SOS9029 – Spatial Data Analysis

Course content

The use of spatial data in the social sciences has an established position, dating back to the first studies of urban poverty in the 1800s, and studies of elections in the 1900s. New sources of spatial data, obtained through self-reporting – for example geotagged tweets and volunteered information – both blur the distinction between quantitative and qualitative data, and between researcher and informant. Using the kinds of data that are becoming available does, however, depend on researchers obtaining an adequate knowledge of the challenges involved in representation and analysis, including spatio-temporal data.

The course is intended to provide a survey of topics in the representation and analysis of spatial data in the social sciences. Research practices vary across disciplines, with opportunities for learning from one-another when using data derived from similar sources. It has been claimed that geographical information is becoming pervasive, that digital representations of our surroundings are increasingly entering into daily life. But are these representations unproblematic? Do the available representations impact our choices with regard to understanding and/or inference? Should we expect to extend aspatial methods of analysis to spatial data without modifying, or at least challenging, their assumptions? These are the key topics to be addressed in the course, which will necessarily be open-ended, because the various disciplines using spatial data may reach different conclusions.

Roger Bivand is a British geographer educated at Cambridge and the London School of Economics, and is Professor of Geography in the Department of Economics at Norwegian School of Economics. He is active in development of contributed software for analysing spatial data using the R statistical language, and is an Ordinary Member of the R foundation.

Learning outcome

At the end of the course, participants will:

  • have a grasp of sources and types of spatial and spatio-temporal data;
  • understand how the choice of observational or aggregation entities may affect analysis;
  • be able to choose between different techniques for visualizing spatial data;
  • understand the concept of spatial autocorrelation, and how it should (and should not) be measured;
  • have an overview of typical modelling approaches used with spatial data.

Admission

Ph.d.-students at the Department of Sociology and Human Geography register for the course in Studentweb.

Participants outside the Department of Sociology and Human Geography shall fill out this application form.

The application deadline is 15th November 2017.

Prerequisites

Formal prerequisite knowledge

Students should have some basic familiarity with elementary statistical and mapping concepts.

Some knowledge of R and RStudio is required. An introduction to R, RStudio and spatial data will be offered on Tuesday 12th December (room 301 Harriet Holters Building) for those who would benefit. If you want to participate on this one-day pre-course, you need to fill out this application form.

Teaching

The course will be taught as lectures with practical examples, most of which may be reproduced using R, RStudio and contributed R packages. If you wish to track the examples as well as the lecture presentation, and/or would like to use the practical examples to assist in absorbing the material and in planning your written assignment, please bring a laptop with R and Rstudio installed. A script to permit required contributed packages to be installed will also be made available.

 

Schedule

The course is in room 301 Harriet Holters Building all days.

Wednesday 13 December:

1. Representing spatial data:
Spatial data is keyed to reference systems just as temporal data i