Beecham, R. and Clarke, S. (2025) Should we use Multilevel Regression and Post-stratification when simulating area-level population outcomes? In: Proceedings of 33rd GISRUK Conference 2025. 33rd GISRUK Conference 2025, 23-25 Apr 2025, Bristol, UK. Zenodo
Abstract
Estimating unknown outcomes at small-area population level is a routine task in GIScience. We ask whether Multilevel Regression and Poststratification (MRP), an approach to simulating public opinion, might overcome deficiencies in spatial microsimulation (SPM), the de facto approach in applied spatial analysis. Using microdata from Health Survey for England and 2021 Census tables, we evaluate MRP and SPM at estimating a known, groundtruthed, health outcome. When parameterised with few constraints, there are only slight differences in estimation between the two approaches. With more, and geographic, constraints, often desired by spatially-inclined researchers, errors begin to accumulate in SPM estimates that do not appear in those arrived at via MRP.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | spatial microsimulation, multilevel modelling and poststratification, small-area estimation, non-representative samples |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/Z531273/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 May 2025 08:09 |
Last Modified: | 20 May 2025 08:09 |
Status: | Published |
Publisher: | Zenodo |
Identification Number: | 10.5281/zenodo.15230381 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:226795 |