Measuring Green Exposure Levels in Communities of Different Economic Levels at Different Completion Periods: Through the Lens of Social Equity

Abstract

Exposure to green spaces contributes to residents’ physical and mental health and well-being. The equitable allocation of green space has also become an increasingly important issue for society and the government. This study takes 3281 communities in Shenzhen as the analysis units. Using web crawlers, semantic segmentation based on deep learning, web map path planning and entropy weighting methods, four types of residents’ daily green exposure indicators are calculated, including community green space ratio, green view index (GVI), park accessibility, and the weighted composite green exposure index. The results reveal inequalities in the level of green exposure in Shenzhen’s communities across economic classes, mainly in GVI and comprehensive green exposure. We also found that the level of composite green exposure is relatively stable; however, green space ratio attainment levels for newer communities are increasing and GVI and park accessibility attainment levels are decreasing. Finally, among the newly built communities: compared to the low-income level communities, the high-income level communities have a significant advantage in green space, but the mid-income level communities do not have such an advantage. The main findings of this study can provide policy implications for urban green space planning, including the need to prioritize the addition of public green space near older communities with poor levels of green exposure, the addition of street greenery near communities with poor levels of composite green exposure, and ensuring that parks have entrances in all four directions as far as possible.

Resident Preferences for Urban Green Spaces in Response to Pandemic Public Health Emergency: A Case Study of Shanghai

“…There was a significant difference in the frequency of access to green space during the epidemic across income levels, and inequality in access to green space across communities with different economic levels [70]. This suggests that high-income neighborhoods have a significant advantage in green space access compared to low-income neighborhoods, while middle-income neighborhoods do not have such an advantage.…”

Women-oriented Evaluation of Perceived Safety of Walking Routes Between Home and Mass Transit: A Case Study and Methodology Test in Guangzhou

“…This wide coverage can compensate for possible omissions in sample surveys or small-scale representative studies, making it possible to understand large-scale urban scenarios. In addition, with the development of Computer Vision (CV), the automatic extraction of SVI features has become possible, including measuring the percentage of greenery within the line of sight [40] and measuring the proportion of visual elements such as pedestrians, vehicles, traffic signs, the sky, and buildings [41][42][43][44].…”

“…This study divides and labels different pixel parts of the SVI using the pyramid scene parsing network (PSPNet) method, which is often used in other urban planning studies [5,40], to identify the physical spatial properties of the street environment. Since training directly based on data is time-consuming, pre-trained models are usually used for prediction.…”

“…The average sum of these 118 categories of visual elements in all SVIs in this study is only 0.6%, which basically does not affect the essential research content of the subsequent study. It should also be noted that the visual element that appears most frequently in the SVI is "tree", which was one of the most common visual elements in studies using SVI-based data, and this visual element has been shown to profoundly influence perceptual evaluations [5,40,52].…”

Fairness Evaluation of Landscape Justice in Urban Park Green Space: A Case Study of the Daxing Part of Yizhuang New Town, Beijing

“…With the improvement of comprehensive equity evaluations, many scholars have derived spatial allocation equity by establishing an evaluation system using spatial autocorrelation to analyze the relationship between equity and residential consumption level. For example, Cui et al [34] derived the daily green exposure index of residents in Shenzhen using community green space ratio, green landscape index (GVI), park accessibility, and weighted composite green exposure index, and concluded that different economic classes differ in community green exposure. Xu et al [35] evaluated the accessibility, area, and quality of park green space allocation in different income neighborhoods in Nanjing.…”