Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China
The rapid increase in urban population density driven by urban development has intensified inequity in urban green space distribution. Identifying the causes of changes in green equity and developing strategies to improve urban greening are crucial for optimizing resource allocation and alleviating...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/742 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589193049538560 |
|---|---|
| author | Xiaohua Guo Chang Liu Shibo Bi Yuling Tang |
| author_facet | Xiaohua Guo Chang Liu Shibo Bi Yuling Tang |
| author_sort | Xiaohua Guo |
| collection | DOAJ |
| description | The rapid increase in urban population density driven by urban development has intensified inequity in urban green space distribution. Identifying the causes of changes in green equity and developing strategies to improve urban greening are crucial for optimizing resource allocation and alleviating social inequalities. However, the long-term spatio-temporal evolution of green visibility and equity remains underexplored. This study utilized the “Time Machine” feature to capture street view images from 2014, 2017, and 2021, analyzing changes in green visibility and its equity across residential communities in Wuhan. Deep learning techniques and statistical methods, including the Gini coefficient and location quotient (LQ), were employed to assess the distribution and spatial equity of street-level greenery. The results showed that overall green visibility in Wuhan increased by 4.18% between 2014 and 2021. However, this improvement did not translate into better spatial equity, as the Gini coefficient consistently ranged between 0.4 and 0.5. Among the seven municipal districts, only the Jiang’an District demonstrated relatively equitable green visibility in 2017 and 2021. Despite a gradual reduction in disparities in green visibility, a spatial mismatch persisted between UGS growth and population distribution, leading to uneven patterns in UGS equity. This study explores the factors driving inequities in green visibility and proposes strategies to enhance urban greening. Key recommendations include integrating the green visibility equity evaluation framework into urban planning to guide fair green space allocation, prioritizing greenery in low-income neighborhoods, and reducing hardscapes to support the planting and maintenance of tall canopy trees. These measures aim to enhance accessible and visible green resources and promote equitable access across communities. |
| format | Article |
| id | doaj-art-7a77c419d8464a3486c3e6fc7ab6f926 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-7a77c419d8464a3486c3e6fc7ab6f9262025-01-24T13:20:40ZengMDPI AGApplied Sciences2076-34172025-01-0115274210.3390/app15020742Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, ChinaXiaohua Guo0Chang Liu1Shibo Bi2Yuling Tang3College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, ChinaCollege of Horticulture and Gardening, Yangtze University, Jingzhou 434025, ChinaSchool of Design Art & Media, Nanjing University of Science and Technology, Nanjing 210094, ChinaCollege of Horticulture and Gardening, Yangtze University, Jingzhou 434025, ChinaThe rapid increase in urban population density driven by urban development has intensified inequity in urban green space distribution. Identifying the causes of changes in green equity and developing strategies to improve urban greening are crucial for optimizing resource allocation and alleviating social inequalities. However, the long-term spatio-temporal evolution of green visibility and equity remains underexplored. This study utilized the “Time Machine” feature to capture street view images from 2014, 2017, and 2021, analyzing changes in green visibility and its equity across residential communities in Wuhan. Deep learning techniques and statistical methods, including the Gini coefficient and location quotient (LQ), were employed to assess the distribution and spatial equity of street-level greenery. The results showed that overall green visibility in Wuhan increased by 4.18% between 2014 and 2021. However, this improvement did not translate into better spatial equity, as the Gini coefficient consistently ranged between 0.4 and 0.5. Among the seven municipal districts, only the Jiang’an District demonstrated relatively equitable green visibility in 2017 and 2021. Despite a gradual reduction in disparities in green visibility, a spatial mismatch persisted between UGS growth and population distribution, leading to uneven patterns in UGS equity. This study explores the factors driving inequities in green visibility and proposes strategies to enhance urban greening. Key recommendations include integrating the green visibility equity evaluation framework into urban planning to guide fair green space allocation, prioritizing greenery in low-income neighborhoods, and reducing hardscapes to support the planting and maintenance of tall canopy trees. These measures aim to enhance accessible and visible green resources and promote equitable access across communities.https://www.mdpi.com/2076-3417/15/2/742urban greeninggreen view indexgreen space equitystreet view imagesdeep learning |
| spellingShingle | Xiaohua Guo Chang Liu Shibo Bi Yuling Tang Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China Applied Sciences urban greening green view index green space equity street view images deep learning |
| title | Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China |
| title_full | Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China |
| title_fullStr | Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China |
| title_full_unstemmed | Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China |
| title_short | Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China |
| title_sort | identification of inequities in green visibility and ways to increase greenery in neighborhoods a case study of wuhan china |
| topic | urban greening green view index green space equity street view images deep learning |
| url | https://www.mdpi.com/2076-3417/15/2/742 |
| work_keys_str_mv | AT xiaohuaguo identificationofinequitiesingreenvisibilityandwaystoincreasegreeneryinneighborhoodsacasestudyofwuhanchina AT changliu identificationofinequitiesingreenvisibilityandwaystoincreasegreeneryinneighborhoodsacasestudyofwuhanchina AT shibobi identificationofinequitiesingreenvisibilityandwaystoincreasegreeneryinneighborhoodsacasestudyofwuhanchina AT yulingtang identificationofinequitiesingreenvisibilityandwaystoincreasegreeneryinneighborhoodsacasestudyofwuhanchina |