The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.

Unlike residential properties which are recently decreasing in transaction due to high interest rates, multi-owned retail properties have gradually increasing in transaction. With the expansion of transaction, issues for property tax such as equity of taxpayers are also raised. The assessed values of multi-owned retail properties are divided into land and building. The current base value, an assessed value of buildings, does not reflect a market value of partitioned buildings such as multi-owned retail properties largely affected by horizontal and vertical usefulness for fixing a price of real-estates. Since the assessed value is based on market value, the analysis of value influencing factors for market prices can be utilized as an alternative of market value to improve the estimation of assessed values. As a result, the value of the multi-owned retail properties is significantly influenced by characteristics of individual property such as exclusive possession area, escalator, main exit, corner store and locational characteristics including floor total building area, building age and individual public land prices. In the case of the vertical usefulness which is not considered as a factor to fix the current base value, it is expected to alleviate inequity of payers for the tax of multi-owned retail properties.

This article focuses on the relationship between church population and sustainability. We carried out the study on a sample of Presbyterian churches in South Korea, and implemented dynamic optimization of the church population based on the Susceptible–Infected–Recovered (SIR) epidemic model. In particular, System Dynamics (SD) and Agent-Based Model (ABM) simulations are performed for a prototype model with key parameters that contribute to church growth. Potential parameters reflecting sustainability for churches trigger dramatic growth in church populations. We categorized five dimensions of sustainability with various multi-dimensional indicators in order to measure the level of sustainability, and we obtained the values of the indicators by analyzing a number of news articles searched with a text mining technique. As time-dependent values of sustainability are imposed on the generic SD model for church population dynamics as sustainable potential parameters, the optimized result reproduces specific features for the church population. We discuss the roles of key parameters for sustainable church growth, and the contributions of the churches to sustainability.

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