Earticle

초록

영어

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

Abstract
1. 서론
2. 이론적 고찰
3. 선행연구 검토
4. 연구 방법
4.1. 연구의 범위
4.2. 분석자료 및 변수구성
4.3. 분석모형
5. 연구 결과
5.1. Feature Importance
5.2. Partial Dependence Plot
6. 결론
참고문헌

키워드

저자

• 이상완 [ Lee, Sanggwan | 한국국토정보공사 공간정보연구원 ] 단독저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국지역개발학회지 [Journal of the Korean Regional Development Association]

• 간기

  연5회

• pISSN

  1225-9055

• 수록기간

  1989~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 322 DDC 330