| Title |
Influencing Factors and Spatial Patterns of Traditional Market Vacancy Rates Across Different City Sizes in Gyeongsangnam-do, South Korea |
| Authors |
이다니엘(Lee, Daniel) ; 최규진(Choi, Kyujin) ; 남기정(Nam, Kijung) ; 오경호(Oh, Kyungho) ; 김서영(Kim, Seoyoung) ; 손동욱(Sohn, Dong-Wook) |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.4.111 |
| Keywords |
Traditional Market; Vacancy Rate; Gyeongsangnam-do; Spatial Analysis; Machine Learning |
| Abstract |
This study investigates the causes of increasing vacancy rates in traditional markets across Gyeongsangnam-do and analyzes the main
influencing factors according to urban scale. Traditional markets in the region were examined using public statistical data from 2014 to 2023.
The analysis applied a Random Forest algorithm, classifying markets into three urban types: large, medium, and small cities or towns.
Findings indicate that vacancy rates in Gyeongsangnam-do’s traditional markets display nonlinear patterns. In large cities, metropolitan factors
such as overlapping living areas between adjacent cities were prominent. In medium-sized cities, inland locations and catchment area demand
had the greatest impact. In small towns, market scale and aging infrastructure were key determinants. As these markets serve as essential
consumption hubs in areas with limited competing facilities, changes in the internal demand base emerged as the primary driver of vacancies.
This study offers an objective, data-driven assessment of traditional market vacancies through machine-learning analysis, providing empirical
evidence to support sustainable management and policy development for regional commercial districts. |