This study examines the factors influencing home elevation decisions among participants in Houston’s Homeowner Assistance Program (HoAP) and the Texas General Land Office’s Homeowner Assistance Program (HAP) in the aftermath of Hurricane Harvey and other flood events. Using a mixed-methods approach, we conducted surveys and semi-structured interviews with 50 homeowners, supplemented by secondary data analyses of program records and GIS-based flood risk assessments. Additionally, 25 undergraduate students engaged in a structured field trip, conducting site observations, interacting with residents, and discussing home elevation with experts. The findings reveal disparities in home elevation outcomes, with lower completion rates in socially vulnerable neighborhoods despite program eligibility. The study also identifies key factors influencing elevation decisions, including mobility concerns, financial constraints, neighborhood esthetics, and perceptions of long-term flood risk. Homeowners aged 60–79 were more likely to elevate their homes, while individuals with disabilities faced additional barriers. This research highlights the need for targeted policy interventions to improve program equity and ensure that vulnerable populations receive adequate support. Beyond its case study implications, this research contributes to broader discussions on disaster recovery, climate adaptation, and urban resilience. It also serves as a model for integrating student learning into community-based participatory research. While this study is limited in scope, it offers insights into the intersection of social vulnerability and housing adaptation, informing future policy efforts to enhance flood resilience in historically marginalized communities.

Leisure agriculture has become an essential driver of rural revitalization in China, yet most existing studies focus on provincial or municipal levels and rely on single-method approaches, leaving a gap in understanding spatial distribution patterns and driving mechanisms in highly urbanized metropolitan regions. This study addresses this gap by constructing a comprehensive leisure agriculture database for southern Jiangsu using multi-source data, including POI (Point of Interest), statistical yearbooks, and GIS datasets. Kernel density estimation, nearest neighbor index (NNI), geographic concentration index (GCI), and ordinary least squares (OLS) regression with VIF testing were applied to analyze spatial clustering and influencing factors. Results reveal that leisure agriculture resources exhibit significant clustering with a clear “core–periphery” pattern, concentrated in urban–rural transition zones. Agricultural output value and the number of A-level scenic spots significantly promote clustering, whereas GDP, population, and transportation density show weaker explanatory power. Theoretically, this study integrates multiple spatial statistical methods into a comprehensive analytical framework, enriching the understanding of leisure agriculture evolution under metropolitanization. Practically, it provides empirical evidence to support the optimization of leisure agriculture resource allocation, inform rural revitalization policies, and guide coordinated urban–rural planning in developed regions.

Climate change intensifies natural disasters, requiring enhanced understanding of urban resilience mechanisms. While green infrastructure’s disaster mitigation role has been established through engineering studies, empirical evidence linking green infrastructure quality to post-disaster economic adaptation remains limited. This study examines whether community-level green infrastructure quality correlates with post-disaster economic adaptation following Hurricane Laura’s August 2020 landfall. [Methods] Using a natural experiment design, we analyzed 247 Census Block Groups in two coastal Texas communities (Galveston and Port Arthur) experiencing differential disaster severity. We employed ordinary least squares regression with SafeGraph foot traffic data to measure economic recovery and satellite-derived Normalized Difference Vegetation Index (NDVI) to measure green infrastructure quality. Results demonstrate that green infrastructure quality significantly correlates with post-disaster adaptation (β = 1.27, p < 0.001), independent of socioeconomic characteristics. The NDVI–severity interaction proved non-significant, indicating consistent associations across impact contexts. These findings suggest that green infrastructure supports resilience universally rather than only in moderate-risk areas. From an environmental justice perspective, equitable distribution may reduce disaster-related inequalities, supporting “bouncing forward” adaptation trajectories.

Equitable urban heat adaptation requires knowing not only where socially vulnerable neighborhoods are hotter, but also whether disparities are widening, which dimensions of vulnerability drive change, and whether the hottest places are warming fastest. We link Landsat-derived summer land surface temperature (LST) to a principal-component-based social vulnerability index for 2,130 Chicago census block groups from 2010 to 2024. Spatially corrected regressions, block-group fixed-effects models, and geographically weighted regression distinguish inequities in heat levels, trajectories, and local heat–vulnerability coupling. The results reveal distinct and partly divergent geographies of thermal inequity. Persistent between-neighborhood heat disparities are most strongly associated with Hispanic share and linguistic isolation. A 10-percentage-point increase in Hispanic share is associated with 0.34 °C higher LST, while a one-standard-deviation increase in the Hispanic and linguistic-isolation component is associated with 0.97 °C higher LST. This spatially pervasive gradient persisted but did not widen. Warming trajectories, however, follow housing tenure and older age rather than ethnicity or income. A 10-percentage-point increase in renter share is associated with 0.14 °C per decade of cooling relative to the citywide trend, whereas owner-occupied areas with higher older-adult shares gained heat. This pattern withstands spatial-confounding correction, spatial-error modeling, alternative scene-selection rules, and sensor checks and is not explained by greening. At the same time, the within-block-group association between composite vulnerability and LST nearly tripled from 2014 to 2024. These findings demonstrate that heat levels, warming trajectories, and intensifying heat–vulnerability coupling have different social drivers. Snapshot composite indices may therefore misdiagnose risk and misdirect adaptation resources.