Characteristics for the Distribution of Elderly Population by Utilizing the Census Data

Abstract

After city of Busan has been entered to the aging society in 2000, the city has the highest aging rate among 7 representative cities in 2011. Moreover, while entire population and number of average household are decreasing, over 65 years old of elderly population is rapidly increasing. So, it is possible to enter the super-aged society, where aging rate would be about 20% after 2020. The purpose of this study is that older housing-related analysis is consisted of dong-unit, and this led microscopic analysis has become necessary. Surveys from 2000 through 2010, census aggregate (output area) unit of spatial analysis was conducted. Take advantages of this, aging population and area, soaring area, high-density areas, such as the region of interest were primary extracted, and microscopic location and spatial distribution patterns were analyzed. Upon analysis, aging population is concentrated in the city and adjacent area, the highlands, and 10 years of increasing rate was more than 30 times in certain aggregate. Regarding the characteristic of these areas, the original city center, Busan, especially concentrated and intensified in aging population. Also, 2000 to 2010, the overall distribution pattern of Busan has identified aging population that is increasingly being distributed. This is the result, which is confronted with previous research result. Entering a super aged-society for the future is accordance with migration of social costs and improve the quality of life of elderly. And this could be the basic information to use the spatial dimension for the corresponding.

An Analysis on the Spatial Spillover Patterns of Aging Population in Rural Areas

“…This study intends to suggest an useful information to establish the area-specific policy on aging population through the results of analysis. (Kim, 2008;Lee and Koo, 2012;Nam and Gwon, 2013) (Lowe and Speakman, 2006;Keating, 2008;Glasgow and Brown, 2012 LISA 기법은 index 값을 통해 공간적 자기 상관의 유형을 High-High, High-Low, Low-High, Low-Low와 not significant 지역으로 구분한다 (Anselin, 1995…”