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Spatial Analysis for Social Sciences in R (RSS2.16) - Closed

The course will explore the dynamic intersection of spatial analysis and social sciences using R. It will equip you with essential skills to analyze geographical data, map social phenomena, and uncover spatial patterns. You will gain hands-on experience with R programming to unlock insights into the spatial dimensions of social science research.

Duration: one-week.


    Closed: registration no longer possible. We have some alternatives for you: 

    We would like to recommend that you consider joining our courses on Multilevel Regression Analysis with R or on Causal Inference with Natural Experiments: DiD, RDD, IV, and Matched Designs, both of which may be relevant for your interests. Feel free to contact the instructors, Rob Eisinga (rob.eisinga [at] (rob[dot]eisinga[at]ru[dot]nl)) and Ryan Moore (ryantmoore [at] (ryantmoore[at]hey[dot]com)) to get more information about the courses. 



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    Starting date

    24 June 2024, 9 am
    Educational method
    Main Language
    24 June 2024, 9 am - 28 June 2024, 5 pm
    Unique code


    Type of education
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    See the requirements in cost and admission
    Study load (ECTS)
    Radboud Summer School

    Embark on an exploration of the dynamic interplay between spatial analysis and social sciences through our hands-on course utilizing the versatile R programming language.

    This 5-day course delves into the role of geospatial data in social sciences research and the valuable integration of R for in-depth analysis. The course begins with the introduction to spatial analysis and R, covering fundamental geospatial concepts, an in-depth exploration of the coordinate reference systems and map projections, and a refresher on R programming. 

    You will gain a solid understanding of geospatial data structures using relevant R-packages, establishing a strong foundation for subsequent modules. The vector data module focuses on concepts, plotting techniques, advanced visualization with R-packages, and map overlays. The raster data module provides a deep dive into raster concepts, data-structures with the relevant R-packages, remote sensing, and satellite data. 

    You will master raster reclassification, stacks, algebra, and mapping with both raster and vector data. With real-world applicability in mind, the course explores sources of vector and raster data for social sciences research, including datasets from Radboud University Global Data Lab. The culmination involves spatial analysis fundamentals, covering tools commonly used for the analysis of geospatial data separately and in combination. 

    This course is designed to provide you with the skills needed to navigate the complexities of geospatial data effectively, enhancing their research capabilities in the realm of social sciences.

    Total package & social events

    Olexiy Kyrychenko

    Olexiy Kyrychenko

    I am an applied microeconomist with research interests at the intersection of environmental and health economics, climate change, and sustainable development.
    In my recent work I combine large administrative datasets with satellite-derived estimates and spatial information (i) to quantify the effects of air pollution on infant mortality, (ii) to reexamine the effectiveness of environmental regulations, and (iii) to study the impact of air temperature on output and climate change adaptation strategies of manufacturing firms.
    I am an Assistant Professor at the Nijmegen School of Management and a team member of the Global Data Lab at the Institute for Management Research, Radboud University (the Netherlands). I received a Ph.D. in Economics and Econometrics from CERGE-EI (the Czech Republic, under a U.S. permanent charter) in 2023. I was also awarded a Marie Skłodowska-Curie fellowship and held visiting positions at Princeton University (Health and Wellbeing, 2018) and UC Berkeley (Environmental Economics, 2019-2020). Before CERGE-EI, I held various teaching, research, and administrative positions at Zaporizhzhia National Technical University (Ukraine), including an Associate Professor position at the chair of International Economic Relations. During this period of my career, I obtained a CSc. (Ph.D.) in World Economy and International Economic Relations from Dnipropetrovsk University of Economics and Law (Ukraine) and was awarded an academic rank of a Docent (Associate Professor). I was also a recipient of the Fulbright Scholar Award for conducting his research (International Finance) at UC Berkeley in the 2011-2012 academic year.


    • Regular: €1049 (application deadline 1st of May)
    • Student & PhD's: €699 (application deadline 1st of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

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    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: 

    The course is designed to accommodate learners with varying levels of experience in R and spatial analysis. The necessary R knowledge for the course will be covered during the sessions, ensuring all participants can actively engage in spatial analysis. While there are no strict requirements, participants with prior knowledge of R will find it advantageous.

    Admission documents:


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