Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India
Urban Green Spaces (UGS) offer social and environmental benefits that enhance quality of life of the residents. However, due to the underlying social and economic disparities, different sections of urban population have disproportionate level of access to UGS. The environmental inequity owing to the...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-07-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2018.1549819 |
| _version_ | 1797679118870904832 |
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| author | Vasu Sathyakumar RAAJ Ramsankaran Ronita Bardhan |
| author_facet | Vasu Sathyakumar RAAJ Ramsankaran Ronita Bardhan |
| author_sort | Vasu Sathyakumar |
| collection | DOAJ |
| description | Urban Green Spaces (UGS) offer social and environmental benefits that enhance quality of life of the residents. However, due to the underlying social and economic disparities, different sections of urban population have disproportionate level of access to UGS. The environmental inequity owing to the varied UGS distribution poses a challenge to urban planners in efficient resource allocation. This study attempts to counter this challenge using a novel remote sensing-based approach. The variations in UGS distribution (in terms of quantity, quality and accessibility) across the neighbourhoods in Mumbai vis-à-vis the socio-economic status (SES) of neighbourhood residents are assessed using remote sensing-based indicators. Further, as these indicators are susceptible to the effect of changing scales, a multi-scale approach is adopted to study the potential variations in the relationship between SES and spatial metrics of UGS with spatial resolution. The neighbourhood SES was assessed using the newly developed Socio-Economic Status Index (SESI) and the neighbourhoods were classified into multiple SES categories. The UGS were extracted from remotely sensed data using Normalized Difference Vegetation Index (NDVI), and their spatial distribution aspects were characterized using indicators at neighbourhood level. The variations in indicators of UGS distribution in the neighbourhoods belonging to different SES categories were analysed using a logistic regression model. The results showed that, while quantity of UGS is not statistically associated with neighbourhoods SES, the quality and accessibility aspects of UGS share a statistically significant relation with SES. Also, this relation was found to vary significantly with spatial resolutions. Further, it was found that the neighbourhoods with higher SES in Mumbai have a better access to green spaces, indicating spatial inequities in UGS distribution in Mumbai. This study has important implications for planning equitable green spaces in cities that are currently in urbanization transition. |
| first_indexed | 2024-03-11T23:09:46Z |
| format | Article |
| id | doaj.art-c4bc667343634fb9bf5142f6499b14f5 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:46Z |
| publishDate | 2019-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-c4bc667343634fb9bf5142f6499b14f52023-09-21T12:34:15ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262019-07-0156564566910.1080/15481603.2018.15498191549819Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, IndiaVasu Sathyakumar0RAAJ Ramsankaran1Ronita Bardhan2Indian Institute of Technology BombayIndian Institute of Technology BombayIndian Institute of Technology BombayUrban Green Spaces (UGS) offer social and environmental benefits that enhance quality of life of the residents. However, due to the underlying social and economic disparities, different sections of urban population have disproportionate level of access to UGS. The environmental inequity owing to the varied UGS distribution poses a challenge to urban planners in efficient resource allocation. This study attempts to counter this challenge using a novel remote sensing-based approach. The variations in UGS distribution (in terms of quantity, quality and accessibility) across the neighbourhoods in Mumbai vis-à-vis the socio-economic status (SES) of neighbourhood residents are assessed using remote sensing-based indicators. Further, as these indicators are susceptible to the effect of changing scales, a multi-scale approach is adopted to study the potential variations in the relationship between SES and spatial metrics of UGS with spatial resolution. The neighbourhood SES was assessed using the newly developed Socio-Economic Status Index (SESI) and the neighbourhoods were classified into multiple SES categories. The UGS were extracted from remotely sensed data using Normalized Difference Vegetation Index (NDVI), and their spatial distribution aspects were characterized using indicators at neighbourhood level. The variations in indicators of UGS distribution in the neighbourhoods belonging to different SES categories were analysed using a logistic regression model. The results showed that, while quantity of UGS is not statistically associated with neighbourhoods SES, the quality and accessibility aspects of UGS share a statistically significant relation with SES. Also, this relation was found to vary significantly with spatial resolutions. Further, it was found that the neighbourhoods with higher SES in Mumbai have a better access to green spaces, indicating spatial inequities in UGS distribution in Mumbai. This study has important implications for planning equitable green spaces in cities that are currently in urbanization transition.http://dx.doi.org/10.1080/15481603.2018.1549819urban green spacessocio-economic statusspatial metricsscale effectsmumbai |
| spellingShingle | Vasu Sathyakumar RAAJ Ramsankaran Ronita Bardhan Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India GIScience & Remote Sensing urban green spaces socio-economic status spatial metrics scale effects mumbai |
| title | Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India |
| title_full | Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India |
| title_fullStr | Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India |
| title_full_unstemmed | Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India |
| title_short | Linking remotely sensed Urban Green Space (UGS) distribution patterns and Socio-Economic Status (SES) - A multi-scale probabilistic analysis based in Mumbai, India |
| title_sort | linking remotely sensed urban green space ugs distribution patterns and socio economic status ses a multi scale probabilistic analysis based in mumbai india |
| topic | urban green spaces socio-economic status spatial metrics scale effects mumbai |
| url | http://dx.doi.org/10.1080/15481603.2018.1549819 |
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