An upcoming 3.5 days course on modelling spatial and spatial-temporal data

Following the publication of our book, we will be delivering a training course on modelling spatial and spatial-temporal data. This training course introduces powerful Bayesian spatial and spatial-temporal modelling techniques that enable researchers to analyse social, economic, political and public health data.

Where and when

May 12-15 2020 At Northumbria University, Newcastle, UK. We are sorry that the course will be postponed till later this year.

Course leaders

Prof. Robert Haining (Cambridge) and Dr. Guangquan Li (Northumbria)

Course outline

  • Types and properties of spatial and spatial-temporal data and implications on model building;

  • Techniques for testing spatial heterogeneity and autocorrelation; clusters and hotspot detection;

  • Uses of maps and graphics for visualization (using R);

  • Bayesian inference for spatial and spatial-temporal data;

  • Introduction to Monte Carlo simulation methods;

  • Simple Bayesian regression models;

  • Bayesian hierarchical models for spatial data, both continuous (e.g., normal/log normal data) and discrete (e.g., count/binary data);

  • Implementation of various Bayesian hierarchical models in WinBUGS for spatial data;

  • Introduction to spatial econometric models and their implementations in WinBUGS;

  • Regression diagnostics with particular relevance for spatial data;

  • Introduction to modelling small area time series data (e.g. linear model in time, autoregressive models, interrupted time series models);

  • Introduction to various strategies/structures for modelling spatial-temporal data;

  • Discussion of space-time models using practical examples.

Entry requirements

The course is aimed at PhD students, Post-Doctoral researchers and academic staff in the social, economic, political and public health sciences who have a background in quantitative methods at least up to and including the normal linear regression model. Prior experience of generalized linear modelling will be helpful for some parts of the course as will some familiarity with the basic ideas behind Bayesian inference.