Spatial stratification and socio-spatial inequalities: the case of Seoul and Busan in South Korea

Abstract

This study approaches the spatial stratification phenomenon through a data-based social stratification approach. In addition, by applying a dissimilarity-based clustering algorithm, this study analyzes how regions cluster as well as their disparities, thereby analyzing socio-spatial inequalities. Ultimately, through map visualization, this study seeks to visually identify spatial forms of social inequality and gain insight into the social structure for policy implications. The results determine how the regions are socioeconomically structured and identify the social inequalities between the spaces.

An analysis of Koreans' attitudes towards migrants by application of algorithmic approaches

“…Rudra (2014) applies a clustering algorithm using macro-level data to describe the welfare state typology of developing countries. Han (2022b) describes the spatial form of inequality by collecting structured data provided by the government and applying a clustering algorithm. Oh et al (2019) analyse variables explaining mental depression by applying various algorithms to national health surveys.…”

“…Korean society, on the other hand, is very far from the stage where it discusses how it can embrace a new influx of migrants after co-existing with ethnic minorities or migrants for a long time ( Lee, 2009 ). Recently, a large number of migrants have moved to Korea while the country has been struggling with stagnant economic growth and its weakened ability to integrate citizens due to increasing polarization, inequality, and exclusion ( Han, 2022a , 2022b ). In particular, there is a dearth of experience of coexisting with various ethnic groups since the early-modern period.…”

Exploring Vulnerability in Urban Areas: Housing and Living Poverty in Seoul, South Korea

“…In the context of Korea, the issue of income and wealth disparities has garnered signi cant attention since the Asian nancial crisis. Studies have identi ed various factors contributing to poverty in Korean society, including unequal opportunities, educational inequalities, and regional disparities (Byun and Park, 2017;Han, 2022aHan, , 2022b). These factors restrict individuals' prospects for upward mobility, perpetuating a vicious cycle of poverty, particularly within low-income communities.…”

“…While these studies contribute to understanding the social contextual structure of urban poverty, they have limitations in identifying its characteristics within speci c administrative units in the city, which hinders the design and implementation of poverty-alleviation policies. Although recent literature analyzes socio-economic disparities and inequalities between regions in urban areas of Korea (Han, 2022a; Han and Lee, 2022; Sohn and Oh, 2019), they have not speci cally focused on the urban poor who face multiple risks in a disadvantaged urban environment. This study seeks to address this gap by concentrating on the most socio-economically vulnerable regions rather than the privileged ones.…”

Mixed-integer programming techniques for the minimum sum-of-squares clustering problem

“…Clustering problems form a highly relevant sub-class of unsupervised learning in machine learning and computational statistics. Its relevance is supported by many applications, e.g., in functional data analysis [10,53], image processing [9], bio-informatics [12], economics [32], and social sciences [31]. For a detailed survey of the history of clustering problems we refer to Steinley [58].…”