Multi-Level Spatial Comparative Judgement Models To Map Deprivation

Rowland Seymour*, David Sirl, Simon Preston, James Goulding

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

68 Downloads (Pure)

Abstract

While current comparative judgement models provide strong algorithmic efficiency, they remain data inefficient, often requiring days or weeks of extensive data collection to provide sufficient pair- wise comparisons for stable and accurate parameter estimation. This disparity between data and algorithm efficiency is preventing widespread adoption, especially so in challenging data-collection environments such as mapping human rights abuses. We address the data inefficiency challenge by introducing the finite element Gaussian process Bradley–Terry mixture model, an approach that significantly reduces the number of pairwise comparisons required by comparative judgement mod- els. This is achieved via integration of prior spatial assumptions, encoded as a mixture of functions, each function introducing a spatial smoothness constraint at a specific resolution. These functions are modelled nonparametrically, through Gaussian process prior distributions. We use our method to map deprivation in the city of Dar es Salaam, Tanzania and locate slums in the city where poverty reduction measures can be carried out.
Original languageEnglish
Title of host publicationProceedings of the Joint Statistical Meetings 2023
PublisherZenodo
DOIs
Publication statusPublished - 4 Sept 2023
EventJoint Statistical Meetings 2023 - Metro Toronto Convention Centre, Toronto, Canada
Duration: 5 Aug 202310 Aug 2023
https://ww2.amstat.org/meetings/jsm/2023/

Conference

ConferenceJoint Statistical Meetings 2023
Abbreviated titleJSM2023
Country/TerritoryCanada
CityToronto
Period5/08/2310/08/23
Internet address

Fingerprint

Dive into the research topics of 'Multi-Level Spatial Comparative Judgement Models To Map Deprivation'. Together they form a unique fingerprint.

Cite this