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

BACKGROUND: Limited empirical evidence exists about the extent to which the current HIV epidemic intersects with COVID-19 infections at the area/geographic level. Moreover, little is known about how demographic, social, economic, behavioral, and clinical determinants are jointly associated with these infectious diseases. SETTING: Contiguous US counties (N = 3108). METHODS: We conducted a cross-sectional analysis and investigated the joint association between new HIV infection prevalence in 2018

Background: As scientific findings of air pollution and subsequent health effects have been accumulating, public interest has also been growing. Accordingly, web visualization is suggested as an effective tool to facilitate public understanding in scientific evidence and to promote communication between the public and academia. We aimed to introduce an example of easy and effective web-based visualization of research findings, relying on predicted concentrations of particulate matter ≤ 10 µg/m3

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

Abstract Background Recent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people’s homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed. Objective This simulation study aimed to understand the impact of incomplete addresses on health effect estimation base

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

BACKGROUND: The temporal investigation of high-risk areas of cancer incidence and mortality can provide practical implications in cancer control. We aimed to investigate the changes in spatial clusters of incidence and mortality from 1999 through 2013 by major cancer types in South Korea. METHODS: We applied flexible scan statistics to identify spatial clusters of cancer incidence and mortality by three 5-year periods and seven major cancer types using the counts of new cases and deaths and popu