Finisterra, LXI(131), 2026, e41818

ISSN: 0430-5027

doi: 10.18055/Finis41818

Artigo de Investigação

PORTO ALEGRE'S URBAN PARKS:

ACCESSIBILITY AND SOCIOECONOMIC EXCLUSION

Jennifer Domeneghini¹

Fábio Lúcio Lopes Zampieri²

Amanda Silveira Correa³

ABSTRACT – This article is a study on accessibility to urban parks in Porto Alegre, Rio Grande do Sul, Brazil, which

aims to investigate how different social classes access these spaces. For this purpose, a methodology that considers travel times

was used to determine the spatial accessibility of places/locations, considering the spatial distribution of nine urban parks and

evaluating the performance of walking, cycling, and public transport. To calculate and determine accessibility considering the

cost of travel in terms of time, the RStudio software was used through the {r5r} package. Spatial opportunity maps of

socioeconomic groups were generated by crossing with the accessibility data previously calculated, with the income

information per hexagon, the distribution of accessibility of the population of each income level was calculated, and the

weighting of the accessibility level was made. The distribution of accessibility of people located in the origins of a certain

income decile was obtained, generating graphs according to travel time (30, 60, 90, and 120 minutes). The analysis highlights

significant inequality in access to urban parks in Porto Alegre. Accessibility on foot is the most restricted and unequal,

benefiting mainly central areas and higher income classes. Although cycling increases the reach of parks compared to walking,

it still faces significant inequalities. Public transportation emerges as the most equitable mode, offering the greatest coverage

and potentially reducing disparities.

Keywords: Urban accessibility; urban parks; socio-spatial inequality; urban mobility.

RESUMO

–

PARQUES URBANOS DE PORTO ALEGRE: ACESSIBILIDADE

E

EXCLUSÃO

SOCIOECONÔMICA. Este artigo é um estudo sobre a acessibilidade aos parques urbanos de Porto Alegre, Rio Grande do

Sul, Brasil, que tem como objetivo investigar como diferentes classes sociais acessam esses espaços. Para tal, foi utilizada uma

metodologia que considera os tempos de deslocamento para determinar a acessibilidade espacial dos locais, levando em conta

a distribuição espacial de nove parques urbanos e avaliando o desempenho dos modos a pé, de bicicleta e de transporte público.

Para calcular e determinar a acessibilidade considerando o custo do deslocamento em termos de tempo, foi utilizado o software

RStudio por meio do pacote {r5r}. Mapas de oportunidade espacial dos grupos socioeconômicos foram gerados cruzando-se

os dados de acessibilidade previamente calculados com as informações de renda por hexágono, calculando-se a distribuição da

acessibilidade da população em cada nível de renda e ponderando-se o nível de acessibilidade. Obteve-se a distribuição da

acessibilidade das pessoas localizadas nas origens de um determinado decil de renda, gerando-se gráficos conforme o tempo

de viagem (30, 60, 90 e 120 minutos). A análise destaca uma desigualdade significativa no acesso aos parques urbanos de Porto

Alegre. A acessibilidade a pé é a mais restrita e desigual, beneficiando principalmente áreas centrais e classes de maior renda.

Embora o uso da bicicleta amplie o alcance aos parques em comparação com o deslocamento a pé, ainda persistem

desigualdades significativas. O transporte público surge como o modo mais equitativo, oferecendo maior cobertura e

potencialmente reduzindo disparidades.

Palavras-chave: Acessibilidade urbana; parques urbanos; desigualdade socioespacial; mobilidade urbana.

HIGHLIGHTS

•

Income-based inequality in park access in Porto Alegre.

Public transport enables most equitable park access.

City centre has best pedestrian/cycling infrastructure.

Wealthier groups reach parks faster.

Peripheral areas lack urban green spaces.

Recebido: 29/05/2025. Aceite: 11/12/2025. Publicado: 11/05/2026.

Fábio Lúcio Zampieri: fabio.zampieri@ufrgs.br

¹Independent researcher affiliated with the research group Spatial Dynamics and Society, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.

²Department of Urbanism, Graduate Program in Urban and Regional Planning, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.

³Graduate Program in Urban and Regional Planning, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.

Published under the terms and conditions of an Attribution-NonCommercial-NoDerivatives 4.0 International license.

Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

1.

INTRODUCTION

Urban parks play a crucial role in cities, especially in a context of growing environmental awareness,

such as adaptation to climate change (Ramyar et al., 2021) and the pursuit of urban quality of life (Markevych

et al., 2017; Mouratidis, 2021). These spaces are essential to improve the quality of life in cities and have

become an increasingly important issue for urban planning (Mouratidis, 2021), as they provide people with

physical, psychological, and social well-being (Buckley, 2020; Kim & Miller, 2019; Wan et al., 2020).

In addition to promoting quality of life, urban parks play vital environmental roles, such as improving

the urban climate (Ramyar et al., 2021; Zhao et al., 2024), filtering air (Kabisch et al., 2024; Zhao et al.,

2024; Zhou et al., 2024), and preserving biodiversity (Aronson et al., 2017; Beninde et al., 2015; Egerer et

al., 2024; Lepczyk et al., 2017). These areas also foster social development by serving as inclusive spaces

that promote social and community cohesion (Delshad, 2022; Jennings & Bamkole, 2019). Green spaces, in

particular, have been shown to strengthen neighbourhood ties and trust, contributing to collective well-being

and social resilience (Jennings & Bamkole, 2019). However, it is important to distinguish the effects of public

open spaces in general, which facilitate social encounters regardless of vegetation (Francis et al., 2012), from

those of urban green spaces, which also provide restorative and environmental benefits that contribute to

individual and collective well-being (Delshad, 2022; Kaplan & Kaplan, 1989). Additionally, urban parks

function as places for environmental education (Lu et al., 2024; Park et al., 2021), recreation (Barbosa et al.,

2024), and health promotion (Barbosa et al., 2024; Park et al., 2021; Shanahan et al., 2015).

As Tucci (2005) argues, urban parks emerge as a viable solution to balance the urbanization process

with the need for environmental preservation. These spaces play an important function, not only in ecological

conservation but also serve as accessible social arenas that foster everyday encounters, strengthen

neighbourhood bonds, and enhance social cohesion and community wellbeing. (Egerer et al., 2024).

In this context, the interaction of urban parks with their surroundings becomes fundamental, both for

their effectiveness and relevance in cities (Patino et al., 2023). However, as Jacobs (2011) argues, such

interactions are not always positive since specific urban conditions, including mixed uses, active edges, and

continuous presence, are essential for parks to function as vibrant and safe public spaces. These spaces

influence and are influenced by environmental dynamics, directly impacting the social, aesthetic, and

economic functions of urban areas, in addition to reflecting significant differences in access to these green

areas (Delshad, 2022; Lu et al., 2024; Park et al., 2021; Patino et al., 2023). The study by Patino et al. (2023)

indicates that, in some cities, a higher socioeconomic level is associated with greater proximity to green

areas, while in others, populations of middle socioeconomic status face the lowest access. Thus, planning

parks in an integrated manner with their surroundings and adjacent infrastructure is essential to ensure their

efficient use by the community (Jacobs, 2011).

Accessibility to parks is directly related/associated with the use of the park by the population. Comber

et al. (2008) found that the distribution of access to green spaces is unequal among ethnic groups. Similarly,

Dash and Chakraborty (2023, p. 64) identified that in one study in Bhubaneswar, India, public green spaces

are larger in “least deprived communities”.

In this sense, the research aims to answer the following question: are the urban parks of Porto Alegre

accessible to people of all social classes? The central hypothesis of this work is that the city’s urban parks

are more accessible to higher-income populations, who, according to Rigolon (2016), have control over the

city’s accessibility and can choose where they will be located and how the means of conveyance used.

Therefore, the objective of this work is to investigate access to urban parks by the Porto Alegre population,

analysing how different social classes access each city space. The accessibility method of capturing and

processing information is a noteworthy contribution of this work, as it utilises the urban network used by the

population on foot, bicycle, buses and trains, considering lines and stops, to calculate the space distribution

of urban parks.

2.

EVALUATED ACCESSIBILITY

Accessibility in urban planning links the urban network and transport system performance within a

city’s spatial structure, influencing population, economic activities, and public services. Pereira and

Herszenhut (2023) identify three core components: infrastructure, land use, and individual characteristics.

This aligns with accessibility as the ease of reaching desired destinations (Geurs & Van Wee, 2004; Hansen,

1959). Infrastructure facilitates urban access. Quality transport services and infrastructure are crucial,

including network connectivity, road efficiency, and high-capacity corridors (Ryan & McNally, 1995). Its

efficient spatial-temporal integration is fundamental (Cervero & Kockelman, 1997).

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

Land use affects accessibility through the proximity of people to activities like schools and healthcare.

Closer activities mean easier access. Mixed-use and denser areas generally boost accessibility by cutting

travel distances and encouraging sustainable transport (Ewing & Cervero, 2010). Jacobs (2011) also

highlighted how diverse land uses foster vibrant, accessible urban life.

Individual characteristics significantly impact accessibility. Mobility/cognitive impairments, age,

gender, ethnicity, and income affect movement, transport use, and feelings of safety (Imrie, 2000). The

United Nations (2006) stresses accessibility as a fundamental right, requiring barrier removal. In this context,

Van Wee (2016) emphasize its social value for equity and inclusion.

Urban accessibility levels are determined by the interaction of these three components, which must be

considered in an integrated manner to foster a more accessible and inclusive city.

3.

ACCESSIBILITY AND SOCIO-SPATIAL EXCLUSION

Understanding how these three components interact within the urban space is essential for analysing

inequalities in access to parks and public spaces. Socio-spatial exclusion, as discussed by Carlos (2020), is

not limited to the physical separation between social groups but rather concerns the structural form of city

production based on capitalist logic, in which space is a commodity and access to it is determined by unequal

power relations. For the author, urban segregation expresses a process in which certain groups are deprived

of the conditions to enjoy and appropriate urban space, which implies restrictions on the use and right to the

city. Thus, socio-spatial exclusion manifests itself not only in the physical distance between poor and rich

areas but also in the unequal distribution of public facilities, leisure opportunities, and infrastructure.

Furthermore, it is important to examine how different social classes use and access these

environments, considering the various factors that influence the appropriation and effective use of these

spaces. As Macedo (1995, p. 49) states, “the solutions adopted are generally partial and rarely reach the entire

population, and certainly very little those most in need”. In this study, accessibility is understood not only as

a factor measured by travel time, but also as a social condition involving the population’s use and

appropriation of parks.

As a result of these exclusions, an impoverished space emerges and becomes impoverished:

materially, socially, politically, and culturally, showing that the space we live in is a space without citizens

(Santos, 2011). This reality is aggravated by the fact that urban space produces inequality. For Rolnik (2015),

the systematic exclusion of most of the population is a central element of the machinery of inequality in the

city. In this sense, the dynamics of exclusion affect access to public spaces and perpetuate the marginalisation

of the most vulnerable social classes, compromising their right to the city and leisure. As Oliveira and Neto

(2020, p.7) argue, the right to the city should reclaim “the place of dwelling, of use value, of public spaces,

of encounter, of and through simultaneities”, thinking of cities through their “horizontality”, associating the

places themselves with cultural issues. In the city of Porto Alegre, as throughout Brazil, public space is still

thought of as urban residue, unoccupied land, a lack of capital investment, and a disregard for the cultural

aspects of territories, perhaps even depriving people with lower incomes of the very concept of “place” as a

neoliberal idea.

Inequality in access to public and leisure spaces becomes evident when we observe the search by the

wealthiest classes for alternative solutions to the urban crisis. According to Macedo (1995), these classes

have increasingly created private spaces that aim to meet emerging needs, resulting in the internalization of

leisure in areas such as squares, parks, and private clubs, often within condominiums. This privatization of

urban space leads to the exclusion of those who cannot pay for access to goods that should be public (Santos,

2011). Thus, urban life and the use of public spaces are transformed into a scenario where leisure becomes a

consumer privilege, marginalizing those who do not have access to these facilities, since “those who cannot

pay for the stadium, the swimming pool, the mountain, the fresh air, the water, are excluded from the

enjoyment of these goods, which should be public because they are essential” (Santos, 2011, p. 64).

Open spaces for the lower classes are reduced and fragmented, limiting their use to basic activities,

such as washing clothes or playing with small children (Macedo, 1995). This reflects the statement by Santos

(2011, p. 63), who asks: “And the right to the environment? It is in the books and official speeches, but it is

still a long way from being implemented”. This lack of access and the privatization of public spaces create

an urban environment that not only ignores the needs of the population but also intensifies existing social

inequalities, transforming the city into a space where the right to leisure is denied to the lower income. In

this study, incomes were used in deciles, that is, dividing the total population into 10 intervals based on

income, so the lowest incomes presented correspond, for example, to the poorest 10%.

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4.

PORTO ALEGRE'S URBAN PARKS

Briefly, in the late 18^thcentury, Porto Alegre’s first squares were large open spaces for religious

festivals, popular events, and food sales. During the 19^thcentury, urban core expansion introduced sanitation

improvements and effectively urbanized public spaces (Menegat & Almeida, 2004). This period marked

initial concerns about organizing these spaces, resulting in significant transformations, high urbanization,

and over 70 new squares (Menegat et al., 1998). Following metropolitanization, maintaining a balance

between greenspaces and population became particularly important (Menegat & Almeida, 2004).

Porto Alegre is one of Brazil’s most forested capitals. Growing ecological awareness led to Brazil’s

first Municipal Department of the Environment in 1976. In 1979, the new Master Plan required 2% of new

subdivisions for the city’s park network (Menegat et al., 1998). Porto Alegre officially has 11 urban parks

(fig. 1) (Prefeitura Municipal de Porto Alegre, 2024).

Fig. 1 – Urban parks, income, and population density in Porto Alegre, Brazil.

Fig. 1 – Parques urbanos, renda e densidade populacional em Porto Alegre, Brasil.

Source: IBGE; Google Satellite; Porto Alegre Municipal Government & OpenStreetMap

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

This study considers nine, as Orla do Guaíba sections 1 (Moacyr Scliar) and 3 (Jaime Lerner) were

analysed together. Pontal Park was excluded due to its significantly smaller area. Park selection is justified

by Dash and Chakraborty (2023, p. 63), stating “parks are considered a better indicator for Urban Green

Spaces (UGS) due to their presence across all neighbourhoods”.

According to data from the Porto Alegre municipality (2022), the oldest of these parks is Farroupilha

Park (Redenção, with 37.51ha), inaugurated in 1935. In the following decades, other important parks were

established, such as Moinhos de Vento Park (Parcão, with 11.50ha) in 1972 and Marinha do Brasil Park

(70.70ha) in 1978. In the 1980s, Harmonia Park (17.00ha) was inaugurated in 1981, and Marechal

Mascarenhas de Moraes Park (18.30ha) in 1982.

The 1990s saw the creation of only one park, Chico Mendes Park (25.29ha), inaugurated in 1992. In

the 21^stcentury, new parks were created, such as Gabriel Knijnik Park (11.95ha) in 2004 and Germânia Park

(15.11ha) in 2006. More recently, the city revitalized the Orla do Guaíba, with sections Moacyr Scliar in

2018 and Jaime Lerner in 2021 (sections 1 and 3, totalling 23.24ha). The parks in Porto Alegre vary in size,

with some areas standing out for their vast expanses. Porto Alegre’s parks vary in size, some with vast

expanses. According to Instituto Brasileiro de Geografia e Estatística (IBGE, 2019), Porto Alegre’s total

territory is 495.390km², with 214.91km² of urbanized area (43.4%). The parks’ combined area totals

233.50ha, only 1.09% of the urbanized area.

5.

METHODOLOGY

Porto Alegre has a cycling network consisting of bike lanes and cycle paths, primarily concentrated in

the central region, where the infrastructure is more developed. However, the city faces significant challenges

due to disconnected segments in the southern and northern areas, limiting cyclist mobility and access to

urbans outside the city centre (fig. 2).

The public transportation network is well distributed, with bus routes and stops covering the entire

urbanized area, aligning with conventional bus corridors. Though one train line extends from central region

northwards, connecting to other cities, the rail system primarily serves intermunicipal travel. Nonetheless,

the proximity of bus stops facilitates intermodal connections between these transport modes.

For the development of Porto Alegre’s urban parks accessibility map, was adopted of Pereira and

Herszenhut (2023) methodology, which uses travel times to assess the spatial accessibility of places. This

approach assumes that all people in the same area have the same opportunities to access activities distributed

throughout the city (Pereira & Herszenhut, 2023).

In this context, the spatial distribution of urban parks and the urban configuration are analysed, along

with the efficiency of walking, cycling, and public transportation networks. However, this method does not

consider individual characteristics when analysing spatial accessibility.

To calculate and determine accessibility regarding the cost of travel in terms of time, the RStudio

software was used through the {r5r} package, which combines all this data into a multimodal transport

network operated in routing trips between origin-destination matrix pairs and in calculating travel time.

According to Pereira and Herszenhut (2023), the calculation procedure involves three steps: (i)

compilation and formatting of the data required for the analysis; (ii) calculation of accessibility levels; (iii)

generation of the resulting data: maps of accessibility levels and graphs of income deciles x accessibility

level. The data used for the analysis were obtained from different sources, processed in the QGIS software,

and inserted into RStudio and are described in the table 1 below:

Table I – Source of data used.

Quadro I – Fonte dos dados utilizados.

Data

Source

Street Network

Website https://export.hotosm.org/ from OpenStreetMap data

Public Transit Network

Topography

General Transit Feed Specification (GTFS) files – Access to Opportunity Project/Ipea

Raster data file – https://search.earthdata.nasa.gov/

Income Data

Information obtained through the {aopdata} package

Source: Authors

The file contains the geographic coordinates of the centroids of a regular hexagonal grid covering the

entire city of Porto Alegre (Pereira & Herszenhut, 2023), along with data on the resident population.

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

These data, obtained from IBGE, were processed in QGIS and distributed across the hexagonalⁱgrid.

The dataset includes the insertion of centroids for each urban park analysed and the centroids of each

hexagon, identified by a unique identifier (ID).

The table records, in addition to the ID, latitude, longitude, the presence or absence of a park, and the

population. All these hexagonalⁱⁱcentroids were used to compute the travel time matrix, independently of

their direct relationship with urban parks.

Fig. 2 – Cycling network infrastructure, public transport, and green areas in Porto Alegre/Brazil.

Fig. 2 – Infraestrutura cicloviária, transporte público e áreas verdes em Porto Alegre/Brasil.

Source: IBGE; Google Satellite; Porto Alegre Municipal Government & OpenStreetMap

The accessibility level calculations were conducted in two stages. First, the accessibility levels of

Porto Alegre’s urban parks were estimated by computing a travel time cost matrix between multiple

origins – the centroids of the hexagons covering the entire city – and the destinations, which are the urban

parks.

The travel time matrix was computed using the travel_time_matrix() function, which defines origins

and destinations. Regarding transport modes, walking was analysed using the WALK mode, cycling with the

BICYCLE mode, and public transit with the TRANSIT mode.

The departure time was set to 14:00:00 on May 13, 2019, with maximum travel times of 30, 60, 90,

and 120 minutes, as exemplified by Pereira and Herszenhut (2023).

This function aims to identify the fastest route from each origin to all possible destinations, considering

the travel mode, departure time, and other user-defined parameters (Pereira & Herszenhut, 2023).

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

These thresholds were defined after preliminary tests using a 15-minute cutoff produced minimal

accessibility results, particularly for public transport. The 30-minute interval was therefore adopted as the

baseline, as it provided more meaningful spatial coverage and better reflected typical urban travel-time

tolerances. Subsequent thresholds were established in continuous 30-minute increments (30-120 minutes) to

ensure comparability across transport modes and to capture progressive changes in accessibility levels.

The selected intervals are consistent with those applied by Pereira (2019) in public transport

accessibility analyses for Rio de Janeiro, supporting methodological comparability.

This approach also aligns with findings from El-Geneidy et al. (2016), who emphasize that cumulative

accessibility is highly sensitive to the choice of time thresholds, and from Tomasiello et al. (2023), who

recommend the use of multiple time intervals to reduce the arbitrariness of such selections.

Although a 120-minute threshold is uncommon for walking, it is commonly applied in public transport

analyses, as in Pereira (2018).

Moreover, the inclusion of this upper limit allows illustrating that even at extended travel durations,

portions of the population, especially in peripheral areas, remain without effective access to parks, revealing

structural spatial inequalities in opportunity distribution.

In addition, as demonstrated in Domeneghini (2019), cyclists may cover considerable distances to

reach urban parks, further justifying the use of broader time thresholds for comparative multimodal analyses.

This function aims to identify the fastest route from each origin to all possible destinations, considering

the travel mode, departure time, and other user-defined parameters (Pereira & Herszenhut, 2023). For

walking trips (WALK), the displacement calculation is based on the Euclidean distance between the origin

and destination points, adjusted to the actual road network.

The algorithm employs a street network to determine the shortest path, considering the average

walking speed. The default speed for the WALK mode in {r5r} is set at 3km/h. Furthermore, the package

incorporates topography and street characteristics, such as sidewalks and crossings, to provide an accurate

estimate of walking travel time (Pereira & Herszenhut, 2023).

For bicycle trips (BICYCLE), {r5r} employs a similar approach, identifying the most efficient route

within the street network. The default speed for the BICYCLE mode is 12km/h. In addition to the road

network, {r5r} considers bike lanes, elevation changes, and other cycling-specific infrastructure, ensuring

that travel time is calculated based on real-world route conditions (Pereira & Herszenhut, 2023).

The travel time calculation for public transit (TRANSIT) in {r5r} is conducted using a door-to-door

approach. For a TRANSIT trip, the total travel time consists of: (i) walking time to the nearest public transport

stop, (ii) waiting time at the stop, (iii) in-vehicle travel time, and (iv) walking time from the drop-off stop to

the destination (Pereira & Herszenhut, 2023).

For the second stage, accessibility levels were calculated using the accessibility() function from the

{r5r} package, which evaluates the accessibility of different locations based on various criteria. This

calculation required generating a cost (travel time) matrix that connects the origins and destinations,

represented by the centroids of the hexagons.

Additionally, the transport modes (walking, cycling, and public transit) were specified, along with the

points of interest for assessing accessibility levels (urban parks), the exact departure time (May 13, 2019, at

14:00:00), and the maximum travel time to be considered. Separate analyses were conducted for maximum

travel times of 30, 60, 90, and 120 minutes, enabling a detailed evaluation of accessibility across different

time intervals.

After completing the accessibility calculations, accessibility maps were generated based on travel

times, incorporating origin and destination points represented by hexagons. The resulting maps were

imported into the Geographic Information System (GIS) environment, highlighting the most accessible areas

and presented in the results.

Similarly, spatial opportunity maps for socioeconomic groups were generated by intersecting

previously calculated accessibility data. Income levels were determined using income decile data for each

origin, classified through natural break intervals based on the average income of residents. With income data

assigned to each hexagon, the distribution of accessibility across different incomeⁱⁱⁱlevels were analysed, and

accessibility levels were weighted accordingly.

As a result, the accessibility distribution for individuals from origins within a given income decile was

determined. The boxplots were produced using the ggplot2 package in R. Graphs were generated for different

travel time intervals (30, 60, 90, and 120 minutes) and transport modes (walking, cycling, and public transit),

and are presented in the results.

The boxes represent the interquartile range (IQR), the central line indicates the median, and the

whiskers extend to the most extreme values within 1.5×IQR.

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The dots correspond to statistical outliers automatically identified by the boxplot geometry, that is,

values beyond 1.5×IQR.

This representation highlights spatial units with exceptionally high or low accessibility values within

each income decile, following the standard convention adopted in previous studies from the Access to

Opportunities Project conducted by Instituto de Pesquisa Econômica Aplicada (IPEA) (Pereira et al., 2019;

Pereira et al., 2021; Pereira & Herszenhut, 2023).

6. RESULTS

It was possible to conduct a comparative analysis of how accessibility patterns vary according to the

mode of transport and how these variations reinforce or mitigate existing socio-spatial inequalities in the city.

Thus, to understand who has better access to parks and through which modes of transport, income-based

accessibility maps were produced for the entire city (figs. 3, 4, 5, and 6).

As expected, the results show that public transport provides the widest service coverage; however,

higher-income populations are the most advantaged, particularly within shorter travel-time thresholds, as

they tend to reside in more central areas. In contrast, lower-income populations require longer travel times

to access parks.

Walking accessibility follows the same spatial logic, with its effective range concentrated in the city

core and favouring higher-income groups. This pattern indicates that walking accessibility is selective, as it

primarily benefits central areas – where the most accessible parks are located – while peripheral areas remain

excluded or experience very limited access.

Parks with the highest levels of accessibility include Park Harmonia, the Orla do Guaíba and

Farroupilha Park, reflecting areas with a higher density of accessible points.

Cycling presents a broader spatial reach than walking; however, due to the lack of connectivity

between existing bicycle lane segments and the significant absence of cycling infrastructure in lower-income

and peripheral areas, its effectiveness in providing access to parks is constrained.

This finding indicates that the increased spatial reach associated with cycling does not result in a

significant redistribution of accessibility, which remains concentrated in central areas. Even with its wider

coverage, the city core continues to exhibit the highest levels of accessibility, although other parks – such as

Marinha do Brasil Park – also demonstrate good accessibility, particularly as travel time thresholds increase.

As travel time increases, all transport modes provide greater access; nevertheless, both walking and

cycling remain largely restricted to the central area, offering limited access to peripheral neighbourhoods.

The importance of public transport becomes evident as a key mechanism for integrating peripheral and

central areas.

It should be noted, however, that the need to pay fares to access public transport may hinder or exclude

a significant portion of the population from accessing parks. Overall, the accessibility pattern remains

consistent, with central parks being the most accessible.

The central area features a higher concentration of urban parks located in proximity, including the

Orla do Guaíba, Park Farroupilha, Park Marinha do Brasil, Park Harmonia, and Park Moinhos de Vento. In

addition, the city’s main public transport routes are also concentrated in this central area, facilitating park

access for residents living there.

The relationship between public transport corridors and highly accessible parks reinforces existing

centralities, suggesting that public transport infrastructure, rather than redistributing access, tends to amplify

spatial advantages already embedded in the urban structure.

Overall, the accessibility maps and distributions indicate that parks are more accessible to higher-

income populations. Conversely, lower-income residents must travel longer distances to reach parks. Xu et

al. (2017) suggest that improvements in public transport can increase leisure opportunities for low-income

populations.

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Fig. 3 – Accessibility of urban parks in Porto Alegre: distribution by income decile for walk, bicycle, and public transport

within 30 minutes.

Fig. 3 – Acessibilidade a parques urbanos em Porto Alegre: distribuição por decil de renda para deslocamentos a pé, de

bicicleta e por transporte público em 30 minutos.

Source: Authors

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Fig. 4 – Accessibility of urban parks in Porto Alegre: distribution by income decile for walk, bicycle, and public transport

within 1 hour.

Fig. 4 – Acessibilidade a parques urbanos em Porto Alegre: distribuição por decil de renda para deslocamentos a pé, de

bicicleta e por transporte público em 1 hora.

Source: Authors

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Fig. 5 – Accessibility of urban parks in Porto Alegre: distribution by income decile for walk, bicycle, and public transport

within 1 hour and 30 minutes.

Fig. 5 – Acessibilidade a parques urbanos em Porto Alegre: distribuição por decil de renda para deslocamentos a pé, de

bicicleta e por transporte público em 1 hora e 30 minutos.

Source: Authors

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

Fig. 6 – Accessibility of urban parks in Porto Alegre: distribution by income decile for walk, bicycle, and public transport

within 2 hours.

Fig. 6 – Acessibilidade a parques urbanos em Porto Alegre: distribuição por decil de renda para deslocamentos a pé, de

bicicleta e por transporte público em 2 horas.

Source: Authors

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6.1. Socioeconomic gradients in accessibility

Regarding income deciles, the results across all figures indicate that higher-income groups are

consistently the most privileged (deciles 9 and 10) in terms of access to parks, regardless of the transport

mode considered. In contrast, lower-income deciles (such as D1, D2, and D3) only begin to experience

improved accessibility at travel times of 90 minutes or more, and even then, with substantial gaps in the areas

reached.

The analysis of park accessibility by walking reveals a strong concentration in higher-income areas,

with particular emphasis on the highest income deciles (D9 and D10), which enjoy easy access to parks. In

contrast, lower-income deciles (D1 to D4) face limited access to parks. Lower and middle deciles (D1 to D7)

have access to, at most, one park. Accessibility improves substantially from D8 to D10, which have access

to up to four parks, demonstrating a clear advantage for higher-income groups.

As travel time increases to 1 hour and 30 minutes, intermediate deciles (D6 and D7) begin to gain

access to a greater number of parks; however, lower-income deciles (D1 to D5) continue to experience

limited accessibility. At a travel time of 2 hours, the highest deciles (D8 to D10) maintain the best access,

reaching up to six parks. Although extending travel time to 2 hours improves access for intermediate deciles,

the lowest-income groups (D1 to D4) remain highly constrained. The persistence of this pattern indicates

that, even with longer walking times, accessibility remains strongly conditioned by economic privilege.

Park accessibility by bicycle follows a pattern similar to that observed for walking. Higher-income

deciles continue to have greater access to parks, reflecting the advantage of centrally located areas that are

better equipped with cycling infrastructure. Intermediate-income deciles (D5 to D8) show an improvement

relative to the lowest deciles; although they still face constraints, these groups achieve moderate levels of

park access. Meanwhile, the highest-income deciles maintain superior accessibility, with access to up to six

parks, indicating a slight improvement in equity when compared to walking.

As travel time increases, lower-income deciles experience a modest increase in park access, although

higher-income deciles continue to hold the highest levels of accessibility, reaching up to 7.5 parks. Equity

improves marginally in comparison to walking; however, the gap between higher and lower-income deciles

remains substantial. This suggests that, while cycling provides broader spatial coverage, inequality persists,

possibly due to disparities in cycling infrastructure and the spatial distribution of parks.

Park accessibility by bus follows a pattern similar to that observed in the previous modes. Higher-

income deciles exhibit broader and superior access to parks, reflecting the more efficient coverage of public

transport in central areas. In contrast, lower-income deciles continue to experience more restricted

accessibility, highlighting the persistence of inequalities in access even when public transport is considered

as a means of reaching parks.

All income deciles, except the lowest-income groups (D1 to D3), show considerable access to parks.

Higher-income deciles retain an advantage, with access to up to eight parks; however, the gap between

income groups is less pronounced. With a travel time of 1 hour and 30 minutes, nearly all deciles – including

the lowest-income groups (D1 to D3) – achieve reasonable access to up to 7.5 parks. Differences across

income deciles become less marked, indicating that public transport functions as a major equalizer in park

accessibility. At a travel time of 120 minutes, lower-income groups gain access to up to eight parks, while

the remaining income groups have access to all nine urban parks analysed. The 2-hour travel-time scenario

represents the most equitable condition, with both the lowest- and highest-income deciles accessing up to 7.5

parks. Overall, the analysis demonstrates that accessibility via public transport is the most equitable among

the transport modes examined.

6.2. Spatial patterns and centrality versus Income and Travel Mode

Regarding spatial patterns and urban centrality, the distribution of parks itself clearly reveals their

concentration in more central areas, which directly influences population access – particularly for active

transport modes such as walking and cycling – even when a 2-hour travel-time threshold is considered. This

spatial configuration clearly produces “islands of exclusion” in the more peripheral parts of the city. Public

transport partially mitigates, but does not eliminate, these gaps: the six new Orla lines operate exclusively on

weekends and holidays (13:40-20:40), with a total of 37 trips and an aggregate headway of 20 minutes across

the network. However, roughly 2 hours elapse between departures serving each neighbourhood. As an

illustrative case, line O210 (Orla–Restinga Nova) serves the southern sector only during these limited hours

(three departures per direction), thereby restricting southern residents to narrow temporal windows for

reaching centrally located parks.

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

The analysis of the generated data, crossing interactions between transport modes, income, and urban

space, indicates that inequalities in access to parks in Porto Alegre are produced by three interrelated factors:

the location of parks, which are predominantly concentrated in the central area; the quality and availability

of mobility infrastructure, which are better developed and more structured in the city core – such as cycling

lanes and a higher number of bus routes; and the spatial distribution of the population itself, with lower-

income deciles concentrated in peripheral areas.

Walking accessibility to parks is concentrated in the city core, and residents of peripheral

areas – particularly in the southern and far northern portions of the city – are effectively excluded from the

system, even when a 2-hour walking threshold is considered. Cycling expands spatial coverage; however,

due to the lack of interconnected, continuous, and integrated cycling infrastructure, its reach is largely limited

to the expanded central area and does not eliminate existing accessibility gaps. Peripheral areas, characterized

by the absence of cycling infrastructure and steep slopes, prevent cycling from functioning as a moderator of

spatial accessibility. Public transport, in contrast, represents the most inclusive mode among those analysed,

provided that bus corridors and routes offer adequate coverage and frequency to connect green amenities to

peripheral areas. Accessibility constraints arise primarily from reduced service frequency during

weekends – when the population is more likely to use parks – and from fare costs.

Unfortunately, the creation of new parks in peripheral areas without simultaneous investment in

supporting infrastructure is unlikely to produce meaningful outcomes, given that lower-income populations

in Brazilian cities tend to be located in urban peripheries. An approach based on the creation of new peripheral

centralities – where urban land values remain lower – combined with the implementation of parks, even of

smaller size, and integrated with efficient and multimodal public transport, could contribute to addressing

this issue.

The results reveal spatial patterns that reflect deeply embedded dynamics in the production of urban

space in Porto Alegre. The concentration of high-income populations in central areas – historically favoured

by investments in infrastructure, transport, and public facilities – reinforces the spatial advantages of these

groups in terms of accessibility. In contrast, peripheral zones, which have long suffered from insufficient

investment in public transport, continue to face structural barriers to accessing green spaces and mobility.

Similar dynamics have been observed in other Latin American cities, where socio-spatial segregation and

infrastructure inequality shape urban accessibility patterns (Patino et al., 2023; Pereira et al., 2019). Thus,

the disparities identified are not merely statistical outcomes but reflect long-standing political and spatial

processes embedded in the city’s urban development.

The Brazilian context, as in many countries of the Global South, differs from that of other regions,

particularly regarding public safety. Green areas located near remote and low-income regions are often

informal or privately owned and lack adequate public security, posing potential risks to the population.

Consequently, even when vegetated areas are located nearby, they cannot be considered suitable leisure

spaces. For this reason, the present study was restricted to public parks that are formally regulated and

managed by the government.

7. CONCLUSION

This work presents studies that demonstrate the importance of green areas, such as parks, in addressing

sustainability issues and their impact on the population. However, it is known that not all residents have equal

access to parks, with higher-income populations living in closer areas. Assuming that access to parks is

necessary for their use, this work focused on discovering how access to Porto Alegre’s parks occurs by bus,

bicycle and on foot, and considering the population’s income.

The maps and graphs clearly illustrate that, regardless of transportation mode, higher-income deciles

consistently enjoy better urban park access. This suggests urban mobility infrastructure and park locations

are intentionally structured to favour central areas, where resources and services are more concentrated.

These regions provide a broader range of public transportation options and are closer to a denser park

network, further enhancing accessibility for higher-income groups.

On the other hand, peripheral areas with concentrated low-income populations often face significant

transportation infrastructure deficits, limiting urban park accessibility. This disparity not only restricts access

to the benefits of green parks but also reinforces socioeconomic inequalities in cities. Spatial segregation,

therefore, extends beyond access to services and employment opportunities; it also affects access to

recreational and communal spaces, perpetuating a cycle of exclusion that negatively impacts the quality of

life of the most vulnerable communities.

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Domeneghini, J., Zampieri, F. L. L., Correa, A. S., Finisterra, LXI(131), 2026, e41818

This inequality pattern in accessibility in public transportation access is supported by Pereira et al.

(2019) and Pereira et al. (2021) studies, which examined unequal urban transport service access among social

groups. These studies highlight that central areas, predominantly occupied by high-income individuals, have

significantly better accessibility, while peripheral areas, home to low-income populations, face severe

limitations. Similarly, Patino et al. (2023) emphasize in some cities, higher socioeconomic status links to

green space proximity; in others, the opposite. In certain locations, populations with a middle socioeconomic

status experience the least access to green environments.

These findings reinforce this study’s conclusions, which revealed that low-income individuals face

greater challenges in accessing urban parks due to their location in more distant areas with limited public

transportation infrastructure. In contrast, higher-income residents living in central areas enjoy greater

accessibility to urban parks, benefiting from more frequent and efficient public transport options.

In a sense, public transportation is not as comprehensive in integrating this population as green

infrastructure is in more central areas. This situation appears to be replicated in other cities in the Global

South, demonstrating the failure of public policies to integrate their low-income populations into the formal

city. However, although improvements in transportation access for lower-income populations are necessary,

perhaps the creation of parks in peripheral areas should also be considered.

Finally, it is acknowledged that the analysis presented relies exclusively on quantitative dimensions

of spatial accessibility, without encompassing qualitative aspects of urban experience, such as perceptions of

safety, environmental comfort, or perspectives associated with gender and race. Although these factors are

fundamental for a broader understanding of access to public spaces, they fall beyond the scope of this study,

however, they may constitute important directions for future research.

These disparities highlight the importance of public policies aimed at improving and expanding public

transportation to promote more equitable access to green spaces and other urban resources. Investments in

mobility infrastructure that accommodates pedestrians, cyclists, and public transport users are necessary for

reducing inequalities in access to urban parks and enhancing the overall quality of life in the city.

Transportation planning – whether for public transit or cycling – plays a key role in expanding the

range of park accessibility, enabling longer trips in less time. However, the creation and maintenance of well-

equipped parks near low-income areas ensure better access for the population, fostering social interactions

and community engagement (Xu et al., 2017). A more inclusive and equitable urban planning approach can

help ensure that all residents, regardless of income or location, can benefit from urban green spaces.

ACKNOWLEDGEMENTS

The authors acknowledge funding from the Coordination for the Improvement of Higher Education

Personnel (CAPES) and institutional support from the Federal University of Rio Grande do Sul (UFRGS).

AUTHORS’ CONTRIBUTIONS

Jennifer Domeneghini: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation,

Resources, Data curation, Writing – original draft preparation, Writing – review and editing, Visualization. Fábio

Lúcio Lopes Zampieri: Conceptualization, Data curation, Writing – original draft preparation, Writing – review

and editing, Visualization, Supervision, Project administration. Amanda Silveira Correa: Writing – review and

editing, Visualization.

ORCID

Jennifer Domeneghini

https://orcid.org/0000-0001-9466-0322

Fábio Lúcio Lopes Zampieri

Amanda Silveira Correa

https://orcid.org/0000-0002-8020-4083

https://orcid.org/0000-0002-7421-0137

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ⁱGeodetic CRS (Coordinate Reference System) WGS 84 (World Geodetic System 1984) was used.

ⁱⁱEach spatial unit (hexagon) covers approximately 0.11 km², an area comparable to a city block (Pereira & Herszenhut, 2023). The data were provided by the

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ⁱⁱⁱIncome decile data are divided into ten intervals, each with a specific average income. The first decile ranges from 0 to 220 BRL, with an average of 109 BRL.

The second decile spans from 220 to 363 BRL, averaging 294 BRL, while the third covers 363 to 500 BRL, with an average of 436 BRL. The fourth decile

ranges from 500 to 665 BRL, with an average of 572 BRL, followed by the fifth, which spans 665 to 860 BRL, averaging 747 BRL. The sixth decile covers 860

to 1000 BRL, with an average of 950 BRL, and the seventh ranges from 1000 to 1320 BRL, averaging 1133 BRL. The eighth decile spans from 1320 to 1760

BRL, with an average of 1522 BRL, while the ninth ranges from 1760 to 2800 BRL, averaging 2184 BRL. Finally, the tenth decile covers incomes from 2800 to

116,667 BRL, with an average of 5994 BRL. Income decile classifications were obtained from: https://blogdoibre.fgv.br/posts/distribuicao-de-renda-no-brasil-e-

o-papel-dos-rendimentos-alem-do-trabalho-para-desigualdade, accessed on July 16, 2024.

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