earticle

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.