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.
Upon completion of the course, the candidate..
- has an overview of representations of spatial data, including classes in R
- will be able to import, export, and project spatial data
- will be able to explore spatial data, and how it may be visualised
- will be able to decide whether specifically spatial techniques are - required in analysing spatial data
- will be able to apply the concepts underlying spatial interpolation, and be able to make interpolations
- will be able to apply the concepts involved in the analysis of point patterns and be able to carry out such analyses
- will be able to define spatial weights for testing for and modelling spatial autocorrelation
- will be able to test for spatial autocorrelation, and understand the assumptions underlying tests
- will have an overview of spatial econometrics techniques, and be able to apply them
- 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
- 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 in wiseflow after course ends. The project must be written in English.
Pass / fail.
R, additional contributed packages, RStudio, possibly a GIS (GRASS or other).
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, https://www.r-spatial.org/book.
Recommended reading: Information will be given later
- ECTS Credits
- Teaching language
Autumn. Offered autumn 2020. Last time offered.
Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.
Professor Roger Bivand, Department of Economics