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Early Bird
virtual

Getting Started with Spatial Analysis using R

Member rate £492.50
Non-Member rate £985.00

* If you attended our Methods School during the calendar years 2024 or 2025, you qualify for £45 off your course fee.

Course Dates and Times

Date: Monday 26 – Friday 30 May 2025
Time: 10:00 – 13:00 CEST

Fernando De la Cuesta

fercuest@ucm.es

Universidad Complutense de Madrid

This course offers a hands-on, step-by-step approach to spatial data analysis. You will begin by mastering essential R skills and tools for handling spatial data. As the course progresses, you will explore core principles of spatial patterns and relationships through statistical techniques.

This course aims to provide a solid foundation to geospatial data and its practical applications in the social and political sciences. You will learn how to organise and utilise spatial data for analysis in diverse political science contexts, visualise it through maps, and identify various social patterns through gelocation. You will also gain experience in applying quasi-experimental methods and using GIS in R to design natural experiments.

Purpose of the course

You will have a strong foundation for analysing spatial data with R, and be equipped to perform spatial analysis on your own research projects.

ECTS Credits

3 ECTS credits awarded for engaging fully in class activities.
1 additional ECTS credit awarded for completing a post-course assignment.


Instructor Bio

Fernando De la Cuesta is an Assistant Professor in the Department of Applied, Public, and Political Economics at the Complutense University of Madrid. He holds a PhD in Social Sciences. His research combines applied geography and economic methods to deepen the understanding of political and electoral behaviour. Fernando integrates his expertise in spatial analysis of political and electoral phenomena into both his research and teaching.

Geography and spatial analysis are now indispensable in social science research, providing valuable insights into patterns and relationships in spatial data—whether it’s mapping historical events or examining social, political and electoral differences across regions. Traditionally, using Geographic Information Systems (GIS) required specialised skills and equipment that were often exclusive to GIS professionals. However, the growth of open-source software, especially R, has changed the game. With R's expanded GIS capabilities, researchers can now access powerful spatial tools more easily, making GIS more approachable and unlocking new possibilities for political and electoral behaviour research. This course will help you build a foundation in geospatial data and its practical applications for examining electoral and political behaviour with R. By the end of the course, you’ll be ready to enhance your research by integrating these valuable spatial analysis methods.

Key topics covered

Day 1: Introduction to Spatial Data in R

  • Getting Started with R for Spatial Analysis
  • Working with Spatial Data in R
  • Data Exploration and Visualisation

Day 2: Fundamentals of Spatial Analysis

  • Core Principles of Spatial Analysis
  • Geospatial Statistics in R

Day 3: Advanced Spatial Techniques

  • Geolocation Techniques in R
  • Distance calculation in R

Day 4: Spatial Autocorrelation and Clustering

  • Spatial Clustering Techniques
  • Measures of Spatial Autocorrelation

Day 5: Spatial Causality and Natural Experiments

  • Spatial Quasi-Random Distributions
  • Using R and GIS for Natural Experiments

How the course will work

This course consists of 10 sessions over 5 days, with two 1.5-hour sessions daily separated by a 15-minute break. Each session blends theoretical lectures and practical labs, all conducted via Zoom. The lectures will cover key concepts for using spatial analysis to study political behaviour, while the hands-on labs will let you dive into R/RStudio as a Geographic Information System. You'll get to practice analysing electoral, political, and social data.

The course is designed to encourage interaction, with opportunities for questions and answers, as well as dedicated office hours. To pass the course, you will need to complete a short paper applying the analysis techniques learned in R to a topic relevant to your ongoing research projects. Throughout the process, you will receive guidance and support from the instructor to help you apply these methods effectively.

Prerequisite Knowledge

This is an introductory course that does not require prior experience with GIS software or spatial analysis. However, since the course emphasises practical applications in R, some familiarity with R and RStudio is strongly recommended. If you’re new to R but have experience with other statistical software like Stata, we suggest reviewing basic R/RStudio tutorials beforehand to make the learning experience smoother.

Additionally, a basic understanding of quantitative analysis—such as statistics and introductory econometrics—will be beneficial, as will some experience in applying ordinary least squares (OLS) regression.

Learning commitment

You will engage in a variety of activities designed to deepen your understanding of the subject matter. While the cornerstone of your training experience will be daily live teaching sessions, the learning commitment will extend beyond these. This ensures that you engage deeply with the course material, partcipate actively, and complete assessments to solidify your learning.

If you have registered and paid for the course, you will be given access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you can view course materials such as pre-course readings. You will be expected to commit approximately 20 hours per week leading up the start date to familiarise yourself with the content and complete any pre-course tasks.

During the course week, you will need to dedicate approximately 1–3 hours per day to prepare and work on assignments.

Each course offers the opportunity to earn three ECTS credits. Should you wish to earn a fourth credit, you will need to complete a post-course assignment, which will involve 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 these prerequisite items. If in doubt, please contact us before registering.