Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach

被引:2
|
作者
Wang, Qian [1 ]
Li, Guie [2 ]
Weng, Min [3 ]
机构
[1] Hubei Univ Econ, Sch Sports Econ & Management, Wuhan 430205, Peoples R China
[2] China Univ Min & Technol, Sch Publ Policy & Management, Xuzhou 221116, Peoples R China
[3] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China
关键词
walkability; walk score; neighborhood deprivation; socioeconomic status; built environment; China; BODY-MASS INDEX; SOCIAL INEQUALITIES; STREET WALKABILITY; BUILT-ENVIRONMENT; OBESITY; COMMUNITIES; LIFE;
D O I
10.3390/land13050667
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Creating a walkable environment is an essential step toward the 2030 Sustainable Development Goals. Nevertheless, not all people can enjoy a walkable environment, and neighborhoods with different socioeconomic status are found to vary greatly with walkability. Former studies have typically unraveled the relationship between neighborhood deprivation and walkability from a temporally static perspective and the produced estimations to a point-in-time snapshot were believed to incorporate great uncertainties. The ways in which neighborhood walkability changes over time in association with deprivation remain unclear. Using the case of the Hangzhou metropolitan area, we first measured the neighborhood walkability from 2016 to 2018 by calculating a set of revised walk scores. Further, we applied a machine learning algorithm, the kernel-based regularized least squares regression in particular, to unravel how neighborhood walkability changes in relation to deprivation over time. The results not only capture the nonlinearity in the relationship between neighborhood deprivation and walkability over time, but also highlight the marginal effects of each neighborhood deprivation indicator. Additionally, comparisons of the outputs between the machine learning algorithm and OLS regression illustrated that the machine learning approach did tell a different story and should contribute to remedying the contradictory conclusions in earlier studies. This paper is believed to renew the understanding of social inequalities in walkability by bringing the significance of temporal dynamics and structural interdependences to the fore.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation
    Fleet, Thomas
    Kamei, Khangamlung
    He, Feiyang
    Khan, Muhammad A.
    Khan, Kamran A.
    Starr, Andrew
    SENSORS, 2020, 20 (23) : 1 - 13
  • [22] Changing Neighborhood Income Deprivation Over Time, Moving in Childhood, and Adult Risk of Depression
    Sabel, Clive E.
    Pedersen, Carsten Bocker
    Antonsen, Sussie
    Webb, Roger T.
    Horsdal, Henriette Thisted
    JAMA PSYCHIATRY, 2024, 81 (09) : 919 - 927
  • [23] Association Between Neighborhood Factors and Adult Obesity in Shelby County, Tennessee: Geospatial Machine Learning Approach
    Brakefield, Whitney S.
    Olusanya, Olufunto A.
    Shaban-Nejad, Arash
    JMIR PUBLIC HEALTH AND SURVEILLANCE, 2022, 8 (08):
  • [24] Unraveling climate trends in the mediterranean: a hybrid machine learning and statistical approach
    AlShafeey, Mutaz
    MODELING EARTH SYSTEMS AND ENVIRONMENT, 2024, 10 (05) : 6255 - 6277
  • [25] The Moderating Effects of Current Substance Use on the Relationship between Neighborhood Deprivation and Cognitive Abilities
    Ande, Namitasai
    Murphy, Blakely
    Ray, Arjun
    Puente, Antonio
    ARCHIVES OF CLINICAL NEUROPSYCHOLOGY, 2024, 39 (07) : 1183 - 1184
  • [26] Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor
    Ng, Hui R.
    Sossa, Isidore
    Nam, Yunwoo
    Youn, Jong-Hoon
    SENSORS, 2023, 23 (01)
  • [27] How the Predictors of Math Achievement Change Over Time: A Longitudinal Machine Learning Approach
    Lavelle-Hill, Rosa
    Frenzel, Anne C.
    Goetz, Thomas
    Lichtenfeld, Stephanie
    Marsh, Herbert W.
    Pekrun, Reinhard
    Sakaki, Michiko
    Smith, Gavin
    Murayama, Kou
    JOURNAL OF EDUCATIONAL PSYCHOLOGY, 2024, 116 (08) : 1383 - 1403
  • [28] A machine learning approach to rank the determinants of banking crises over time and across countries
    Casabianca, Elizabeth Jane
    Catalano, Michele
    Forni, Lorenzo
    Giarda, Elena
    Passeri, Simone
    JOURNAL OF INTERNATIONAL MONEY AND FINANCE, 2022, 129
  • [29] A Personalized Approach To Asthma Control Over Time: Discovering Phenotypes Using Machine Learning
    Ross, M. K.
    Yoon, J.
    Moon, K. Ho
    Van der Schaar, M.
    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2017, 195
  • [30] Learning from main streets - A machine learning approach identifying neighborhood commercial districts
    Oh, Jean
    Hwang, Jie-Eun
    Smith, Stephen F.
    Koile, Kimberle
    INNOVATIONS IN DESIGN & DECISION SUPPORT SYSTEMS IN ARCHITECTURE AND URBAN PLANNING, 2006, : 325 - +