Indicators for green spaces in contrasting urban settings - Ecosistemas ...

Ecological Indicators 62 (2016) 212–219

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Indicators for green spaces in contrasting urban settings Francisco de la Barrera a,∗ , Sonia Reyes-Paecke b , Ellen Banzhaf c a

Pontificia Universidad Católica de Chile, Instituto de Geografía and Centro del Desarrollo Urbano Sustentable, Vicu˜ na Mackenna 4860, Macul, Chile Pontificia Universidad Católica de Chile, Departamento de Ecosistemas y Medio Ambiente and Centro del Desarrollo Urbano Sustentable, Vicu˜ na Mackenna 4860, Macul, Chile c UFZ – Helmholtz Centre for Environmental Research, Department Urban and Environmental Sociology, Working Group Geomatics, Permoserstr. 15, 04318 Leipzig, Germany b

a r t i c l e

i n f o

Article history: Received 14 April 2015 Received in revised form 7 October 2015 Accepted 11 October 2015 Available online 2 December 2015 Keywords: Quality of green spaces Accessibility to green spaces Vegetation cover Social equity Urban planning Environmental inequalities

a b s t r a c t Urban green spaces (GS) are essential for the well-being of the population. Several works have shown a positive correlation between the amount of GS and the household incomes in both developed and developing countries. Thus, the higher the incomes, the larger the total area covered by GS, the better the quality of these spaces, the higher the amount of private GS. Public policies seek to correct this inequality, but existing indicators, especially the amount of GS per inhabitant, do not provide enough information for effective decision-making. Our aim was to provide tools to evaluate and plan better the location and quality of GS in complex urban areas. For this we applied a set of indicators for GS at two spatial scales city-level and local-level, in order to disclose existing inequalities. The indicators considered (i) the total area of GS in relation to population and urban context, (ii) the quality of GS based on its size, shape and vegetation cover, and (iii) the spatial distribution and accessibility of GS. The proposed indicators were tested in three municipalities, belonging to the Metropolitan Area of Santiago (Chile), with different household incomes. The indicators showed large differences in terms of quantity of GS per inhabitant, vegetation cover and accessibility. The GS proved to be an effective strategy to reduce areas that lack vegetation cover. The sustainability assessments must consider how the diversity of structural attributes of GS has an impact on the well-being of urban inhabitants. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Green spaces (GS) are key elements for urban quality of life. They contribute to human well-being by providing ecosystem services such as climate regulation, capture of pollutants or flood regulation; they also promote the encounter of neighbors and community integration, and deliver a favorable place for health, relaxation and nature contemplation (Chiesura, 2004; Lee and Maheswaran, 2011; Dobbs et al., 2014; Larondelle et al., 2014; Carrus et al., 2015; Marselle et al., 2015). Because of their importance delivering ecosystem services within densely populated cities (Niemelä, 2014; Yao et al., 2014) and for the purpose of this study we considered GS as public goods which allow free access to all citizens and represent pockets of nature for all residents (e.g. urban parks, squares, median strip, roadsides, sidewalks, etc). Given this, GS of restricted access such

∗ Corresponding author. Tel.: +56 2 23547404. E-mail addresses: [email protected] (F. de la Barrera), [email protected] (S. Reyes-Paecke), [email protected] (E. Banzhaf). http://dx.doi.org/10.1016/j.ecolind.2015.10.027 1470-160X/© 2015 Elsevier Ltd. All rights reserved.

as private gardens, golf courses and institutional gardens were excluded; despite their provision of ecosystem services and amendment to the quality of well-being (Colding and Folke, 2009; Cilliers et al., 2013; Balooni et al., 2014; Zhang and Jim, 2014). The most widely used indicator to assess green spaces is their total area in respect to the total population (m2 /inhabitant) (Taylor et al., 2011; Van Herzele and Wiedemann, 2003; Caspersen et al., 2006; Kabisch and Haase, 2013; ISO, 2014). However, this indicator – area of GS per inhabitant – does not inform on how this is distributed throughout the city or administrative unit and also doesn’t inform on ecosystem services provided (Yao et al., 2014). It does not provide enough information about the actual distribution of GS within the city, neither about the accessibility of these spaces for different population groups, since it assumes a fair distribution for all inhabitants (Reyes-Paecke and Figueroa, 2010; Weiland et al., 2011; Zhou and Kim, 2013; La Rosa, 2014). The effective assessment of GS and its ecosystem service provision depends on quantity, quality and accessibility of GS (Chen et al., 2009; Wright Wendel et al., 2012; Zhou and Kim, 2013; Yao et al., 2014). Only few studies have evaluated these aspects in an integrated manner.

F. de la Barrera et al. / Ecological Indicators 62 (2016) 212–219

Quantitatively, size and shape of GS matters: the larger the size, the greater the magnitude and diversity of ecosystem services provided. For cultural services, larger green spaces allow various activities and thereby facilitate the simultaneous presence of different users (e.g. children, youth and adults), favoring social interactions (Sugiyama and Ward Thompson, 2008; Maas et al., 2009; Krellenberg et al., 2014). The GS also foster biodiversity (Uno et al., 2010; Young, 2010; Gong et al., 2013), have larger portions cover by trees and are more effective in providing regulating services, such as climate regulation and flood control (Chiesura, 2004; Jenerette et al., 2007; Cavan et al., 2014; Larondelle et al., 2014; Niemelä, 2014). The concept of green infrastructure has been widely used to refer to include GS in a more extensive list of nature features which contribute to the urban performance and resilience at various scales (e.g. Tzoulas et al., 2007; Charlesworth, 2010; Lafortezza et al., 2013; Fletcher et al., 2014). The quality and accessibility of GS are key attributes to strengthen the effectiveness of ecosystem services provided to citizens. The quality of GS is a function of size, shape and the elements inside the GS (Taylor et al., 2011; Dobbs et al., 2014; Tian et al., 2014). One key element of green spaces is vegetation, being one of the main provider of ecosystem services; hence, vegetation cover can be used as a measure of the quality of GS (Cilliers et al., 2013; Zhou and Kim, 2013). Accessibility to GS relates to the spatial distribution of GS throughout the urban area, which is measured through a variety of GIS-based methodologies combined with social surveys (Schipperijn et al., 2010; Krellenberg et al., 2014). In addition, Yao et al. (2014) proposed three indicators to measure quality and accessibility of GS: (i) the Effective Green Equivalent (EGE) referring to the GS area that effectively has a benefit for each inhabitant; (ii) the Average Effective Green Equivalent (AEGE), which is the average of EGE for all inhabitants; and (iii) the Inequality Coefficient (IC) which is based on the Gini coefficient of income inequality, exchanging income with the EGE of residents (Yao et al., 2014). Besides the multi-dimensional attributes for better assessing GS, it is necessary to determine the spatial scale in which the indicators are relevant given that urban planners require effective indicators for municipal and local scales. Indicators at municipal scale can be used for temporal comparisons and to compare cities. Indicators at local level are useful to recognize intra-urban inequalities that are not evident by applying municipal-level indicators. Local indicators can aid detecting areas requiring governmental actions given their lack of GS. This study aims at understanding how to evaluate GS better for maintaining or enhancing human well-being from a multidimensional framework. The main objective is to develop a set of indicators for GS that allows recognizing inequalities in different urban contexts. The quantity refers to the total area of GS in relation to population and urban context. The quality is evaluated through GS’ morphologies and vegetation cover. Especially, the spatial distribution tested the accessibility of residents and existing territorial inequalities. All indicators were applied in three contrasting urban municipalities of the Metropolitan Area of Santiago (MAS) Chile, to test their sensitivity to existing differences inside the same political and economic system. Like other Latin American cities, Santiago has a strong sociospatial segregation, so that groups of different income levels live separately to each other (Kabisch et al., 2012; Romero et al., 2012). Previous research showed that low-income municipalities in Santiago tend to have less GS with smaller average sizes and dramatic variations in the proportion of GS between municipalities at city level (Escobedo et al., 2006; Reyes-Paecke and Figueroa, 2010; Romero et al., 2012; Banzhaf et al., 2013). However, such evaluations are very scarce in Latin-American cities did not follow standards about what is a GS and did not assess quality and

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spatial distribution of GS (Celemín and Velazquez, 2012; UNHABITAT, 2012). 2. Methodology 2.1. Dataset preparation 2.1.1. Mapping green spaces To map GS, we used the geo-database provided by Reyes-Paecke and Figueroa (2010) containing all GS for the MAS. That study distinguished 11,606 GS covering 3825 ha located in 34 municipalities, obtained from a mosaic of aerial photography from June 2006. We add the vegetation cover to this database derived from satellite imagery and select only GS of public use, i.e. urban parks, squares and median strips (so-called bandejones), sidewalks, etc. 2.1.2. Scale-dependent mapping of urban land use and its structure All features of the urban land-use structure were derived from QuickBird satellite imageries analyses. This sensor system comprises four spectral bands in the visual and near-infrared spectra (ground resolution of 2.4 m) and one panchromatic band (0.6 m ground resolution). Our acquired data set is from 19December-2006 and 06-January-2007. Preprocessing comprises geographical and radiometric corrections, followed by mosaicking and pan-sharpening the imageries to capture the structural features precisely. We processed the data developing object-based image analysis (OBIA) to map land uses within neighborhoods in local districts (Banzhaf and Höfer, 2008: 130). The urban land-use structure was thus explained by various compositions of vegetation cover, bare soils and imperviousness detecting the homogeneity and heterogeneity between neighborhoods. In an earlier study, similar analyses were performed just at municipal level by Banzhaf et al. (2013) for different time scales. At that level, no differentiation was undertaken for vegetation cover. 2.1.3. Getting the demographic data Based on census data we quantified three products for a basic understanding of the socio-spatial differences between selected municipalities: (1) absolute number of population for each block in 2002; (2) absolute number of population in 2006 based on official projection and (3) population density for the residential areas accordingly (INE, 2014). No projection of population could be gathered for each block, because this is executed at municipal level (INE, 2014). More recent census data by block level do not exist in Chile. These demographic indicators were then combined to describe the urban land-use structure of the case study areas. 2.2. Case study The MAS is the capital of Chile, and the largest urban agglomeration of the country with a current population of 6.5 million inhabitants (INE, 2014). It covers 2274 km2 of which approximately 616 km2 are built-up areas concluding in a population density of 9540 inhabitants per km2 (Banzhaf et al., 2013). The population of the MAS has a high degree of spatial disparity segregated by extremely varying income levels. The spatial distribution of GS is also highly correlated with the income level of residents: the higher the income, the higher the land cover of GS per inhabitant (Escobedo et al., 2006; Reyes-Paecke and Figueroa, 2010; Ministerio del Interior, 2009). Neighborhoods constructed by social housing programs only possess few GS (Vasquez, 2008; Romero et al., 2012). The Chilean institutional structure divides the national territory into regions and municipalities, defining three levels of administration: national, regional and local. Hence, the MAS is not one

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Fig. 1. Location of the Metropolitan Area of Santiago (MAS) in Chile, gray feature (i.e. built-up area) of the MAS, and the three selected municipalities Cerro Navia (low incomes), La Florida (middle incomes), and Vitacura (high incomes).

administrative unit, but a conglomerate of 34 local municipalities. As study area, three municipalities were selected, according to their characteristics by high, medium and low incomes, respectively. As representative of low incomes we chose Cerro Navia in the northwest, for medium incomes La Florida was selected in the south-east, and as a good example for municipalities with high incomes we picked Vitacura in the north-east (Fig. 1).

Similarly, the land cover per GS of the built-up municipal territory is obtained by dividing the sum of GS areas by the built-up area of each municipality. Finally, the total area of GS is respectively compared to the sum of land cover per impervious cover, per bare soils and per vegetation cover (according to FAO land cover classification system; FAO, 2005), for a better understanding of the urban structure and the location of the GS.

2.3. Proposed indicators The set of indicators elaborated to analyze GS consider the three main dimensions quantity, quality and spatial distribution (Table 1) and are explained in the subsequent sections. 2.3.1. Indicators associated to the quantity of GS The sum of GS areas is compared to the total population per municipality to get the indicator of GS per inhabitant (in m2 ). Table 1 Description of the proposed indicators. Indicators

Name

Scale

Quantity of GS

GS per inhabitant GS per built-up area GS per impervious cover GS per bare soils GS per vegetation cover

Municipal Municipal Municipal Municipal Municipal

Quality of GS

Mean size of GS (±SD) Shape index of GS (±SD)* Vegetation cover on GS (mean ± SD) Vegetation cover on GS per inhabitant (mean ± SD)

Municipal Municipal Municipal

Spatial distribution and accessibility to GS

Aggregation index of GS* Share of blocks served by GS > 0.5 ha Share of population served by GS > 0.5 ha

Source: Elaborated by the authors. * McGarigal et al. (2012).

Municipal Municipal Local Local

2.3.2. Indicators associated to the quality of GS We evaluate the quality of GS using physical attributes. The mean size per municipality is based on the individual size of each GS as well as their standard deviation. The shape index compares perimeter to area in order to inform how regular each GS is, being equal to 1 if the GS has a square shape and increases beyond 1 if the shape of the GS becomes irregular (Fragstat 4.2 by McGarigal et al. (2012)). The vegetation cover per GS (mean ± SD) was calculated by extracting the land use information for all GS shapes and then the land-use statistics was processed for all patches. In addition, the vegetation cover on GS per inhabitant was calculated (m2 /inh.) to deduce how much ecosystem services could be produced by each GS, and therefore enjoyed by each inhabitant. This indicator puts the share of GS in context to the population density. 2.3.3. Indicators associated to the spatial distribution of and the accessibility to GS How the GS are distributed in the municipal territory is important to complement the indicator of the total area of GS. In theory, the most extreme distributions are (i) the restriction of all GS to just one neighborhood or (ii) the equal distribution of GS in all neighborhoods. The indicator of the total area would be exactly the same for both examples, but the spatial allocation would be missed. A well-designed indicator has to enable the reflection on the spatial distribution. Therefore, we first calculate the aggregation index (AI) to get a reference of how clustered GS in each municipality are. This index pictures the range from fragmentation to aggregation.


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Table 2 Statistics of demographic development and land use in the studied municipalities. Categories of land uses were calculated within the built-up areas of each municipality. Statistics

Low income municipality Cerro Navia

Demographic information 143,045 Population in 2002 [abs. no. inh.] Population in 2006 [abs. no. inh.] 143,035 166.0 Population density in 2006 per built-up area of each municipality [inh./ha] Urban land uses within the urbanized space of a municipality Impervious areas within the 54.4 urbanized space of a municipality [%] 30.2 Bare soils [%] 15.3 Vegetation cover [%] 0.2 Other land cover [%]

The AI is close to 100 when all the evaluated fragments are adjacent to each other and 0 if all of the fragments are isolated (McGarigal et al., 2012). To measure accessibility we combine the most refined demographic data available (population per block in 2002) with the location of GS (database of 2006). After geo-processing, we obtain the blocks with any GS in a distance less than 100 m, and those with GS larger than 0.5 ha in a distance less than 300 m. The population supplied by GS is derived from the population living in each block.

3. Results At the municipal scale, Table 2 describes the demographic information on the three municipalities together with their urban structure. It becomes obvious that their numbers of inhabitants differ in number, and more importantly, their population densities varied significantly. Vitacura has the highest family income and lowest urban density, the least impervious areas and bare soil and the highest vegetation cover. The largest municipality with rather mixed social strata is La Florida. Here, more than a quarter of its urban territory is covered by vegetation, and almost half of the urbanized area is built-up by residential houses, and public infrastructure. Cerro Navia has low family income and the highest population density, and more than 50% impervious areas and a low share of vegetation cover. The correlation of GS per inhabitant (m2 /inh.) and incomes indicates environmental justice. Hence, we found a correlation between low family income and low GS per capita (Table 3). The pattern

Medium income municipality La Florida

High income municipality Vitacura

350,255 395,720 108.9

79,053 81,588 39.6

45.0

35.6

28.4 25.5 1.0

20.3 40.6 3.5

of the most affluent municipality had a higher proportion of their inhabitants supplied by different types of GS, and a high share of population supplied by GS (Table 3). On average, the GS possess larger vegetation cover than the other two municipalities; those two being rather similar despite their different incomes (Table 3, Fig. 2). The poorest municipality (Cerro Navia) has a larger fraction of its territory covered by GS compared to the other two municipalities, and its GS contributes more to the total vegetation cover in the municipal territory than in the other two municipalities of higher household incomes (Table 3). Here, the GS are located closer to each other. In La Florida only 34% of GS are larger than 0.5 ha, a low proportion compared to the poorest and richest municipalities with 46 and 47% respectively. On average, the GS in Cerro Navia are larger compared to Vitacura and La Florida, and tend to have more square shapes, showing a smaller shape index. In Vitacura, the shape index reaches the highest value, explaining the large number of linear vegetated structures.

3.1. Quantity of GS In Cerro Navia, GS add up to 37 ha representing 4.3% inside the built-up area, which signifies a higher share compared to the other two municipalities. In La Florida, GS cover an area of 111 ha (3.1%), and in Vitacura the accumulative cover is 63 ha, representing 3.0% inside the built environment. GS per capita show low values of only 3 m2 /inh. in Cerro Navia and in La Florida respectively, and 8 m2 /inh. in Vitacura. The World

Table 3 Set of indicators for GS evaluated in three contrasting urban settings. Indicators Quantity of GS GS per inhabitant (2006) [m2 /inh.] GS per built-up area [%] GS per impervious cover [%] GS per bare soils [%] GS per vegetation cover [%] Quality of GS Mean size of GS [m2 ] (SD in brackets) Shape index of GS [+/- SD] (in brackets ±SD)a Mean vegetation cover per GS [%] (in brackets + - SD) Vegetation cover on GS per inhabitant [m2 /inh.] Spatial distribution and accessibility to GS Aggregation index of GS Share of population supplied by GS (buffer = 100 m; all GS) [%] Share of GS > 0.5 ha [%] Share of population supplied by GS > 0.5 ha (buffer = 300 m) [%]

Low income municipality Cerro Navia

Medium income municipality La Florida

High income municipality Vitacura

2.59 4.3 7.9 14.3 28.1

2.81 3.1 6.8 10.8 12.0

7.68 3.0 8.5 15.0 7.5

2454 (4075) 1.446 (0.393) 39.6 (0.184) 1.29

1507 (2274) 1.528 (0.686) 41.2 (0.214) 1.30

2053 (3471) 1.929 (1.126) 52.7 (0.270) 4.56

97.60 70.0 46.4 41.2

96.50 84.1 34.0 49.2

96.20 81.6 47.3 67.3

SD, standard deviation. a Shape index is an a-dimensional indicator, the value is 1 when the patch is square and increases without limit as patch shape becomes more irregular.

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Fig. 2. Overview over the amount and distribution of vegetation cover in the three selected municipalities and the location of green spaces.

Health Organization defines the standard of 9–11 m2 /inh. of GS to secure the quality of life in cities (Kuchelmeister, 1998; Thaiutsa et al., 2008; UNEP, 2010:157; Darkwah and Cobbinah, 2014). Only Vitacura approaches this standard, leaving Cerro Navia and La Florida far below this threshold. The relationship of bare soils and GS is equivalently covered in Cerro Navia and Vitacura with 14 and 15% respectively (Table 3), although in Cerro Navia bare soils represent 30% of the built-up area, and in Vitacura they reach only 20% (Table 2). So differences in availability of GS are not necessarily a lack of GS or excessive bare soils. Share of GS per vegetation cover reports that in municipalities with the least resources, GS contribute in a greater way to the municipal vegetation cover, while in wealthier municipalities the vegetation cover is so high that GS are immersed in a vegetated matrix. 3.2. Quality of GS In contrary to the assumption that municipalities with lower incomes have smaller GS, Cerro Navia has an average size of 0.24 ha, superior even to the average size Vitacura (0.20 ha) has and well above the average size of La Florida (0.15 ha). Poor Cerro Navia with the highest average GS cover has a shape index closer to a square shape for residential stay, while the shape index is higher in Vitacura, GS have more elongated or complex shapes, typical for such linear GS as sidewalks or median strips to pass by.

Another novelty of this research is the measurement of the vegetation cover on GS, not measured before, as an important attribute to GS. In arid and semiarid environments the proportion of bare soil inside the GS depends on irrigation. In addition, GS could have a high proportion of impervious surfaces. Both attributes are relevant to understand the functionality and quality of GS. Regarding the percentage of the total vegetation cover on GS, we can draw the following statements: in Cerro Navia GS cover only 4.3% of the municipal area (37.1 ha), but provide 14% of the total vegetation cover in this municipality, i.e. 18.5 ha. For local residents of low incomes who are the majority in Cerro Navia and who live in densely populated environments, it obviously becomes vital to have and even increase the vegetation cover on GS as a positive contribution to environmental quality. In contrast, in La Florida the GS represent 3.1% of the municipal area (111.2 ha) and provide only 5.5% of the respective vegetation cover (i.e. 51.4 ha), which is a much lower proportion than in Cerro Navia. In Vitacura, the situation is similar to La Florida, but the contribution of GS is still less important as GS hold 4% of the municipal area and provide only 4.5% of the total vegetation cover (Table 3, Fig. 3). Significantly, GS have a higher contribution to the entire vegetation cover in Cerro Navia than in more affluent municipalities. The share of vegetation cover on GS adds up to 50%, higher than in La Florida with 46%, but lower than in Vitacura (59%). Vitacura and La Florida have areas with a high vegetation cover and a low number of GS, suggesting that vegetation is mainly

Fig. 3. Left: Differences of vegetation cover in total and on GS of each municipality (own figure). Right: Vegetation cover on GS per inhabitant (own figure). Cerro Navia, La Florida and Vitacura represent the low, the medium and the high income municipality, respectively.


concentrated in GS of restricted access (e.g. gardens and backyards). This is consistent with their urban structure as private lots are larger and the proportion of impervious and bare soils is lower. A previous study shows that in the MAS the vegetation cover in home gardens is 3.7 times higher than on GS (Reyes-Paecke and Meza, 2011). Vegetation cover contained on GS shows a fairly similar pattern in the three municipalities with about 48–60% respectively (Table 3). Regarding its ratio per inhabitant, GS in Vitacura have a higher vegetation cover per inhabitant (4.6 m2 /inh.) than in Cerro Navia and La Florida, showing the same proportion (1.3 m2 /inh.) (Table 3, Fig. 3) and thus making a tremendous difference for the environmental quality of residents. As part of the urban structure the built environment is a main determinant for the optional or restricted expansion of vegetation cover. Beyond, it is associated with housing and population density (Table 2). So the indicators which help to compare any quantifiable variable of GS (e.g. number of entities, area covered) with the number of inhabitants will show extremely low figures as a consequence of the huge differences in urban structure and population density. Examples given are the differences on the figures of GS per inhabitant, vegetation cover on GS per inhabitant, share of population supplied by GS comparing a highly dense populated urban area with a less densely populated one. 3.3. Spatial distribution and accessibility to GS All GS are highly aggregated. The share of population supplied by all GS in a buffer of 100 m from the boundary of each GS ranges from 70–84%. Considering only the larger areas (>0.5 ha) and an area of influence within 300 m from the edge of each GS, however, shows the fraction of population supplied with gives a wide range from 70 to 40% in Cerro Navia, 84–49% in Florida and 82–67% in Vitacura. Less than half of the GS exceed 0.5 ha in all municipalities, reaching the lowest value in La Florida, middle income and higher population growth. The higher vegetation cover on GS in the municipality of highest incomes is not enough to explain the differences on vegetation cover in the municipal territory as we mentioned above. Indeed, Cerro Navia with the highest share of low income households has proportionally more GS that the other municipalities. 4. Discussion The developed indicators for GS tackled the differentiations in the quality and quantity of urban vegetation and the reflection of this differentiation on the socio-spatial distribution of residents in the selected municipalities. Only few case studies undertake research on different land cover on green spaces in general, so there are still few sources in literature that allow to compare our results and to find some common trends. It needs to be stated that shortages in total vegetation cover of a municipality (public and private) cannot be compensated by vegetation cover on GS because the urban structure limits any available space to be converted into new GS. Growing cities face a constant pressure on GS, due to the need to build housing and infrastructure. The combination of population and economic growth (which facilitates real-estate investment) and high density of urbanization restricts the presence of environmental amenities (Livert and Gainza, 2014) which include vegetation, and even threatens existing GS. The proportion of vegetation cover on GS with respect to total vegetation cover is an important indicator in cities with a high socio-spatial differentiation. The proportion of vegetation cover on GS is higher in dense urban areas, which usually are also the poorest ones. Conversely, there is a lower proportion of vegetation

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on GS in zones with large private gardens. The proposed indicator shows that despite the scarcity of GS of public access in the MAS, the GS are the main providers of vegetation cover and their associated benefits. The positive correlation between the household incomes and the abundance of vegetation has been demonstrated in other cities, e.g. city of Tampa (Florida, USA; Landry and Chakraborty, 2009) and Montreal (Canada; Pham et al., 2012). In the MAS, poorer and richer municipalities invest a similar share (4.1–4.7%) of their budget to tree management on GS (Escobedo et al., 2006) achieving different results, not only because their municipal budgets are quite different. This study shows how a high income municipality keeps an abundant vegetation cover in private gardens and green spaces of public use that would not survive unattended in a semi-arid environment like in the MAS where urban vegetation is dominated by exotic species requiring irrigation and maintenance. In cities located in arid and semiarid regions the correlation between income level and vegetation cover is rather evident (Jenerette et al., 2007; Halper et al., 2012). In these municipalities the low-income population faces a double constraint in regards to the maintenance of vegetation. Those contrasts are land and water scarcity. The lowest income groups live in smaller properties which again reduce the area that could be planted, and cannot devote a lot of water to irrigate the plants, due to the cost of drinking water. It also emphasizes the demand of GS for poorer municipalities to balance out the deficiency of vegetation cover on GS of restricted access. However, using photointerpretation techniques, a previous study qualitatively proved that even smaller lots have vegetation, although tree and shrub cover is slightly higher than on average lawn (ReyesPaecke and Meza, 2011). The mentioned evidences also suggested that the higher the household incomes, the richer the structural diversity (e.g. tree, shrub and herbs), which is an issue that still needs to be studied more deeply. This contribution shows that traditional indicators of green spaces calculated for a large territory and standardized by the population have a positive bias to areas of lower density, where they tend to state better results as a consequence of having less population per GS. Thus, inequalities in the distribution of GS are not only explained by the household incomes, but also by the urban structure and especially the size of residential lots. The proposed indicators will be useful for municipalities and urban planners, allowing compare different neighborhoods to steer and evaluate public investment toward the more deprived sectors. These public investments should not only focus on the provision of new GS but also on long-term maintenance in order to guarantee such provision and use of GS as ecosystem services. They should also be applied in other cities of developing countries with similar characteristics (Wright Wendel et al., 2012).

5. Conclusions The study presented a spatially explicit methodology not only to portray the complex role of GS for different municipalities. This mixed approach also deepens our understanding how GS should be developed to serve as contribution to a good quality of life in different urban settings. To do so, the set of indicators should consider standardized indicators of its spatial context. Such a set must also assess quality and size of GS. Consequently, this combination of indicators avoids a bias when evaluating the urban structure. The accessibility is a piece of valuable information beyond the urban structure. In addition, the quality of GS (size, shape and vegetation cover) is not necessarily correlated to population density and distribution or the affluence of local residents. Therefore the results also support a more sustainable urban planning which implies positive commitment by differentiating areas and providing a

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well-distributed accessibility to GS. We demand future research to focus on standards: standards of accessibility for different types of GS and standards of “sustainable” quality of GS considering the natural environment in which cities are embedded in. Acknowledgments This study was framed during the project Evaluating Environmental and Life Quality to analyse Urban Vulnerability in Santiago de Chile (2011–2013). Therefore we want to thank the German BMBF-IB (FKZ 01DN12033), CONICYT/BMBF (229/2010) and CONICYT/FONDAP (15110020) for their support. References Balooni, K., Gangopadhyay, K., Kumar, B.M., 2014. Governance for private green spaces in a growing Indian city. Landsc. Urban Plan. 123, 21–29. Banzhaf, E., Höfer, R., 2008. Monitoring urban structure types as spatial indicators with CIR aerial photographs for a more effective urban environmental management. J. Sel. Top. Appl. Earth Obs. Remote Sens. 1 (2), 129–138. Banzhaf, E., Reyes-Paecke, S., Müller, A., Kindler, A., 2013. 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