Yan Wang

Survey research faces mounting structural challenges: declining response rates, sample bias, block-wise missingness among at-risk respondents, and AI-assisted fraudulent completions in online panels. Large language models (LLMs) offer potential remedies, yet rigorous evaluations across the full survey workflow remain scarce, particularly in disaster contexts where data quality matters most. We present a five-stage framework for LLM integration covering questionnaire design, sample selection, pilot testing, missing-data imputation, and post-collection analysis, evaluated on the 2024 Hurricane Milton preparedness survey of Florida residents (n = 946). We introduce a Protection Motivation Theory (PMT)-constrained co-occurrence knowledge graph and benchmark seven LLM configurations against three classical imputation baselines. Four findings stand out. First, organizing retrieval around PMT causal structure and integrating all evidence in a single model call outperforms both unstructured retrieval and staged sequential inference on response prediction (mean absolute error 0.993 vs. 1.097 for standard retrieval-augmented generation). Second, our proposed Anchored Marginal Theory-Informed LLM (A-TLM) outperforms all classical baselines on root-mean-square error under disaster-relevant block-wise missing-not-at-random conditions (RMSE 1.439 vs. 1.496 for the next-best method) and achieves near-zero signed bias (-0.121) where the random-forest imputer produces the largest absolute bias (-0.631). Third, near-zero aggregate bias can mask opposing subgroup errors of substantial magnitude, with at-risk respondents systematically under-predicted; subgroup-stratified auditing is proposed as a minimum reporting standard. Fourth, hallucination risk in LLM-based survey analysis is architecturally manageable through grounded refusal. Together, these findings provide actionable guidance for disaster researchers seeking to integrate AI tools responsibly into survey workflows under urgent conditions.

Social infrastructure plays a critical role in shaping neighborhood well-being by fostering social and cultural interaction, enabling service provision, and encouraging exposure to diverse environments. Despite the growing knowledge of its spatial accessibility, time use at social-infrastructure places is underexplored due to the lack of a spatially resolved national dataset. We address this gap by developing scalable Social-infrastructure Time Use (STU) measures that capture length and depth of activity engagement, diversity, and spatial inequality, supported by the first-of-their-kind datasets spanning multiple geographic scales—from census tracts to metropolitan areas. Our datasets leverage anonymized and aggregated foot traffic data collected between 2019 and 2024 across 49 continental U.S. states. The data description reveals variances in STU across time, space, and differing neighborhood socio-demographic characteristics. Validation demonstrates generally robust population representation, consistent with established national survey findings while revealing more nuanced patterns. Future analyses could link STU with public health outcomes and environmental factors to inform targeted interventions aimed at enhancing population well-being and guiding social infrastructure planning and usage.

Social infrastructure plays a critical role in shaping neighborhood well-being by fostering social and cultural interaction, enabling service provision, and encouraging exposure to diverse environments. Despite the growing knowledge of its spatial accessibility, time use at social infrastructure places is underexplored due to the lack of a spatially resolved national dataset. We address this gap by developing scalable Social-Infrastructure Time Use measures (STU) that capture length and depth of engagement, activity diversity, and spatial inequality, supported by first-of-their-kind datasets spanning multiple geographic scales from census tracts to metropolitan areas. Our datasets leverage anonymized and aggregated foot traffic data collected between 2019 and 2024 across 49 continental U.S. states. The data description reveals variances in STU across time, space, and differing neighborhood sociodemographic characteristics. Validation demonstrates generally robust population representation, consistent with established national survey findings while revealing more nuanced patterns. Future analyses could link STU with public health outcomes and environmental factors to inform targeted interventions aimed at enhancing population well-being and guiding social infrastructure planning and usage.

Natural disasters such as hurricanes and wildfires increasingly introduce unusual disturbance on economic activities, which are especially likely to reshape commercial land use pattern given their sensitive to customer visitation. However, current modeling approaches are limited in capturing such complex interplay between human activities and commercial land use change under and following disturbances. Such interactions have been more effectively captured in current resilient urban planning theories. This study designs and calibrates a Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change (Urban-STA4CLC) to predict both the yearly decline and expansion of commercial land use at census block level under cumulative impact of disasters on human activities over two years. Guided by urban theories, Urban-STA4CLC integrates both spatial and temporal attention mechanisms with three theory-informed modules. Resilience theory guides a disaster-aware temporal attention module that captures visitation dynamics. Spatial economic theory informs a multi-relational spatial attention module for inter-block representation. Diffusion theory contributes a regularization term that constrains land use transitions. The model performs significantly better than non-theoretical baselines in predicting commercial land use change under the scenario of recurrent hurricanes, with around 19% improvement in F1 score (0.8763). The effectiveness of the theory-guided modules was further validated through ablation studies. The research demonstrates that embedding urban theory into commercial land use modeling models may substantially enhance the capacity to capture its gains and losses. These advances in commercial land use modeling contribute to land use research that accounts for cumulative impacts of recurrent disasters and shifts in economic activity patterns.

Importance: Mobility, defined as the ability to move freely, decreases with increasing age. The increasing frequency and intensity of extreme weather and climate events (EWCEs), such as hurricanes, pose a considerable risk for older adults (aged ≥65 years) to maintain their mobility (ie, mobility resilience).Objective: To examine whether an association exists between older-aged neighborhoods and mobility resilience following an EWCE.Design, Setting, and Participants: This case series study used 2022 demographics with prospectively observed social infrastructure point-of-interest visitations before and after Hurricane Ian (September 23-30, 2022) to characterize neighborhood mobility resilience. Analyses were conducted for all residents (age categories, 53 years) of central and southern Florida neighborhoods.Main Outcomes and Measures: Using a resilience triangle framework, daily point-of-interest visitation data before, during, and after the event were used to measure the duration of recovery, ratio of recovery, ratio of impact, and area of resilience triangles.Results: Among 1819 neighborhoods including 8 084 335 residents (median [range] of neighborhood ages, 46 [19-82] years; 51% female), a total of 225 218 social infrastructure points of interest with 75.4 million visitation records were included. Compared with younger-aged neighborhoods, neighborhoods in the oldest age quartile had a longer duration of mobility recovery after Hurricane Ian (1.137 days; 95% CI, 0.844-1.431 days), lower mobility recovery ratio (−2.1%; 95% CI, −4.0% to −0.2%), higher ratio of impact (1.2%; 95% CI, 0.3%-2.1%), and higher cumulative losses of daily mobility (17.0 percentage-days; 95% CI, 8.4-26.3 percentage-days). These associations were substantially attenuated by hurricane wind exposure (ie, the strength of the storm) (Lindemann, Merenda, and Gold relative importance, 0.919-0.960 vs 0.031-0.065 for age quartiles).Conclusions and Relevance: In this case series study, neighborhoods with an older population showed compromised mobility resilience associated with the aftermath of Hurricane Ian. These findings may inform neighborhood-targeted mobility interventions for climate resilience.