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Determinants Occurrence Factors of Vacant Houses Using Spatial Regression Analysis - Focusing on Vacant Houses of Detached Houses in Gyeongsangnam-do -

공간회귀 분석을 활용한 빈집 발생 요인 분석 - 경상남도 단독주택 빈집을 대상으로 -

  • 이소영 (경상대학교 도시공학과) ;
  • 주희선 (경상대학교 도시공학과)
  • Received : 2021.07.12
  • Accepted : 2021.08.25
  • Published : 2021.09.30

Abstract

Vacant houses, which are directly linked to various social problems, are increasing rapidly throughout the country. In this study sought to understand the occurrence of vacant houses as a spatial phenomenon and to demonstrate factors affecting the occurrence of vacant houses. Therefore, the purpose of this study is as follows: First, it was intended to identify the distribution and analyze the current status of empty houses in Gyeongsangnam-do by utilizing actual empty house address data. Second, the spatial characteristics of vacant houses were understood based on spatial autocorrelation analysis of vacant houses. Third, variables were selected based on prior studies and the factors affecting empty houses were analyzed through spatial regression analysis. Finally, it was intended to explore the connection between regional characteristics and the occurrence of vacant houses, and to derive influencing factors related to the occurrence of vacnat houses. Analysis results shows that the distribution of vacant houses is distributed in large numbers around geographically poor areas. The increase in vacant houses could also be observed centered around areas near existing vacant houses. Furthermore, the spatial regression analysis showed that the occurrence of vacant houses was highly distributed in areas with poor terrain and environment, such as administrative boundary areas, river boundary areas, and wastewater discharge sites. Based on these results, it seems necessary to take measures against vacant houses in dangerous areas in order to prepare for the increase in vacant houses in advance in the future, taking into account regional characteristics.

Keywords

Acknowledgement

이 연구는 2020년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호:2020R1G1A1101214

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