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A recommendation and risk classification system for connecting rough sleepers to essential outreach services
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Wilde, Harrison, Chen, Lucia L., Nguyen, Austin, Kimpel, Zoe, Sidgwick, Joshua, De Unanue, Adolfo, Veronese, Davide, Mateen, Bilal A., Ghani, Rayid and Vollmer, Sebastian (2021) A recommendation and risk classification system for connecting rough sleepers to essential outreach services. Data & Policy, 3 . e2. doi:10.1017/dap.2020.23 ISSN 2632-3249.
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WRAP-A-recommendation-and-risk-classification-system-for-connecting-rough-sleepers-to-essential-outreach-services-Vollmer-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (956Kb) | Preview |
Official URL: http://dx.doi.org/10.1017/dap.2020.23
Abstract
Rough sleeping is a chronic experience faced by some of the most disadvantaged people in modern society. This paper describes work carried out in partnership with Homeless Link (HL), a UK-based charity, in developing a data-driven approach to better connect people sleeping rough on the streets with outreach service providers. HL's platform has grown exponentially in recent years, leading to thousands of alerts per day during extreme weather events; this overwhelms the volunteer-based system they currently rely upon for the processing of alerts. In order to solve this problem, we propose a human-centered machine learning system to augment the volunteers' efforts by prioritizing alerts based on the likelihood of making a successful connection with a rough sleeper. This addresses capacity and resource limitations whilst allowing HL to quickly, effectively, and equitably process all of the alerts that they receive. Initial evaluation using historical data shows that our approach increases the rate at which rough sleepers are found following a referral by at least 15% based on labeled data, implying a greater overall increase when the alerts with unknown outcomes are considered, and suggesting the benefit in a trial taking place over a longer period to assess the models in practice. The discussion and modeling process is done with careful considerations of ethics, transparency, and explainability due to the sensitive nature of the data involved and the vulnerability of the people that are affected.
Item Type: | Journal Article | ||||||||||||
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Subjects: | H Social Sciences > HV Social pathology. Social and public welfare | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||
Library of Congress Subject Headings (LCSH): | Homelessness -- Prevention -- Statistics, Homeless persons -- Services for -- Statistics | ||||||||||||
Journal or Publication Title: | Data & Policy | ||||||||||||
Publisher: | Cambridge University Press | ||||||||||||
ISSN: | 2632-3249 | ||||||||||||
Official Date: | 22 January 2021 | ||||||||||||
Dates: |
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Volume: | 3 | ||||||||||||
Number of Pages: | 16 | ||||||||||||
Article Number: | e2 | ||||||||||||
DOI: | 10.1017/dap.2020.23 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 31 August 2022 | ||||||||||||
Date of first compliant Open Access: | 31 August 2022 | ||||||||||||
RIOXX Funder/Project Grant: |
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