3.1 Green Space Accessibility

Public green and blue spaces (referred to as “green space” for simplicity) have positive impacts on quality of life and wellbeing (see e.g., Baidu at al., 2016; Chiesura et al., 2004). Different types of green spaces such as urban parks and gardens (including community gardens), cemeteries, sportsgrounds, public plazas, urban forests, orchards, arable lands, undeveloped lands, and other partly or completely vegetated areas demonstrated capacity to clean air, cool local temperature and manage surface runoff. Urban green space also plays a role in increasing the value of local real estate (Roebeling et al. 2017). The environmental, ecological and social benefits of urban green spaces are strongly influenced by green space size and their accessibility in terms of distance and travel time.

16

17

Publicly accessible green spaces provide opportunities for a wide range of different types of nature-based recreational activities, which have been shown to deliver multiple co-benefits (e.g., Eigenschenk et al., 2019; Triguero-Mas et al., 2017). Green space accessibility is an important metric to evaluate the potential for the realisation of recreational opportunities and related co-benefits. Accessibility of green space can also be used to evaluate the relative success of urban greening policies focused on the provision of and equal access to urban green spaces, and to assess NBS co-benefits as a function of distance from accessible public green space.

18

19

Many methods for the evaluation of accessibility are available (Handy and Niemeier, 1997). Here, we propose a simplified cumulative measure (Páez, Scott and Morency, 2012) based upon the World Health Organisation Regional Office for Europe recommendations related to urban green space accessibility for public health (WHO, 2016) and European Common Indicator of the availability of local public open areas and services (Lavalle et al., 2002)

20

)))

21

|(% style="width:211px" %)(((

22

====== **Scale as linked to D2.2 framework** ======

23

)))|(% style="width:730px" %)(((

* City

* Neighborhood

)))

|(% style="width:211px" %)(((

28

====== **Data source [ link to data framework]** ======

29

)))|(% style="width:730px" %)(((

30

* Census data

31

* Satellite-derived land use data

32

)))

33

|(% style="width:211px" %)(((

34

====== **Data needs** ======

35

)))|(% style="width:730px" %)(((

36

* Size, location and types of green spaces, including public accessibility (land use maps, green space maps, green space qualification, etc.).

37

* Population data, e.g., census data (municipal departments, statistical services, etc.)

38

* Optional data: total urban area (municipal departments, statistical services etc.); specific points of departure from large residential buildings (buildings)

39

40

This KPI can be measured using specific software, such as GIS software and spreadsheet software. QGIS is the GIS software suggested, as it is an open source and multiplatform software that is distributed under Creative Commons Attribution-Share Alike 3.0 licence (CC BY-SA).

41

)))

42

|(% style="width:211px" %)(((

43

====== **Expected Outputs** ======

44

)))|(% style="width:730px" %)(((

45

* Unit: % of a population

46

* Annual measurements ideal, or before/after NBS implementation

47

)))

48

|(% style="width:211px" %)(((

49

====== **Calculation methods** ======

50

)))|(% style="width:730px" %)(((

51

* Data processing using QGIS (or other GIS software) has been designed to obtain one KPI value for the whole city. Steps:

52

* 1. Identify and map arrival points of public green, blue and blue/green spaces equal to or greater than 0.5 ha in size. Data can be provided by the relevant municipality or derived from publicly available land cover maps (e.g., Urban Atlas or Open street maps).

53

* 2. Identify and map buildings or census blocks (departure points). Data can be provided by the municipality or national/international statistics institutes.

54

* 3. Define circles with radii 300 m from the access point(s) to each identified public green space. This 300 m distance most likely represents a walk of five (Natural England) to 15 (European Common Indicator5) minutes, depending on walking pace.

55

* 4. Using census area or similar data, determine the total number of residents within all the mapped 300 m walking distance circles.

Alternative method:

* 1. Spatially join each (building) departure point to its nearest park access point (tool Distance to the nearest hub). As a result, a new shape-file is obtained with an attribute field containing the shortest distance to the closest park.

60

* 2. Classify proximity to the parks. Tamosiunas et al (2014) classified the distance to the closest park using a tertiles method. The resultant three categories classify proximity as high, moderate and low based upon distance (shown as an example)

61

** (((

62

(% class="table-bordered" style="width:367px" %)

63

|(% style="width:145px" %)**Proximity category**|(% style="width:48px" %)**High**|(% style="width:102px" %)**Moderate**|(% style="width:71px" %)**Low**

64

|(% style="width:145px" %)**Distance (m)**|(% style="width:48px" %)≤347.8|(% style="width:102px" %)347.8-629.6|(% style="width:71px" %)>629.6

65

)))

66

67

To obtain this KPI in terms of walking time, the Field calculator tool can be used. A conversion factor has to be set to measure a pedestrian walking speed (Bosina & Weidmann, 2017). For example, the average pedestrian walking speed in Spain is 1.59 m/s, or 95.4 m/min. The distance value in minutes can be obtained by diving the distance in metres by the distance walked per minute. Note that it is generally not possible to walk in a completely straight line “as the crow flies” in urban areas. Thus, estimates of walking times based upon linear distances between two points in built-up urban areas (e.g., point of departure from building A to point of access to park B) are unlikely to be highly accurate.

68

69

Complementary data that may be useful in contextualizing the green space proximity index:

70

71

* City level descriptive statistics: It will measure the impact of the NBS at municipal level: Overall statistics can also be calculated by a QGIS tool called Basic statistics for numeric fields. As a result a set of measures is derived:

72

** Descriptive statistics in terms of distance and travel time:

73

*** Minimum / maximum distance to the closest park (m)

74

*** Average distance to the closest park (m).

75

** Statistics regarding number of inhabitants from each defined starting point (building) are also useful to contextualize the index.

76

*** Number of people that live in the proximity of the facility

77

*** Proportion of people having the closest park in the high, moderate or low proximity category.

78

* District level descriptive statistics: A neighborhood level study is also recommended in order to find deficient areas in greenspace availability, or probability of overcrowded green areas.

79

)))

80

|(% style="width:211px" %)(((

81

====== **Links and references** ======

82

)))|(% style="width:730px" %)(((

83

* Badiu, D.L., Ioja, C.I., Patroescu, M., Breuste, J., Artmann, M., Nita, M.R., Gradinaru, S.R., Hossu, C.A., & 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.

84

* Bosina, E., Weidmann, U. 2017. Estimating pedestrian speed using aggregated literature data, Physica A: Statistical Mechanics and its Applications, 468, 1-29. https:~/~/doi.org/10.1016/j.physa.2016.09.044

85

* Chiesura, A. (2004). The role of urban parks for the sustainable city. Landscape and Urban Planning, 68(1), 129-138.

86

* Eigenschenk, B., Thomann, A., McClure, M., Davies, L., Gregory, M., Dettweiler, U. and Inglés, E. 2019. Benefits of outdoor sports for society: A systematic literature review and reflections on evidence. Int J Environ Res Public health 16(6), 937. doi: 10.3390/ijerph16060937

87

* Handy, S. L. and Niemeier, D. A. 1997. Measuring accessibility: an exploration of issues and alternatives, Environment and Planning A, 29. doi: 10.1068/a291175.

88

* Lavalle, C., Demicheli, L., Kasanko, M., McCormick, N., Barredo, J. and Turchini, M. Towards an urban atlas. Assessment of spatial data on 25 European cities and urban areas. Copenhagen: European Environment Agency. ISBN 92-9167-470-2. https:~/~/www.eea.europa.eu/publications/environmental_issue_report _2002_30

89

* Le Texier, M., Schiel, K. and Caruso, G. 2018. The provision of urban green space and its accessibility: Spatial data effects in Brussels. PLoS ONE 13(10): e0204684. [[https:~~/~~/doi.org/10.1371/journal.pone.0204684>>https://doi.org/10.1371/journal.pone.0204684]]

90

* Páez, A., Scott, D. M. and Morency, C. (2012) ‘Measuring accessibility: positive and normative implementations of various accessibility indicators’, Journal of Transport Geography, 25, pp. 141–153. doi: http:~/~/dx.doi.org/10.1016/j.jtrangeo.2012.03.016.

91

* QGIS 3 – Userguide. https:~/~/www.qgis.org/en/site/ QGIS Development Team 2013.

92

* QGIS Geographic Information System. Open Source Geospatial Foundation. URL http:~/~/qgis.osgeo.org

93

* Roebeling, P., Saraiva, M., Palla, A., Gnecco, I., Teotónio, C., Fidélis, T., … Rocha, J. (2017). Assessing the socio-economic impacts of green/blue space, urban residential and road infrastructure projects in the Confluence (Lyon): a hedonic pricing simulation approach. Journal of Environmental Planning and Management, 60(3), 482- 499.

94

* Tamosiunas, A., Grazuleviciene, R., Luksiene, D., Dedele, A., Reklaitiene, R., Baceviciene, M., Vencloviene, J., Bernotiene, G., Radisauskas, R., Malinauskiene, V., Milinaviciene, E., Bobak, M., Peasey, A., Nieuwenhuijsen, M.J., 2014. Accessibility and use of urban green spaces, and cardiovascular health: Findings from a Kaunas cohort study. Environ. Heal. 13, 20. doi:10.1186/1476-069X-13-20

95

* Triguero-Mas, M., Donaire-Gonzalez, D., Seto, E., Valentín, A., Martínez, D., Smith, G., Hurst, G., Carrasco-Turigas, G., Masterson, D., van den Berg, M., Ambròs, A., Martínez-Íñiguez, T., Dedele, A., Ellis, N., Grazulevicius, T., Voorsmit, M., Cirach, M., Cirac-Claveras, J., Swart, W., Clasquin, E., Ruijsbroek, A., Maas, J., Jerret, M., Gražulevičienė, R., Kruize, H., Christopher J. Gidlow, C.J. and Nieuwenhuijsen, M.J. 2017. Natural outdoor environments and mental health: Stress as a possible mechanism. Environmental Research 159, 629-638. https:~/~/doi.org/10.1016/j.envres.2017.08.048

96

* URBAN GreenUP Deliverable D2.4 - Monitoring program to Valladolid. https:~/~/www.urbangreenup.eu/insights/deliverables /d2-4~-~-- monitoring-program-to-valladolid.kl

97

* URBAN GreenUP Deliverable D3.4 - Monitoring program to Liverpool https:~/~/www.urbangreenup.eu/insights/deliverables /d3-4~-~-- monitoring-program-to-liverpool.kl

98

* URBAN GreenUP Deliverable D4.4 – Monitoring program to Izmir https:~/~/www.urbangreenup.eu/insights/deliverables /d4-4~-~- monitoring-program-to-izmir.kl

99

* URBAN GreenUP Deliverable D5.3: City Diagnosis and Monitoring Procedures https:~/~/www.urbangreenup.eu/insights/ deliverables/d5- 3-city-diagnosis-and-monitoring-procedures.kl

100

* WHO Regional Office for Europe, 2016. Urban green spaces and health. Copenhagen: WHO Regional Office for Europe16.

)))

(% class="lead" %)

Revisions

(% class="success" %)|(% style="width:68px" %)**Version**|(% style="width:179px" %)**Name**|(% style="width:112px" %)**Date**|(% style="width:636px" %)**Changes made**

107

(% class="active" %)|(% style="width:68px" %)**0.1**|(% style="width:179px" %)Emily (EcoWise)|(% style="width:112px" %)14/01/2022|(% style="width:636px" %)Factsheet created

108

(% class="active" %)|(% style="width:68px" %) |(% style="width:179px" %) |(% style="width:112px" %) |(% style="width:636px" %)

Wiki source code of 3.1 Green Space Accessibility