Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data

Jingjing Luo; Shiyan Zhai; Genxin Song; Xinxin He; Hongquan Song; Jing Chen; Huan Liu; Yuke Feng

doi:10.3390/ijerph19105798

Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data

Int J Environ Res Public Health. 2022 May 10;19(10):5798. doi: 10.3390/ijerph19105798.

Authors

Jingjing Luo^{1

2}, Shiyan Zhai^{1

2}, Genxin Song^{1

2}, Xinxin He^{1

2}, Hongquan Song^{1

2}, Jing Chen^{1

2}, Huan Liu^{1

2}, Yuke Feng^{1

2}

Affiliations

¹ Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China.
² College of Geography and Environmental Science, Henan University, Kaifeng 475004, China.

Abstract

Green space exposure is considered an important aspect of a livable environment and human well-being. It is often regarded as an indicator of social justice. However, due to the difficulties in obtaining green space exposure data from a ground-based view, an effective evaluation of the green space exposure inequity at the community level remains challenging. In this study, we presented a green space exposure inequity assessment framework, integrating the Green View Index (GVI), deep learning, spatial statistical analysis methods, and urban rental price big data to analyze green space exposure inequity at the community level toward a "15-minute city" in Zhengzhou, China. The results showed that green space exposure inequality is evident among residential communities. The areas in the old city were with relatively high GVI and the new city districts were with relatively low GVI. Moreover, a spatially uneven association was observed between the degree of green space exposure and housing prices. Especially, the wealthier communities in the new city districts benefit from low green space, compared to disadvantaged communities in the old city. The findings provide valuable insights for policy and planning to effectively implement greening strategies and eliminate environmental inequality in urban areas.

Keywords: deep learning; green space exposure; inequity; street view images.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Big Data
China
Cities
Deep Learning*
Humans
Parks, Recreational*

Grants and funding

This research was funded by the Science and Technology Department of Henan Province (222102320397); The National Natural Science Foundation of China (42171294); Key scientific research projects of colleges and universities in Henan Province (grant number 21A170007); The National Experimental Teaching Demonstrating Center of Henan University (grant number 2020HGSYJX004); The Young Elite Scientists Sponsorship Program by Henan Association for Science and Technology (2022HYTP027); and the “Outstanding Talents Program” for Graduate Students of Henan University (grant number SYL20060109).