SGO9010 - Quantitative Methods and Spatial Analysis for Geographers

Course content

During the last decades the computing calculating capacities of computers, and the access to abundant and disaggregate sources of statistics has increased tremendously. This new world of Big Data has already changed how quantitative methods are perceived and used in many scientific fields. However, only in the last few years has computers gained so much computational power that Big Data handling is possible also in a Spatial Analysis and GIS setting.

This offers a unique opportunity for geographers to create and test geographical hypotheses build around these new data registers.

This course is designed to close this knowledge gap and aiming to increase the knowledge of advanced methods within quantitative and spatial analysis among doctoral students in human geography.

The course will include:

  • Lectures and discussions
  • Supervised computer labs

The course will focus on a certain field within quantitative and spatial analysis. The four themes are:

  • Regression techniques.
  • Spatial analysis and GIS.
  • Advanced statistical and spatial analytical tools: Survival analysis, Clustering techniques, Scripting, etc.
  • The presentation of quantitative and spatial analysis output.

Learning outcome

The course shall give the participants:

  • Increased ability to select the quantitative and spatial methods in relation to research questions
  • Acquired skills to assemble, collect and manage big data resources so that they facilitate both statistical as well as geographical studies
  • Acquire skills to inference output from statistical and spatial analysis Tools.


The course is open for all interested Ph.D. students. Priority is given to Ph.D.-candidates in human geography enrolled at Norwegian universities.

Interested partisipants shall fill out this application form.

The deadline for registration is  1st April 2017.


Formal prerequisite knowledge

We will use SPSS and ArcGIS during class. Some skills in SPSS and basic knowledge in descriptive statistics, and hypothesis testing (using t-test, correlation analysis) is assumed. Registered students will be offered a possibility to use a terminal server connection for the access of SPSS and ArcMap for self-studies and to solve course-related tasks.


Department of Social and Economic Geography, Uppsala University

Adress: Kyrkogårdsgatan 10, 4th floor (Ekonomikum), Uppsala, Sweden


Contact person:

Norway: Lena Magnusson Turner, (, +47 412 90 227)

Sweden: John Östh, (, +46 18 471 73 88)



Monday May 8th

11.30-12.00                Lunch together

12.00 – 13.00              John Östh & Lena Magnusson Turner: Introduction to the course: Quantitative Methods and Spatial Analysis for Geographers and establishing working Groups

13.00 – 14.00               Lecture: Logistic Regression and OLS

14.00 – 14.30              Tea/Coffee

14.30 -17.00               Supervised computer lab: Logistic Regression


Tuesday May 9th

9.00 - 12.00                  Lecture and discussion  Spatial analysis and GIS (with a break for Tea/Coffee) (John Östh)

12.00 - 13.00                Lunch

13.00 -17.00                 Supervised computer lab: Spatial analysis and GIS (with a break for Tea/Coffee)


Wednesday May 10th

9.00 - 12.00                   Supervised computer lab: Spatial analysis and GIS (with a break for Tea/Coffee)

12.00 - 13.00                 Lunch

13.00 -17.00                  Supervised computer lab: Spatial analysis and GIS (with a break for Tea/Coffee)


Thursday May 11th

9.00 – 10.30                     Lecture and discussion: Survival analysis (Lena Magnusson Turner)

10.30 – 11.00                   Tea/Coffee

11.00 – 12.30                    Lecture and discussion: Logistic regression and marginal effect (Terje Wessel)

12.30 – 13.00                    Lunch

13.00 – 15.00                    Summing up the course. Information about examination.


Reading List


The book Discovering Statistics using SPSS by Andy Field is a handbook, and we recommend you to use it as such. However, on Monday's lecture, we focus especially on:

Chapter 5 (The beast of bias) and Chapter 19 (Logistic regression)


The following three chapters are a useful background to statistics and logistic regression:

Chapter 2 (Everything you never wanted to know about statistics), Chapter 8 (Regression) and Chapter 18 (Categorical data)


The following chapter is an important extension:

Chapter 20 (Multilevel linear models)



Openshaw, Stan (1983) The modifiable areal unit problem. Geo Abstracts University of East Anglia. Read here.

Fischer & Getis, 2010 Handbook of Applied Spatial Analysis Software Tools, Methods and Applications (useful handbook, and can be downloaded for free)



Openshaw, Stan (1983) The modifiable areal unit problem. Geo Abstracts University of East Anglia

Fischer & Getis, 2010, Handbook of Applied Spatial Analysis Software Tools, Methods and Applications



Survival analysis and COX regression:

Turner, Lena Magnusson & Hedman, Lina (2014). Linking Integration and Housing Career: A Longitudinal Analysis of Immigrant Groups in Sweden. Housing Studies 29(2), pp 270- 290.

Marginal effects:

Mood, C. (2010) Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It. European Sociological Review 26, pp 67-82.

Williams, R. (2012) Using the margins command to estimate and interpret adjusted predictions and marginal effects. The Stata Journal 12(2), pp 308-331.

Contact Lena in case you have problems to get access to these articles.



This is a 5-day course, 6 ECTS. Requested readings consist of approximately 1000 pages (a detailed reading list will be made available before the course). Attendance and active participation is expected throughout the course. Examination will be in the form of a written paper (15 - 20 pages, reference list and tables included) on a topic agreed upon with the course coordinators (Lena Magnusson Turner and John Östh). The deadline for handing in the paper is 30th June 2017. The paper should be sent to Katalin Godberg by email to:

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Facts about this course






Spring 2017

8-11th May 2017


Spring 2017

Teaching language