Geographically varying associations between mentally unhealthy days and social vulnerability in the USA

Lee, Kyung Hee; Lee, Sunwoo; Ryu, Jae-Yong; Chun, Sanghee; Heo, Jinmoo

doi:10.1016/j.puhe.2023.06.033

Cited by 3 publications

(4 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Qualitative research on social capital emphasizes that key parameters of social vulnerability, such as education levels, income levels, and unemployment rates in a region, are linked to social capital formation [5,14,15]. Additionally, housing stability is a crucial social context within neighborhoods that affects social capital.…”

Section: Considering Environmental Contexts In Social Vulnerabilitymentioning

confidence: 99%

“…Conversely, neighborhoods with socio-environmental advantages can facilitate access to a wide array of services and amenities, fostering the development of social capital [13]. In this vein, previous studies have focused on the relationships between the levels of neighborhood social vulnerability and individual social capital [14,15]. Additionally, due to the importance of environmental features, some studies have accounted for built environment attributes when identifying the levels of social vulnerability in neighborhoods [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impacts of Streetscape Features on Individual Social Capital: Applying Korea’s Neighborhood Data to Street View Images to Improve Lives of the Socially Vulnerable

Kim,

Jeon,

Noh

et al. 2024

Land

View full text Add to dashboard Cite

Neighborhood social vulnerability is a dimension of vulnerability that influences society’s response to external factors, encompassing various social factors tied to socioeconomic and demographic attributes at the neighborhood level. While previous studies have explored the associations between vulnerable neighborhoods and stressors like environmental hazards, understanding of their impact on residents’ social capital remains limited. Moreover, the role of built environments in mitigating these effects is uncertain. This study investigates the impact of neighborhood social vulnerability, alongside streetscape features, on individual social capital in Seoul, South Korea. Using a stratified random sampling method, we surveyed 1000 Seoul residents, assessing their social capital and demographics. In particular, six questionnaires based on a four-point Likert scale were used to measure the individual level of social capital: two questions for social networks, three questions for social participation, and one question for sense of community. Additionally, we employed a deep learning-based model to analyze Google Street View images for detailed streetscape features. Our analysis, utilizing multiple and ordinal logistic regression models, reveals that walkable environments and neighborhood prosperity, indicated by factors like income, education, and street greenery, are linked to higher social capital. Moreover, less urbanized areas with lower land prices and greater openness to the sky also show positive associations. These findings underscore the potential of improving streetscape features to mitigate the negative impacts of neighborhood vulnerability, particularly in neighborhoods where low-income and less-educated residents are concentrated, offering insights for urban planners and community leaders.

show abstract

Section: Considering Environmental Contexts In Social Vulnerabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impacts of Streetscape Features on Individual Social Capital: Applying Korea’s Neighborhood Data to Street View Images to Improve Lives of the Socially Vulnerable

Kim,

Jeon,

Noh

et al. 2024

Land

View full text Add to dashboard Cite

show abstract

“…In statistics or econometrics, the OLS method has been carried out to fit a linear relationship between a dependent or response variable and one or more independent or predictor variables by minimizing the loss function (Cole & Neumayer, 2004;Lee, Lee, Ryu, Chun & Heo, 2023;Middya & Roy, 2021). In the OLS method, the global relationship has been observed between predictor and response variables.…”

Section: Olsmentioning

confidence: 99%

“…Being an extension of the global regression model, GWR has been applied to capture the spatial variability by considering local parameter estimation in a continuous manner (Lee et al, 2023;Middya & Roy, 2021). The model assumes that the regression coefficients are the function of geographical locations or coordinates.…”

Section: Gwrmentioning

confidence: 99%

Spatial and seasonal association study between P M2.5 and related risk factors in India

Sengupta,

Middya,

Dutta

et al. 2024

Preprint

View full text Add to dashboard Cite

Global environmental degradation and climate change causing persistent health issues due to increased exposure to pollution have become a serious matter of concern and studies. This human-generated environmental pollution is caused due to rapid industrialization, construction-related issues, different festive occasions, etc. Remarkable seasonal and spatial distributions have been observed in this context. Among all the existing pollutants, the fine airborne particles P M2.5(with aerodynamic equivalent diameter ≤ 2.5μm) and P M10 (with aerodynamic equivalent diameter ≤ 10μm) are associated with chronic pulmonary and cardiovascular diseases. This leads to carrying out the study regarding the varying relationship between P M2.5 and other related risk factors so that its concentration level might be under control. Existing literature has explored the geographical association between the pollutants with a few other important factors. To address this problem, the present study aims to explore the widespatiotemporal relationships between the particulate matter (P M2.5) with the other associated risk factors (e.g., socio-demographic, meteorological factors, and air pollutants). For this analysis, the geographically weighted regression (GWR)model with different kernels (viz. Gaussian and Bisquare kernels) and the ordinary least squares (OLS) model have been carried out to analyze the same from the perspective of the four major seasons (i.e., autumn, winter, summer, and monsoon) in different districts of India. It may be inferred from the results that the local model (i.e., GWR model with Bisquare kernel) captures the spatial heterogeneity in a better way with R2 values 0.994, 0.993, 0.992, 0.991, and 0.994respectively for four different seasons and for the corresponding 1-year cycle during October 2022-September 2023. The GWR model using the Bisquare kernel method performs better in terms of smaller corrected Akaike Information Criterion (AICc) (i.e., -896.883, -790.469, -618.686, -834.706, and -867.802 for four specific seasons and the corresponding 1-year cycle period respectively).

show abstract

Localized effects of neighborhood park exposure on mental illness mortality in the Pacific Northwest United States

Song,

Luan

2024

Applied Geography

View full text Add to dashboard Cite

Geographically varying associations between mentally unhealthy days and social vulnerability in the USA

Cited by 3 publications

References 45 publications

Impacts of Streetscape Features on Individual Social Capital: Applying Korea’s Neighborhood Data to Street View Images to Improve Lives of the Socially Vulnerable

Impacts of Streetscape Features on Individual Social Capital: Applying Korea’s Neighborhood Data to Street View Images to Improve Lives of the Socially Vulnerable

Spatial and seasonal association study between P M2.5 and related risk factors in India

Localized effects of neighborhood park exposure on mental illness mortality in the Pacific Northwest United States

Contact Info

Product

Resources

About