Mapping the Heartbeat of America with ChatGPT-4: Unpacking the Interplay of Social Vulnerability, Digital Literacy, and Cardiovascular Mortality in County Residency Choices DOI Open Access
Mohammed M. Ali, Subi Gandhi, Samian Sulaiman

и другие.

Journal of Personalized Medicine, Год журнала: 2023, Номер 13(12), С. 1625 - 1625

Опубликована: Ноя. 21, 2023

Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible US for cardiovascular research, digital literacy (DL) has not been explored as potential factor influencing mortality, although Social Vulnerability Index (SVI) used previously variable predictive modeling. Utilizing large language model, ChatGPT4, we investigated variability CVD-specific that could be explained by DL SVI using regression We fitted two models to calculate crude adjusted CVD rates. Mortality ICD-10 codes were retrieved from CDC WONDER, geographic level was Department Agriculture. Both datasets merged Federal Information Processing Standards code. The initial exploration involved 1999 through 2020 (n = 65,791; 99.98% complete all Counties) (CCM). Age-adjusted (ACM) had 3118 rows; 99% Counties), with inclusion model (a composite internet access). By leveraging on advanced capabilities ChatGPT4 linear regression, successfully highlighted importance incorporating predicting mortality. Our findings imply just availability may sufficient without significant variables, such SVI, predict ACM. Further, our approach enable future researchers consider key variables study other health outcomes public-health importance, which inform clinical practices policies.

Язык: Английский

Spatial and temporal patterns of supply and demand risk for ecosystem services in the Weihe River Main Stream, NW China DOI

Dan Men,

Jinghu Pan,

Xuwei Sun

и другие.

Environmental Science and Pollution Research, Год журнала: 2022, Номер 30(13), С. 36952 - 36966

Опубликована: Дек. 23, 2022

Язык: Английский

Процитировано

15
Improving Home Insurance Ratemaking with Geographically Weighted Poisson Regression (GWPR) Model: Assessing Water Damage Risk DOI Creative Commons
M.V. Lopez, Mariano Matilla‐García, Román Mı́nguez

и другие.

Applied Spatial Analysis and Policy, Год журнала: 2025, Номер 18(1)

Опубликована: Фев. 11, 2025

Язык: Английский

Процитировано

0
Current and Future Trends for Resilient Inland Waterway Transportation Systems during Flood Disruptions DOI
Shokoufeh Ahmadi, Jennifer I. Lather, Christine E. Wittich

и другие.

Journal of Construction Engineering and Management, Год журнала: 2025, Номер 151(6)

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

0
Assessing the Environmental Justice Implications of Seismic Risk in Ottawa-Gatineau and Montreal Metropolitan Areas DOI Creative Commons
Liton Chakraborty, Angela Spinney, Daniele Malomo

и другие.

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер unknown, С. 105516 - 105516

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province DOI
Weiting Yue, Yunji Gao, Yao Xiao

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Abstract Climate change and increased human activity have resulted in an increase the frequency intensity of wildfires. Effective wildfire risk assessment is essential for disaster prevention, resource protection, regional stability. Existing studies often overlook spatial heterogeneity temporal patterns wildfires, with limited county-scale quantitative assessments. To address these gaps, multidimensional framework Sichuan Province was proposed, combining characterization modeling. Temporal trends mutation wildfires from 2001 to 2023 were analyzed using Mann-Kendall test. Additionally, model constructed by hazard vulnerability Specifically, assessed Multiscale Geographically Weighted Regression (MGWR) capturing driving factors. Vulnerability through Multi-Criteria Decision Analysis (MCDA) approach identify areas high their factor importance. The results indicated a significant rise particularly during winter non-fire prevention periods. MGWR effectively captured heterogeneity, identifying highest levels southwestern Sichuan, Liangshan Prefecture Panzhihua City. High scattered, mainly across southwestern, southern, northern Sichuan. integrated revealed that its surrounding counties exhibited significantly higher than other regions, while eastern northeastern regions demonstrated lowest risk. This study provides scientific foundation targeted management, emergency response strategies Province, offering valuable insights policymakers managers.

Язык: Английский

Процитировано

0
Exploring Socio-Spatial Inequalities in Flood Response Using Flood Simulation and Social Media Data: A Case Study of 2020 Flood in Nanjing, China DOI Open Access
Yi Chen, Yang Zhang,

Dekai Tao

и другие.

Climate, Год журнала: 2025, Номер 13(5), С. 92 - 92

Опубликована: Апрель 30, 2025

Identifying socio-spatial inequalities in flood resilience is crucial for effective disaster risk management. This study integrates susceptibility simulations and Weibo activity data to construct a index incorporates differentiation represent residents’ coping capacities. By combining awareness capacity, we develop comprehensive response capability model examine the spatial patterns of inequality. The findings reveal that (1) high concentrated near Yangtze River major lakes based on social media simulations; (2) capacity floods exhibits central–periphery pattern, with higher urban centers gradually decreases suburban exurban areas; (3) communities are classified into four types combination Multiple linear regression analysis indicates both natural factors significantly influence capacity. research provides critical insights resilience, offering valuable guidance formulating targeted adaptation strategies.

Язык: Английский

Процитировано

0
Spatial heterogeneity in social vulnerability to flood exposure DOI Creative Commons
Shelley Hoover, Eric Tate

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Май 15, 2025

Язык: Английский

Процитировано

0
Multiscale Analysis of the Relationship between Toxic Chemical Hazard Risks and Racial/Ethnic and Socioeconomic Groups in Texas, USA DOI
Guangxiao Hu, Kuishuang Feng, Laixiang Sun

и другие.

Environmental Science & Technology, Год журнала: 2023, Номер 57(5), С. 2019 - 2030

Опубликована: Янв. 24, 2023

Although quantitative environmental (in)justice research demonstrates a disproportionate burden of toxic chemical hazard risks among racial/ethnic minorities and people in low socioeconomic positions, limited knowledge exists on how groups across geographic spaces experience hazards. This study analyzed the spatial non-stationarity associations between risk community characteristics census block Texas, USA, for 2017 using multiscale geographically weighted regression. The results showed that percentage Black or Asian population has significant positive with meaning racial suffered more from wherever they are located state. By contrast, Hispanic Latino relationship risk, varies locally is only eastern areas Texas. Statistical variables not stationary state, showing sub-state patterns variation terms sign, level, magnitude coefficient. Income negative association around Dallas–Fort Worth–Arlington Metropolitan Area. Proportions without high school diploma unemployment rate both have relationships area Our findings highlight importance identifying at group level addressing inequality.

Язык: Английский

Процитировано

6
Flooding and inequality: A multilevel analysis of exposure to floods and poverty in French cities DOI
Kenji Fujiki, Olivier Finance,

Joanne Hirtzel

и другие.

Applied Geography, Год журнала: 2024, Номер 164, С. 103193 - 103193

Опубликована: Фев. 2, 2024

Язык: Английский

Процитировано

2
Urban–Rural Exposure to Flood Hazard and Social Vulnerability in the Conterminous United States DOI Creative Commons
Bishal Dhungana, Weibo Liu

ISPRS International Journal of Geo-Information, Год журнала: 2024, Номер 13(9), С. 339 - 339

Опубликована: Сен. 22, 2024

This study investigates the spatial disparities in flood risk and social vulnerability across 66,543 census tracts Conterminous United States (CONUS), emphasizing urban–rural differences. Utilizing American Community Survey (ACS) 2016–2020 data, we focused on 16 factors representing socioeconomic status, household composition, racial ethnic minority housing transportation access. Principal Component Analysis (PCA) reduced these variables into five principal components: Socioeconomic Disadvantage, Elderly Disability, Housing Density Vehicle Access, Youth Mobile Housing, Group Quarters Unemployment. An additive model created a comprehensive Social Vulnerability Index (SVI). Statistical analysis, including Mann–Whitney U test, indicated significant differences between urban rural areas. Spatial cluster analysis using Local Indicators of Association (LISA) revealed high clusters, particularly regions along Gulf Coast, Atlantic Seaboard, Mississippi River. Global local regression models, Ordinary Least Squares (OLS) Geographically Weighted Regression (GWR), highlighted vulnerability’s variability localized impacts risk. The results showed substantial regional disparities, with areas exhibiting higher risks vulnerability, especially southeastern centers. also that Unemployment, Access are closely related to areas, while relationship such as Disability is more pronounced. underscores necessity for targeted, region-specific strategies mitigate enhance resilience, where converge. These findings provide critical insights policymakers planners aiming address environmental justice promote equitable management diverse geographic settings.

Язык: Английский

Процитировано

2