Analysing spatial data

ECS530 Analysing spatial data

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

    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.

  • 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

    Recommended reading: Information will be given later

Overview

ECTS Credits
7.5
Teaching language
English.
Semester

Autumn. Offered autumn 2019.

Course responsible

Professor Roger Bivand, Department of Economics