POL8527 GIS and the Geography of Armed Conflict – Applying Geographical Information Systems (GIS) and georeferenced data for Peace and Conflict Research


05 - 09 Jun 2023

NTNU Trondheim, Campus Dragvoll

Application through NTNU - contact adviser Einar Gimse-Syrstad, Department of Sociology and Political Science: einar.syrstad@ntnu.no



Andreas Forø Tollefsen is Associate Professor at the Department of Sociology and Human Geography, University of Oslo, and Senior Researcher at the Peace Research Institute Oslo (PRIO). His research focuses on the use of georeferenced data such as surveys and event data, to explore the local causes and consequences of armed conflict, primarily focusing on migration and human mobility. He has an extensive experience with ArcGIS and QGIS, as well as open-source GIS databases such as PostGIS and the use of spatial data in the statistical software package R. 

Elisabeth Lio Rosvold is Associate Professor at the Department of Sociology and Political Science, NTNU. Her research focuses on understanding how the geographic overlap of disasters and armed conflict can impact conflict dynamics as well as the wider security implications of climate adaptation and mitigation. She has extensive experience with geographic data collection and has experience with spatial data in R.

Short outline

As a research tool in the social sciences, GIS has not been used to nearly the same depth relative to applications in natural science, where GIS have a longer history dating back to the late 1960s. With increased availability of spatial data and software, current and future opportunities for the application of GIS in the social sciences are considered tremendous. This course aims to point at some of the many opportunities in managing and combining spatial data, modifying spatial data, and analyzing spatial data, with a focus on applications in peace and conflict research. Whether the research design is based on qualitative or quantitative methods, GIS can provide the researcher with added analytical capabilities. Examples on how GIS can support both qualitative and quantitative methodologies will be given during the course, but with an emphasis on the latter. The course will give students practical skills to manage and analyze spatial data using open-source software such as QGIS and R.

Full information and application information on NTNU website

Course Description:

The course aims to give participants an extensive hands-on experience with the use of GIS operations and to apply georeferenced data. A number of exercises will be given to allow students to become familiar with the essential GIS functionalities. Many of these exercises will provide students a model on how to populate a data table (that can later be used for a statistical analysis) with geographic or disaggregated variables. Lectures will be balanced between the theoretical and the practical with several examples. Examples are mostly drawn from the use of GIS for the sub-national study of civil armed conflict, but as the techniques are generic, course participants should be able to see their relevance for other purposes.  
The course further aims to give participants and understanding on what GIS is as well as to provide participants with the necessary understanding of basic GIS concepts and tools, such as: 

  • GIS as Geographical Information System, Science and Studies.
  • The nature of geographical data, the measurement levels after Stevens (nominal, ordinal, interval and ration) and some of their shortcomings when applied to geographical data. Spatial autocorrelation and how this may be problematic for conventional statistics such as regression. 
  • Representation of geographical data in GIS, discrete and continuous geographic data as well as their associated common representation: vector and raster data. Topological properties for vector data and how these facilitate spatial analysis.  
  • Representing statistical surfaces and terrain surfaces.
  • Coordinate systems; both coordinate system at the globe (geographical co-ordinates or latitude / longitude) and Cartesian (‘flat’) coordinate system.
  • Map projections and their properties, how measurements on a global scale becomes distorted because of map projections.  
  • Data capture methods (Global Positioning Systems (GPS), Remote Sensing, Screen and table digitizing, scanning, from table to map. Georeferencing a satellite image, aerial photograph or a scanned map) 
  • Queries based on attributes, queries based on location 
  • Area and distance measurement. How to measure distance and area correctly on a global scale? Euclidean distance versus geodesic distances. 
  • Basic vector based GIS tools: buffer and overlay 
  • Basic raster based tools: map algebra, local, focal, zonal, and global map algebra functions and spatial modelling 
  • Presenting geographical data, thematic mapping (choropleth maps), map design, geovisualization and participatory GIS 

The course participants will be working with geographic representations of armed conflicts as well as spatial data on conflict-related factors, such as demographic, political, and physical variables. Dataset on armed conflicts could include geocoded conflict data from Uppsala/PRIO (point) and conflict zones from localized Military Interstate Disputes (MID) data (polygon). Examples of other relevant georeferenced datasets that will be mentioned in lectures and may be used in exercises during the course include diamond sites, petroleum fields, ethnic groups, forests, mountainous terrain, and population density.   
Lecture and lab constitute five days of teaching and tentatively more than half of these teaching hours will be lab hours. Examples of exercises would be:

  • Understanding map projections
  • From table to map – convert a table with coordinates with the ‘add events’ tool. Examples of tables with geographical coordinates that we can use are dataset on armed conflict events (the Uppsala/PRIO dataset) and the dataset on locations of military intrastate disputes (MIDLOC). 
  • Representing the extension of internal armed conflicts by applying buffer and overlay operations. 
  • Combining dataset on the extension of internal armed conflicts with a population raster in order to estimate the number of people living in conflict affected areas with the use of zonal map algebra function (zonal statistics).  
  • Distance measures – how to get the measurement correct on a global scale by using geodesic distance measures.
  • Thematic mapping – how to make appealing and understandable maps 
  • Use of scripting to automate work-flows and to manage large dataset (for instance by batch processing) 

As GIS also is a technology and since there are many different formats involved, some focus on the more technical side of GIS will also be covered. When working with other software for statistical analysis or word processing, course participants may be used to be working with one single file (or a limited number of files). Working with GIS, for one GIS project, it is not uncommon that the number of files becomes several hundred or more. For native users, this is often challenging, and the course therefore also aims to make course participants able to manage huge amount of GIS data. 


​1 March 2023


Requested prior knowledge

Participants should be computer literate, knowing how to pack and unpack zip or tar files and knowing how to navigate, copy, and paste files in a file manager program. A minor basic knowledge of statistics and mathematics is useful. While not a requirement, students should have some basic knowledge of R. Online preparatory course material will be circulated.

Software used

Students should bring their own laptops. The software used will be QGIS and R. Students should have QGIS, R, and RStudio installed already at course start, and they should have rights to install R packages on their computer. Instructions for how to download this will be circulated.