Spatial Analysis in R: Online - (2 days)


Designed for the applied users of R, this master-class will show you how to access spatial data from a number of sources, match this with geographic shape files, analyse spatial patterns, link these data to information from surveys, and create interactive maps to highlight important findings.


This course will be run over 2 days in three sessions per day:

  • 9.30 am - 11.30 am - Session 1
  • 12.00 pm - 1.30 pm - Session 2
  • 2.30 pm - 4.30 pm - Session 3


This course is being held online via Zoom and run on Australian Eastern Standard Time (GMT +10)

Master Class - runs over 2 days

Dr Shaun Ratcliff is a quantitative political scientist working at the United States Studies Centre, the University of Sydney.

His academic research focuses on the issue preferences and behaviour of political actors in the United States and Australia, including voters, interest groups and elites, and the role of parties as interest aggregators. Recently his focus has been on citizens’ attitudes towards COVID-19 and government responses to the pandemic. At the University of Sydney he has taught data science, public opinion, research methods and the use of data in politics in the US Studies Centre, the Department of Government, and the Faculty of Engineering and IT.

He has a PhD in political science from Monash University, and has previously worked at the University of Melbourne and Monash. He has also held government and media relations roles, and provided statistical and political consulting services, for industry associations, trade unions and political campaigns.

For further details visit his website

About this course: 

With terabytes of information on consumer behaviour, public transport use, crime statistics and election results sourced from across the world now available almost anywhere in minutes, our ability to answer important questions about the world has never been greater.


Much of human behaviour can be understood (at least in part) as a function of geography. This includes crime, public health and election outcomes. Taught by a quantitative political scientists from the University of Sydney, this masterclass will show you how to access spatial data from a number of sources, match this with geographic shape files, analyse geospatial patterns, link these data to information from surveys, and create interactive maps to highlight important findings.


Designed for the applied users of R who want to take their spatial analysis to a higher level. You will master the use of R and Markdown to clean and manipulate spatial data, produce reports and presentations with high quality visualisations.


This course is for participants who have some experience using R, and is most useful for those who have completed the ACSPRI course Data Analysis in R or equivalent.


Course syllabus: 

Day 1 - Engaging with spatial data

There are some important differences between spatial data and some other kinds of information we usually work with. In particular, we are often operating with data tied to specific spatial coordinates. This includes working with some quite particular data formats, including shapefiles. These are polygons, which store spatial coordinates (latitude and longitude) and other information on spatial units. During the first day of the masterclass, we will look at how these types of spatial data can be accessed, manipulated and analysed. We will look at loading and working with shapefiles, and combining them with other forms of spatial data, including information on geographic units from the Australian Bureau of Statistics. We will finish the day combining what we have learned to explore predictors of the county-level outcomes at the 2020 US presidential election.


    1. Working with shapefiles and other forms of spatial data.
    2. Exploring spatial patterns.
    3. Problem solving: What predicts spatial distribution of votes in US presidential elections?



Day 2 -  Linking survey microdata with spatial data, and visualising our results

On the second day of the course, we will expand on this, linking spatial data with microdata from surveys. This allows us to connect data on individuals with spatial information connected to their location. By doing this, we are able to examine the association between individual attitudes and behaviours and aggregate spatial variables. We will learn how to do this using survey panel data collected in Australia before and after the 2019 bushfires, allowing us to examine how proximity to serious fires is associated with changing attitudes to climate change. Using these data we will also create interactive maps plotting the location of our respondents and major fires. We will then fit some regressions to these data to model spatial trends in attitudes.


    1. Linking survey microdata with spatial information.
    2. Creating engaging interactive maps using these data.
    3. Bringing it all together: Modelling spatial trends.

Course format: 

You will need your own computer preloaded with R and an internet connection.


We will be in contact prior to the course to ensure you have the software you will need.
Data and course notes will be provided.

Recommended Background: 

This course is designed for applied users of R.

Prior experience with R is necessary.

Completion of the ACSPRI course Data Analysis in R, or equivalent, is helpful.


Recommended Texts: 

Course notes will be supplied.

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