Understanding spatial inequalities and stratification in transportation accessibility to social infrastructures in South Korea: multi-dimensional planning insights.

This research investigated spatial inequalities in transportation accessibility to social infrastructures (SIs) in South Korea, using a multi-dimensional methodological approach, including descriptive/bivariate analysis, explanatory factor analysis (EFA), K-Mean cluster analysis, and multinomial logit model (MNL). Our study confirmed pronounced spatial disparities in transportation accessibility to SIs, highlighting significantly lower access in rural and remote regions compared to urban centers and densely populated areas, consistent with existing literature. Building on prior findings, several additional findings were identified.

Temporal dynamics of public transportation ridership in Seoul before, during, and after COVID-19 from urban resilience perspective.

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases.

The Effects of Urban Containment Policies on Public Health.

Public health risks such as obesity are influenced by numerous personal characteristics, but the local spatial structure such as an area's built environment can also affect the obesity rate. This study analyzes and discusses how a greenbelt plan as a tool of urban containment policy has an effect on obesity. This study conducted spatial econometric regression models with five factors (13 variables) including transportation, socio-economic, public health, region, and policy factors.

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching.

This paper proposes a method to detect corresponding vertex pairs between planar tessellation datasets. Applying an agglomerative hierarchical co-clustering, the method finds geometrically corresponding cell-set pairs from which corresponding vertex pairs are detected. Then, the map transformation is performed with the vertex pairs.

New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System.

This research introduces an improvement plan for the reliability of Global Navigation Satellite System (GNSS) positioning solutions. It should be considered the most suitable methodology in terms of the adjustment and positioning of GNSS in order to maximize the utilization of GNSS applications. Though various studies have been conducted with regards to Bridge Health Monitoring System (BHMS) based on GNSS, the outliers which depend on the signal reception environment could not be considered until now.