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Network analysis to identify patterns of use in the public space: A gender perspective

guilherme stecanella SaVlzqe9068 unsplash1 | Network analysis to identify patterns of use in the public space: A gender perspective | Coletividad

Gender issues in Urban Design

In the last decades, increasing attention has been given to gender issues in urban design.

However, research on the urban environment continues to show large gender inequalities, which are especially evident when studying the use and enjoyment of the public space. This study aims to identify predominant patterns of use in public places, and to explore the possible existence of traditional gender roles in the urban space.

The study uses, from three public spaces of the city of Barcelona as case study, an innovative combination of systematic observation techniques and network analysis procedures. Variables collected by EXOdES, a dedicated software analysis tool for systematic observation, are represented as nodes of a network system, and analyzed using network analysis tools.

Findings confirmed that, in spite of the progressive consolidation of feminist urbanism, uses in the public realm resulting from traditional gender roles remained explicitly recognizable. Whereas women occupation of space was related to playground and resting areas, generally involving care activities concerned with children or elderly people, men were primarily located in resting and sport areas, practicing sports or participating in leisure activities.

These patterns of use were more prone to emerge when users were part of a group than when they were alone. From a gender perspective, a contribution of the study is that it informed about main aspects of the analyzed public spaces reconfirming the existence of traditional roles in society, and the significance of exploring the public space as a key scenario where social features are explicitly exposed.

From a methodological perspective, processing of observational data with network analysis tools proved to be relevant and suitable for dealing with the intricacies of urban place analysis.

Compared to more classical approaches and systems, these techniques allowed to identify and interpret complex systems composed of many variables and relationships in a relatively straightforward manner, what turns it into a useful aid for urban designers and architects.

Analysis of public urban places

Nowadays, the interest in the public space has been renewed by actualizing the ideas of Lefebvre (1968) and Jacobs (1961), among others. Beyond the modern urbanism movement, the quality of urban life is, nowadays, drawn not only in terms of urban design and functionality but considering the social quality of city life. Indeed, public places are social places, where multiple interactions occur. As public space dynamics reflect what is socially relevant in a definite context (Andretta et al., 2015), the analysis of public urban places from a socio-environmental perspective leads to identifying social phenomena that affect the vitality of public life. Among these phenomena, the gender perspective analysis is one of the most relevant issues in the current urban debate. This study aims to identify predominant patterns of use in three public places in the city of Barcelona and to explore the possible existence of traditional gender roles in the urban space.

In traditional Western societies, women were assigned primary responsibility for childcare (Hartmann, 1981) and household activities (Schooler et al., 1984). These roles have dramatically affected their experience in public space, as well as their participation in leisure activities (Mowl and Towner, 1995). Franck and Paxson (1989) referred to several effects because of these. The most prominent one is that women’s mobility tends to be more restricted than that of men. The engagement in housekeeping and childcare led women to be less prone to benefit from the public space for discretionary use, often accompanied by little children or older people. In this regard, the presence of men is predominant in public spaces characterized by amenities such as betting, chess playing, or sports facilities such as baskets, football, and baseball courts. When alone, women tend to avoid public spaces perceived as unsafe, especially those with low maintenance or dirty. According to these scholars, compared with men, women are more likely to interact with others and are inclined to stay in populated spaces, particularly those inhabited by other women. Tackling these effects implies enhancing the publicness of public spaces, that is, increasing social diversity, variety of uses, and the acceptance of differences in terms of aspects such as gender, culture, ethnicity, and age. As stated by feminist urbanism, and the program for urban resilience from a gender perspective (Un-Habitat, 2018) urban design must rule over a key principle: if public spaces will be good for women, then they will also be good enough for everyone.

From the feminist urbanism perspective, the social role of high-quality urban places is crucial to promoting egalitarian use and enjoyment in the city. Urban life must reflect the diversity of existing social life, and the presence of disparate social groups -in terms of gender, race, origin, or status- because of their citizenship. In the words of Paravicini: “Urban living, particularly from the viewpoint of women, promises liberation from social control, from traditional gender roles and spatial designations” (Paravicini, 2003, p. 58). Accordingly, the urban role of high-quality public spaces can be regarded as of large importance since social, cultural, and gender-specific differences are perceived and experienced as an enhancement of urban living. However, it is fundamental to understand the extent to which these gender traditional roles are eradicated from the public realm. More research is needed to gain insight into the extent that which women are equally considered to men in what is called, following Lefevbre (1968), the right of the city. Much attention has been devoted to this issue in the feminist urbanism debate nowadays (Beebeejaun, 2017; see also www.right2city.org).

In spite of the increasing endorsement of the gender perspective in urban thinking and urban design (Little et al., 1988; McDowell, 1993; Bondi and Rose, 2003; Sánchez de Madariaga and Roberts, 2016), feminist urbanism studies show that with respect to urban settings, women’s needs and requirements are not considered as much as men. Recent research on urban analysis has shown large gender inequalities that involve mobility systems (Hanson, 2010; Pirra et al., 2021), urban facilities, and urban structure (Spain, 2014). To some extent, this situation occurs because cities are designed and managed mainly by men (Beebeejaun, 2017). Another reason is that urban space production is the outcome of social patriarchal structures, which impose barriers to women—both functional and symbolic—for the use and enjoyment of the urban public space (Blackstone, 2003; Bondi and Rose, 2003; Neaga, 2014).

Based on many key studies (Jacobs, 1961; Barker, 1968; Gehl, 1987, 2010; Barker, 2016; Sennett, 2017; Delclòs-Alió et al., 2019), public places can be conceptualized as complex systems. As such, they are made up of many variables (related to urban design, topological features, weather conditions, social interactions, individual dispositions, etc.) relating to each other in intricate configurations. It was Jane Jacobs who was among the first to view the public space as a place of complex and changing order. In the public space, people and groups—like dancers of a ballet—perform together in apparent chaos creating order within a hidden system. She referred to this aspect as “The art of the street” (Jacobs, 1961). This poetic, yet rigorous, account of street life offers a landmark example of what can be regarded as an ecological or systemic approach to space. This idea supposes considering human behavior as an outcome of socio-environmental system requirements. The assumption is that human behavior is a function of socio-environmental system demands. Among the many disciplines that have endorsed this approach are urban ecology and sociology. Consequently, in the words of Redman et al. (2004), an environment can be described as “a coherent system of (…) physical and social factors that regularly interact in a resilient, sustained manner, that is, a perpetually dynamic, complex system with continuous adaptation.” While understanding the public space as a complex network is a powerful concept, studies in environmental psychology literature that adopted methodological approaches from a systemic perspective are scarce. One of these is Roger Barker’s Behavior Setting Theory, which aims to investigate human behavior in daily contexts. According to Baker (2016), in a behavior setting, individuals, actions, and objects follow recognizable patterns. This theory sought to explain, perhaps for the first time scientifically, small-scale socio-environmental systems, as well as to study behavior in natural settings. However, as Popov and Chompalov (2012) highlighted, the behavior setting concept had very little impact on mainstream psychological theories (Wicker, 2002). In spite of this, Barker pioneered the ecological approach to psycho-environmental analysis, representing one of the most important perspectives in the discipline (Winkel et al., 2009).

Although the Behavior Setting Theory aimed at identifying environmental patterns of use, few studies have analyzed patterns involving social use of public places. Cao and Kang (2019) defined a pattern of use as “the ways that people use a space, which usually comprises activity and spatial occupancy” (op. cit., p. 189). The relationship between functionality and urban design was explored by Alexander (1977, 1979), who defined a pattern language as a way to analyze prototypical ill-structured urban design problems. He suggested a comprehensive list of standardized solutions to recurring urban uses and design situations that have influenced the way urban spaces are analyzed and designed today (Casakin, 2018). Gehl (1987) investigated environmental patterns and identified three types of human activities in urban places, including the necessary, optional, and social activities. For him, a public place ought to offer possibilities to develop all types of activities at different moments of the day, and for various users. This researcher proposed a set of valuable tools and a list of recommendations to observe urban social phenomena (Gehl and Svarre, 2013). Considering the process of privatization of public spaces and their progressive control surveillance, Cybriwsky (1999) studied the changing patterns of use of urban centers such as Battery Park City in New York and Yebisu Garden Place in Tokyo. These processes were found to restrict the social use of the space and reduce the quality of urban life. Goliènik and Thompson (2010) analyzed patterns of use in parks based on GIS and behavioral mapping. Cao and Kang (2019) studied four public places in the United Kingdom and China and using Hall’s (1966) social distance typology, they identified patterns of use that differ depending on if people were alone or in a group. In particular, women were more likely to stay in the group. Single users, on the other hand, were prone to be in the periphery, while groups were spatially distributed. Based on this literature, the current study depicts a pattern of use in the urban public space as a set of three interacting variables dealing with a user’s profile, a specific physical environment or space, and certain use of the space (Casakin and Valera, 2020; Valera, 2020).

What is common to the studies that analyze patterns of use is the systematic observation as a methodological approach (Cybriwsky, 1999; Goliènik and Thompson, 2010; Koen and Durrheim, 2010; Gehl and Svarre, 2013; Metha, 2014; Cao and Kang, 2019; Park, 2020)? Such an approach showed to be effective in the analysis of natural contexts (Anguera, 2003; Anguera et al., 2019) it is widely used in many recent studies. Differentiating itself from other empirical approaches such as self-report, systematic observation is a direct method that offers the possibility of collecting objective information with strong internal validity, while it allows the concurrent generation of data about the physical and social environment where an activity occurs (McKenzie and van der Mars, 2015; Park, 2020). This is, for instance, the methodology adopted by Iqbal and Ceccato (2016) to study the safety of urban parks in Stockholm considering CPTED criteria, and by O Caughy et al. (2001) to assess the impact of parks in urban neighborhoods’ on the well-being of families and children.

Finally, in previous studies, we dealt with the issue of gender analysis in public places by using an observational methodology (Pérez-Tejera et al., 2011, 2018). By analyzing a wide sample of public places, results offered much evidence on gender uses of the space (i.e., gender occupancy, preeminent activities, race profile, etc.). However, it was difficult to identify with precision, specific patterns of use that clearly related to gender roles. This issue is mainly because of the use of Polar Coordinates Analysis for exploring the collected data. This is a reduction data technique based on multievent sequential analysis (Bakeman and Quera, 2011) for exploring relationships between a focal behavior and one or more conditional behaviors (Gorospe and Anguera, 2000). Significant relationships are depicted in several vector maps indicating the extent to which the conditional behaviors are excitatory or inhibitory of the focal behavior. Despite its strengths, the Polar Coordinates Analysis technique presents some limitations, mainly concerned with the need to generate as many vector maps as the number of values of the observed category (known as focal behaviors), and the rest of the observed categories (known as conditional behaviors). Consequently, numerous separate analyses must be carried out, each one revealing only a partial aspect of the pattern of use. Hence, to arrive at more comprehensive conclusions, hard post-integration analysis of the partial results is required. To deal with this problem, it is necessary to explore techniques that allow an integrative analysis of a large amount of observational data straightforwardly.

Bearing this research gap in mind, the goal of this study is to detect predominant patterns of use in public places, as well as to analyze the extent to which the existence of traditional gender roles can be still detected. For this purpose, the current study relies on the innovative combination of systematic observation techniques and network analysis procedures (Casakin and Valera, 2020; Valera, 2020). To achieve these goals, it will be necessary to establish: (1) what are the main user’s profiles in public places, (2) what type of activities they are involved in, and (3) where they are located.

Network Analysis

After collection, data were analyzed as a network system. Based on Graph Theory, studies on Network Analysis argued that a large part of the systems found in nature, even in society, can be described in terms of networks, which allows capturing the intricate web of connections between the units of which these networks are made (Wasserman and Faust, 1994; Cartwright and Harary, 1956; Palla et al., 2005; de Nooy et al., 2018). In our study, each record of EXOdES is considered a set of variables that are represented as nodes in a network and are related to each other in terms of co-occurrence. This principle is repeated throughout all observed events. As a result, a system of relationships between nodes is configured, generating non-directional networks that reveal how specific public space functions and behaves. To this aim, network analysis freeware software was employed. The collected observational information was analyzed using Pajek (de Nooy et al., 2018). This software represents data as networks through a combination of vertices and lines according to definite criteria. In our case, vertices (or nodes) were variables related to the selected observational categories, and lines were defined as co-occurrence relationships identified between these variables. Furthermore, Pajek provided indexes of both the network itself (centralization indexes) and the nodes involved (centrality indexes). Additionally, visualizations, clustering, and centrality indices provide critical information for understanding the dynamics of the network. Particularly, visualizations and clustering – i.e., grouping nodes by specific criteria – allow the representation of key components of the network. Once created, networks were exported from Pajek to VosViewer software (Van Eck and Waltman, 2011) via an available function of this application. This software was used for network visualization and clustering analysis. Options in normalization methods and clustering resolution allowed for obtaining optimal layouts for visualizing results.

This work was conducted in accordance with the Declaration of Helsinki. The study was exempt from ethics committee review and written informed consent, since: (a) it concerned the observation of people in public places where there was no expectation of privacy for those being observed; (b) The observer did not implement interventions or interactions with individuals or groups; and (c) No personal information captured by using photographs, films, or videos footage was shown in the research findings.

Results

According to the definition of patterns of use adopted in the present study, several categories were selected for these results considering the proposed macro-criteria that included: (i) functional areas; (ii) gender profile of persons and groups; (iii) profile of groups (number of persons and age); (iv) activities; and (v) complimentary items such as dogs and vehicles. Categories of the environmental macro-criteria were excluded in the analysis due to their impossibility to discriminating between the potential patterns of use. In this regard, after testing them in preliminary network analyses, the regularity of variables related to these categories collected from different observation sessions did not allow to relate definite environmental features to identifiable patterns of use.

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Finally, data were processed to obtain three networks, that is one per case study. Table 2 presents the main parameters and corresponding values for each of these networks. Three Chimneys Park is the biggest network, both in number of Vertices (nodes) and Lines (links between nodes). It also has the highest average Degree (average of connected nodes). However, St Pau del Camp has the highest Degree of Centralization, that is, the most compacted network. The density between networks is similar in all cases.

While centralization indexes refer to the network as a whole, centrality indexes describe nodes that are involved in a specific network. Among these, Degree (d) and Weighted Degree (Wd) are crucial. The former refers to the number of nodes that are directly connected to a definite one. Its centrality derives from how many nodes appear together with it. Nevertheless, one node can be connected to another several times. The Weighted Degree index informs the number of connections (lines) received by a node: Its centrality derives from the number of times that this node appears in the observation sessions. Table 3 shows the main 40 nodes of each site’s network ordered by the Weighted Degree index and their respective Degree indexes.

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The main activity carried out in the observed places is Resting (when people are alone) or Resting/Talking (in the case of groups). In fact, this activity represents one of the most relevant nodes in the network. In all three case studies, it is the first ranked activity with respect to Weighted Degree, and the most important one regarding Degree measures. Together with Passage areas related to Walking activity, resting areas (Resting_area) are the largest visited places, except in Three Chimneys Park where Esplanade is also relevant. Furthermore, in general, people appear in the public space in groups rather than individually. The presence of males, especially groups of men (Group_men), is dominant in the places studied. In contrast, the presence of women is less frequent, and when it happens, they appear in group (Group_women). This effect is more relevant in St. Pau del Camp Gardens (WdGroup_men = 682; dGroup_men = 53; WdMan = 470; dMan = 20 in front of WdGroup_women = 223; dGroup_women = 28; WdWoman = 157; dWoman = 18) and Three Chimneys Park (WdGroup_men = 1307; dGroup_men = 61; WdMan = 540; dMan = 31 in front of WdGroup_women = 368; dGroup_women = 40; WdWoman = 165; dWoman = 21) than in Pegaso Park (WdGroup_men = 657; dGroup_men = 53; WdMan = 486; dMan = 26 in front of WdGroup_women = 493; dGroup_women = 43; WdWoman = 317; dWoman = 18). The presence of dogs is prominent in St. Pau del Camp Gardens and Pegaso Park, while the skaters (Skater) and Skating activity are preeminent in the Three Chimneys Park.

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As noted, a pair of nodes can be interconnected with each other several times. Depending on the number of connections, the value of the line (i.e., link) between two nodes can vary accordingly. The value of the lines that link gender profile options with both, functional areas and activities was calculated. Since these values are absolute, to normalize them in relation to the network we elaborated an index representing the percentage of the number of connections that link two nodes (value of the line) of the total number of connections of the network. The resulting indexes allowed comparison among the three case studies. Table 4 shows the index value between gender profile and functional areas, and Table 5 depicts the index value between gender profile and use of the space in each case study.

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Analyzed by case studies, St. Pau del Camp Gardens’ functional areas are linked with different gender profiles. Playground_area relates to groups of women (Group_women), while resting (Resting_area) and leisure areas (Esplanade, Sport_court, Petanque_court, Blue_space, and Green_area) are connected to Man or groups of men (Group_men) (Table 4). Playing activities relate to groups of women (Group_women), whereas resting and talking activities (Resting/Talking) are linked to male users (Man, Group_men) (Table 5).

Similar results are observed in Three Chimneys Park. The playground area is linked to groups of women (Group_women), leisure areas (Esplanade, Sport_court, Petanque_court, and Blue_space) to groups of men (Group_men). Both Resting_area and Passage_area are largely related to male users (Man, Group_men) (Table 4). While there is no difference between men and women in Playing and Walking activities, the male gender is clearly linked to resting, talking (Resting/Talking), and sports activities (mainly Sport_skate, Sport_biking, and Sport_football) (Table 5).

As regards Pegaso Park, like, in the other sites, Playground_area is related to groups of women (Group_women), as well as resting (Resting_area) and leisure areas (Esplanade, Sport_court, Petanque_court, Blue_space, and Green_area) either composed of solitary men (Man) or groups of men (Group_men). Conversely, passage zones (Passage_area) are mainly connected to singles (mainly Man but also Woman) (Table 4). In addition, activities such as resting or talking (Resting/Talking) are predominant for male users (Man, Group_men), although no differences exist in Playing activities regarding gender (Table 5).

When the analysis of the main components of the networks is carried out integrally, it is possible to identify patterns of use of the sites based on the visualizations of the resulting networks. For a better visualization of the results, networks generated by Pajek were exported to VOSViewer. The network visualizations generated by VOSViewer allowed clearly capturing the global structure of the nodes and their relationships. In our case, the size of the nodes is represented according to the Weighted Degree index. Hence, the bigger the node, the larger the Weighted degree, and the larger the presence of this node on the network. The larger the central location of the node, the larger its relevance. Moreover, the closer one node is to another, the larger their interconnection in terms of co-presence. The size of the links between nodes are represented according to the value of the line (i.e., number of connections between pairs of nodes). Therefore, the thicker the line the higher the value of the line. Additionally, VOSViewer allows the application of cluster algorithms to the network, highlighting sets of nodes that repeatedly appear together. Depending on what information the sets or clusters contain, nodes related to users’ profile, activities, and functional areas, they can be related to specific as patterns of use.

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From a gender perspective, three main clusters define St. Pau del Camp Gardens’ network (Figure 5). The green cluster is defined by single persons of both genders (Man, Woman), accompanied by dogs (Dog, 2/more_dogs), preferentially walking (Walking) in passage zones (Passage_area) or green areas (Green_area). The red cluster is related to groups of men (Group_men). This node is linked with the main nodes regarding Weighted Degree: talking and resting activities (Resting/Talking), and resting areas (Resting_area). Finally, the blue cluster outlines groups of women (Group_women) with children (2_child, 3to5_child/adult) and older people (2_child_old) (with presence of Wheelchair), involved in Playing activities are located in the Playground area. Interestingly, this cluster is well separated from the others, suggesting that the pattern of use is highly specific for this site.

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In Three Chimneys’ Park, three major clusters and a second one are observed in the network (Figure 6). The former ones are clearly separate in the network layout, and from each of these patterns of use related to gender can be identified. The red cluster depicts two of the higher Weighted Degree nodes dealing with the Resting_area and the resting or talking activity (Resting/Talking) (see Table 3). Users in this cluster are mostly males (Man, Old_man, and Boy), in addition to mixed groups of gender (Group_mixt). Sleeping and Reading are the dominant activities appropriate for places like the Resting_area. The green cluster is characterized by groups of men (Group_men): young people (2_young, 3to5_young, 5to10_young) or children (5to10_child, 10_child) practicing skating (Sport_skate) in an Esplanade. The yellow cluster is mainly defined by groups of women (Group_women) with children (Child_female, 3to5_child, 2_child/adult, 3to5_child/adult, and 5to10_child/adult), who are primarily located in Playground_areas, use Strollers, and are involved in Playing activities. The blue cluster is closely located to the red one. It is defined by the concurrence of single Woman, Walking alone, in Passage_areas. Remarkably, the red and blue clusters reflect the presence of single persons, while the green and yellow clusters represent groups of users.

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Moreover, three well-defined clusters represent Pegaso Park’s network (Figure 7). The green cluster is related to single persons (Man, Woman, and Old_man), accompanied by a Dog, Walking in Passage_areas or in the Esplanade. These nodes have a large weight in the entire network (see Table 3). The red cluster includes nodes with a similar weight: groups of men (Group_men) talking or staying (Resting/Talking) in Resting_areas. In the extreme of the network, there are elder men (5to10_old) practicing petanque sport (Sport_petanque) in the Petanque_court. As in previous cases, the blue cluster is defined by groups of women (Group_women) with children (2_child/adult, 3to5_child/adult, 5to10_child/adult, 2_child/old, 5to10_child, and 3to5_child/young) and Strollers, involved in Playing activities in the Playground area. The fact that Group_men (red cluster) and Group_women (blue cluster) are relatively close to each other in the network may suggest that, although clearly defined, the pattern of use belonging to the blue cluster, might not be as exclusive for women as in Three Chimneys and St. Pau Gardens.

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