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A Multilevel Spatial Model to Investigate Voting Behaviour in the 2019 UK General Election.

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Summary
This summary is machine-generated.

This study introduces a new model to identify vertical and horizontal spatial processes. The findings reveal distinct associations between socio-economic factors and UK voting behavior across different regions.

Keywords:
Butler swingHierarchical modelIntrinsic conditional autoregressive modelsSpatial regressionUK general election 2019

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Area of Science:

  • Spatial statistics
  • Geographical analysis
  • Statistical modeling

Background:

  • Spatial data often exhibits hierarchical structures and local dependencies.
  • Understanding these spatial processes is crucial for accurate geographical analysis.
  • Previous models may not effectively disentangle multi-scale spatial variations.

Purpose of the Study:

  • To present a novel modelling framework for detecting simultaneous vertical and horizontal spatial processes.
  • To estimate the relative contributions of different spatial scales to overall variation.
  • To apply the framework to analyze socio-economic attributes and voting behavior in the UK.

Main Methods:

  • Utilizing a multi-level modelling structure to capture hierarchical (vertical) spatial variation.
  • Incorporating an autoregressive component at the lowest level to account for local (horizontal) spatial influence.
  • Estimating spatially-varying parameters and the contribution of each spatial process.

Main Results:

  • Evidence of both vertical and horizontal spatial effects was found in the analysis of UK voting behavior.
  • The model successfully estimated the distinct contributions of different spatial scales.
  • Socio-economic attributes showed varying associations with voting patterns across England and Wales.

Conclusions:

  • The developed framework effectively distinguishes and quantifies multi-level spatial processes.
  • Both hierarchical and local spatial factors significantly influence socio-economic associations with voting behavior.
  • The findings highlight the importance of considering geographical scale in understanding complex spatial relationships.