Introduction to Spatial Data Analysis with R

Key topics covered

No prior GIS experience is needed. The course moves step by step from the basics of spatial data to more advanced applications, with each day building on the previous one. Every session follows the same structure: a short conceptual introduction, a guided live-coding tutorial in R Markdown with a short break, and a wrap-up with questions and discussion. You will receive the slides and commented R Markdown files before each session, so you can follow along and replicate every analysis at home. You are encouraged to bring your own research question or dataset and apply each day’s techniques to it.

Day 1: Foundations of spatial data and mapping

Why does location matter, and what makes spatial data different from an ordinary data frame? You will learn how geographic information is represented (vector and raster data), why we need coordinate reference systems, and how the sf package stores geometries alongside attributes. We then turn these concepts into maps and discuss the design choices that make a map informative rather than misleading. In the hands-on session, you will load spatial boundaries, join them with tabular data and produce thematic, bivariate and interactive maps.

Day 2: Geocoding: turning addresses into data

Much of the information social scientists need, such as polling stations, schools or public services, comes as addresses or place names rather than coordinates. You will learn how geocoding works, where it fails and how to check its quality, and how to use OpenStreetMap as an open source of geographic information. In the hands-on session, you will geocode lists of addresses, extract points of interest, validate the results and map them across several locations.

Day 3: Distance, proximity and spatial operations

Once units are located in space, we can measure how they relate to each other. You will learn to treat distance and proximity as explanatory variables, and to use the core spatial operations: distances, buffers, intersections and spatial joins. In the hands-on session, you will compute distances between sets of locations, build exposure measures and use them in correlation and regression analyses, testing how sensitive the results are to different distance thresholds.

Day 4: Spatial autocorrelation: detecting patterns and clusters

Near things tend to be more related than distant ones, so observations are often not independent. You will learn how to define neighbours through spatial weights, test for global clustering with Moran’s I, and locate clusters and outliers with local indicators (LISA, Getis-Ord hot spots). We also discuss common pitfalls, such as multiple testing and the modifiable areal unit problem. In the hands-on session, you will analyse both point and polygon data and produce cluster and hot-spot maps.

Day 5: From spatial patterns to causal inference

Spatial patterns are not causal effects. You will learn how geography can help move from correlation to causation through natural experiments, focusing on geographic regression discontinuity designs and their assumptions. In the hands-on session, you will implement a geographic regression discontinuity around a policy boundary, estimate the effect and run standard robustness checks. We close by reviewing the complete workflow built during the week and how to apply it to your own research.

Post-course assignment

You will apply at least three techniques from the course to a research question of your own, using spatial data relevant to your work. You can choose one of three formats: a short research paper (around 3,000 words); a policy brief (around 1,500 words plus an executive summary); or a replication that extends a published study with spatial methods. All formats must be submitted with a reproducible R Markdown file containing the code and data sources. Submissions are assessed on the appropriate use of spatial methods, research design, interpretation of results and reproducibility.

Prerequisite knowledge

Basic working knowledge of R: importing data, working with data frames, using simple tidyverse functions (e.g. filter, mutate, summarise) and running an R Markdown file. Familiarity with linear regression is helpful for Days 3 and 5. No prior knowledge of GIS or spatial statistics is required.

You will need recent versions of R and RStudio installed on your computer. Before the course, you will receive an installation script for all required R packages; please run it and check that everything loads correctly. All software is free and open source.

Before the course

Around 2–3 hours: installing R, RStudio and the course packages (30 minutes); an optional R refresher, shared in advance, for participants who are new to R or want to brush up (about 2 hours); and, optionally, thinking of a research question or dataset you would like to apply the methods to.

Learning commitment

The course includes three hours of live teaching each day for five days, alongside readings and other learning activities.

Once you have registered and paid, you will receive access to our Learning Management System (LMS) approximately two weeks before the course start date. Course materials, including pre-course readings, will be available here. Allow approximately 20 hours per week before the course to review the materials and complete readings and any pre-course tasks.

During the course week, you should allow approximately two to three hours per day for preparation and assignments, in addition to the live teaching sessions.

The course offers the opportunity to earn three ECTS credits. To earn a fourth credit, you will need to complete a post-course assignment involving approximately 25 hours of work.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach prerequisite items. If in doubt, please contact us before registering.