Roger S. Bivand, Edzer J. Pebesma and Virgilio Gómez-Rubio.
Applied Spatial Data Analysis with R
Springer. August 2008.

 

One-day introductory course on Spatial Data Analysis with the R Programming Language

Faculty of Medicine

Imperial College London

St. Mary's Campus, London

9:00 - 17:30, 31 August 2007

Course outline

This one-day introductory course is aimed at researchers which have to deal with the analysis of spatial data. The course will tackle the problem of analysing spatial data with the R programming language. Different types of spatial data will be covered, such as point patterns, lattice data and data coming from irregular measurements of continuos processes (geostatistics). In addition, different worked examples will be presented showing how to proceed with the analysis of a wide range of spatial data sets.

The topics of the course will contain an introduction to various R packages for the analysis of spatial data. This includes data import/export, data management and visualisation, and how to fit a broad range of models for spatial data. The worked examples will focus on particular real data sets from Epidemiology, Environmental Sciences, Ecology, Economics and others.

Although most of the lectures will include live demonstrations of the software, a working knowledge of the R software is desirable to follow the examples. R is distributed as free software, and it can be downloaded from http://cran.r-project.org . Similarly, the course will introduce the statistical concepts behind the analysis, but a basic knowledge of statistics and regression analysis will be necessary.

The course will take place in the Hynds Computer Lab of the Faculty of Medicine and the participants will be able to use these facilities. Note that the course will take at the facilities of the Faculty of Medicine, Imperial College London at St. Mary's Campus, and NOT at the main campus in South Kensington.

The complete address is

Faculty of Medicine
Imperial College London
St. Mary's Campus, Norfolk Place
W2 1PG London - UK

More information on how to arrive can be found at http://www1.imperial.ac.uk/medicine/contacts/campuses/stmarys/

This course is part of the ESRC National Centre for Research Methods trainning activities and there are discount fees for ESRC-funded researchers.

Summary of course contents

Summary of course instructors

Prof. Roger Bivand, Norwegian Shool of Economics and Business Administration

Dr. Virgilio Gómez-Rubio (course organiser), Imperial College London

Booking and course fees

Fees for the course are as follows:

£20 or postgraduate students registered at UK academic institutions

£40 for staff at UK academic institutions, ESRC-funded researchers and researchers from charity organisations

£175 for all other participants

This includes the course materials, refreshment and a buffet lunch. In order to book a place for the course, you should contact Virgilio Gomez-Rubio (v.gomezrubio@imperial.ac.uk).

More Info

Additional information on the analysis of spatial data with R, including a gallery of figures, can be found via http://cran.r-project.org/src/contrib/Views/Spatial.html.

You can find more information about the course at the following web site: http://www.bias-project.org.uk

Course materials

CD with the course materials

Lecture notes

Pre-course material

Given that the course will be very intensive (and intense!!), we have prepared a list with some materials to get the attendants familiar with R.

R

R software and packages
This is the main site to download R and the packages that we will use in the course.

An Introduction to R (HTML, PDF)
Introductory tutorial to R, ideal for beginners. It contains a description of data types, commands, etc. and how to make basic statistical analysis.

Other software

This software will be used (or, at least, mentioned) in the course. No prior knowledge will be assumed, but it will help to understand the topics covered in the course.

GRASS
Geographic Information System

WinBUGS
Software for Bayesian data analysis.

Mondrian
Software for visualizing data.