Analysing spatial data

ECS530 Analysing spatial data

Spring 2019

Autumn 2019
  • 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

    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

  • Teaching

    Combined lectures and computer practicals, concluding with draft project presentations on final day. Participants use their own laptop computers.

  • 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 after course ends

  • 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)

    Lovelace, Nowosad & Muenchow (forthcoming) Geocomputation with R

    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

Autumn. Offered autumn 2018

Course responsible

Professor Roger Bivand, Department of Economics