Title：The inequalities of different dimensions of visible street urban green space provision: A machine learning approach
- A machine-learning approach is used to mine street view data.
- Multiple dimensions of visible street urban green space are identified.
- Inequalities of urban green space are distributed unevenly over space.
Awareness is growing that the uneven provision of street urban green space (UGS) may lead to environmental injustice. Most previous studies have focused on the over-head perspective of street UGS provision. However, only a few studies have evaluated the disparities in visible street UGS provision. While a plethora of studies have focused on a single dimension of visible UGS provision, no previous studies have developed a framework for systematically evaluating visible street UGS provision. This study therefore proposes a novel 4 ‘A′ framework, and aims to assess different dimensions (namely: availability; accessibility; attractiveness; and aesthetics) of visible street UGS provision, using Beijing as a case study. It investigates inequities in different dimensions of visible street UGS provision. In addition, it also explores the extent to which a neighbourhood’s economic level is associated with different dimensions of visible street UGS. Our results show that, in Beijing, the four chosen dimensions of visible street UGS provision significantly differ in terms of spatial distribution and the association between them. Furthermore, we found that the value of the Gini index and Moran’s I index for attractiveness and aesthetics are higher than those for availability and accessibility, which indicates a more unequal distribution of visible street UGS from a qualitative perspective. We also found that a community’s economic level is positively associated with attractiveness and aesthetics, while no evidence was found to support the claim that the economic level of a community associated with availability and accessibility. This study suggests that visible street UGS provision is unequal; therefore, urban planning policy should pay more attention to disparities in visible street UGS provision, particularly in urban areas.
4 ‘A′ framework; Disparity; Visible street urban green space; Street view data; Machine learning; Beijing
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