in modeling vector-borne diseases.

Module 4: Validation and Accuracy Assessment

Learn how to validate your spatial models and maps against ground-truth data using confusion matrices, ROC curves, and other key statistical metrics.

Module 5: Analyzing Spatial Clustering

Move from visualization to formal testing. This module introduces spatial statistics like Moran’s I and Ripley’s K-function to identify disease clusters and hotspots.

Module 6: Resources & Best Practices

A curated list of key textbooks, R packages, and online resources to continue your learning journey. This module also covers principles of ethical and reproducible research.