This study aims to suggest an approach that integrates multilevel models and eigenvector spatial filtering methods and apply it to a case study of self-rated health status in South Korea. In many previous health-related studies, multilevel models and single-level spatial regression are used separately. However, the two methods should be used in conjunction because the objectives of both approaches are important in health-related analyses. The multilevel model enables the simultaneous analysis of both individual and neighborhood factors influencing health outcomes. However, the results of conventional multilevel models are potentially misleading when spatial dependency across neighborhoods exists. Spatial dependency in health-related data indicates that health outcomes in nearby neighborhoods are more similar to each other than those in distant neighborhoods. Spatial regression models can address this problem by modeling spatial dependency. This study explores the possibility of integrating a multilevel model and eigenvector spatial filtering, an advanced spatial regression for addressing spatial dependency in datasets.

Tuberculosis (TB) incidence and corresponding mortality rates in S. Korea are unusual and unique compared to other economically developed countries. Korea has the highest TB incidence rate in Organization for Economic Co-operation and Development (OECD) countries. TB is known as a disease reflecting socio-economic and environmental conditions of a society. Besides, TB is an infectious disease spread through the air, naturally forming spatial dependence of its incidence. This study investigates TB incidences in Korea in socio-economic and environmental perspectives. Eigenvector spatial filtering applied accounts for spatial autocorrelation in the TB incidence, and Getis-Ord statistic tracks the changes of TB clusters at given time. The results show that population composition ratio, population growth rate, health insurance payment, and public health variables are significant throughout the study period. Environmental variables make minor effects on TB incidence. This study argues that unique demographic features of Korea are a potential threat to TB control in the future.

In a competitive business environment, retailers brand themselves using unique location strategies. New retailers, especially, use their location strategy to construct a different brand concept from that of the first mover. Therefore, retail chains in the same industry show different location patterns. This article aims to investigate how a new retailer uses its location strategy to differentiate itself from the first mover by comparing the location patterns of two coffee chains. The location patterns of Starbucks Coffee (a “first mover” that employs a premium brand concept) and Ediya Coffee (a “new retail chain” that promotes an economical brand concept) are analyzed for the study. We use a Bayesian spatial model to explore the two retailers' location patterns in Seoul, Korea. Considerable differences are found in the spatial distribution patterns of the two coffee franchises. Starbucks has formed store groupings in prime areas such as the city center based on its cluster location strategy, reflecting its premium brand concept. Conversely, many Ediya Coffee shops have been located in less desirable areas such as fringe areas with low land prices, reflecting their economical brand concept and differentiating the company from Starbucks. These findings lead to a useful marketing implication for a retail startup formulating its location strategy for differentiation. Furthermore, this study's company-level analysis of retail location patterns provides a better understanding of complex retail geography.

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul’s COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

Since its re-emergence in 1993, the spatial patterns of malaria outbreaks in South Korea have drastically changed. It is well known that complicated interactions between humans, nature, and socio-economic factors lead to a spatial dependency of vivax malaria occurrences. This study investigates the spatial factors determining malaria occurrences in order to understand and control malaria risks in Korea. A multilevel model is applied to simultaneously analyze the variables in different spatial scales, and eigenvector spatial filtering is used to explain the spatial autocorrelation in the malaria occurrence data. The results show that housing costs, average age, rice paddy field ratio, and distance from the demilitarized zone (DMZ) are significant on the level-1 spatial scale; health budget per capita and military base area ratio are significant on the level-2 spatial scale. The results show that the spatially filtered multilevel model provides better analysis results in handling spatial issues.

Objectives: This study aims to identify the spatial inequality of tuberculosis (TB) and related socio-environmental factors in Gyeongsangbuk-do, Korea. Methods: This study explains TB incidences in a spatial perspective. A spatial multilevel regression model was applied to account for the spatial inequality of TB and related factors. Applied socio-environmental factors were average age, population density, Normalized Difference Built-up Index (NDBI), particulate matter (PM10), medical insurance, financial independence rate, and medical doctors per 1,000 people. Results: This study finds evident spatial inequality of TB in Gyeongsangbuk-do. Most TB hotspots are identified around Yeongyang-gun, the area with the lowest socio-economic status in Korea. The regression result shows that level 1 factors (Average age, NDBI, PM10) and level 2 factors (medical insurance, financial independence rate) were statistically significant related to TB incidences. Furthermore, spatial filtering applied to multilevel regression accounts for spatial autocorrelation of TB incidence. Conclusions: Regional socio-economic and demographic conditions in Gyeongsangbuk-do influence TB incidences. Given low expectations on Gyeongsangbuk-do’s socio-economic and demographic growth, reducing the spatial inequality of TB would be challenging.