Elaborating Spatiotemporal Associations Between the Built Environment and Urban Vibrancy: A Case of Guangzhou City, China

Wang, Bo; Lei, Yaqin; Xue, Dayuan; Liu, Jixiang; Wei, Chunzhu

doi:10.1007/s11769-022-1272-6

Cited by 14 publications

(6 citation statements)

References 43 publications

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“…Controlling for weather and air pollutants, Li, Liu, et al (2021) found that higher street accessibility (calculated with Space Syntax Method), among other built-environment factors, was conducive to urban vibrancy in both winter and summer/autumn. Public transport was found to be significantly positively associated with urban vibrancy in Shanghai (Huang et al, 2019) and Guangzhou ( Wang et al, 2022 ), China. The emergence of big data has enabled more studies to adopt more plentiful vibrancy indicators/index and explore broader built environment correlates in various urban contexts ( Fu et al, 2021 ; Long & Huang, 2017 ; Meng & Xing, 2019 ; Tu et al, 2020 ; Wu, Ta, et al, 2018 ; Ye et al, 2018 ; Yue et al, 2017 ; Zhang, Li, et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong

Xiao

Liu

2023

Applied Geography

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Section: Literature Reviewmentioning

confidence: 99%

Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong

Xiao

Liu

2023

Applied Geography

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“…Due to its accuracy in spatial and temporal scales, it has been widely used in studies closely related to human activities [12][13][14]. Urban social vibrancy are closely related to residents' activities and trips, and can be quantified by population density [28,29]. Zhang proposed a method for determining the population aggregation density using the pixel value of the BHM [30].…”

Section: Urban Vibrancy Indicators (1) City Social Vibrancy Indexmentioning

confidence: 99%

“…Considering that the distribution of the population on weekends and weekdays shows special differences [12,13,28], according to the proportion of weekdays and weekends in a week, we weighted the average of USV1 h and USV2 h , and then normalized the average to obtain the social vibrancy index of each fishnet grid (USV h ).…”

Section: Urban Vibrancy Indicators (1) City Social Vibrancy Indexmentioning

confidence: 99%

How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment

Gong

Wang

et al. 2022

IJERPH

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Quantitative assessment of urban vibrancy is crucial to understanding urban development and promoting sustainability, especially for rapidly developing areas and regions that have experienced post-disaster reconstruction. Taking Dujiangyan City, the hardest-hit area of the earthquake, as an example, this paper quantifies the urban economic, social, and cultural vibrancy after reconstruction by the use of multi-source data, and conducts a geographic visualization analysis. The purpose is to establish an evaluation framework for the relationship between the urban built environment elements and vibrancy in different dimensions, to evaluate the benefits of post-disaster restoration and reconstruction. The results show that the urban vibrancy reflected by classified big data can not be completely matched due to the difference in the data generation and collection process. The Criteria Importance Though Inter-criteria Correlation and entropy (CRITIC-entropy) method is used to construct a comprehensive model is a better representation of the urban vibrancy spatial characteristics. On a global scale, comprehensive vibrancy demonstrates high continuity and a bi-center structure. In the old town, the distribution of various urban vibrancies show diffusion characteristics, while those in the new district demonstrated a high degree of aggregation, and the comprehensive vibrancy is less sensitive to land-use mixture and more sensitive to residential land.

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“…Moreover, the dynamic urban function structure over different timescales has been analyzed using a random forest model to determine the temporal changes in different urban function ratios through studying the relationship between the POI and Tencent location request (TLR) data [38]. Multi-source datasets such as Baidu heat map data, POI data, and floor area and land use data, as well as geographic and timeweighted regression (GTWR) models have all been utilized to reveal the spatiotemporal relationship between built environments and urban vitality [39,40].…”

Section: Introductionmentioning

confidence: 99%

Mining the Spatial Distribution Pattern of the Typical Fast-Food Industry Based on Point-of-Interest Data: The Case Study of Hangzhou, China

Shen

Wang

Liao

et al. 2022

IJGI

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There is a Chinese proverb which states “Where there are Shaxian Snacks, there are generally Lanzhou Ramen nearby”. This proverb reflects the characteristics of spatial clustering in the catering industry. Since the proverbs are rarely elucidated from the perspective of geographical proximity and mutual attraction, we aimed to explore the spatial clustering characteristics of the fast food industry from the proverb perspective. Point-of-interest, OSM road network, population, and other types of data from the typical fast-food industry in Hangzhou were used as examples. The spatial pattern of the overall catering industry in Hangzhou was analyzed, while the spatial distribution of the four types of fast food selected in Hangzhou was identified and evaluated. The “core-edge” circle structure characteristics of Hangzhou’s catering industry were fitted by the inverse S function. The common location connection between the Western fast-food KFC and McDonald’s and the Chinese fast-food Lanzhou Ramen and Shaxian Snacks and the spatial aggregation were elucidated, being supported by correlation analysis. The degree of mutual attraction between the two was applied to express the spatial correlation. The analysis demonstrated that (1) the distribution of the catering industry in Hangzhou was northeast–southwest. The center of the catering industry in Hangzhou was located near the economic center of the main city rather than in the center of urban geography. (2) The four types of fast food were distributed in densely populated areas and exhibited an anti-S law, which first increased but then decreased as the distance from the center increased. Among these, the number of four typical fast foods was the highest within a distance of 4–10 km from the center. (3) It was concluded that 81.6% of KFCs had a McDonald’s nearby within 2500 m, and 68.5% of Shaxian Snacks had a Lanzhou Ramen nearby within 400 m. McDonald’s attractiveness to KFC was calculated as 0.928448. KFC’s attractiveness to McDonald’s was 0.908902. The attractiveness of the Shaxian Snacks to Lanzhou Ramen was 0.826835. The attractiveness of Lanzhou Ramen to Shaxian Snacks was 0.854509. McDonald’s was found to be dependent on KFC in the main urban area. Shaxian Snacks were strongly attributed to Lanzhou Ramen in commercial centers and streets, while Shaxian Snacks were distributed independently in the eastern Xiaoshan and Yuhang Districts. This study also helped us to optimize the spatial distribution of a typical fast-food industry, while providing case references and decision-making assistance with respect to the locations of catering industries.

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Elaborating Spatiotemporal Associations Between the Built Environment and Urban Vibrancy: A Case of Guangzhou City, China

Cited by 14 publications

References 43 publications

Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong

Exploring non-linear built environment effects on urban vibrancy under COVID-19: The case of Hong Kong

How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment

Mining the Spatial Distribution Pattern of the Typical Fast-Food Industry Based on Point-of-Interest Data: The Case Study of Hangzhou, China

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