Aims:

1. To provide a solid understanding of the method and applications
2. To teach its implementation in R in general terms
3. To provide guidance on next steps

• Housekeeping
• About the course and its teachers
• Lectures and practicals
• Getting help

9:30 - 11:00

• Lecture: what is spatial microsimulation?
• Getting used to working with RStudio (and GitHub)
• Demonstration of what we'll be working on
• Loading the input data (Chapter 3)

Refreshments: 11 - 11:15

11:15 - 1:00

• Working through Chapter 3 and 4
• Performance
• (Parallel processing in R)

1:30 - 2:45

• Finishing up and questions about SimpleWorld
• Lecture: Introduction to spatial microsimulation in the wild
• Cleaning messy input data for spatial microsimulation (Chapter 5)

3 - 4:30

• Performing IPF on CakeMap Data (5.2)
• Description and demonstration of integerisation (5.3)
• Re-cap and questions on key concepts

9:30 - 11

• Demonstration analysis of CakeMap data
• Model checking and validation

11:15 - 1:30

• Visualisations
• Lecture: next steps
• Applying the methods to your data

• Major update of course materials from May
• New improved code is much faster
• And easier to write
• Booklet -> Book

1. A method
2. An approach

• Wide variety of potential applications
• So far main applications have been in health, poverty mapping and transport
• What do you want to use spatial microsimulation for?

• Powerful command-line interface
• Fast - if you know how
• Steep learning curve but lots of help available

• Creating, modifying and subsetting datasets
• Functions
• Features of RStudio