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Inference on the difference in male and female populations in the metropolitan area, non-metropolitan area, and local governments
Journal of the Korean Data & Information Science Society 2021;32:853-65
Published online July 31, 2021;  https://doi.org/10.7465/jkdi.2021.32.4.853
© 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, 2019.
1 Professor, Division of Mathmatics and Big Data Science, Daegu University, Gyeongbuk 38453, Korea.
E-mail: jtkim@daegu.ac.kr
Received March 31, 2021; Revised June 21, 2021; Accepted June 29, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
The purpose of this study is to infer the difference in the population ratio by gender between men and women in the metropolitan area, non-metropolitan area and local government of South Korea’s resident registration population 80 years later, until December 2100. And it’s to show how serious the gender difference is. The seriousness of the gender disparity and gender disparity in the metropolitan area and non-metropolitan area shown in the inference results of this study will have a very adverse effect on balanced regional development. The female population continues to grow, peaking at 25,987,994 in 2020. However, after peaking at 25,866,129 in 2018, the male population declined to 25,841,029 in 2020. In 2100, the female population in the non-metropolitan area will increase by 321,000, and the female population in the metropolitan area will be 1,889,000 more than the male population. In conclusion, South Korea’s population deadcross phenomenon is influenced by the rapid decrease of the male population than the rate of increase of the female population, and the centralization phenomenon in the metropolitan area and the gender inequality in the metropolitan area and non-metropolitan area will become more serious.
Keywords : Linear model, metropolitan, population, prediction.