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.
arXiv:2510. 06473v3 Announce Type: replace-cross Abstract: Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health.
arXiv:2608. 00815v1 Announce Type: new Abstract: The 15-minute city promotes access to everyday services within a short walk or bicycle ride, but its relationship with observed mobility remains difficult to quantify.
arXiv:2606. 06174v1 Announce Type: new Abstract: Childhood asthma is a common illness exacerbated by air pollution as well as meteorological and neighborhood-level socioeconomic factors.
arXiv:2607. 22640v1 Announce Type: cross Abstract: Short-term environmental exposures have been linked to cognitive and behavioral outcomes, although many reported associations may reflect broader geographic and contextual differences.
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.
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.
arXiv:2608. 14663v1 Announce Type: new 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.
The paper introduces the Activity Chain Encoder (ACE), a self‑supervised deep learning model that transforms passively collected mobile phone location data into daily activity representations. ACE integrates pre‑trained urban embeddings, visit timing, and duration, using a Transformer to capture the sequential structure of stays, and is trained via masked activity modelling and contrastive learning without explicit activity labels. The resulting user‑level profiles are clustered and interpreted with temporal‑functional patterns and Census demographics, revealing six distinct weekday activity‑pattern groups in London that differ in daily rhythms, urban contexts, and demographic characteristics.
arXiv:2507. 00945v2 Announce Type: replace Abstract: Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require substantial mobility history and learn spatial structure implicitly through grids or graphs.
arXiv:2405. 17468v3 Announce Type: replace-cross Abstract: Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations.
arXiv:2608.21567v1 Announce Type: new Abstract: Human mobility serves as an essential proxy for understanding social, economic, and environmental dynamics in urban systems. Geospatial transferability...
arXiv:2510. 08762v2 Announce Type: replace Abstract: Causal inference in spatial domains faces two intertwined challenges: (1) unmeasured spatial factors, such as weather, air pollution, or mobility, that confound treatment and outcome, and (2) interference from nearby treatments that violate standard no-interference assumptions.