Social challenge/ impact linked to:

Co-creation phase linked to:

A) Understanding & establishing co-creation   

Level of importance

1 - High Importance / must have

Description

Measure of the distribution of public greenspace (total surface or per capita) and categories (i.e., street trees, residential gardens, school green areas, parks) using more applied and participatory approaches as an index to increase quality/quantity of green/blue existing, restored and new NBS with a high degree of multifunctionality (informed by ES Valuation e.g., includes cultural ES value, needs of residents, socio-economics etc) and adapted to the type of urban area (e.g., size of urban area/landscape structure).

Public greenspace in cities contributes to quality of life in terms of environmental services and social and psychological services. Public greenspace distribution can therefore be an important factor for making a city sustainable. Decisions on where to create greenspace/NBS should be based on criteria related to maximising the equitability of distribution, focusing on areas lacking greenspace and in areas where ES valuation identifies greatest benefit/need. 

Mears and Brindley (2019) provide a comprehensive review of metrics for assessing the equity of greenspace in urban areas. Within this process, they highlight the importance of generating comparable data across cities and projects, and the importance of incorporating the neighbourhood as experienced by residents as accurately as possible. Data on public greenspace distribution generated in these ways can be used to:  

• Quantify the benefits of nature-based solution project in terms of improving the distribution of public greenspace;
•  Support the planning of new nature-based solution greenspace initiatives;
• Underpin other indicators that require an understanding of greenspace distribution as a foundation. 

Scale as linked to D2.2 framework

• National
• City
• Neighborhood

Data source and input type

• Satellite imagery
• Census

Connection with SDGs

• Goal 3: Good health and well-being
• Goal 11: Sustainable cities and communities

Required Data

• Land use data derived from satellite imagery
• Basic population statistics by area 

Expected Outputs

• it can be used as a baseline to compare with 2.2.3 Changes to the size and distribution of public green space (However, the difference might not that significant)
• 

• or it can be used to diagnose or identify the project area compared to other neighbourhoods within the city
• in this case, we need to have population/green spaces data at the neighbourhood level over all neighbourhoods in city
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(source: https://www.researchgate.net/publication/275965917_Urban_green_space_distribution_and_accessibility_in_Berlin_Germany)

Calculation methods

• A variety of methods exist from applied/public participation techniques through to earth observation/remote sensing approaches.
• Mapping green spaces with GIS: Whilst it is possible to physically map green spaces across cities, this tends to be a laborious and expensive process. As such, typically, public greenspace distribution would be measured through a mapping exercise that interrogates aerial photography and/or satellite imagery in a GIS environment (e.g. QuickBird satellite imageries analyses).
• Combining with census data: This can be combined with census data to determine demographics in relation to population distribution (de la Barrera et al. 2016).  Such methods provide very basic insight into the distribution of green space in relation to population patterns. Supplementing these with methods to categorise urban greenspace (e.g. differentiating street trees, residential gardens, school green areas, parks, etc) and including variables that cover socio-economic, geographic and administrative aspects can provide greater evidence for supporting equality in urban greenspace distribution.
• Optional- Participatory mapping: Participatory approaches provide an opportunity for generating such data and/or ground-truthing the results from remote sensing data analyses. This includes the use of public participation GIS to map greenspaces overlooked by automated methods, and the use of public knowledge to categorise landuse types (Rall et al. 2019). 
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Level of expertise required

• Expertise in relation to mapping and modelling will be necessary. Also expertise in leading participatory processes would be of value to maximise the quality of outputs. 
• Some map datasets and satellite imagery are freely available online, but higher resolution images and more comprehensive data needed for network-based measures potentially can involve licence fees/higher costs. Costs for GIS specialists if not available in-house. Participatory GIS can also involve costs in relation to designing a portal, hosting the webpage, generating engagement, and analysing data. 

Links and references

• Applied Methods:

• de la Barrera, F., Reyes-Paecke, S. and Banzhaf, E. (2016) Indicators for green spaces in contrasting urban settings. Ecological Indicators 62, 212-219.
• Mears, M and Brindley, P (2019) Measuring Urban Greenspace Distribution Equity: The Importance of Appropriate Methodological Approaches. ISPRS International Journal of Geo-Informatics 8, 286.
• Rall, E, Hansen, R and Pauleit, S (2019) The added value of public participation GIS (PPGIS) for urban green infrastructure planning. Urban Forestry & Urban Greening 40, 264-274.

• EO/RS methods:

• Atiqul Haq SM. (2011) Urban green spaces and an integrative approach to sustainable environment. Journal of Environmental Protection.;2:601–608. 10.4236/jep.2011.25069
• Badiu, D.L., Iojă, C.I., Pătroescu, M., Breuste, J., Artmann, M., Niță, M.R., Grădinaru, S.R., Hossu, C.A. and Onose, D.A. (2016) Is urban green space per capita a valuable target to achieve cities’ sustainability goals? Romania as a case study. Ecological indicators, 70: 53-66.
• Comber, A., Brunsdon, C., & Green, E. (2008) Using a GIS-based network analysis to determine urban green space accessibility for different ethnic and religious groups. Landscape and Urban Planning, 86(1): 103–114.
• Daniels B, Zaunbrecher BS, Paas B et al. (29018) Assessment of urban green space structures and their quality from a multidimensional perspective. Science of the Total Environment 615 (2018) 1364–1378. https://doi.org/10.1016/j.scitotenv.2017.09.167
• Davies, Z.G., Edmondson, J.L., Heinemeyer, A., Leake, J.R. and Gaston, K.J. (2011) Mapping an urban ecosystem service: quantifying above‐ground carbon storage at a city‐wide scale. Journal of applied ecology, 48(5): 1125-1134.
• de la Barrera, F., Reyes-Paecke, S. and Banzhaf, E. (2016) Indicators for green spaces in contrasting urban settings. Ecological Indicators, 62: 212-219
• Foster A, Dunham IM. (2015) Volunteered geographic information, urban forests, and environmental justice. Computers, Environment and Urban Systems.; 53:65–75. 10.1016/j.compenvurbsys.2014.08.001
• Joseph, M., Wang, L. & Wang, F.H. (2012) Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti. GI Science & Remote Sensing, 49:2, 228-250.
• Le Texier M, Schiel K, Caruso J (2018) The provision of urban green space and its accessibility: Spatial data effects in Brussels. PLoS One 13(10): e0204684. doi: 10.1371/journal.pone.0204684
• Mitchell R, Astell-Burt T, Richardson EA. (2011) A comparison of green space indicators for epidemiological research. Journal of Epidemiology and Community Health.;54(10):853–858 10.1136/jech.2010.119172
• Nicholls S. Measuring the accessibility and equity of public parks: a case study using GIS. Managing Leisure. 2001;6(4):201–219. 10.1080/13606710110084651
• Oh, K. and Jeong, S., 2007. Assessing the spatial distribution of urban parks using GIS. Landscape and urban planning, 82(1- 2): 25-32.
• Ren Z., He X., Zheng H., Zhang D., Yu X., Shen G., Guo R. Estimation of the Relationship between Urban Park Characteristics and Park Cool Island Intensity by Remote Sensing Data and Field Measurement. Forests. 2013;4:868– 886. doi: 10.3390/f4040868.
• Santos T, Tenedório JA, Gonçalves JA (2016) Quantifying the City’s Green Area Potential Gain Using Remote Sensing Data. Sustainability 2016, 8, 1247; doi:10.3390/su8121247
• Seiferling I, Naik N, Ratti C, Proulx R. (2017) Quantifying and mapping urban trees with street-level imagery and computer vision. Landscape and Urban Planning.;165:93–101
• Taylor BT, Fernando P, Bauman AE, Williamson A, Craig JC, Redman S. (2011) Measuring the quality of public open space using Google Earth. American Journal of Preventive Medicine.;40(2):105–112. 10.1016/j.amepre.2010.10.024
• Sister C, Wolch J, Wilson J. Got green? Addressing environmental justice in park provision. GeoJournal. 2010;75:229–248. 10.1007/s10708-009-9303-8
• Van De Voorde T. (2016) Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: a case study on Brussels, Belgium. International Journal of Digital Earth.;10(8):798–813. 10.1080/17538947.2016.1252434
• Van Herzele A, Wiedemann T. A monitoring tool for the provision of accessible and attractive urban green spaces. Landscape and Urban Planning. 2003;63:109–126. 10.1016/S0169- 2046(02)00192-5