An Introduction to Spatial Microsimulation using R (fully booked)
Date:
08/05/2014 - 09/05/2014
Organised by:
University of Leeds
Presenter:
Dr Robin Lovelace
Level:
Entry (no or almost no prior knowledge)
Contact:
Amy O'Neill at a.oneill@leeds.ac.uk
Map:
View in Google Maps (LS2 9JT)
Venue:
Lab G19, East Building, School of Geography, University of Leeds, Leeds
Description:
Spatial microsimulation is a statistical method by which individual-level data is generated for small areas by combining national surveys with geographically aggregated census data. Much has been written about the technique in academic journals, but there has been much less work explaining the practicalities of its implementation, generally, and its customisation for specific purposes. Journal articles have tended to present key results, but not the underlying code, therefore hindering reproducibility. This 1.5 day course fills this knowledge gap taking an explicitly practical and pragmatic approach to spatial microsimulation. Its aim is to allow attendees to harness spatial microsimulation for their own needs by providing example data and code. To this end the course uses R, a free, open source and extensible statistical language that is becoming the de facto standard for statistical analysis.
No prior knowledge in either R or spatial microsimulation is assumed: we will begin from first principles, using simple examples to ensure understanding of every step. Content too will be directed to participants' needs; all teaching material will be provided with explanation of how to modify example code for tackling specific research problems.
Cost:
£45 for postgraduate students; £90 for staff at UK academic institutions, public sector staff and staff at registered charity organisations; £220 for all others.
Region:
Yorkshire and Humberside
Keywords:
Statistical Theory and Methods of Inference, Spatial Data Analysis, Simulation , R , Spatial analysis , Microsimulation , Coding , Geographical data
Related publications and presentations:
Statistical Theory and Methods of Inference
Spatial Data Analysis
Simulation