Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning Approach Article Swipe
Haijun Wang
,
Guie Li
,
Min Weng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/land13050667
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/land13050667
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/land13050667
- https://www.mdpi.com/2073-445X/13/5/667/pdf?version=1715505685
- OA Status
- gold
- Cited By
- 2
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396888917
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396888917Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/land13050667Digital Object Identifier
- Title
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Unraveling the Dynamic Relationship between Neighborhood Deprivation and Walkability over Time: A Machine Learning ApproachWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-12Full publication date if available
- Authors
-
Haijun Wang, Guie Li, Min WengList of authors in order
- Landing page
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https://doi.org/10.3390/land13050667Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-445X/13/5/667/pdf?version=1715505685Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-445X/13/5/667/pdf?version=1715505685Direct OA link when available
- Concepts
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Walkability, Built environment, Metropolitan area, Computer science, Socioeconomic status, Geography, Machine learning, Transport engineering, Econometrics, Environmental health, Mathematics, Ecology, Engineering, Medicine, Archaeology, Population, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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58Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W32393404, https://openalex.org/W2082491593, https://openalex.org/W2901564266, https://openalex.org/W4386064300, https://openalex.org/W2411583672, https://openalex.org/W2724177178, https://openalex.org/W3048485637, https://openalex.org/W1976661687, https://openalex.org/W4213387502, https://openalex.org/W2953225340, https://openalex.org/W4389307698, https://openalex.org/W2948018644, https://openalex.org/W2035764748, https://openalex.org/W1698806803, https://openalex.org/W4296907309, https://openalex.org/W4310067663, https://openalex.org/W4390384789, https://openalex.org/W2947466881, https://openalex.org/W4388078806, https://openalex.org/W2605924126, https://openalex.org/W3045435521, https://openalex.org/W4281681183, https://openalex.org/W4229030855, https://openalex.org/W4391930673, https://openalex.org/W4392180289, https://openalex.org/W2969228194, https://openalex.org/W3035551722, https://openalex.org/W2281922929, https://openalex.org/W4309075839, https://openalex.org/W2146964491, https://openalex.org/W4281701669, https://openalex.org/W4394904567, https://openalex.org/W4394567588, https://openalex.org/W2522940576, https://openalex.org/W2485522290, https://openalex.org/W2781696151, https://openalex.org/W2987374530, https://openalex.org/W4383262700, https://openalex.org/W4313403228, https://openalex.org/W4388176904, https://openalex.org/W6857233133, https://openalex.org/W4388520468, https://openalex.org/W4313518611, https://openalex.org/W4200585767, https://openalex.org/W2372377263, https://openalex.org/W3124700334, https://openalex.org/W2780912313, https://openalex.org/W2055533274, https://openalex.org/W2471269811, https://openalex.org/W2073587934, https://openalex.org/W2045508278, https://openalex.org/W2020246247, https://openalex.org/W6669571269, https://openalex.org/W2009142344, https://openalex.org/W1978712769, https://openalex.org/W2048400826, https://openalex.org/W2076883797, https://openalex.org/W4388084092 |
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| corresponding_author_ids | https://openalex.org/A5109419866 |
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