ECS530 Analysing spatial data (expired)

• Topics

  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.

• Learning outcome

  Upon completion of the course, the candidate..

  Knowledge

  • 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

  Skills 

  • 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 

  • can manage complex interdisciplinary assignments and projects

• Teaching

  Combined lectures and computer practicals, concluding with draft project presentations on final day. Participants use their own laptop computers. Autumn semester 2020: the talks will be streamed and recorded. It will be possible to participate fully remotely, but for those able to travel to Bergen, interaction with other participants will be more fruitful.

• Recommended prerequisites

  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.

• Assessment

  Individual written project due 6 weeks in wiseflow after course ends. The project must be written in English.

• Grading Scale

  Pass / fail.

• Computer tools

  R, additional contributed packages, RStudio, possibly a GIS (GRASS or other).

• Literature

  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.

  https://www.rspatial.org/

   

  Recommended reading: Information will be given later