Event

Introduction to Spatial Analysis in R

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Course type: Short Course
Date: 2 June - 3 July 2026
Location: Online

Overview

Spatial analysis is becoming an increasingly useful tool throughout public health research with increasing amounts of spatial health data generated each year. Whether you’re a humanitarian aid worker looking to add map-making to your growing rapid analysis skillset or an early-stage PhD student who wants to learn the fundamentals before progressing to geostatistics, this short course will be well suited to your needs.

Our hands-on, practical approach to teaching, with real-life examples, means you can progress from no previous experience with R to applying R to your own work with confidence. We also place a strong emphasis on enabling students to continue their learning independently allowing your skillset to continue growing beyond the end of the course.

Who is this course for?

Practising public health professionals and health researchers interested in adding expertise in spatial data analysis to their existing skills. Operational researchers and in particular those working in humanitarian crises/emergency deployments are particularly encouraged.

No previous experience with R or spatial data analysis is required, but some experience with quantitative data analysis using programmable computer software, e.g. plotting and analysing data in Stata, SAS, Python or MATLAB is expected. It is also expected that students are familiar with the use of the Generalised Linear Model (e.g. logistic regression, Poisson regression, multiple explanatory variables) and that computing is, or will be, part of their regular day-to-day role.     

Course objectives

At the end of the course, students should be able to:

  • Read in spatial and non-spatial datasets into R and perform basic data manipulation tasks using the “dplyr” package, make a variety of plots using the “ggplot2” package and demonstrate an understanding of why different plot types are used for different types of data
  • Manipulate and visualise spatial data using maps with the “ggplot” package and be able to identify when different types of data projections should be used.
  • Understand how to analyse areal data and be able to implement and interpret simple regression analyses on areal datasets including the use of multi-level models
  • Be able to write clear, tidy and intuitive R code that can be reproduced by others and know how to conduct a “code review” of the work of others.
  • Identify the key characteristics of point data and understand and implement a variety of point data analysis techniques, such as kriging and Gaussian process regression. 

How to Apply

For more information and how to register, please click here!

Application Deadline: 2 May 2026

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