Quantifying the spatial quality of urban streets with open street view images: A case study of the main urban area of Fuzhou

Rui, Quanquan; Cheng, Huishan

doi:10.1016/j.ecolind.2023.111204

Cited by 9 publications

(2 citation statements)

References 43 publications

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“…This study explores the emotional health benefits of campus green spaces from the perspective of this important group of college students. Not only do we fully consider the influencing factors of campus green space with the help of remote sensing satellites and online street-view platforms [40][41][42][43], but we also combine social media data as a relatively objective and effective method of emotional evaluation, while simultaneously considering the differences in the influence of campus green space on students' emotions in the context of space, time, and epidemics.…”

Section: Introductionmentioning

confidence: 99%

Using Social Media Data to Research the Impact of Campus Green Spaces on Students’ Emotions: A Case Study of Nanjing Campuses

Wang,

Meng,

Zhao

et al. 2024

Sustainability

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Campus green space, as a component of urban green space and the main natural place for college students’ daily contact, has a subliminal effect on their mental health. This study aims to investigate the degree of influence of campus green space on college students’ emotions as well as the main indicators of influence and other scientific issues. Taking the campus green spaces of 44 college campuses in Nanjing as the object of the study, with the help of social media data to research the issue of green spaces and emotional preference, we conducted a difference analysis, constructed an individual-time, double fixed-effects regression model and obtained the corresponding results: (1) significant seasonal and individual differences existed in all green space indicators across the 44 campuses; (2) a significant positive correlation existed between each of the campus green space indicators and college students’ positive emotions; (3) compared with the regression results of the data prior to the New Crown Pneumonia Outbreak (COVID-19), college students’ green sensitivity increased substantially during the outbreak control period, and the health benefits of the campus green spaces were more significant.

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Section: Introductionmentioning

confidence: 99%

Using Social Media Data to Research the Impact of Campus Green Spaces on Students’ Emotions: A Case Study of Nanjing Campuses

Wang,

Meng,

Zhao

et al. 2024

Sustainability

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“…Further, how can we scientifically identify the multi-stage evolution characteristics of the spatial pattern of Shanghai's regional green space? To address these scientific problems, we used the Google Earth Engine (GEE) platform [27], machine learning [28], biophysical composition (biophysical composition index) [29], CA binarization, and other technical methods to construct a technical method for the identification of regional green spaces. We extracted dynamic data information for the regional green space in Shanghai and adopted a Sankey diagram and spatial pattern index [30] to discriminate the multi-stage evolution characteristics of regional green space pattern in Shanghai.…”

Section: Introductionmentioning

confidence: 99%

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

Ji,

Zhang,

et al. 2023

Land

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The spatial pattern of regional green space is an important dimension to describe and quantitatively express the characteristics of regional green spaces outside the built-up area of a city. With the expansion of urban and rural construction land, regional green space has been continuously encroached upon. This leads to a decline in regional ecological well-being and the loss of biodiversity. Based on the remote sensing data of Shanghai city from 2000 to 2020, we quantitatively studied the spatial morphological change characteristics of regional green space outside the built-up area of Shanghai city. Firstly, with the help of the GEE platform, the optimal decoding accuracy classification method was selected through machine learning (random forest, support vector machine, classification regression tree); then, based on the biophysical component (BCI) and CA binarization, the built-up area ranges for up to five time nodes were obtained; finally, through GIS spatial data analysis and processing technology, the regional green space dynamic data of Shanghai for five time nodes were extracted. Based on the above data, an analysis index system was constructed to quantitatively analyze the spatial morphology characteristics of the regional green space outside the built-up area of Shanghai. The results show that (1) the area of regional green space outside the built-up area of Shanghai had a fluctuating growth pattern of “decreasing and then increasing”. The arable land and water areas in Shanghai decreased, and the woodland area increased steadily, while the wetland and grassland areas showed a trend of first decreasing and then increasing. (2) The regional green patch fragmentation shows a fluctuating development trend of increasing, decreasing, and increasing. (3) The change in the spatial center of gravity of the regional green space in Shanghai had a high degree of consistency with the overall green space change. The center of gravity of the grasslands in the regional green space moved substantially to the northwest, while the center of gravity of the other types remained basically unchanged. This study reveals the spatial morphology characteristics of regional green spaces and provides a research method to study the dynamic changes in regional ecological resources. The results of this study can provide a scientific basis for the identification, protection, and development of regional ecological resources.

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How to Enhancing Urban Space Renewal Through Visual Landscape Perception? An Approach From Street View Image Recognition

Yuan,

Mu,

Jiao

et al. 2024

Soc Indic Res

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Quantifying the spatial quality of urban streets with open street view images: A case study of the main urban area of Fuzhou

Cited by 9 publications

References 43 publications

Using Social Media Data to Research the Impact of Campus Green Spaces on Students’ Emotions: A Case Study of Nanjing Campuses

Using Social Media Data to Research the Impact of Campus Green Spaces on Students’ Emotions: A Case Study of Nanjing Campuses

Study on the Spatial and Temporal Evolution of Regional Green Space Morphology Outside Built-Up Areas based on the Google Earth Engine and Biophysical Component Modeling

How to Enhancing Urban Space Renewal Through Visual Landscape Perception? An Approach From Street View Image Recognition

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