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Spatial data analysis course session

Add to calendar 2024-04-04 09:00 2024-04-04 18:00 Europe/Rome Spatial data analysis course session Sala Triaria Villa Schifanoia YYYY-MM-DD


04 April 2024

9:00 - 18:00 CEST


Sala Triaria

Villa Schifanoia

MWP Multidisciplinary Workshop

Many social science research questions are spatially dependent, such as voting outcomes, housing prices, protest behavior, or migration decisions. This dependence can stem from spatial contagion, spatial spillovers, or common confounders. Spatial regression models can help to detect this spatial dependence and explicitly model spatial relations, identifying spatial spillovers or diffusion, thus adding another layer of insights.

The main objective of the course is the theoretical understanding and practical application of spatial regression models to model and analyze spatial processes. The course will offer a brief refresher on handling spatial data in R and will then mainly focus on spatial regression techniques. This will start with the construction of suitable spatial relationships (W) and the detection and diagnostic of spatial dependence. We will then discuss various spatial regression techniques to model processes, clarify the assumptions of these models, and show how they differ in their applications and interpretations. We will use R for practical applications.

The workshop provides a mixture of lectures and practical sessions in which the researchers can apply the newly learned skills directly to their data of interest.


09:00 – 09:30 Refresher on R for spatial data

09:30 – 11:00 Spatial Relationships (W) and Spatial Dependence

11:15 – 12:00 Practical exercise

Lunch break

13:00 – 14:30 Spatial Regression Models (SLX, Error, lagged DV)

14:45 – 16:00 Interpreting Results: Spatial Impacts

16:15 – 18:00 Practical exercise


Participants should have experience using R to analyse quantitative and spatial data. Participants without experience with spatial data in R should acquire basic knowledge of importing and visualising spatial objects in R before taking the course. We will send some resources and exercises to participants in advance.


Tobias Ruttenauer (University College London, Social Research Institute)


Eva Krejcova (EUI)

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