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Analysis of Factor Influencing Vacant House Occurrence in Depopulation Regions and Development of an Estimation Model using Machine Learning: A Case Study of Gongju-si
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머신러닝을 활용한 인구감소지역의 빈집 발생 요인 분석 및 추정 모델 개발 - 충청남도 공주시를 대상으로

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Type
Academic journal
Author
Jeong, Yeon-Jun Lee, Kyung-Hwan (공주대학교)
Journal
Urban Design Institute of Korea Journal of the Urban Design Institute of Korea Urban Design Vol.25 No.4(Wn.124) KCI Accredited Journals
Published
2024.8
Pages
39 - 55 (17page)
DOI
10.38195/judik.2024.08.25.4.39

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Analysis of Factor Influencing Vacant House Occurrence in Depopulation Regions and Development of an Estimation Model using Machine Learning: A Case Study of Gongju-si
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Abstract· Keywords

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The purpose of this study is to derive factors influencing the occurrence of vacant houses and develop a vacant houses prediction model using machine learning to improve the efficiency of the vacant house estimation method in depopulation region. To summarize the research results, first, in a review of previous research on factors affecting the occurrence of vacant houses, it was confirmed that vacant houses occur due to a complex effect of influencing factors of various scales and fields, which were divided into 20 influencing factors in Three sectors. Second, as a result of learning three models by setting the vacant house impact factors derived through previous research review and the confirmed vacant house data in Gongju City as learning data and target data, the XGBoost model showed the highest performance. In addition, it was confirmed that social and economic factors were also factors of high importance and that it was necessary to estimate vacant homes based on complex data. Third, when we compared the prediction results with the spatial environmental characteristics of Gongju City, it was predicted that areas with dense multi-household housing in the new downtown were likely to have vacant houses. In the old downtown and rural areas, it was predicted that areas with old detached house on slopes would have a high possibility of vacant houses. This study suggested a way to streamline the vacant house estimation process by developing a comprehensive data-based vacant house estimation model.
#빈집 #머신러닝 #추정모델 #인구감소지역 #Vacant Houses #Machine Learning #Prediction Model #Depopulation Regions

Contents

국문요약
Abstract
1. 서론
2. 이론적 고찰
3. 데이터 구축 및 분석방법
4. 분석 결과
5. 결론
참고문헌

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