About

spatial-epi-101

An Introduction to Spatial Epidemiology using R

About This Page

This workbook is an introduction to spatial epidemiology using R. It covers how to analyze, visualize, and interpret spatial health data, with worked examples that apply spatial statistical methods to disease patterns, clustering, and the environmental determinants of health.

What You’ll Learn

By the end of this workbook, you will be able to handle spatial datasets, produce maps and visualizations, run spatial statistical analyses, and interpret the results in a public health context. The material also covers reproducible research practices and the ethical considerations of working with health data.

Who This Page Is For

Ideal for:

• Epidemiologists who want to add spatial methods to their toolkit
• Graduate students in public health, geography, or related fields
• Data scientists working with health or environmental data
• Anyone interested in spatial patterns in health outcomes

Prerequisites:

• Basic knowledge of R programming
• A working understanding of epidemiological concepts
• Familiarity with basic statistical methods
• No prior experience with spatial analysis required

Learning Approach

The workbook takes a hands-on approach: each concept is applied directly through coding exercises and case studies. The modules build on one another, moving from basic spatial data handling to more advanced statistical modeling. You will work with real datasets and public health questions throughout.

Questions about the workbook?
Contact: max.lang[at]stx.ox.ac.uk