Whose City Is It? Mapping Perceived Urban Livability with Citizen-Guided AI

Whose City Is It? Mapping Perceived Urban Livability with Citizen-Guided AI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Whose City Is It? Mapping Perceived Urban Livability with Citizen-Guided AI Florencio V Campomanes, Angela Abascal, Lorraine Trento Oliveira, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7047996/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in npj Urban Sustainability → Version 1 posted 10 You are reading this latest preprint version Abstract Urban livability is shaped by dominant values, often economic or aesthetic, and power dynamics that often overlook the lived experiences of deprived urban area (DUA) residents. As a result, conventional livability indicators risk reinforcing existing inequalities unless these are grounded on inclusive and participatory approaches. To address this issue, we developed lightweight deep learning models – ‘AI-voters’ – trained on livability preferences from both DUA residents and city planners, using open-source satellite imagery. Applied in Ghana’s Greater Accra Metropolitan Area, our approach reduced data requirements to map urban livability by 90% through a two-step urban form sampling strategy that enabled scalable participatory mapping. Training separate ‘AI-voters’ for planners and DUA residents revealed systematic differences: planners not only disagree among themselves but also consistently assign higher livability scores and overlook the preferences of DUA residents, such as avoiding coastal area exposure. The AI-voters mirrored human-voter behavior based on physical urban features such as greenery and urban density, especially when trained on the preferences of DUA residents, demonstrating their potential as scalable proxies for local insights. These results highlight the importance of integrating community perspectives into AI models trained to map urban livability to expose hidden spatial inequities and promote more inclusive urban development. Scientific community and society/Geography Scientific community and society/Developing world Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in npj Urban Sustainability → Version 1 posted Editorial decision: Revision requested 17 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers invited by journal 14 Jul, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 04 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7047996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485556119,"identity":"055863ba-19b1-47c3-a3b9-fd02fc50ce31","order_by":0,"name":"Florencio V Campomanes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBAC9gZk3gcE0wKnFp4DSBzGGQwGQIoZxJYgTgszD1FaGJgffrq5wyaaf3bz4c+2bX8S+6X7Dz5g3IFPC5uxdO6ZtNwZd46lSee2GSTOnHOY2YDxDG4t9kBNQJWHcxtu5JgxA7XkbriRzCbB2IbPFh7m3yAt82/kf/5sCdSy/0Yy+w8CWtjAtmy4kcMgzQiyRSKZjQGvFmY2M2uQXzbeSDOT7DlnXD/jRrKxRCI+LezNj2/n7rDJnXcj+fGHH2VyxvwzEh9++Nhmg1MLOBIYG9BFE3BrgABMLaNgFIyCUTAKkAAAiJhQhinP1QgAAAAASUVORK5CYII=","orcid":"","institution":"University of Twente","correspondingAuthor":true,"prefix":"","firstName":"Florencio","middleName":"V","lastName":"Campomanes","suffix":""},{"id":485556120,"identity":"6a2a097d-23b3-418a-ace7-868e96386407","order_by":1,"name":"Angela Abascal","email":"","orcid":"","institution":"Universidad Publica de Navarra","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"","lastName":"Abascal","suffix":""},{"id":485556121,"identity":"be43cb18-29af-4d4f-85cc-7cee6085756f","order_by":2,"name":"Lorraine Trento Oliveira","email":"","orcid":"","institution":"University of Twente","correspondingAuthor":false,"prefix":"","firstName":"Lorraine","middleName":"Trento","lastName":"Oliveira","suffix":""},{"id":485556122,"identity":"43d7d58c-6391-4674-855d-0c429cc6421d","order_by":3,"name":"Monika Kuffer","email":"","orcid":"","institution":"University of Twente","correspondingAuthor":false,"prefix":"","firstName":"Monika","middleName":"","lastName":"Kuffer","suffix":""},{"id":485556123,"identity":"4a20130f-559e-444a-9259-804137af2dab","order_by":4,"name":"Anne M. 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As a result, conventional livability indicators risk reinforcing existing inequalities unless these are grounded on inclusive and participatory approaches. To address this issue, we developed lightweight deep learning models \u0026ndash; \u0026lsquo;AI-voters\u0026rsquo; \u0026ndash; trained on livability preferences from both DUA residents and city planners, using open-source satellite imagery. Applied in Ghana\u0026rsquo;s Greater Accra Metropolitan Area, our approach reduced data requirements to map urban livability by 90% through a two-step urban form sampling strategy that enabled scalable participatory mapping. Training separate \u0026lsquo;AI-voters\u0026rsquo; for planners and DUA residents revealed systematic differences: planners not only disagree among themselves but also consistently assign higher livability scores and overlook the preferences of DUA residents, such as avoiding coastal area exposure. 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