Spatial analysis of public health data
Andy MacLachlan1 Laura Sheppard2 Adam Dennett3 Chiara Gericke4 Ruth Neville 5
Last updated: 2026-02-19
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holds the material used to support the Spatial Analysis of public health data, part of the MSc in Modelling for Global Health at the University of Oxford.
Overview:
Much of the data encountered in public health studies has a spatial dimension. The aim is to cover statistical approaches that can be used to make inferences and predictions from spatial data of the kind encountered in epidemiological analyses. We will introduce different types of spatial data, such as point-level and areal data, and understand that these data types require different analytical approaches. We will understand the need to apply statistical approaches that account for spatial dependence, and cover a range of methods of increasing complexity, such as spatial regression, geostatistical models for point level data and conditional autoregressive models for areal data. The students will need a statistical background and familiarity with linear regression techniques and familiarity with programming in R.
Learning outcomes:
- Manipulate and visualise spatial data using R software.
- Critically evaluate quantitative analyses of spatial data through understanding the implications of spatial dependence.
- Apply and critique (spatial) statistical analysis techniques to infer relationships between spatial phenomena.
- Explain and evaluate common issues with geographic data such as representation and uncertainty
- Develop an ability to apply spatial statistical models in R to spatial point-level and areal data to:
- Perform inference of spatial trends
- Make predictions using spatial extrapolation
The Bartlett Centre for Advanced Spatial Analysis, https://www.andymac.uk/↩︎
AQA, https://cls.ucl.ac.uk/team/laura-sheppard/↩︎
The Bartlett Centre for Advanced Spatial Analysis, https://adamdennett.co.uk/↩︎
Centre for Behaviour Change, https://www.chiaragericke.co.uk/about-me↩︎
The Bartlett Centre for Advanced Spatial Analysis, https://profiles.ucl.ac.uk/103748-ruth-neville/about↩︎