Loading…

The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania

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,...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Seymour, R G, Sirl, D, Preston, S, Dryden, I L, Ellis, M J A, Perrat, B, Goulding, J
Format: Article
Language:English
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Seymour, R G
Sirl, D
Preston, S
Dryden, I L
Ellis, M J A
Perrat, B
Goulding, J
description 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of prohibitive costs and poor infrastructures; and mass urbanisation can render manually surveyed figures rapidly out-of-date.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 house-hold 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 amount of data 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.
doi_str_mv 10.48550/arxiv.2010.14128
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2455089633</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2455089633</sourcerecordid><originalsourceid>FETCH-LOGICAL-a958-7800162df7334cc21af28b55d0d71ae90ed49ab9d8d7af10c9e0b0d0304cacba3</originalsourceid><addsrcrecordid>eNotkE1Lw0AYhBdBsNT-AG8Lnre--5XserNVq9BSxHgub7Ib3RI2cdMW4683UE8DMw8zMITccJgrozXcYfoJp7mA0eCKC3NBJkJKzowS4orM-n4PACLLhdZyQt6KL08XOPg-YKTvHR4CNnSR0DV-YKzwKQ100zrf3NOPVI7Mo-9SOI1cG89BiJ80RFpg_MUY8Jpc1tj0fvavU1I8PxXLF7berl6XD2uGVhuWGwCeCVfnUqqqEhxrYUqtHbico7fgnbJYWmdcjjWHynoowYEEVWFVopyS23Ntl9rvo-8Pu317THFc3Ak1_mBsJqX8A8fjUJo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2455089633</pqid></control><display><type>article</type><title>The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania</title><source>ProQuest - Publicly Available Content Database</source><creator>Seymour, R G ; Sirl, D ; Preston, S ; Dryden, I L ; Ellis, M J A ; Perrat, B ; Goulding, J</creator><creatorcontrib>Seymour, R G ; Sirl, D ; Preston, S ; Dryden, I L ; Ellis, M J A ; Perrat, B ; Goulding, J</creatorcontrib><description>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 urbanisation 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 house-hold 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 amount of data 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.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2010.14128</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Bayesian analysis ; Deprivation ; Developing countries ; LDCs ; Smoothness</subject><ispartof>arXiv.org, 2021-10</ispartof><rights>2021. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2455089633?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>780,784,25751,27923,37010,44588</link.rule.ids></links><search><creatorcontrib>Seymour, R G</creatorcontrib><creatorcontrib>Sirl, D</creatorcontrib><creatorcontrib>Preston, S</creatorcontrib><creatorcontrib>Dryden, I L</creatorcontrib><creatorcontrib>Ellis, M J A</creatorcontrib><creatorcontrib>Perrat, B</creatorcontrib><creatorcontrib>Goulding, J</creatorcontrib><title>The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania</title><title>arXiv.org</title><description>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 urbanisation 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 house-hold 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 amount of data 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.</description><subject>Bayesian analysis</subject><subject>Deprivation</subject><subject>Developing countries</subject><subject>LDCs</subject><subject>Smoothness</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNotkE1Lw0AYhBdBsNT-AG8Lnre--5XserNVq9BSxHgub7Ib3RI2cdMW4683UE8DMw8zMITccJgrozXcYfoJp7mA0eCKC3NBJkJKzowS4orM-n4PACLLhdZyQt6KL08XOPg-YKTvHR4CNnSR0DV-YKzwKQ100zrf3NOPVI7Mo-9SOI1cG89BiJ80RFpg_MUY8Jpc1tj0fvavU1I8PxXLF7berl6XD2uGVhuWGwCeCVfnUqqqEhxrYUqtHbico7fgnbJYWmdcjjWHynoowYEEVWFVopyS23Ntl9rvo-8Pu317THFc3Ak1_mBsJqX8A8fjUJo</recordid><startdate>20211028</startdate><enddate>20211028</enddate><creator>Seymour, R G</creator><creator>Sirl, D</creator><creator>Preston, S</creator><creator>Dryden, I L</creator><creator>Ellis, M J A</creator><creator>Perrat, B</creator><creator>Goulding, J</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20211028</creationdate><title>The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania</title><author>Seymour, R G ; Sirl, D ; Preston, S ; Dryden, I L ; Ellis, M J A ; Perrat, B ; Goulding, J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a958-7800162df7334cc21af28b55d0d71ae90ed49ab9d8d7af10c9e0b0d0304cacba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Bayesian analysis</topic><topic>Deprivation</topic><topic>Developing countries</topic><topic>LDCs</topic><topic>Smoothness</topic><toplevel>online_resources</toplevel><creatorcontrib>Seymour, R G</creatorcontrib><creatorcontrib>Sirl, D</creatorcontrib><creatorcontrib>Preston, S</creatorcontrib><creatorcontrib>Dryden, I L</creatorcontrib><creatorcontrib>Ellis, M J A</creatorcontrib><creatorcontrib>Perrat, B</creatorcontrib><creatorcontrib>Goulding, J</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest - Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Seymour, R G</au><au>Sirl, D</au><au>Preston, S</au><au>Dryden, I L</au><au>Ellis, M J A</au><au>Perrat, B</au><au>Goulding, J</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania</atitle><jtitle>arXiv.org</jtitle><date>2021-10-28</date><risdate>2021</risdate><eissn>2331-8422</eissn><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 urbanisation 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 house-hold 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 amount of data 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.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2010.14128</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2021-10
issn 2331-8422
language eng
recordid cdi_proquest_journals_2455089633
source ProQuest - Publicly Available Content Database
subjects Bayesian analysis
Deprivation
Developing countries
LDCs
Smoothness
title The Bayesian Spatial Bradley--Terry Model: Urban Deprivation Modeling in Tanzania
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T14%3A13%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Bayesian%20Spatial%20Bradley--Terry%20Model:%20Urban%20Deprivation%20Modeling%20in%20Tanzania&rft.jtitle=arXiv.org&rft.au=Seymour,%20R%20G&rft.date=2021-10-28&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2010.14128&rft_dat=%3Cproquest%3E2455089633%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-a958-7800162df7334cc21af28b55d0d71ae90ed49ab9d8d7af10c9e0b0d0304cacba3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2455089633&rft_id=info:pmid/&rfr_iscdi=true