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Spatial Analysis and Modelling

These pages outline a one semester (36 contact hours) class in spatial analysis and modelling that was last taught at Victoria University of Wellington as GISC 422 in the second half of 2023.

I am still in the process of cleaning the materials up for potential conversion into training materials. For the time being the materials are provided gratis with no warrant as to their accuracy as a guide to spatial in R but you may still find them useful all the same!

Link to related video content

A consolidated list of relevant video content for this class is available on this page.

Lab and lecture timetable

Here's a 12 week schedule schedule we will aim to follow. Bolded labs have an associated assignment that must be submitted and contributes the indicated percentage of the course credit. General instructions for the labs are here. Relevant materials (lecture slides, lab scripts and datasets) are linked below, when available.

Week#	Lecture	Lab	Videos
1	Course overview	R and RStudio computing environment and Markdown documents	Practical
2	Why ‘spatial is special’	Making maps in R	Lecture<br />Practical
3	Spatial processes	Introducing `spatstat`	Lecture<br />Practical
4	Point pattern analysis	Point pattern analysis (15%)	Lecture<br />Practical
5	Measuring spatial autocorrelation	Moran's I (15%)	Lecture<br />Practical
6	Spatial interpolation	'Simple' interpolation in R	Lecture
7	Geostatistics	Interpolation (15%)	Lecture
8	Multivariate methods	Geodemographics (15%)	Lecture
9	Overlay, regression models and related methods	Lab content
10	Cluster detection
11	Network analysis	Tools for network analysis
12

Readings

The most useful materials are

Bivand R, Pebesma E and Gómez-Rubilio V. 2013. Applied Spatial Data Analysis with R 2nd edn. Springer, New York.
O'Sullivan D and D Unwin. 2010. Geographic Information Analysis 2nd edn. Wiley, Hoboken, NJ.
Brunsdon C and L Comber. 2019. An Introduction to R for Spatial Analysis and Mapping 2nd edn. Sage, London.

There are also many useful online resources that cover topics that are the subject of this class. For example:

Geocomputation with R by Lovelace, Novosad and Muenchow, 2019
Spatial Data Science a book in preparation from Bivand and Pebesma
Course materials for Geographic Data Science by Daniel Arribas-Bel at University of Liverpool
Geospatial Analysis by deSmith, Longley and Goodchild

For the final assignment you will need to do your own research and assemble materials concerning how spatial analysis has been applied in specific areas of study.

Software

Most of the lab work will be completed in the R programming language for statistical computing, using various packages tailored to spatial analysis work. R

We will use R from the RStudio environment which makes managing work more straightforward.

Both R and RStudio are freely downloadable for use on your own computer (they work on all three major platforms). I can take a look if you are having issues with your installation, but are likely to suggest that you uninstall and reinstall.

Course learning objectives (CLOs)

Articulate the theoretical and practical considerations in the application of spatial analysis methods and spatial modelling
Prepare, manipulate, analyse, and display spatial data
Apply existing tools to derive meaningful spatial models
Identify and perform appropriate spatial analysis

Assessment

This course is 100% internally assessed. Assessment is based on four lab assignments worth 15% of overall course credit each, and a final assignment worth 30% of course credit which is due in the exam period.

Assessment item	Credit	Due date	CLOs
Point pattern analysis	15%	4 September	2 3 4
Spatial autocorrelation	15%	11 September	2 3 4
Spatial interpolation	15%	25 September	2 3 4
Geodemographic analysis	15%	9 October	2 3 4
Written report on application of spatial analysis in a particular topic area	30%	20 October	1
Participation (including non-assessed labs)	10%	NA	1 2 3 4

Some guidance on the written report assignment expectations is provided here.