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

Abstract

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. First, we uncovered significant positive correlations among accessibility to different types of SIs in four critical categories: green and recreation spaces, health and aged care facilities, educational institutions, and justice and emergency services, revealing prevalent spatial inequality patterns. Second, we identified three distinct accessibility clusters (High, Middle, and Low) across the critical SI categories. Specifically, residents within the High cluster benefited from the closest average network distances to all SIs, while those in the Low cluster faced significant accessibility burdens (e.g., 22.9 km for welfare facilities, 20.1 km for hospitals, and 19.2 km for elderly care facilities). Third, MNL identified factors such as population density and housing prices as pivotal in spatial stratification of accessibility. Specifically, areas with lower SI accessibility tended to have a higher proportion of elderly residents. Also, decreased accessibility correlated with diminished traffic volumes across all transportation modes, particularly public transportation. This research contributes to enhancing our understanding of spatial inequalities in transportation accessibility to SIs and offers insights crucial for transportation and urban planning.

Keywords: Nationwide spatial analysis; Social infrastructures; Spatial inequality; Transportation accessibility; Transportation and urban planning.

Figure 1

Framework of the multi-dimensional approach used in this study.

Figure 2

Conceptual Diagram of the Method of Explanatory Factor Analysis and K-Mean Cluster Analysis (The maps in this figure were generated using a publically available package in R Studio 2023.03.0 + 386 called “leaflet” and modified using Microsoft PowerPoint).

Figure 3

Spatial Distribution of Transportation Accessibility Variables in the 10th Quantile (The maps in this figure were generated using a publically available package in R Studio 2023.03.0 + 386 called “leaflet” and modified using Microsoft PowerPoint).

Figure 4

Results of Gini Index and Lorenz Curve (The maps in this figure were generated using a publically available package in R Studio 2023.03.0 + 386 called “ineq” and modified using Microsoft PowerPoint).

Figure 5

Correlation Matrix of Transportation Accessibility Variables (The figure was generated using a publically available package in R Studio 2023.03.0 + 386 called “Corrplot” and modified using Microsoft PowerPoint).

Figure 6

Selected Bivariate Choropleth Maps between Transportation Accessibility Variables (The maps in this figure were generated using a publically available package in R Studio 2023.03.0 + 386 called “biscale” and modified using Microsoft PowerPoint).

Figure 7

Three Clusters Found in K-Mean Clustering: (1) High, (2) Middle, and (3) Low (The maps in this figure were generated using a publically available package in R Studio 2023.03.0 + 386 called “leaflet” and modified using Microsoft PowerPoint).