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Conference object . 2024
License: CC BY
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Article . 2024
License: CC BY
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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Leveraging Machine Learning and Street View Imagery to Study the association between social inequalities and visual walkability in the South Africa Context

Authors: Li, Lingshan; Law, Stephen;

Leveraging Machine Learning and Street View Imagery to Study the association between social inequalities and visual walkability in the South Africa Context

Abstract

Walkability is crucial for improving urban sustainability. Most studies on walkability focused in developed countries, while only a few studies have been conducted in the Global South due partially to limited data availability. To address the limitation, this research calculated visual walkability using Street View Imagery and computer vision techniques in Johannesburg, South Africa, and then explored the relationship between social-economic indicators and walkability. The results offer insights on the social and spatial inequality of walkability in the South African context.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average