교통 접근성과 인구･사업체 밀도와의 상관관계에 관한 연구

• 영문명: Application of Interpretable Machine Learning to Explore Associations between Transportation Accessibility and Population and Business Density
• 발행기관: 한국지역개발학회
• 저자명: 이상완
• 간행물 정보: 『한국지역개발학회지』제35권 제1호, 105~120쪽, 전체 16쪽
• 주제분류: 사회과학 > 지역학
• 파일형태: PDF
• 발행일자: 2023.03.30

영문 초록

This study examined associations between diverse accessibility indicators and population and business density using Extreme Gradient Boosting Decision Tree Regressor (XGB) and Interpretable Machine Learning (XAI). The main results are as follows. First, the results of feature importance reveal that the accessibility indicators exerted a more considerable contribution to predicting the density than control variables. Specifically, accessibility to hospitals appeared to have the most significant impact on explaining population and business density. In addition, accessibility to elementary, middle, and high schools showed high importance in explaining the population density, while it was found to have a smaller importance in explaining the business density. Second, findings of partial dependence plots derived non-linear relationships between accessibility and density. For instance, the population or business density was highest when the travel time to elementary, middle, and high school was between 5 and 7 minutes. This study contributes to (1) offering a more detailed understanding of the relationship between transportation accessibility and density and (2) providing associated discussions and policy implications.

목차

1. 서론

2. 이론적 고찰

3. 선행연구 검토

4. 연구 방법

5. 연구 결과

6. 결론

참고문헌

키워드

교통 접근성 토지이용 해석가능한 기계학습 Transportation Accessibility Land Use Interpretable Machine Learning