Infectious diseases and pandemics, including the COVID-19 pandemic, have a huge economic impact on cities. However, few studies examine the economic resilience of small-scale regions within cities. Thus, this study derives neighborhoods with high economic resilience in a pandemic situation and reveals their urban characteristics. It evaluates economic resilience by analyzing changes in the amount of credit card payments in the neighborhood and classifying the types of neighborhoods therefrom. The study conducted the ANOVA, Kruskal–Wallis, and post hoc tests to analyze the difference in urban characteristics between neighborhood types. Accordingly, three neighborhood types emerged from the analysis: high-resilient neighborhood, low-resilient neighborhood, and neighborhood that benefited from the pandemic. The high-resilient neighborhood is a low-density residential area where many elderly people live. Neighborhoods that benefited are residential areas mainly located in high-density apartments where many families of parents and children live. The low-resilient neighborhood is an area with many young people and small households, many studio-type small houses, and a high degree of land-use mix.

Sustainability assessment systems are commonly used to achieve sustainability. However, internationally agreed systems are difficult to locate. Analysis of sustainability assessment systems in various contexts can lead to creating a standardized sustainability assessment framework. Here, we reviewed the experience of the sustainability assessment system applied in Korea for sharing knowledge and providing implications for creating internationally standardized sustainability assessment frameworks. The subject of the study was the assessment of sustainability of cities (ASC, conducted annually by the Korean government) using data from the 2015 assessment and consisting of two parts: Descriptive analysis of the ASC system and analysis of the 2015 ASC assessment results. The ASC assessment unit is the low-ranked municipality, and indicators include social, economic, environmental, and institutional themes. The ASC assesses overall sustainability by incorporating mandatory and voluntary indicators, and the Korean government encourages improved sustainability through awards, diagnostic reports, and incentives. Gun was highly evaluated in social sustainability, and the southern Seoul metropolitan area and Chungcheong area were highly evaluated in economic sustainability. In environmental sustainability, metropolitan cities received high scores, while institutional theme scores were not high in any specific area. Municipalities in metropolitan cities received higher voluntary indicator scores than rural areas.

Zoning is crucial for controlling animal infectious diseases and movement plays a major role in disease transmission. However, movement-based zoning has received little research attention. This study aimed to identify biosecurity zones divided by administrative unit, based on communities detected in movement network. We used vehicle entry data from November 2013 to January 2017. We split the data to analyze changes in networks over time and seasons (3 summer and 4 winter). The HN algorithm for mega-scale networks was used to detect communities. We identified biosecurity zones based on the geographical concentration of facilities belonging to the same communities. Jenks Natural Breaks Method was used to determine whether facilities were agglomerated. The zone classifications derived for seven seasons were overlaid to identify an integrated zone classification. The number of significant communities declined from 10 to 7 over time, from which we inferred that separated communities tended to aggregate. Therefore, biosecurity zones that were separate in the past merged and the number of zones decreased. From the overlay, seven biosecurity zones were derived. These zones are different from the conventional control zones, which do not consider movement. Therefore, these biosecurity zones can be used as an alternative control zone to complement existing zoning systems in Korea.

Since urban areas with high air pollution are known to have higher mortality rates compared to areas with less air pollution, accurately understanding and predicting the distribution of particulate matter (PM) in cities is important for urban planning policies that seek to emphasize the health of citizens. Therefore, this study aims to investigate the relationship between PM and land use in metropolitan cities in South Korea using the land-use regression model. We use daily data from the air quality monitoring stations (AQMS) in seven cities in South Korea for the year 2018. For analysis, K-means clustering is employed to identify the land-use pattern surrounding the AQMSs and two log-lin regression models are used to investigate the effects of each land-use type on PM. The findings show a statistically significant difference in PM concentration and variability in the business, commercial, industrial, mixed, and high-density residential areas compared to parks and green areas, and that PM concentration and variability were less in mixed areas than in single land use, thus verifying the effectiveness of a mixed land-use planning strategy. Moreover, microclimatic, seasonal, and regional factors affect PM concentration and variability. Finally, to minimize exposure to PM, various policies such as mixed land use need to be established and implemented differently, depending on the season and time.

Identifying how the urban environment affects pedestrian volume is a traditional urban planning topic. Recently, because of climate change and air pollution, interest in the effects of urban microclimates has been increasing. However, it is unclear whether the effects of microclimate on pedestrian volume can vary depending on the urban environment. This study determines whether microclimate’s influence on pedestrian volume differs according to land-use in the urban environment in Seoul, Korea. We constructed eight models with microclimate factors (temperature, precipitation, and PM10) as independent variables, using pedestrian volume as the dependent variable. We classified the models according to season and land-use and conducted a negative binomial regression analysis. The results confirmed that the effect of microclimate on pedestrian volume varies by land-use. A summary of the results is as follows. First, residential areas had more microclimate factors that significantly affected pedestrian volume compared to commercial areas. Second, for microclimate variables that had significant influences in commercial areas, the size of their influence was greater in commercial than in residential areas. Third, the influence of microclimatic factors on pedestrian volume in mixed-use areas has intermediate characteristics between residential and commercial areas.

The ongoing debate over whether polycentric urban structures reduce commuting times has yielded conflicting conclusions, highlighting the need for empirical findings in diverse urban contexts and analyses that consider a range of influencing factors. This study analyzed the effects of employment center characteristics on commuting times, using the Seoul Metropolitan Area (SMA) as a case study. A cutoff method identified employment centers within the SMA. Differences in commuting behavior, including average commuting time and mode share, were observed among workers at different employment centers. A multilevel regression model estimated the effect of employment center characteristics, such as industry composition and nearby housing prices, on workers’ commuting time. Key findings include a positive relationship between public transportation (PT) density and commuting time, suggesting that well-designed PT systems may encourage longer commutes. Manufacturing and finance, insurance, and real estate (FIRE) industries negatively impacted commuting times, with manufacturing being associated with the geographic location of centers and FIRE industries being associated with high-income workers, which likely contributed to shorter commutes. On the other hand, the positive relationship between housing prices and commuting times highlights the need for affordable housing near employment centers to reduce commuting times. These findings underscore the complex interactions between each employment center’s characteristics and workers’ socioeconomic factors in shaping commuting behavior.