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Forecasting for the proportion of population distribution in the metropolitan and non-capital regions
Journal of the Korean Data & Information Science Society 2021;32:375-90
Published online March 31, 2021;
© 2021 Korean Data and Information Science Society.

Jongtae Kim1

1Division of Mathematics and Big Data Science, Daegu University
Correspondence to: This research was supported by the Daegu University, 2018.
1Professor, Division of Mathmatics and Big Data Science, Daegu University, Gyeongbuk 38453, Korea. E-mail:
Received February 17, 2021; Revised March 9, 2021; Accepted March 10, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The purpose of this study is to analyze the population imbalance distribution of the resident registration population of Korea in local governments, metropolitan and noncapital regions. This study predicted the future distribution of the proportion of the population of local governments, metropolitan and non-metropolitan areas, compared to the national population for 80 years from December 2020 to 2100. With these prediction results, another purpose of this study is to inform the seriousness of the imbalance in the proportion of the population of local governments and the metropolitan and non-metropolitan areas. After Korea’s population peaked at 51,851,427 in November 2019, a population deadcross occurred. As a result of the impact, Korea’s population decreased by 22,404 for the first time, and Korea’s population reached 51,829,023 in December 2020. The proportion of the population in the metropolitan and nonmetropolitan areas of Korea was around 51% vs. 49% in December 2020. However, the proportion of the population in the metropolitan and non-metropolitan areas is 62.7% vs. 37.3% in 2100, which is a serious gap. This predicts a very biased population distribution of local governments and population imbalances in metropolitan and non-metropolitan areas.
Keywords : Linear model, metropolitan, population, prediction.