This study investigates the factors contributing to bicycle accidents, focusing on four types of bicycle lanes and other exposure and built environment characteristics of census blocks. Using Seoul as a case study, three years of bicycle accident spot data from 2018 to 2020 was collected, resulting in 1,330 bicycle accident spots and a total of 2,072 accidents. The geographically weighted Poisson regression (GWPR) model was used as a methodological approach to investigate the spatially varying relationships between the accident frequency and explanatory variables across the space, as opposed to the Poisson regression model. The results indicated that the GWPR model outperforms the global Poisson regression model in capturing unobserved spatial heterogeneity. For example, the value of deviance that determines the goodness of fit for a model was 0.244 for the Poisson regression model and 0.500 for the far better-fitting GWPR model. Further findings revealed that the factors affecting bicycle accidents have varying impacts depending on the location and distribution of accidents. For example, despite the presence of bicycle lanes, some census blocks, particularly in the northeast part of the city, still pose a risk for bicycle accidents. These findings can provide valuable insights for urban planners and policymakers in developing bicycle safety measures and regulations.

• Klíčová slova
• Bicycle accident, Bicycle lane types, Geographically weighted regression, Seoul,
• MeSH
• bezpečnost statistika a číselné údaje   MeSH
• cyklistika * zranění   statistika a číselné údaje   MeSH
• dopravní nehody * statistika a číselné údaje   MeSH
• lidé MeSH
• Poissonovo rozdělení MeSH
• prostorová regrese MeSH
• rizikové faktory MeSH
• vytvořené prostředí statistika a číselné údaje   MeSH
• životní prostředí - projekt * statistika a číselné údaje   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Soul epidemiologie   MeSH

OBJECTIVES: A growing body of research has incorporated the Social Vulnerability Index (SVI) into an expanded understanding of the social determinants of health. Although each component of SVI and its association with individual-level mental health conditions have been well discussed, variation in mentally unhealthy days (MUDs) at a county level is still unexplored. To systematically examine the geographically varying relationships between SVI and MUDs across the US counties, our study adopted two different methods: 1) aspatial regression modeling (ordinary least square [OLS]); and 2) locally calibrated spatial regression (geographically weighted regression [GWR]). STUDY DESIGN: This study used a cross-sectional statistical design and geospatial data manipulation/analysis techniques. Analytical unit is each of the 3109 counties in the continental USA. METHODS: We tested the model performance of two different methods and suggest using both methods to reduce potential issues (e.g., Simpson's paradox) when researchers apply aspatial analysis to spatially coded data sets. We applied GWR after checking the spatial dependence of residuals and non-stationary issues in OLS. GWR split a single OLS equation into 3109 equations for each county. RESULTS: Among 15 SVI variables, a combination of eight variables showed the best model performance. Notably, unemployment, person with a disability, and single-parent households with children aged under 18 years especially impacted the variation of MUDs in OLS. GWR showed better model performance than OLS and specified each county's varying relationships between subcomponents of SVI and MUDs. For example, GWR specified that 69.3% (2157 of 3109) of counties showed positive relationships between single-parent households and MUDs across the USA. Higher positive relationships were concentrated in Michigan, Kansas, Texas, and Louisiana. CONCLUSIONS: Our findings could contribute to the literature regarding social determinants of community mental health by specifying spatially varying relationships between SVI and MUDs across US counties. Regarding policy implementation, in counties containing more social and physical minorities (e.g., single-parent households and disabled population), policymakers should attend to these groups of people and increase intervention programs to reduce potential or current mental health illness. The results of GWR could help policymakers determine the specific counties that need more support to reduce regional mental health disparities.

• Klíčová slova
• Geographically weighted regression, Mentally unhealthy days (MUDs), Social Vulnerability Index, Spatial modeling,
• MeSH
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• prostorová analýza MeSH
• prostorová regrese * MeSH
• průřezové studie MeSH
• sociální zranitelnost * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Michigan MeSH