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Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China

Understanding how street spatial patterns are related to street vitality is conducive to enhancing effective urban and street design. Such analysis is facilitated by big data technology as it enables more accurate methods. This study cites data from street view imagery (SVI) and points of interest (...

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Autores principales: Yang, Junyue, Li, Xiaomei, Du, Jia, Cheng, Canhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863786/
https://www.ncbi.nlm.nih.gov/pubmed/36674400
http://dx.doi.org/10.3390/ijerph20021646
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author Yang, Junyue
Li, Xiaomei
Du, Jia
Cheng, Canhui
author_facet Yang, Junyue
Li, Xiaomei
Du, Jia
Cheng, Canhui
author_sort Yang, Junyue
collection PubMed
description Understanding how street spatial patterns are related to street vitality is conducive to enhancing effective urban and street design. Such analysis is facilitated by big data technology as it enables more accurate methods. This study cites data from street view imagery (SVI) and points of interest (POI) to assess street vitality strength after the classification of street spatial and vitality types to explore the relationship between street spatial patterns and street vitality with a further discussion on the layout features of street vitality and its strength in various street spatial patterns. First, street spatial patterns are quantified based on SVI, which are further classified using principal component analysis and cluster analysis; POI data are then introduced to identify street vitality patterns and layout, and the strength of street vitality is evaluated using spatial overlay analysis. Finally, relevance analysis is explored to cast light on the relationship between street vitality layout and street spatial patterns by overlaying street spatial pattern, street vitality types, and street vitality strength in the grid cells. This paper takes the urban area of Guiyang, China, as an example and the analysis shows that a pattern is discovered in Guiyang regarding the layout of street vitality types and vitality strengths across different street spatial patterns; compact street spaces should be prioritized in designing street space renovation; and cultural leisure vitality is most adaptive to street spatial patterns. Based on big data and using grids to understand the intrinsic relationship between street spatial patterns and the type and strength of street vitality, this paper brings more options to urban street studies in terms of perspective and methodology.
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spelling pubmed-98637862023-01-22 Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China Yang, Junyue Li, Xiaomei Du, Jia Cheng, Canhui Int J Environ Res Public Health Article Understanding how street spatial patterns are related to street vitality is conducive to enhancing effective urban and street design. Such analysis is facilitated by big data technology as it enables more accurate methods. This study cites data from street view imagery (SVI) and points of interest (POI) to assess street vitality strength after the classification of street spatial and vitality types to explore the relationship between street spatial patterns and street vitality with a further discussion on the layout features of street vitality and its strength in various street spatial patterns. First, street spatial patterns are quantified based on SVI, which are further classified using principal component analysis and cluster analysis; POI data are then introduced to identify street vitality patterns and layout, and the strength of street vitality is evaluated using spatial overlay analysis. Finally, relevance analysis is explored to cast light on the relationship between street vitality layout and street spatial patterns by overlaying street spatial pattern, street vitality types, and street vitality strength in the grid cells. This paper takes the urban area of Guiyang, China, as an example and the analysis shows that a pattern is discovered in Guiyang regarding the layout of street vitality types and vitality strengths across different street spatial patterns; compact street spaces should be prioritized in designing street space renovation; and cultural leisure vitality is most adaptive to street spatial patterns. Based on big data and using grids to understand the intrinsic relationship between street spatial patterns and the type and strength of street vitality, this paper brings more options to urban street studies in terms of perspective and methodology. MDPI 2023-01-16 /pmc/articles/PMC9863786/ /pubmed/36674400 http://dx.doi.org/10.3390/ijerph20021646 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Junyue
Li, Xiaomei
Du, Jia
Cheng, Canhui
Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China
title Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China
title_full Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China
title_fullStr Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China
title_full_unstemmed Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China
title_short Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China
title_sort exploring the relationship between urban street spatial patterns and street vitality: a case study of guiyang, china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863786/
https://www.ncbi.nlm.nih.gov/pubmed/36674400
http://dx.doi.org/10.3390/ijerph20021646
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