arXiv Machine Learning By Shujuan Chen, Yue Li, Ying Jin

Beyond travel mode: urban context shapes active mobility's mental health effects over time

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arXiv:2607. 04520v1 Announce Type: new Abstract: Active mobility is widely promoted for sustainable and healthier living, but whether it translates into equitable mental health benefits across individuals and places over time remains unknown.

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arXiv Machine Learning
Jun 18

UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction

arXiv:2504. 04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy.

By Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia
arXiv Machine Learning
Sep 11

Geospatial Foundation Models Capture Health-Relevant Dimensions of Place Beyond Conventional Social Risk Indices

The study evaluates whether geospatial foundation models derived from 2022 satellite data can capture physical aspects of place that conventional area-based social risk indices miss. Using LightGBM on data from 82,646 census tracts, the models moderately predicted certain survey variables and explained up to 54% of the residual variance in 40 health outcomes, notably improving predictions for annual checkups, arthritis, and high blood pressure. The models’ explanatory power increased with larger tract sizes, suggesting they add valuable, health-relevant information beyond traditional social risk measures.

By Nathaniel Hendrix, Carl Y. Zhang, Chris Heitzig, Andrew Bazemore, David H. Rehkopf