Toward green equity: An extensive study on urban form and green space equity for shrinking cities

Chen, Jie; Kinoshita, Takeshi; Li, Hongyu; Luo, Shixian; Su, Daer; Yang, Xiaoqi; Hu, Yanqing

doi:10.1016/j.scs.2023.104395

Cited by 23 publications

(3 citation statements)

References 139 publications

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“…An urban neighborhood green space index can be highly efficient for obtaining comprehensive data and performing a thorough analysis of urban green spaces for all cities. The related literature provides various methods to evaluate urban green spaces with different components and parameters [71][72][73].…”

Section: Discussionmentioning

confidence: 99%

Reinventing the Urban Neighborhood Green Index in the Context of Urban Ecology as a Conceptual Framework in Northern Nicosia, Cyprus

Bolkaner,

Asilsoy

2023

Sustainability

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Today, one in two people live in urban environments, and this number is expected to rise. Urban ecology is among the main concepts of the ecological urban planning agenda for cities where the consequences of global warming and climate change are increasing day by day. Urban open green spaces provide a variety of ecosystem services for city dwellers. It is important that green spaces are accessible, efficient, walkable and properly planned in the city. Within this framework, via the relevant theoretical evaluation, a conceptual framework was constructed to redefine the urban neighborhood green index. Regarding the existing definitions within the related literature, this developed index includes quantitative, qualitative and perceptual parameters and has been newly introduced to measure and analyze green spaces in a more comprehensive layout. There is also a need to establish an ecological approach to urban planning to increase the quantity and quality of urban green spaces in Cyprus, including in Northern Nicosia. Therefore, Northern Nicosia, with its three selected neighborhoods, was chosen as the study area. As the main methodology of the study, a thorough assessment of the quantitative component of the developed index with its four parameters of per-capita green space, proximity to green space, area percentages and type of green spaces is made. Maps from the Town Planning Department and satellite images from the Nicosia Turkish Municipality and GIS were used for this quantitative measurement. Based on the findings, it can be argued that the urban green spaces are not sufficient when assessed based on most of the parameters evaluated within the study to construct a comprehensive green space index. It was found that the green spaces in the city are accessible, being within 337 m, which is reasonable. However, there are very few active and accessible open green areas in these neighborhoods within the 300 and 500 m buffers, as a remarkable number of the green spaces, which are shown on the map, are in an abandoned condition. In addition, the amount of active green space per capita is 3.35 m2, which is well below the 9 m2 predicted by WHO. The area percentages of the neighborhoods are also below the required standards. Therefore, the findings show that Northern Nicosia can benefit by addressing these deficiencies to achieve a higher urban neighborhood green index. Furthermore, the theoretical model is efficient for the assessment of urban spaces at the neighborhood scale and can be used in other cities worldwide, especially in cities with a relatively low density. In sum, this study, which considered not only the quantitative parameters but also the qualitative and perceptual features, has the potential to expand scientific knowledge on measuring and analyzing urban neighborhood green spaces.

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Section: Discussionmentioning

confidence: 99%

Reinventing the Urban Neighborhood Green Index in the Context of Urban Ecology as a Conceptual Framework in Northern Nicosia, Cyprus

Bolkaner,

Asilsoy

2023

Sustainability

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“…Urban green spaces (UGS) represent a crucial component of urban ecosystems, comprising the vegetative entities within urban areas [4]. UGS play a pivotal role in the sustainable development of cities [5]. On one hand, UGS contribute significantly to enhancing the urban ecological environment [6] and addressing environmental issues like air pollution [7] and noise pollution [8].…”

Section: Introductionmentioning

confidence: 99%

Green Space Reverse Pixel Shuffle Network: Urban Green Space Segmentation Using Reverse Pixel Shuffle for Down-Sampling from High-Resolution Remote Sensing Images

Jiang,

Shao,

Zhu

et al. 2024

Forests

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Urban green spaces (UGS) play a crucial role in the urban environmental system by aiding in mitigating the urban heat island effect, promoting sustainable urban development, and ensuring the physical and mental well-being of residents. The utilization of remote sensing imagery enables the real-time surveying and mapping of UGS. By analyzing the spatial distribution and spectral information of a UGS, it can be found that the UGS constitutes a kind of low-rank feature. Thus, the accuracy of the UGS segmentation model is not heavily dependent on the depth of neural networks. On the contrary, emphasizing the preservation of more surface texture features and color information contributes significantly to enhancing the model’s segmentation accuracy. In this paper, we proposed a UGS segmentation model, which was specifically designed according to the unique characteristics of a UGS, named the Green Space Reverse Pixel Shuffle Network (GSRPnet). GSRPnet is a straightforward but effective model, which uses an improved RPS-ResNet as the feature extraction backbone network to enhance its ability to extract UGS features. Experiments conducted on GaoFen-2 remote sensing imagery and the Wuhan Dense Labeling Dataset (WHDLD) demonstrate that, in comparison with other methods, GSRPnet achieves superior results in terms of precision, F1-score, intersection over union, and overall accuracy. It demonstrates smoother edge performance in UGS border regions and excels at identifying discrete small-scale UGS. Meanwhile, the ablation experiments validated the correctness of the hypotheses and methods we proposed in this paper. Additionally, GSRPnet’s parameters are merely 17.999 M, and this effectively demonstrates that the improvement in accuracy of GSRPnet is not only determined by an increase in model parameters.

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“…The application of remote sensing image data in urban shrinkage mainly focuses on population distribution, land use, environment and climate and night lighting. Among them, the global land cover map [49] (2017 FROM-GLC10) and LandScan high-resolution global population dataset [50] are commonly used in urban shrinkage research, which mainly alleviate the problem of mismatch between traditional macro statistical data and the boundary of urban entities. In order to carry out more detailed research work from the perspective of environment and climate, scholars have used MODIS products to calculate the intensity of urban heat island on the surface [51] or measure the urban morphology indicators of shrinking cities through high-resolution vegetation image data to complete the classification of shrinking cities [52].…”

Section: Introductionmentioning

confidence: 99%

Identification and Measurement of Shrinking Cities Based on Integrated Time-Series Nighttime Light Data: An Example of the Yangtze River Economic Belt

Tan,

Xiang,

Wang

et al. 2023

Remote Sensing

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Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking cities in China’s Yangtze River Economic Belt (YREB). We identified 36 shrinking cities and 644 shrinking counties on the municipal and county scales. Based on this approach, we established the average urban shrinkage intensity index and the urban shrinkage frequency index, attempting to find out the causes of shrinking cities for different shrinkage characteristics, city types and shrinkage frequencies. The results show that (1) the shrinking cities are mainly concentrated in the Yangtze River Delta city cluster, the midstream city cluster and the Chengdu–Chongqing economic circle. (2) Most shrinking cities have a moderate frequency of shrinking, dominated by low–low clusters. Resource-based, heavy industrial, small and medium-sized cities are more inclined to shrink. (3) The single economic structure, the difficulty of industrial transformation and the lack of linkage among county-level cities are possible reasons for the urban shrinkage in the YREB. Exploring the causes of urban shrinkage from a more micro perspective will be an inevitable task for sustainable development in YREB and even in China.

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Toward green equity: An extensive study on urban form and green space equity for shrinking cities

Cited by 23 publications

References 139 publications

Reinventing the Urban Neighborhood Green Index in the Context of Urban Ecology as a Conceptual Framework in Northern Nicosia, Cyprus

Reinventing the Urban Neighborhood Green Index in the Context of Urban Ecology as a Conceptual Framework in Northern Nicosia, Cyprus

Green Space Reverse Pixel Shuffle Network: Urban Green Space Segmentation Using Reverse Pixel Shuffle for Down-Sampling from High-Resolution Remote Sensing Images

Identification and Measurement of Shrinking Cities Based on Integrated Time-Series Nighttime Light Data: An Example of the Yangtze River Economic Belt

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