How the physical inactivity is affected by social-, economic- and physical-environmental factors: an exploratory study using the machine learning approach

作     者：Kangjae Lee Jue Wang Joon Heo 

作者机构：Department of Location-Based Information SystemKyungpook National UniversitySangju-siGyeongsangbukdoRepublic of Korea Department of GeographyGeomatics and EnvironmentUniversity of Toronto MississaugaMississaugaCanada Department of Civil and Environmental EngineeringYonsei UniversitySeoulRepublic of Korea 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：2503-2521页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0703[理学-化学] 0714[理学-统计学（可授理学、经济学学位）] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.2022000000150) 

主　　题：Physical inactivity environmental effects machine learning GIS 

摘      要：Previous studies have utilized regression models to investigate the impact of environmental factors on physical ***,such approaches are inadequate for data-driven analysis seeking to identify robust associations from the intricate and multi-variable interactions between physical activity and environmental *** the emergence of the concept of the exposome,which encompasses the totality of exposures,this paper explores machine learning models for predicting the percentage of physical inactivity in ***,while considering 28 social-,economic-,and physical-environmental *** aim of this study is to address the research gap and gain insight into the complex associations between environmental exposures and physical *** machine learning models were tested,and the performances were compared to select the best classifier for further *** study used data from the Behavioral Risk Factor Surveillance System(BRFSS)of the Centers for Disease Control and *** mean population of all counties was 102,841,and the mean percentage of population below 18 years was 22.3%.The partial dependence plot analysis indicated that only one feature–bachelor’s degree–exhibited a close-to-linear relationship with physical ***-vehicle crash death rate and mean temperature showed nonlinear and non-monotonic relationships with the predicted percentage of physical inactivity.