송인상 교수

Recent cohort studies have relied on exposure prediction models to estimate individuallevel air pollution concentrations because individual air pollution measurements are not available for cohort locations. For such prediction models, geographic variables related to pollution sources are important inputs. We demonstrated the computation process of geographic variables mostly recorded in 2010 at regulatory air pollution monitoring sites in South Korea. On the basis of previous studies, we finaliz

We found that there is a considerable intersection between the current distribution of HIV burden with COVID-19 infections at the area level. We identified areas that federal funding and vaccination campaigns should prioritize for prevention and care efforts.

Our example contributes to future studies that develop the visualization of research findings in further intuitive designs.

Machine learning (ML) is being applied in an increasing volume of geographical research. However, the aspects of spatial autocorrelation (SAC) in the residuals produced by ML models have been understudied compared to the benefit of ML, namely, reduction of prediction errors. In this study, we examined the relationship between predictive accuracy and the reduction in the residual SAC for 597 variables from 25 geographical socio‐economic data sets using spatial and nonspatial cross‐validation of t

Our simulation study focused on a common and practical challenge of limited address information in air pollution epidemiology, and investigated its impact on health effect analysis. Cohort studies of air pollution have developed advanced exposure prediction model to allow the estimation of individual-level long-term air pollution concentrations at people's addresses. However, it is common that address information of existing health data is available at the coarse spatial scale such as city, dist

In geographical literature, numerous studies have demonstrated the differences that arise if spatial autocorrelation (SAC) is incorporated into a conventional nonspatial modeling procedure, but little is known about when these differences might be magnified. This study addressed this query by conducting two sets of regression modeling for 561 variables representing housing prices, metropolitan industry, health, crime, education, and (un)employment across various parts of the United States: (1) n

<b>Objectives.</b> To use activity space assessments to investigate neighborhood exposures that may heighten young Black men's vulnerability to substance use and misuse. <b>Methods.</b> We surveyed young Black men in New Haven, Connecticut in 2019 on the locations (activity spaces) they traveled to in a typical week and their experiences of racism and any alcohol and cannabis use at each location. <b>Results.</b> A total of 112 young Black men (mean age = 23.57 years; SD = 3.20) identified 583 a