The Bayesian Spatial Bradley–Terry model: Urban deprivation modelling in Tanzania

Seymour, Rowland G.; Sirl, David; Preston, Simon P.; Dryden, Ian L.; Ellis, Madeleine J.A.; Perrat, Bertrand; Goulding, James

Abstract

Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated; estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures; and mass urbanization can render manually surveyed figures rapidly out-of-date. Comparative judgement models, such as the Bradley–Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas' affluence, such models can both simplify logistics and circumvent biases inherent to household surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley–Terry model, which substantially decreases the number of comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania.

• Journal Article Type: Article
• Acceptance Date: Oct 27, 2021
• Online Publication Date: Jan 10, 2022
• Publication Date: 2022-03
• Deposit Date: Oct 29, 2021
• Publicly Available Date: Jan 11, 2023
• Journal: Journal of the Royal Statistical Society: Series C (Applied Statistics)
• Print ISSN: 0035-9254
• Electronic ISSN: 1467-9876
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 71
• Issue: 2
• Pages: 288-308
• DOI: https://doi.org/10.1111/rssc.12532
• Keywords: Statistics, Probability and Uncertainty; Statistics and Probability; Comparative Judgement; Preference Learning; Networks
• Public URL: https://nottingham-repository.worktribe.com/output/6544232
• Publisher URL: https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12532