Relation between Proximity to Public Open Spaces

and Socio-economic Level in Three Cities in the Ecuadorian Andes

María Laura Guerrero

, Daniel Orellana

1,2 b

, Jorge Andrade

and Gabriela Naranjo

LlactaLAB – Ciudades Sustentables, Departamento de Espacio y Población, Universidad de Cuenca, Av. 12 de Abril,

Cuenca, Ecuador

Facultad de Ciencias Agropecuarias, Universidad de Cuenca, Av. 12 de Octubre, Cuenca, Ecuador

Escuela de Arquitectura, Pontificia Universidad Católica del Ecuador sede Ibarra, Av. Jorge Guzmán, Ibarra, Ecuador

Facultad de Arquitectura Diseño y Artes, Pontificia Universidad Católica del Ecuador, Av. 12 de Octubre, Quito, Ecuador

Keywords: Public Open Spaces, Proximity, Environmental Justice.

Abstract: Public Open Spaces (POSs) are necessary urban goods for satisfying personal and collective needs for

physical, social and mental wellbeing. Equitable spatial access to POSs is key for guaranteeing that resources

for wellbeing are democratically available for all members of the community. Environmental justice states

that contemporary cities have a biased distribution of public spaces, against socially and economically more

disadvantaged sectors of society. Under these premises, this paper evaluates whether there is a case of

environmental imbalance in access to public spaces in three Ecuadorian cities: Quito, Cuenca and Ibarra,

based on the socio-economic status of the population. A pedestrian impedance street network model was used

for obtaining time to the nearest Public Open Space from each urban block, and socio-economic conditions

were obtained from national census data per household and divided into quartiles. Statistical analyses included

Mood’s Median Test, Dunn’s post-hoc test and notched boxplots for assessment. Results show that there is a

significant difference in time to public spaces between quartiles, where the quartile with the lowest socio-

economic conditions is also further from public spaces than the others in the three cities. These results should

inform planning policies, strategies, designs and decisions for future leisure land use reserves.

1 INTRODUCTION

Urban open spaces, such as parks, forests, streams,

squares and community gardens, provide critical

ecosystem services and benefit physical activity,

social bonding, psychological and general well-being

of urban residents (Wolch, Byrne, & Newell, 2014).

One important characteristic of sustainable urban

development is a spatial pattern where facilities are

spatially distributed in a way that they benefit as

many different social groups as possible (Talen,

2010). However, studies have reported consistently

that neighbourhoods with higher socioeconomic

levels enjoy greater accessibility to green spaces

(Talen, 2010; Wen, Zhang, Harris, Holt, & Croft,

2013). Attention must be paid to avoid racial,

economic and social inequalities in access to urban

https://orcid.org/0000-0001-5164-1455

https://orcid.org/0000-0001-8945-2035

https://orcid.org/0000-0003-0570-7446

goods in planning processes for creating more

democratic, equitable cities.

The vast majority of studies about proximity to

public spaces has been conducted in developed

countries such as the United States, the United

Kingdom, Australia, Europe, and some large Asian

cities (Wolch et al., 2014). Only recently, researchers

are studying the spatial relationship between

socioeconomic conditions and proximity to urban

public spaces in Latin America, and to date there are

only a few cities in the region with published studies

(Fernández-Álvarez, 2017; Mayorga Henao &

García, 2018; Tiznado-Aitken, Muñoz, & Hurtubia,

2018).

This paper investigates whether there is a bias

regarding access to public spaces due to socio-

economic conditions in three Ecuadorian cities. It

Guerrero, M., Orellana, D., Andrade, J. and Naranjo, G.

Relation between Proximity to Public Open Spaces and Socio-economic Level in Three Cities in the Ecuadorian Andes.

DOI: 10.5220/0009396600810091

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 81-91

ISBN: 978-989-758-425-1

studies the spatial distribution of public spaces in

relation to the socio-economic conditions of urban

dwellers, providing evidence for both future public

policies and planning guidelines. It also provides a

transparent and replicable methodology using

available public data from official sources and

Volunteered Geographic Information platforms.

The first section of the paper presents the

theoretical background on public space proximity.

The second section details the methodology, datasets,

data processing and statistical analyses. The third

section presents the results and discussion, and the

last section focuses on conclusions and further

research.

2 BACKGROUND

Environmental justice studies whether the

distribution of urban risks and benefits is biased,

disfavouring racial minorities (environmental racism)

and population with lower socioeconomic status

(environmental classism), based on research on

absolute and relative spatial distribution of amenities,

risks, and population in cities (Bolin et al., 2000;

Fernández-Álvarez, 2017). For environmental

justice, the term Environment is defined as the set of

linked places “where we live, work, learn and play,”

a concept that challenges traditional definitions of

environment and nature by including urban areas,

which allows urban inhabitants to be incorporated

into the environmental debate (Turner et al., 2002).

In most cities, the last two hundred years of urban

development history have been dominated by market

dynamics. These dynamics dictate the pattern of

physical production of space, making product

urbanism, competitive strategies, and private

initiatives the major forces of social and spatial

segregation in cities, leaving public spaces as

residuals from private development (Mayorga Henao

& García, 2018).

The definitions of Public Open Spaces (POSs) are

diverse, subject to constant change and discussion,

and depending on the author, sometimes it has

contradictory characteristics. In a recent review of

POS definitions, Andrade et al. (2019) elaborate on

the wide range of characteristics of POSs in the

international and legal literature. These authors have

proposed a definition applicable to this research:

“Unbuilt urban open space for recreational, civic,

natural or cultural purposes, accessible for the

whole community for free and without restriction;

primarily (although not exclusively) owned by

public subjects, capable of hosting a variety of uses

and accommodating diverse users to enhance

inclusion and social equity, suitable for protecting

ecosystems and the sustainability of human

settlements”

This definition is purposefully multidimensional

and attempts to encompass many aspects, in

accordance with Kohn (2004), who suggests a way to

handle the diverse and somehow contradictory

definitions of POSs. This author proposes the

definition to ensure that it comprises a range of

possible criteria, where a sub-selection of them would

enable a space to be qualified as a POS, and not

having one of the criteria wouldn’t mean that the

space is not a POS.

Public Open Spaces are important in terms of

environmental justice for several reasons. First, they

offer opportunities for physical activity that prevents

the risk for chronic conditions such as obesity and

heart disease (Wolch et al., 2014), and they improve

the psychological health of modern communities

(Mehta, 2014). Second, POSs, such as parks and other

green areas, offer ecological services, including

maintenance of biodiversity, regulation of urban

climate, and mitigation of pollution effects and floods

via water infiltration (Haq, 2015). Lastly, they

provide a relevant social benefit, enriching urban life

with meaning and emotions (Fernández-Álvarez,

2017). This social importance has been described by

Mehta (2014) in four key roles for public spaces: as

an arena for public life, as a meeting place for

different social groups, as a space for displaying

social symbols, and as part of the communications

system between urban activities. All urban dwellers

should have equal rights to access these benefits.

From the many variables that influence the use of

public open spaces, such as size, quality,

attractiveness (amenities within the space), proximity

and accessibility, the latter two have been given more

attention. For instance, Pasaogullari (2004) explored

visibility, physical structure, sidewalks, dispersion,

and proximity, finding that variables like proximity,

dispersed location, travel time and characteristics of

the transport environment affect accessibility to

public spaces directly in Famagusta, Cyprus. Lotfi &

Koohsari (2009) analysed proximity to various urban

services, including POSs, in relation to deprivation

levels of the population, finding, surprisingly, that for

the case of Tehran, neighbourhoods with higher

deprivation have better access to public spaces.

Tiznado-Aitken et al. (2018) focused on analysing

both proximity to public transport stops and urban

walking environment together to understand the

accessibility to POSs in Santiago, Chile, in relation to

the socio-economic situation of the population,

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

finding a correlation between low income and poor

access and urban space quality. Bancroft et al. (2015)

in a systematic review found a variation in the

association between access to parks and physical

activity, possibly due to the heterogeneity of exposure

measurements (variables related to parks like

proximity, density, amenities); therefore, they

recommended improvements in study design and

sampling to clarify the relationship between access to

parks and physical activity. The authors mention that

in the current study designs, perceived park

environment characteristics and smaller (vs. larger)

buffer sizes seemed to be more predictive of physical

activity.

It is worth making a distinction between

proximity and accessibility. Proximity refers to the

direct physical distance between two things.

Accessibility, on the other hand, is a far more

complex definition than just a spatial mismatch. It

involves influential factors such as user

characteristics, social and physical barriers, attributes

of the facility, and interaction with other facilities in

the system (Wang et al., 2013). Furthermore,

accessibility to public spaces also has been defined as

a measure of the spatial distribution of facilities

adjusted for the desire and the ability of people to

overcome distance or travel time to access a POS

(Giles-Corti et al., 2005). In this sense, this paper

focuses on proximity, where distance is a component

of a wider concept that determines how reachable

urban services are for different groups. The wider

concept, accessibility, can be tackled in further

research.

Accessibility is closely related to the urban form,

which makes it possible for citizens to participate in

activities, obtain resources, and benefit from services

and information (Lynch, 1960). Moreover,

accessibility is affected by zoning policies and sprawl

patterns in cities, which tend to make distances

increase for working purposes; therefore, they should

decrease for the satisfaction of other needs, like

leisure in POSs. In terms of democratic access,

assessing proximity makes sense where transport

means are available for the entire population, making

walking the most suitable form of transport, since it

can be used without income, race or gender

distinctions (Mayorga Henao & García García, 2018).

There is a trend in the literature to use 400m as a

distance to estimate the potential 'walk-on' or 'walk-

off’ threshold to urban services (Koohsari, Badland,

& Giles-Corti, 2013); other studies indicate that it is

a good starting point, but a person’s willingness to

walk is also influenced by weather conditions, total

travel time, walking distance to the destination,

footpath access to traffic being negotiated, and the

attractiveness of the route (Daniels & Mulley, 2013;

Ker & Ginn, 2003).

In this paper, we investigate the accessibility to

public open spaces in three cities in terms of

proximity to the place of residence of urban dwellers

for three cities in the Ecuadorian Andes (Quito,

Cuenca and Ibarra). Moreover, we evaluate if there is

a relation between the socio-economic level and the

proximity to POSs.

3 METHODOLOGY

We assessed population’s proximity to POSs by

evaluating walking time to the nearest POS from each

city block. Individual dwellers were categorised

according to their socio-economic conditions to

appraise whether there is a bias in accessibility for

different socio-economic levels.

3.1 Study Area

The three cities were chosen due to their different

sizes (metropolitan, intermediate and small cities),

which allows evaluating the existence of patterns

regarding public space distribution and conditions in

the region that are not dependant on city size.

Quito is the capital of the country and also of

Pichincha province, with an urban population of

1,021,474 million and a surface of 266.75Km2. The

surface for public spaces is 24.37Km2 (7.09%).

According to this data, the green urban index is

23.86m2/inhabitant. It is located at 2,850 m.a.s.l. at

the base of the Pichincha volcano (west) and limited

by the geologic fault EC-31 to the east. Both

geographic landmarks highly determine its

morphological development in a longitudinal way,

hence challenging mobility and distribution of

services.

Cuenca is the capital of Azuay province, with

323,000 urban inhabitants; it covers 73Km2. From

this, 2.43Km2 (3.3%) are dedicated to POSs. The

green urban index is 7.52m2/inhabitant. It is located

in an inter-Andean valley at 2,500 m.a.s.l. Hydrology

is very important in Cuenca, with 4 main rivers and

11 secondary water courses, like creeks, crossing the

city. The city shape is strongly influenced by a

terraced geomorphology and several rivers and

streams, which makes connectivity one of the main

challenges for urban development.

Relation between Proximity to Public Open Spaces and Socio-economic Level in Three Cities in the Ecuadorian Andes

Figure 1: Distribution of Public Open Spaces in Ibarra, Cuenca and Quito.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

Ibarra is the capital of Imbabura province and has

an urban population of 129,305 and a 43.45Km2

extension. Public spaces occupy 1.36Km2 (3.13% of

the total surface). The green urban index is

10.52m2/inhabitant. It is located at the base of the

Imbabura volcano at 2,225 m.a.s.l. and limits with the

Yaguarcocha Lake to the northeast. The closeness to

both the volcano and the lake are to be considered;

however, being a small city, it has not yet reached a

problematic limit regarding natural conditions, which

makes planning decisions timely.

For this analysis only the area inside the official

urban boundary was analysed. The distribution of

POSs in each city is represented in Figure 1.

3.2 Datasets

The unit of analysis is each person in the urban areas

of the three cities. The socio economic condition of

each person was represented by the Living Condition

Index (LCI) (Orellana & Osorio, 2014) of their

corresponding household. The LCI was computed

using official data from the 2010 census (INEC,

2011), which included an identification code of the

city block where the household is located. This code

was used to geo-reference each individual at the block

level. There are no data protection constraints, given

that although the index is calculated per household,

the household’s precise location is unknown, because

census cartography is released up to city block as the

most disaggregated geographical level.

A second dataset contained the public spaces in

each city. First, local authority databases were

obtained. Then, a team in each city revised the

databases, confirming that they corresponded to

existing public sites and updating them to only public

open spaces (leaving aside roofed playing courts, for

example) using aerial imagery and on-site

verification.

Finally, the third dataset comprised the urban

street network of each city obtained from

OpenStreetMap (OpenStreetMap, 2019). The dataset

was revised and corrected for topological consistency

and connectivity.

3.3 Process

First, a pedestrian impedance model was built to

calculate network walking time in minutes for each

street segment and intersection using Network

Analyst in ArcGIS 10.3.. The impedance model was

computed based on the street hierarchy, the existence

of pedestrian infrastructure such as sidewalks and

footways, and the existence of facilities at

intersections, like pedestrian traffic lights or zebra

crossings.

Second, the Living Conditions Index (Orellana &

Osorio, 2014) socio-economic conditions of each

individual was computed in IBM SPSS Statistics 25.0

at the household level. The LCI takes into account

physical household characteristics (flooring, walls

and roofing materials, and overcrowding), access to

basic services (water, electricity, sewage and

communications), level of education of household

dwellers and access to health insurance (public or

private). The spatial location of each individual was

represented as the centroid of the block the household

belongs to. The mean LCI was assigned to each city

block for visual exploratory purposes.

Third, the walking time to the closest POS was

obtained for each individual using the “closest

facility” algorithm in ArcGIS’s Network Analyst.

Usually, origins and destinations are represented by

centroids of polygons (city blocks and POSs).

However, since the algorithm will automatically snap

the centroid to the nearest network edge, centroids of

large POSs (larger than 10000m2) may not be a useful

representation of destinations, because origins at the

other side of the POS will have artificially large

values of network distances and times (the algorithm

will compute the distance as if they must walk all

around the POS to where the centroid was snapped).

Therefore, destinations were represented as points at

the perimeter of each POS each 100m. To select the

perimeter lines that actually have street fronts, a 10-

meter buffer from road centre line was computed,

based on the average width of streets in the three

cities. Then points were generated along the selected

lines each 100m. Finally, large fenced POSs and

those with designated entrances or limited by

topography were individually analysed to determine

their connections to the street network. This approach

implied a considerable pre-processing effort

compared with the traditional method of centroids.

3.4 Statistical Analyses

To assess the potential bias on accessibility to POSs

for different socio-economic levels, the population

was classified into quartiles according their LCI.

Then, the Mood’s Median Test was used to identify if

there were significant differences on the walking time

to the nearest point for different LCI quartiles.

Moreover, Dunn’s post-hoc test with Bonferroni

correction was used for pairwise comparisons.

Boxplots were also used for visual exploration of the

differences. Statistical analyses were conducted in R.

Relation between Proximity to Public Open Spaces and Socio-economic Level in Three Cities in the Ecuadorian Andes

Figure 2: Socio-economic conditions for Ibarra, Cuenca and Quito.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

Figure 3: Time to access POS in Ibarra, Cuenca and Quito from each city block.

Relation between Proximity to Public Open Spaces and Socio-economic Level in Three Cities in the Ecuadorian Andes

4 RESULTS AND DISCUSSION

4.1 Descriptive Statistics of Proximity

to Public Open Spaces

The mean time in minutes to reach a POS was 4.7 in

Quito, 6.6 in Cuenca and 3.8 in Ibarra. The median

for the three cities was approximately 3 minutes. Also

in all three cities the value for the third quartile is less

than 6 minutes, which means that 75% of people are

about 6 minutes away from a POS. This might seem

encouraging, but as we will discuss further along,

what matters in terms of environmental justice is the

socio-economic profile of those who are further away

and the differences they might have with the rest of

the population. There are some possible causes for

that distribution of POS in each city. In Quito,

planning became important from the 1950s onwards,

under the tradition of the Modern Movement, which

promoted big and small public space reserves before

most Ecuadorian cities experimented rapid growth

(Cifuentes, 2016). The downside of this initial

planning process is that the model was polarized,

segregating (both geographically and economically)

and served the interests of landlords (Carrión &

Erazo, 2012), which planted the seed for the evident

socio-economic divide between the north and the

south that is currently experimented. In Cuenca,

planning also played an important role around the

same time that in Quito, by keeping river banks in a

natural state to be converted into formal POSs later

on (Hermida, Hermida, Cabrera, & Calle, 2015). As

rivers transversally cross the city, mainly from west

to east, the banks became crucial for the POS

distribution. Ibarra, a smaller and younger city, has

experimented horizontal sprawl from the 1980s

onwards, but without replacing previously existing

public spaces for other land uses. Even though the

Table 1: Descriptive statistics of time (minutes) from city

block centroids to the closest POS.

Ibarra Cuenca Quito

Minimum

0.000 0.000 0.000

Maximum

24.679 72.743 95.841

Mean

3.838 6.620 4.712

Median

3.110 3.046 3.224

Standard deviation

3.103 10.264 5.661

Coeff. of Variation

0.808 1.551 1.201

First quartile

1.851 1.579 1.639

Third quartile

4.863 5.982 5.919

average time might be encouraging, the longest time

to the nearest POS in Quito is 95.8 minutes, 72.7

minutes in Cuenca, and 24.7 minutes in Ibarra.

4.2 Socio-economic Spatial Distribution

A spatial pattern of socio-economic conditions

becomes evident: higher levels tend to gather in or

around the city centres, while lower LCI values were

located mainly in the periphery. This pattern is clearer

for Cuenca and Ibarra. Quito, on the other hand,

shows a very strong division by socio-economic

status between the north and the south of the city, the

south being the most disadvantaged one. Blocks with

the lowest LCI are located on the extreme south and

the extreme north of the urban area. See Figure 2 for

details.

Besides this global pattern, there are also small

clusters of high LCI values on peripheral areas. These

are usually modern residential developments where

families with high socio-economic conditions move

to the suburban areas looking for larger parcels and

houses with private gardens or inside gated

communities. There are also clusters and single

blocks of low LCI values near the city centres where

some impoverished neighbours are located.

4.3 Relation between Socio-economic

Distribution and Proximity to

Public Open Spaces

Visual analysis showed that there seems to be a

perceivable bias regarding access to public open

spaces in all three cities, with longer travel times

towards the periphery where blocks with the lowest

socio-economic conditions are usually located.

Figure 3 shows the spatial assignment of each city

block to the closest POS. Although the analysis was

conducted using network distance and walking time,

the assignment of blocks to POSs was represented

using straight lines for the sake of visual

interpretation.

Results of Mood’s median test (Table 2) evidenced

that there are significant differences between the

medians of travel times to POS for different LCI

quartiles in all three cities (alpha = 0,05).

Table 2: Mood’s Median Test Results.

Ibarra Cuenca Quito

X squared

1353.3 6958.7 7541.6

P value

<0.0001 <0.0001 <0.0001

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

Table 3: Dunn’s Test Results.

Ibarra Cuenca Quito

Value

q1 q2 q3 q1 q2 q3 q1 q2 q3

z q2 13.519 11.258 44.870

p-value 0.000* 0.000* 0.000*

z q3 20.780 7.270 20.557 9.304 58.843 9.922

p-value 0.000* 0.000* 0.000* 0.000* 0.000* 0.000*

z q4 17.882 4.357 2.920 20.928 9.674 0.370 7.459 36.443 49.191

p-value 0.000* 0.000* 0105* 0.000* 0.000* 1.000 0.000* 0.000* 0.000*

The boxplots showed that, for all cities, people in

the first LCI quartile were further away from a POS

than the people in Q2 and in turn, they were further

compared to people in Q3. These differences were

statistically significant as revealed by Dunn’s test

with Bonferroni’s correction. Quartile Q4, however,

behaved in a different way. Median travel time for Q4

was not significantly different from Q1 in Quito and

from Q3 in Cuenca. In the case of Ibarra, was

significantly higher than for Q3. A possible

explanation might be that dwellers in the best socio-

economic conditions (Q4) don’t prioritize living near

a POS and tend to look for more remote locations with

the possibility of private green space. This might be

due to self-segregation by acquisitive capacity (Table

3 and Figures 4-5).

5 CONCLUSIONS

In this paper we explored the relation between public

space distribution and socio-economic status of the

population in three Ecuadorian cities in the Andes

region. For this purpose, we used a pedestrian

impedance street network model to calculate the

walking time from city blocks centroids to the

entrances to public spaces for different socio-

economic levels. Although people may access to POS

using different transportation means, walking time to

POS will better reflect accessibility for different

socio-economic groups.

There results were consistent on the three cities,

independently of their size and particular historical or

physical conditions. First, although the walking time

to the nearest public space was relatively low from

most city blocks, there was a statistically significant

difference among socio-economic levels. This means

that there is a case of environmental injustice in the

studied cities, regardless of their surface, population

size or particular geographies, a condition that should

be addressed through detailed spatial planning and

Figure 4: Boxplots of time for each socio-economic quartile

(Ibarra-Cuenca).

land use public policy. For example, parcels should

be reserved for public spaces in areas that are in

process of consolidation, with special care for those

Relation between Proximity to Public Open Spaces and Socio-economic Level in Three Cities in the Ecuadorian Andes

where the inhabitants have vulnerable socio-

economic conditions. Also, in already consolidated

areas where the need of public space is identified,

some publically owned parcels could be partially or

totally to POSs. Finally, some work can be done with

the community to form public-private alliances for

the provision of POSs and collective spaces.

Figure 5: Boxplots of time for each socio-economic quartile

(Quito).

Second, there is a trend in the three cities in which

walking time to the nearest POS diminishes from the

first to the third LCI quartile. This is, as the socio-

economic condition improves, there are more

accessibility to POSs. However, this trend is

disrupted for the fourth LCI quartile, where the

walking time to the nearest POS increases again or is

not different from Q3, and therefore accessibility to

POSs drops again. Given that their economic status is

not an impediment, it is assumed that distancing from

urban services, like POSs, is intentional which

indicates a trend with an impact on urban growth that

should be properly analysed and addressed. Authors

like Talen (2010) support this affirmation, indicating

that public parks can substitute for private open space

for apartment dwellers, but not for owners of single-

family detached homes, who are likely to have their

own private outdoor space. This pattern is consistent

with the urban sprawl process characterising

Ecuadorian and Latin American cities, where urban

planning is usually weak, producing low-quality

urban neighbourhoods in new expansion areas where

even wealthier families are located. Overall, we were

able to confirm a bias on spatial proximity to POSs

regarding the socio-economic level of population in

the three studied cities.

In methodological terms, one improvement of this

work is that the spatial and statistical analysis was

based on high-detailed datasets. We were able to

assess walking time to nearest POS for each person in

the three cities. Moreover, the representation of

destination as points at the border of the POS instead

of centroids is a more accurate representation for

modelling routes to the closest facility in network

analysis.

Further research could take proximity to POSs to

a deeper level, by analysing accessibility as it was

defined in the Background section of the paper, taking

into account the attractiveness of public spaces and

their relation to space use. At the same time other

accessibility metrics should be explored beyond time-

based proximity (e.g. different transportation modes).

Also, from a planning perspective, potential sites

for public space should be identified in the areas

where the deprived population is, to decrease the bias

against the more vulnerable sectors. Finally, the

methodology could be reproduced to more cities in

the country, and also to other urban services, to

inform public policy.

ACKNOWLEDGEMENTS

This research is part of the project “Estudio

comparativo integral de inventario, distribución y

evaluación del Espacio Público Abierto (EPA) en las

ciudades de Quito, Cuenca e Ibarra” founded by

Pontificia Universidad Católica del Ecuador-PUCE

in collaboration with the LlactaLAB research group

of the University of Cuenca. Authors are grateful with

Prof. Daniela Ballari for her advice on the statistical

analyses.

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