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.

 

 
Master Class - runs over 2 days
Instructor: 

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 he has also worked 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.


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

During the first day of the masterclass, we will look at how spatial data can be accessed, manipulated and analysed. We will finish the day combining what we have learned to explore predictors of the county-level outcomes at the 2020 US presidential election.

Content:

    1. Accessing, loading and cleaning spatial data
    2. Exploring spatial patterns
    3. Problem solving: What predicts the 2020 US presidential election outcome?
    

 

Day 2 -  Visualising spatial patterns

On the second day of the course, we will expand on this, linking spatial data with microdata from surveys. This allows us to connect information on individuals with details on their geographic region. By doing this, we are able to examine the association between individual attitudes and behaviours and aggregate spatial variables. This includes, for instance, how the seriousness of COVID-19 in a given area was related to concern about the virus and support for stronger measures to fight the pandemic. We will also spend part of the day looking at how we can make interactive maps with our data.

Content:

    1. Linking survey microdata with spatial information
    2. Making interactive maps in R
    3. Brining it all together: What predicts the spread of COVID-19?

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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