In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults

[2608.14663] In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14663 (cs) [Submitted on 1 Aug 2026] Title: In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults Authors: Houhao Liang , Kresimir Friganovic , Joanne Kua , Noor Hafizah Ismail , Su Su , Bryan Yijia Tan , Navrag B. Singh , Panos Mavros View a PDF of the paper titled In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults, by Houhao Liang and 7 other authors View PDF HTML (experimental) Abstract: As global populations age, enhancing neighborhood walkability through inclusive urban design is important for mitigating built environment (BE) barriers that discourage physical activity and social participation among older adults. This study investigates the utility of in-context learning (ICL), using the transformer-based foundation model TabPFN, to determine how BE features influence perceived walkability, as measured by the Neighborhood Environment Walkability Scale (NEWS-A) survey. Using a small-scale dataset (N = 257) comprising a unique demographic of older adults with knee osteoarthritis or a history of falls, TabPFN achieved a macro F1 score of 54.89% for walkability perceptions categorized as Low, Neutral, and High using equal-width binning. This result outperformed optimized, grid-searched baseline models, including Random Forest (45.85%) and XGBoost (50.56%). To interpret these results, we employed Shapley Interaction Quantification (SHAP-IQ) to identify the hierarchical importance of feature interactions. Preliminary results revealed that the model's predictive logic was primarily driven by higher-order interactions. For example, the interaction between average street circuity and the ratio of drivable roads emerged as the primary discriminator of perceived walkability. Neighborhood greenery was found to have substantial predictive importance only when combined with an individual's fear of falling or perception of age-friendliness. Overall, ICL using TabPFN demonstrates superior performance on small-scale datasets, enhancing the fidelity of the resulting interpretive insights. Furthermore, SHAP-IQ provides a synergistic perspective on how higher-order feature interactions drive the model's predictions. Comments: 8 pages, 2 tables, 1 figure Subjects: Machine Learning (cs.LG) ; Applications (stat.AP) ACM classes: I.2; J.3 Cite as: arXiv:2608.14663 [cs.LG] (or arXiv:2608.14663v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14663 Focus to learn more arXiv-issued DOI via DataCite Journal reference: COSIT 2026 Poster Paper Submission history From: Houhao Liang [ view email ] [v1] Sat, 1 Aug 2026 01:46:10 UTC (352 KB) Full-text links: Access Paper: View a PDF of the paper titled In-Context Learning to Assess Built Environment Impacts on Perceived Neighborhood Walkability Among Mobility-impaired Older Adults, by Houhao Liang and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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