Identification of Inequities in Green Visibility and Ways to Increase Greenery in Neighborhoods: A Case Study of Wuhan, China
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Urban Green Space and Health · Land Use and Ecosystem Services · Urban Heat Island Mitigation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China
School of Design Art & Media, Nanjing University of Science and Technology, Nanjing 210094, China
Funding
National Natural Science Foundation of China · Philosophy and Social Science Foundation of Hubei Province
References (72)
Show source-provided references
1. Assessing the Role of Urban Green Spaces for Human Well-Being: A Systematic Review10.1007/s10708-021-10474-7 ↗
2. Green Justice or Just Green? Provision of Urban Green Spaces in Berlin, Germany10.1016/j.landurbplan.2013.11.016 ↗
3. Assessing the Ecosystem Service of Air Pollutant Removal by Urban Trees in Guangzhou (China)10.1016/j.jenvman.2007.03.035 ↗
4. Relationship between Green Space-Related Morphology and Noise Pollution10.1016/j.ecolind.2016.09.032 ↗
5. How the Landscape Features of Urban Green Space Impact Seasonal Land Surface Temperatures at a City-Block-Scale: An Urban Heat Island Study in Beijing, China10.1016/j.ufug.2020.126704 ↗
6. Multi-Stage Optimization Framework for Synergetic Grey-Green Infrastructure in Response to Long-Term Climate Variability Based on Shared Socio-Economic Pathways10.1016/j.watres.2025.123091 ↗
7. Using GIS and Landscape Metrics in the Hedonic Price Modeling of the Amenity Value of Urban Green Space: A Case Study in Jinan City, China10.1016/j.landurbplan.2006.02.013 ↗
8. Natural Outdoor Environment, Neighbourhood Social Cohesion and Mental Health: Using Multilevel Structural Equation Modelling, Streetscape and Remote-Sensing Metrics10.1016/j.ufug.2019.126576 ↗
9. Who Has Access to Urban Vegetation? A Spatial Analysis of Distributional Green Equity in 10 US Cities10.1016/j.landurbplan.2018.08.007 ↗
10. Deciphering the Changes in Residential Exposure to Green Spaces: The Case of a Rapidly Urbanizing Metropolitan Region10.1016/j.buildenv.2020.107508 ↗
11. Robinson, J.M., Mavoa, S., Robinson, K., and Brindley, P. (2022). Urban Centre Green Metrics in Great Britain: A Geospatial and Socioecological Study. PLoS ONE, 17.10.1371/journal.pone.0276962 ↗
12. Green Justice in the City: A New Agenda for Urban Green Space Research in Europe10.1016/j.ufug.2016.07.004 ↗
13. Environmental Inequities in Terms of Different Types of Urban Greenery in Hartford, Connecticut10.1016/j.ufug.2016.06.002 ↗
14. Zhang, Y., Wu, Q., Wu, L., and Li, Y. (2021). Measuring Community Green Inequity: A Fine-Scale Assessment of Beijing Urban Area. Land, 10.10.3390/land10111197 ↗
15. Normalized Difference Vegetation Index (NDVI) as a Marker of Surrounding Greenness in Epidemiological Studies: The Case of Barcelona City10.1016/j.ufug.2016.07.001 ↗
16. Global Inequities in Population Exposure to Urban Greenspaces Increased amidst Tree and Nontree Vegetation Cover Expansion10.1038/s43247-023-01141-5 ↗
17. Hong, Z., Xu, W., Liu, Y., Wang, L., Ou, G., Lu, N., and Dai, Q. (2023). Retrieval of Three-Dimensional Green Volume in Urban Green Space from Multi-Source Remote Sensing Data. Remote Sens., 15.10.3390/rs15225364 ↗
18. Wang, X., Meng, Q., Liu, X., Allam, M., Zhang, L., Hu, X., Bi, Y., and Jancsó, T. (2022). Evaluation of Fairness of Urban Park Green Space Based on an Improved Supply Model of Green Space: A Case Study of Beijing Central City. Remote Sens., 15.10.3390/rs15010244 ↗
19. Parks and Safety: A Comparative Study of Green Space Access and Inequity in Five US Cities10.1016/j.landurbplan.2020.103841 ↗
20. Zhang, S., Yu, P., Chen, Y., Jing, Y., and Zeng, F. (2022). Accessibility of Park Green Space in Wuhan, China: Implications for Spatial Equity in the Post-COVID-19 Era. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph19095440 ↗
21. Remotely-Sensed Imagery vs. Eye-Level Photography: Evaluating Associations among Measurements of Tree Cover Density10.1016/j.landurbplan.2016.07.010 ↗
22. Accessing Eye-Level Greenness Visibility from Open-Source Street View Images: A Methodological Development and Implementation in Multi-City and Multi-Country Contexts10.1016/j.scs.2024.105262 ↗
23. Street View Imagery in Urban Analytics and GIS: A Review10.1016/j.landurbplan.2021.104217 ↗
24. Using Deep Learning to Examine Street View Green and Blue Spaces and Their Associations with Geriatric Depression in Beijing, China10.1016/j.envint.2019.02.013 ↗
25. Lu, Y. (2018). The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents’ Exposure to Urban Greenness. Int. J. Environ. Res. Public Health, 15.10.3390/ijerph15081576 ↗
26. Visible Green Space Predicts Emotion: Evidence from Social Media and Street View Data10.1016/j.apgeog.2022.102803 ↗
27. Exploring Non-Linear and Synergistic Effects of Green Spaces on Active Travel Using Crowdsourced Data and Interpretable Machine Learning10.1016/j.tbs.2023.100673 ↗
28. Pedestrian and Cyclist Preferences for Tree Locations by Sidewalks and Cycle Tracks and Associated Benefits: Worldwide Implications from a Study in Boston, MA10.1016/j.cities.2018.06.024 ↗
29. Relationship between percieved greenery and width of visual fields
30. Analyzing the Effects of Green View Index of Neighborhood Streets on Walking Time Using Google Street View and Deep Learning10.1016/j.landurbplan.2020.103920 ↗
31. Using Machine Learning to Examine Street Green Space Types at a High Spatial Resolution: Application in Los Angeles County on Socioeconomic Disparities in Exposure10.1016/j.scitotenv.2021.147653 ↗
32. Peng, L., Zhang, L., Li, X., Wang, P., Zhao, W., Wang, Z., Jiao, L., and Wang, H. (2022). Spatio-Temporal Patterns of Ecosystem Services Provided by Urban Green Spaces and Their Equity along Urban–Rural Gradients in the Xi’an Metropolitan Area, China. Remote Sens., 14.10.3390/rs14174299 ↗
33. Socioecological Justice in Urban Street Greenery Based on Green View Index-A Case Study within the Fuzhou Third Ring Road10.1016/j.ufug.2024.128313 ↗
34. Measuring Socio-Economic Disparities in Green Space Availability in Post-Socialist Cities10.1016/j.habitatint.2021.102434 ↗
35. Xue, C., Jin, C., and Xu, J. (2022). Inequality in Urban Green Space Benefits: Combining Street Greenery and Park Greenery. PLoS ONE, 17.10.1371/journal.pone.0273191 ↗
36. Luo, J., Zhai, S., Song, G., He, X., Song, H., Chen, J., Liu, H., and Feng, Y. (2022). 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, 19.10.3390/ijerph19105798 ↗
37. The Inequalities of Different Dimensions of Visible Street Urban Green Space Provision: A Machine Learning Approach10.1016/j.landusepol.2022.106410 ↗
38. The Distribution of Greenspace Quantity and Quality and Their Association with Neighbourhood Socioeconomic Conditions in Guangzhou, China: A New Approach Using Deep Learning Method and Street View Images10.1016/j.scs.2020.102664 ↗
39. School of Design, Shanghai Jiao Tong University, Shanghai 200240, China Spatial Accessibility of Park Green Space in Huangpu District of Shanghai Based on Modified Two-Step Floating Catchment Area Method10.18306/dlkxjz.2021.05.005 ↗
40. Towards a Green Equal City: Measuring and Matching the Supply-Demand of Green Exposure in Urban Center10.1016/j.jenvman.2024.121510 ↗
41. Wuhan Government (2024, July 20). Wuhan New-Type Urbanisation Plan (2014–2020) Announced, Available online: https://zrzyhgh.wuhan.gov.cn/zwgk_18/fdzdgk/ghjh/zzqgh/202001/t20200107_602779.shtml.
42. (2024, July 20). Baidu Baidu Map Api Sdk. Available online: https://lbsyun.baidu.com/.
43. OpenStreetMap: User-Generated Street Maps10.1109/MPRV.2008.80 ↗
44. A Geographic Information System-Based Logical Urban Growth Model for Predicting Spatial Growth of an Urban Area10.1177/0265813515618565 ↗
45. Cartograms, Hexograms and Regular Grids: Minimising Misrepresentation in Spatial Data Visualisations10.1177/2399808319873923 ↗
46. Wang, J., and Kwan, M.-P. (2018). Hexagon-Based Adaptive Crystal Growth Voronoi Diagrams Based on Weighted Planes for Service Area Delimitation. ISPRS Int. J. Geo-Inf., 7.10.3390/ijgi7070257 ↗
47. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation10.1007/978-3-030-01234-2_49 ↗
48. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. (2017, January 21–26). Scene Parsing through ADE20K Dataset. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.544 ↗
49. Semantic Understanding of Scenes Through the ADE20K Dataset10.1007/s11263-018-1140-0 ↗
50. Dynamic Greenspace Exposure and Residents’ Mental Health in Guangzhou, China: From over-Head to Eye-Level Perspective, from Quantity to Quality10.1016/j.landurbplan.2021.104230 ↗
51. Do Various Dimensions of Exposure Metrics Affect Biopsychosocial Pathways Linking Green Spaces to Mental Health? A Cross-Sectional Study in Nanjing, China10.1016/j.landurbplan.2022.104494 ↗
52. Cui, Q., Huang, Y., Yang, G., and Chen, Y. (2022). Measuring Green Exposure Levels in Communities of Different Economic Levels at Different Completion Periods: Through the Lens of Social Equity. Int. J. Environ. Res. Public Health, 19.10.3390/ijerph19159611 ↗
53. (2006). Orihara Research on green landscape evaluation--Considerations on green evaluation methods for creating good landscapes. IBEC, 27, 32–35.
54. On the Measure of Concentration with Special Reference to Income and Statistics, Colorado College Publication
55. Methods of Measuring the Concentration of Wealth
56. Location Quotient: A Basic Tool for Economic Development Analysis
57. (2024, September 15). Gardening and Forestry Bureau of Wuhan City Wuhan City Greening Work Plan in 2022, Available online: https://www.wuhan.gov.cn/zwgk/xxgk/zfwj/bgtwj/202203/t20220331_1948390.shtml.
58. Quantifying the Green View Indicator for Assessing Urban Greening Quality: An Analysis Based on Internet-Crawling Street View Data10.1016/j.ecolind.2020.106192 ↗
59. Inequalities in the Quality and Proximity of Green Space Exposure Are More Pronounced than in Quantity Aspect: Evidence from a Rapidly Urbanizing Chinese City10.1016/j.ufug.2022.127811 ↗
60. Disentangling the Effects of the Surrounding Environment on Street-Side Greenery: Evidence from Hangzhou10.1016/j.ecolind.2022.109153 ↗
61. (2024, September 15). Wuhan Jiang’an District People’s Government Jiangan District Garden Development “14th Five-Year Plan”, Available online: https://www.jiangan.gov.cn/.
62. Tan, C., Tang, Y., and Wu, X. (2019). Evaluation of the Equity of Urban Park Green Space Based on Population Data Spatialization: A Case Study of a Central Area of Wuhan, China. Sensors, 19.10.3390/s19132929 ↗
63. Investigation of Urban Green Space Equity at the City Level and Relevant Strategies for Improving the Provisioning in China10.1016/j.landusepol.2020.105144 ↗
64. Mapping Urban Green Equity and Analysing Its Impacted Mechanisms: A Novel Approach10.1016/j.scs.2023.105071 ↗
65. Evaluation of the Accessible Urban Public Green Space at the Community-Scale with the Consideration of Temporal Accessibility and Quality10.1016/j.ecolind.2021.108231 ↗
66. Can Smaller Parks Limit Green Gentrification? Insights from Hangzhou, China10.1016/j.ufug.2021.127009 ↗
67. Understanding the Spatially Heterogeneous Relationships between Urban Green Amenities and Residential Dynamics: Evidence from Wuhan, China10.1016/j.apgeog.2024.103396 ↗
68. Examining Equity: A Multidimensional Framework for Assessing Equity in Payments for Ecosystem Services10.1016/j.envsci.2012.10.006 ↗
69. Urban Green Corridor Construction Considering Daily Life Circles: A Case Study of Wuhan City, China10.1016/j.ecoleng.2022.106786 ↗
70. (2024, August 15). UN THE 17 GOALS|Sustainable Development. Available online: https://sdgs.un.org/goals.
71. Global Streetscapes—A Comprehensive Dataset of 10 Million Street-Level Images across 688 Cities for Urban Science and Analytics10.1016/j.isprsjprs.2024.06.023 ↗
72. Balawejder, M., Warchoł, A., and Konttinen, K. (2023). Energy Efficiency in Agricultural Production—Experience from Land Consolidation in Poland and Finland. Energies, 16.10.3390/en16227598 ↗