ECS530 Analysing spatial data
This intensive course is addressed to doctoral and other students who need to learn applied spatial data analysis, most likely for thesis use, and to applied researchers with similar requirements; relevant disciplines include among others economics and econometrics, political science, biology, epidemiology and public health, environmental science, applied statistics, geography.
Knowledge - The candidate...
- has an overview of representations of spatial data, including classes in R
- be able to import, export, and project spatial data
- understand how to explore spatial data, and how it may be visualised
- have an understanding of how to decide whether specifically spatial techniques are - required in analysing spatial data
- understand the concepts underlying spatial interpolation, and be able to make interpolations
- understand the concepts involved in the analysis of point patterns and be able to carry out such analyses
- understand how to define spatial weights for testing for and modelling spatial autocorrelation
- be able to test for spatial autocorrelation, and understand the assumptions underlying tests
- have an overview of spatial econometrics techniques, and be able to apply them
Skills - The candidate...
- can formulate problems, plan and carry out research and scholarly and development work
- can contribute to research of a high international standard
- can handle complex academic issues and challenge established knowledge and practice in the field
Competence - The candidate...
- can manage complex interdisciplinary assignments and projects
Combined lectures and computer practicals, concluding with draft project presentations on final day. Participants use their own laptop computers.
Basic knowledge of R and applied statistics/empirical analysis of quantitative data relevant for participants' disciplines is an advantage
Requirements for course approval
Attendance and draft project presentation
Individual written project due 6 weeks after course ends
Pass / fail
R, additional contributed packages, RStudio, possibly a GIS (GRASS or other)
Autumn. Offered autumn 2018
Bivand, Pebesma & Gómez-Rubio: Applied Spatial Data Analysis with R, Springer, 2013 (2nd edition) http://www.asdar-book.org/
Lovelace, Nowosad & Muenchow (forthcoming) Geocomputation with R https://geocompr.robinlovelace.net/
Bivand, Piras (forthcoming) Introduction to Applied Spatial Econometrics
Bivand, Pebesma (forthcoming) Title TBA - bookdown site forthcoming
Recommended reading: Information will be given later
- ECTS Credits
- Teaching language
- Spring, Autumn
Professor Roger Bivand, Department of Economics