Indicators for green spaces in contrasting urban settings

Barrera, Francisco de la; Reyes‐Paecke, Sonia; Banzhaf, Ellen

doi:10.1016/j.ecolind.2015.10.027

Cited by 179 publications

(101 citation statements)

References 45 publications

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“…The private green spaces refer to the outdoor amenities within private urban residence whose accessibility is restricted unless permitted by the household owners. Conversely, the public green spaces (PGSs) represent those that can be accessed freely and treated as public goods (de la Barrera, Reyes-Paecke, & Banzhaf, 2015). The PGSs in urban cities include the natural places dominated by green vegetation (e.g., forests, woods, gardens, and parks) and the artificial green spaces (e.g., roadside greenbelts, riverside greenbelts, green spaces around residence, green spaces around institution, squares and plazas) (Chen, Liu, & Liu, 2016;Shan, 2014;de la Barrera et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Characterizing the inequalities in urban public green space provision in Shenzhen, China

You

2016

Habitat International

179

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Section: Introductionmentioning

confidence: 99%

Characterizing the inequalities in urban public green space provision in Shenzhen, China

You

2016

Habitat International

179

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“…Widely used indicators for green spaces usually refer to green space coverage (Fuller and Gaston, 2009;Zhao et al, 2013) or green space per inhabitant (ISO 37120;WHO, 2010). Others include also the proximity to green areas (Herzele and Wiedemann, 2003;Hillsdon et al, 2006;Kabisch et al, 2016;Morar et al, 2014;Natural England, 2010;Ståhle, 2010) or more complex indices referring to the balance between green and built up areas (De la Barrera et al, 2016). Furthermore, there are shape-oriented indices, which can also measure the distribution of green spaces (Margaritis and Kang, 2016;McGarical and Marks, 1994;Verani et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Relationship between green space-related morphology and noise pollution

Margaritis

Kang

2017

Ecological Indicators

126

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Green spaces have been proved to have a positive effect on traffic noise pollution in the local scale; however their effects have not been explored on the urban level. This paper investigates the effects of green space-related parameters from a land cover viewpoint on traffic noise pollution in order to understand to what extent greener cities can also be quieter. A triple level analysis was conducted in the agglomeration, urban and kernel level including various case study cities across Europe. The green space parameters were calculated based on land cover data available in a European scale, while traffic noise data were extracted from online noise maps and configured in noise indices. In the first level 25 agglomerations were investigated, six of which were further analyzed in the urban and kernel levels. It was found that the effect of green spaces on traffic noise pollution varies according to the scale of analysis. In the agglomeration level, there was no significant difference in the cluster of the higher green space index and the percentage of people exposed in the lowest (55-59 dB(A)) or the highest noise band of more than 70 dB(A). In the urban level it was found that lower noise levels can possibly be achieved in cities with a higher extent of porosity and green space coverage. Finally, in the kernel level a Geographically Weighted Regression (GWR) analysis was conducted for the identification of correlations between noise and green. Strong correlations were identified between 60% and 79%, while a further cluster analysis combined with land cover data revealed that lower noise levels were detected in the cluster with higher green space coverage. At last, all cities were ranked according to the calculated noise index.

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“…This result provides a credible quantitative basis for follow-up surveys, planning, and design practices of the project team. Traditional approach to urban green space planning is mostly based on per capita index, which often fails to effectively guide the spatial distribution of green spaces [32]. Moreover, site selection of green spaces is usually based on service radius without consideration of actual population distribution.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method

Liu

et al. 2018

Sustainability

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Urban green space is closely related to the quality of life of residents. However, the traditional approach to its planning often fails to address its actual service capacity and users' demand. In this study, facilitated by mobile phone location data, more specific features of the spatial distribution of urban residents are identified. Further, population distribution in relation to traffic analysis zones is mapped. On this basis, the two-step floating catchment area method (2SFCA) is adopted in combination with urban green space planning to evaluate the per capita area of green space and its accessibility in practice. Subsequently, classification of per capita area and spatial distribution of green spaces within the study area are obtained; thus, urban districts currently with low accessibility to green areas are identified and can be deemed as key areas for the planning of green areas in the future. The study concludes that mobile phone data can be used to more accurately map the spatial distribution of residents; while, the 2SFCA offers a more comprehensive quantitative measuring of the supply and demand of green spaces. The two combined can be used as an important basis for decision-making in the planning of urban green spaces. Since urban green space can be regarded as a kind of public facility, the methodology of the present study is also believed to be applicable in studies of other types of urban facilities.

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Indicators for green spaces in contrasting urban settings

Cited by 179 publications

References 45 publications

Characterizing the inequalities in urban public green space provision in Shenzhen, China

Characterizing the inequalities in urban public green space provision in Shenzhen, China

Relationship between green space-related morphology and noise pollution

Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method

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