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Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea

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  • Sangwan Lee

    (Department of Urban Planning and Engineering, Hanyang University, 206, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea)

Abstract

This study investigated the relationship between the degree of satisfaction with the pedestrian environments in their neighborhoods and the degree of neighborhood satisfaction in Seoul, South Korea. This study employed proportional odds logistic regression and gradient boosting decision tree models, using the 2021 Seoul Urban Policy Indicator Survey. The key findings are as follows. First, there was a significant and positive relationship between the two factors. Second, respondents’ satisfaction levels with pedestrian environments showed higher feature importance than other factors. Third, the partial dependence plots show non-linear relationships; specifically, when respondents reported being satisfied or very satisfied with pedestrian environments, the partial dependence on the dependent variable increased significantly. This study contributes to (1) finding the association between the two factors, (2) offering insights into how to improve residents’ satisfaction with their neighborhood through pedestrian environment satisfaction, and (3) unfolding what active mobility means to people.

Suggested Citation

  • Sangwan Lee, 2022. "Satisfaction with the Pedestrian Environment and Its Relationship to Neighborhood Satisfaction in Seoul, South Korea," Sustainability, MDPI, vol. 14(15), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9343-:d:875845
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    References listed on IDEAS

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    1. Guo, Zhan & Loo, Becky P.Y., 2013. "Pedestrian environment and route choice: evidence from New York City and Hong Kong," Journal of Transport Geography, Elsevier, vol. 28(C), pages 124-136.
    2. Lanza, Kevin & Burford, Katie & Ganzar, Leigh Ann, 2022. "Who travels where: Behavior of pedestrians and micromobility users on transportation infrastructure," Journal of Transport Geography, Elsevier, vol. 98(C).
    3. Jiyun Lee & Donghyun Kim & Jina Park, 2022. "A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    4. Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
    5. Soongbong Lee & Myungjoo Han & Kyoungah Rhee & Bumjoon Bae, 2021. "Identification of Factors Affecting Pedestrian Satisfaction toward Land Use and Street Type," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    6. Nantian Huang & Guobo Lu & Dianguo Xu, 2016. "A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest," Energies, MDPI, vol. 9(10), pages 1-24, September.
    7. Sangwan Lee, 2022. "Exploring Associations between Multimodality and Built Environment Characteristics in the U.S," Sustainability, MDPI, vol. 14(11), pages 1-16, May.
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    Cited by:

    1. Silvia Stuchi & Sonia Paulino & Faïz Gallouj, 2022. "Social Innovation in Active Mobility Public Services in the Megacity of Sao Paulo," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

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