Introduction

Various studies on urban quality of life emphasize the importance of indicators for urban planning and management (Massam, 2002; Nahas, 2002; Martins; Cândido, 2015; Garau; Pavan, 2018; Mittal; Chadchan; Mishra, 2021; Kupíec; Wojtowicz, 2022; Potluka, 2023). Agenda 21 marked a significant step in establishing urban quality of life indicators aimed at sustainable urban development (Spangenberg; Pfahl; Deller, 2002), committing countries to create and implement a broad set of indicators at global, regional, and local levels (Woods et al., 2016). Following this, various initiatives have been developed by both international and local actors to adapt urban quality of life indicators (ECI, 2003; Socco et al., 2003; Scussel, 2007; Delsante et al., 2014; Delsante, 2016; Verma; Raghubanshi, 2018). Notably, the European Commission emphasized the importance of assessing local efforts and exploring methods from Agenda 21. The European Common Indicators (ECI) project involved local authorities in refining a broad set of indicators for European cities (ECI, 2003).

Building on the ECI project, Socco et al. (2003) proposed the Residential Environmental Quality Index (REQI) based on a study in Reggio Emilia, Italy. This model, adaptable to various urban contexts, measures urban quality of life across three dimensions:

1. housing;

2. perceptible environmental context; and

3. urban services at the neighborhood level.

Scussel (2007) identified the model as a relevant tool for local-scale assessments, applying it in the Menino Deus neighborhood in Porto Alegre, Brazil. This adaptation included the Adjusted Quality of the Residential Space (QER) Index, which assigned weights to indicators, and the Expanded QER Index, which tested the hypothesis that urban quality could support, but not guarantee, sustainable practices.

Based on the proposals by Socco et al. (2003), Scussel (2007), Delsante et al. (2014) and Delsante (2016) developed an expanded and adapted version of the conceptual model through a comparative case study of Lodi and Genoa, Italy. Delsante’s approach reinforced the model’s adaptability to different urban contexts and demonstrated its flexibility in adding or excluding indicators, making it a useful tool for local-scale assessments. The models proposed by Socco et al. (2003), Scussel (2007), Delsante et al. (2014) and Delsante (2016) emphasize primary data collection (such as questionnaires, interviews, and document analysis), incorporating residents’ perception (subjective dimension) alongside quantitative data analysis (objective dimension), with weights assigned by researchers. However, these studies do not include the participation of public officials, relying solely on the researchers’ analysis.

In Brazil, some municipalities have developed urban quality of life indicators. Belo Horizonte proposed an Urban Quality of Life Index to guide public investments (PMBH, 1996), São Paulo created a Map of Social Exclusion to address priority actions (Sposati, 2000), and Curitiba established a Synthetic Quality of Life Satisfaction Index to monitor local living conditions (IPPUC, 1996). However, these efforts relied on secondary data and neglected residents’ perceptions, which are essential for a comprehensive assessment (Ballas, 2013).

Urban quality of life is shaped by how residents perceive their environment (subjective dimension). A holistic approach should include both subjective (residents’ perceptions) and objective (quantitative data) dimensions. Previous research highlights the need for a more reliable and comparative data collection method across cities and countries (Nahas, 2002; Martins; Cândido, 2015). Based on the above, it can be inferred that the concept of urban quality of life is complex, encompassing multiple disciplines and dimensions.

Therefore, the assessment of urban quality of life should be considered from its objective and subjective dimensions, as well as the balance between these three constructs: equity of access, environmental quality, and sustainability. An environment susceptible to the promotion of urban quality of life should ensure ease of access to goods and services to the entire population (equity), environmental quality of the urban space (quality), and the minimization of short-, medium- and long-term impacts to the environment (sustainability). This study adopts the urban quality of life framework proposed by Nahas (2002) and Nahas et al. (2006), which focuses on three main constructs:

1. equitable access to urban resources (spatial and social access);

2. environmental quality; and

3. urban sustainability.

Assessing urban quality of life requires balancing these constructs, ensuring accessibility, quality, and sustainability.

Therefore, there is a need to develop assessment methods that incorporate both residents’ perceptions and public officials’ participation into existing urban quality of life indicator models (Castro Bonaño, 2004; Martins; Cândido, 2015; Delsante et al., 2014; Delsante, 2016). Given these considerations, this study aims to propose a method for assessing urban quality of life at the neighborhood scale in Brazil, incorporating both residents’ perceptions and public officials’ input. It seeks to improve assessment models that combine quantitative and qualitative analyses at the neighborhood level.

Method

The research strategy adopted is Design Science Research, as it aims to develop an innovative method to help with a real problem (Lukka, 2003). Figure 1 presents the research design, which is divided into three main stages, described below:

1. understanding;

2. development; and

3. assessment of the proposed method, based on the perception of public official.

Understanding (A)

In the Understanding Stage (A), the selection and analysis of studies on indicator models to assess urban quality of life were conducted. This stage was divided into two phases:

1. exploratory; and

2. descriptive.

During the exploratory phase, a literature review was carried out on studies concerning urban quality of life indicators, urban indicators (objective dimension) and residents’ perception (subjective dimension). In the descriptive phase, semi-structured interviews¹ were conducted individually and in person during the first quarter of 2019, with the participation of 21 public officials involved in the urban planning for Brazilian neighborhoods. Among these officials, 17 worked at the City Council of Porto Alegre, 10 from the Municipal Housing Department (DEMHAB), 6 from the Municipal Department of Environment and Sustainability (SMAMS), and 1 from ObservaPOA (Observatory of the City of Porto Alegre). Additionally, four officials from the Brazilian Institute of Geography and Statistics (IBGE) were interviewed, considering the national context.

Development (B)

The Development Stage (B) included the following steps described below:

Outline of the method for assessing urban quality of life (a)

The method proposed in this study is based on the conceptual model proposed by Socco et al. (2003) and adapted by Scussel (2007) and Delsante (2016). The conceptual model consists of four levels:

1. “Quality of the Residential Space” (QRS);

2. “Architecture and Urban Design” (Qarch), “Uses and Accessibility” (Qacc), “Landscape and Environment” (Qenv), and “Society and Community” (Qsoc);

3. “Macro-indicators” adapted to the specific neighborhood being assessed; and

4. “Urban indicators” used to evaluate blocks and land units.

Selection of the object of study (b)

Subsequently, the Farrapos neighborhood in southern Brazil, was selected as the study area. When assessing urban livability and sustainability, the scale of coverage is a key consideration. A study by the Municipality of Porto Alegre entitled “Maps and Indicators of Social Vulnerability” showed that not all neighborhoods are equally prepared to promote the quality of life of their residents. Of the 82 neighborhoods evaluated, 11 had the lowest indices, indicating high social vulnerability. This highlights the need to assess urban quality of life at a local level to capture specific urban contexts, as a municipal-level index may not reflect the different realities within the same territory.

Based on this study, the neighborhood of Farrapos, with the highest level of social vulnerability, was selected for analysis. The methodology developed in this research aims to identify the weaknesses of the neighborhood, to help public managers decide on interventions to improve the local quality of life. The settlement history of Farrapos is linked to the rapid population growth of Porto Alegre. The predominantly residential neighborhood also includes green spaces, recreational areas, urban health facilities, social housing, and informal settlements lacking infrastructure and basic services.

The blocks (subunits of analysis) were delineated, the data collection instrument was customized, and the sample planning was developed. All these steps were presented and discussed with public officials for validation and refinement of the data collection instrument. Following criteria outlined in the literature, the subunits of analysis were selected based on the neighborhood’s specificities and distinct characteristics observed in the area (Scussel, 2007). The data collected on land use in the Farrapo’s neighborhood identified three distinct types of areas:

1. single-family homes (1,813 units);

2. social housing (1,800 units); and

3. irregular occupations (1,062 units).

The delimitation of these subunits was based on the 4,675 housing units in the neighborhood. Table 1 presents the sampling plan calculation used in this research.

After calculating the sampling plan, land units were selected for assessment using simple random sampling per block:

1. four blocks with single-family residences were selected, totaling 100 sample units;

2. two blocks of social housing, totaling 94 sample units; and

3. two blocks of irregular occupations, totaling 92 sampling units.

In addition, the data collection instrument² was structured into six sections:

1. researcher identification;

2. laddering;

3. permanence or evasion;

4. families’ intention to stay in the area;

5. assessment form of the land units; and

6. resident profile.

The Laddering in-depth interview technique (Reynolds; Gutman, 1988) was incorporated into the instrument with open-ended questions to identify the elements that residents valued most in their homes and neighborhood (subjective dimension). In this technique, respondents are first asked to identify the five most important characteristics of their place of residence. For each of these characteristics, the following question was asked repeatedly: “Why is it important to you?”

The questionnaire was refined through ten pilot tests conducted in October 2019 to improve the internal consistency of the instrument and facilitate the interaction between researcher and respondent. Data collection was carried out in person by researchers between September 29 and October 27, 2019³.

Delimitation of procedures for data analysis (c)

To analyze the data obtained through the Laddering technique, the steps recommended by Reynolds and Gutman (1988) were followed:

1. content analysis;

2. formulation of the implication matrix;

3. formulation of the hierarchical value map; and

4. determination of the dominant perception orientations through a cut-off point.

The following software was used for data analysis: Excel^® (to tabulating raw data); Statistical Package for the Social Sciences (SPSS^®) (for quantitative and statistical analysis of data); and LadderUX (for analyzing qualitative data from the Laddering technique, based on participants’ responses to the five most important characteristics of their place of residence).

In the content analysis step, sentences resulting from the Laddering technique were categorized as: (A) attributes of the built environment; (C) consequences of using this environment; and (V) individuals’ personal values, creating “A-C-V” chains. After classifying each keyword, these chains (A-C-V) were sequentially inputted into the LadderUX tool. Next, we aimed to determine whether there were differences in the residents’ perceptions across three distinct living situations: single-family homes; social housing; and irregular occupations. To reduce the subjectivity resulting from the Laddering technique, several meetings were held with researchers in order to refine the sentences obtained through users’ responses.

By inputting each ladder in the LadderUX tool, an implication matrix was generated. According to Reynolds and Gutman (1988), the implication matrix represents the frequency of connections between elements, i.e., how often each element leads to an attribute, consequence, or value. These connections, derived from both direct and indirect relationships between the elements, are used to create the Hierarchical Value Map – HVM (Reynolds; Gutman, 1988). The HVM is a tree-like diagram that graphically represents the associations between the attributes, consequences, and values, based on the survey responses. The strength of these associations is indicated by the thickness of the connecting lines, with thicker lines signifying stronger relationships, highlighting the elements most perceived by residents.

Data analysis for each land unit followed the methodology established by Socco et al. (2003) and Delsante et al. (2014), with the following steps:

1. each indicator is associated with a status condition or qualitative judgment, which can vary on a scale of “very bad”, “insufficient” and “good/excellent”; and

2. the scale for each indicator is linked to a corresponding weight that must be accurately quantified.

To assess the status condition or qualitative judgment (step a), the responses to the closed-ended questions in the questionnaire were considered. For each indicator, based on the perception of residents in the Farrapos neighborhood, weights were assigned according to the values used in previous studies (Socco et al., 2003; Scussel, 2007): 0.05 for “very bad”; 0.5 for “insufficient”; and 1.0 for “good/ excellent”. These weights were applied to all 36 indicators considered in this study. Subsequently, to calculate the weight “k” of each indicator (step b), qualitative data obtained from the Laddering technique were used.

After calculating the weight “k” per block, the next involved calculating the indicators using the pairwise comparison technique. This method entails comparing indicators in pairs, arranged in a matrix. The matrix represents the number of connections between elements, i.e., how frequently each element contributes to achieving each attribute, consequence, or value through direct and indirect relationships. These relationships help form the coordinates for the Hierarchical Value Map (HVM). Based on the number of connections, the criteria scale to be used was determined. In this study, Saaty’s (2016) scale, as shown in Table 2, was applied.

Based on the definition of the scale, we proceeded with calculating the pairwise conflict matrix. After determining the “k” weights for all indicators, the next step was to calculate the indicators by multiplying the values from “step a” (the scale of 0.05 for “very bad”; 0.5 for “insufficient”; and 1.0 for “good/ excellent”) by the corresponding “k” weights from step b. Finally, all the values for all indicators were entered into a spreadsheet for each block, which included the following categories: “Qarch: Architecture and Urban Design; Qacc: Uses and Accessibility; Qenv: Landscape and Environment; and Qsoc: Society and Community”. The result of this calculation is the “Residential Space Quality Index” for each land unit in the Farrapos neighborhood.

Once the indicators were calculated, value maps were generated using the free geographic information system software QGIS (version 3.4 LTR) and the georeferenced cartographic base of Porto Alegre. The maps were produced in “graded” symbology mode, with color gradients ranging from dark orange (representing the highest values) to light orange (representing the lowest values). The values were grouped into 19 intervals, each assigned a specific color, with a minimum value of 0.05 and a maximum value of 1.00.

The assessment method was developed and implemented using Google Data Studio, a tool that generates reports and dashboards. This tool was chosen for its ease of use, free availability, and features that support creation, editing, sharing, and the customization of interactive layouts.

Assessment of the proposed method (C)

The Assessment Stage, which aimed to evaluate the proposed method, was conducted in parallel with the previous stage and focused on two constructs: utility and applicability. Semi-structured interviews were held with 26 specialists and professionals involved in the urban planning and management of Brazilian neighborhoods. Among the participants, 14 public officials worked in the City Council of Porto Alegre, 3 in the management of DEMHAB, 11 in the management and direction of SMAMS, and 12 were academic researchers.

This stage included presenting the results from the assessment of the three specific situations observed at the Farrapos neighborhood (single-family homes; social housing; and irregular occupations). The presentation aimed to facilitate discussions with public officials and researchers about the method’s potential usefulness and its applicability in other Brazilian neighborhoods. It is important to note that this stage of the study was conducted via videoconferencing, in accordance with WHO (World Health Organization) guidelines due to the COVID-19 pandemic⁴.

Results: the assessment method

The main result of this study is the proposal of a method for assessing the quality of urban life in Brazilian neighborhoods, which is structured into three phases:

1. preparation;

2. implementation; and

3. discussion.

The phases and results of its implementation are described below.

First phase of the method: preparation of the assessment (1)

The first phase of the method involves adapting the conceptual model to the specific context of Brazilian neighborhoods and customizing the data collection instrument (the questionnaire). The assessment method was operationalized using the Google Data Studio tool. This enabled the creation of a dashboard – a visual interface that presents the proposed method for assessing urban quality of life in Brazilian neighborhoods in an interactive, real-time manner.

At the top of the dashboard, the logo (representing the identity of the proposed method) and a map of Brazil are displayed. Below these elements, there is a bar allowing users to choose the following categories: State; City; Neighborhood; and Year of Assessment. When the method is applied to other neighborhoods in different states and cities, the interactive dashboard spreadsheet must be updated with the new data, which will automatically refresh the map in real time. Figure 2 illustrates the first phase of the Assessment Method (visual dashboard), providing a step-by-step guide for preparing the assessment.

As presented at the bottom of the dashboard in Figure 2 (Preparing for the Assessment), the conceptual model is depicted as a tree-shaped structure with four levels that represent the Environmental Quality of the Residential Space (QSR). The model follows a top-down structure.

At the top of the structure (first level), the Quality of the Residential Space (QRS) is defined, derived from the weighted sum of the subsequent levels in the conceptual framework. The second level includes the “domain groups,” which are the elements of the residential space (e.g., building units; basic services related to the neighborhood; and environmental context). These “domain groups” were structured based on the model by Delsante et al. (2014), as follows: “Architecture and Urban Design” (Qarch); “Uses and Accessibility” (Qacc); “Landscape and Environment” (Qenv); and “Society and Community” (Qsoc).

The third level of the structure contains the “macro-indicators”, which should be adapted according to the urban context and the scale of application. The fourth and final level consists of the “urban indicators”⁵, which are identified and adapted for the assessment. This structure allows flexibility, as items from different levels can be added or excluded depending on the purpose of each assessment and the specific characteristics of the urban context. This adaptability makes the model suitable for varying research objectives.

The conceptual model should be used in an exploratory manner to identify the four levels: “quality of QSR residential space” (1^st level); “domain groups” (2^nd level); “macro-indicators” (3^rd level); and “urban indicators” (4^th level). In the proposed method, the structuring of these four levels is based on a database that should be constantly updated as new constructs emerge from different urban contexts and realities.

Once the four levels are established in the conceptual model, the questionnaire must be customized to fit the urban context of the neighborhood being assessed. Questions related to the Laddering interview technique should be incorporated into the questionnaire. The data collected through this technique will be used to create a HVM based on residents’ perceptions. Figure 3 presents the HVM from the assessment conducted in this research for Study 1 (single-family residences).

Figure 3 presents two representative chairs in the HVM:

1. the chain “location and accessibility – accessibility to health facilities – improvement in health condition – well-being” forms the strongest combination on the map. This is because the terminal value “well-being” has nine indirect relationships with the concrete attribute “location and accessibility”, nine indirect relationships with the functional consequence “accessibility to health units”; and ten direct relationships with the functional consequence “health conditions”. The concrete attribute “location and accessibility” and the functional consequence “accessibility to health units” are the two most perceived benefits, according to the residents’ evaluations. This data supports the quantitative evaluation, which indicated that 65% of residents gave a positive assessment of access to health units in the neighborhood; and

2. the chain “location and accessibility – neighborhood location – centrality – accessibility to urban amenities – visibility – vitality – social recognition” results in the second strongest combination on the HVM, as the terminal value “social recognition” has four direct relationships with the concrete attribute “location and accessibility”; three direct relationships with the abstract attribute “location of the neighborhood in relation to the city”; four indirect relationships with the functional consequence “centrality”, three direct relationships with the functional consequence “accessibility to urban facilities”; two indirect relationships with the psychosocial consequence “visibility”; and three direct relationships with the psychosocial consequence “vitality”. This cognitive chain aligns with the quantitative evaluation, where 90% of residents demonstrated a positive perception regarding the neighborhood’s location in relation to the city.

The results from the HVM are used to assign weights to the urban quality of life indicators. The stronger the chain (as in the examples 1 and 2), the greater the weight given to the indicator after it is weighted. Including residents’ perceptions (subjective dimension) in the process of creating and weighting urban indicators (objective dimension) for evaluating quality of life helps minimize some of the limitations observed in the existing models – such as weighting being performed solely by researchers and the restricted use of secondary data. Furthermore, this approach generates data with a higher degree of reliability (primary data), enabling comparisons with new assessments that use similar methodologies.

Second phase of the method: implementation of the assessment and analysis of results (2)

The second phase of the method involves data collection, analysis, and processing. During this phase, the neighborhood to be assessed must be defined, along with the sample units included in the study. To facilitate this, reports and studies that provide up-to-date information about the city should be consulted, including data on population profiles, existing neighborhoods, housing units, public spaces, commerce, and services. Meetings with public officials involved in urban planning should also be held at this stage.

For data collection process, preliminary contacts should be made with community leaders and other neighborhood representatives to determine the most suitable date for data gathering. The assessment team must be trained in both data collection and analysis techniques. Data tabulation can be handled by the researcher with the assistance of statisticians, depending on the complexity of the analyses. It is recommended that the resulting data be organized into two separate databases: quantitative and qualitative. Quantitative data can be analyzed using SPSS^® and pairwise conflict matrices, while qualitative data (Laddering) should be analyzed using the LadderUX tool.

Figure 4 presents the second phase of the Assessment Method, outlining the steps to be followed for successful implementation.

Third phase of the method: discussion and dissemination of results (3)

The third phase of the method involves the presentation, discussion, and dissemination of the key results of the assessment with public officials involved in urban planning. It is important to emphasize the value of jointly analyzing both quantitative data (from the questionnaire) and qualitative data (from the Laddering technique) derived from the perceptions of residents in Brazilian neighborhoods. This combined analysis should be presented in clear formats that allow the results to be examined from various perspectives, thereby facilitating the transmission of knowledge and enabling the potential use of the information in urban planning.

Additionally, a visual dashboard was created to present and disseminate the results of the assessment carried out in this study (Figure 5). The main findings of this research were illustrated through Value Hierarchy Maps (Figure 3), which combine an analysis of the quantitative data from the questionnaire evaluation (%) with qualitative data from the evaluation with the in-depth Laddering interviews conducted with the residents. These data served as the foundation for calculating the “Residential Space Quality Index”, ultimately generating value maps for each block investigated in the neighborhood, as shown in Figure 5.

As mentioned – First Phase of the Method: Preparation of the Assessment (1) – the “Residential Space Quality Index” is the result of the composition of the other levels of the conceptual model, which are made up of the domain groups, the macro-indicators and the urban indicators adopted for the assessment in this research, which can be consulted in full in the thesis that led to this article.

In summary, the value maps show that the indicators linked to “Qarch - Architecture and Urbanism” are the main ones responsible for the lowest score in the “Residential Space Quality Index” in the studies investigated. On the other hand, the evaluation also shows that the indicators related to “Qacc - Accessibility Uses” are mainly responsible for the highest score in the “Residential Space Quality Index”.

A disaggregated analysis of the overall index was carried out to identify opportunities for improvement based on the indicators that scored low in terms of environmental quality. This analysis showed, for example, that the indicators responsible for the low score for “Qarch - Architecture and Urbanism” are related to “thermal comfort of the housing unit”, “construction quality”, “physical space of the housing unit’s courtyard” and “acoustic comfort of the housing unit”.

The method proposed in this study was evaluated by the public managers based on two main constructs: “utility” and “applicability” (ease of use of the method). According to managers, the main potential of the tool in terms of its “utility” includes:

1. its ease of understanding, allowing information to be compiled and customized, which facilitates comprehension and use within public institutions;

2. the real-time data entry and the creation of a municipal database that can be accessed by various departments;

3. the potential to create a municipal database for assessing and monitoring Brazilian neighborhoods; and

4. its free access.

Assessment of the proposal for an urban quality of life assessment method

In terms of “applicability”, it is recommended that the preparation and implementation of the evaluation involve a multidisciplinary team of public managers. Another point highlighted by the public managers was the need for periodic evaluations to ensure that the method becomes systemic within the public agency. Therefore, it is recommended that this evaluation be conducted periodically, ideally in alignment with of each management (every four years).

Identification and analysis of the theoretical contributions of the Method in relation to existing studies

This research helped to overcome one of the main limitations of urban indicator studies by incorporating the perception of local residents in the construction and weighting of urban quality of life indicators, thus reducing the dependence on secondary data (PMBH, 1996; Sposati, 2000; IPPUC, 1996). The participation of users made it possible to collect more reliable primary data in less time, allowing comparisons to be made in subsequent evaluations using the same methodology.

In addition, the research innovated in the way the indicators were weighted, using the Laddering technique, in contrast to previous studies that based the weighting on the researcher’s judgment (Socco et al., 2003; Delsante, 2016). This approach produced a weighting that was more in line with the local context. The research also contributed by exploring the potential of a qualitative technique (Laddering) to generate quantitative data, which was used to calculate urban indicators, demonstrating the effectiveness of mixed techniques to deepen the analysis of urban phenomena.

In addition to improving the selection of indicators and the calculation model, the research innovated in the presentation of results, using free online tools such as visual dashboards to facilitate the sharing and updating of interactive information.

By focusing on the perception of value, the research advanced on previous studies (Socco et al., 2003; Sposati, 1996, 2000; Delsante et al., 2014) by not only identifying the numerical value of the urban quality of life index, but also exploring the subjective aspects present in the perception of residents, adding a richer analysis.

Discussion and final remarks

This study aimed to “propose a method for assessing urban quality of life adaptable to the scale of Brazilian neighborhoods”. The development was divided into three stages. In the Understanding Stage (A), the existing literature about urban quality of life assessment was reviewed. The conceptual model adopted is the result of contributions from Socco et al. (2003), Scussel (2007) and Delsante (2016), consisting of four levels:

1. Quality of the Residential Space (QRS);

2. “Architecture and Urban Design” (Qarch);

3. “Uses and Accessibility” (Qacc);

4. “Landscape and Environment” (Qenv); and

5. “Society and Community” (Qsoc);

6. Adapted macro-indicators, considering the neighborhood to be assessed;

7. “Urban indicators” to assess blocks and land units.

This conceptual model was chosen because it incorporates, across its four levels, components related to equity, environmental quality and sustainability (Nahas, 2002; Nahas et al., 2006). Based on these “domain groups” and “macro-indicators”, a database was created to select “urban indicators”. Once structured, this database allows for the addition or exclusion of urban indicators, which makes this model adaptable to different urban contexts.

In the Development Stage (B), an outline of the method for assessing urban quality of life adaptable to the scale of Brazilian neighborhoods was created. Based on Scussel’s (2007) study, the method was operationalized and implemented through three empirical studies:

1. single-family residences;

2. social housing; and

3. irregular occupations.

In these studies, the method underwent cycles of preparation, implementation, and analysis, with results disseminated for further refinement through the participation of public officials and urban planning researchers.

During the assessment preparation phase, the method requires adjustments to the conceptual model based on the urban context being assessed. The model is used exploratively to identify and operationalize levels within a database. The data collection instrument is tailored to collect primary data through a questionnaire with both closed (objective) and open-ended (subjective) questions. The open-ended questions use the Laddering technique (Reynolds; Gutman, 1988), an interview method that uses cognitive associations to identify key aspects of residential space that are valued by users. The Implication Matrix and Value Hierarchy Map derived from this technique were used to calculate the “k” weight of urban indicators using pairwise conflict matrices, following studies by Socco et al. (2003), Scussel (2007), and Delsante et al. (2014).

The use of the Laddering approach for calculating urban indicators is an original contribution of this study, distinguishing it from existing reference studies (Socco et al., 2003; Delsante et al., 2014; Delsante, 2016; PMBH, 1996; Sposati, 2000, IPPUC, 1996), especially regarding the weighting of urban indicators. While previous studies relied on researchers’ judgment for this weighting (Socco et al., 2003; Scussel, 2007; Delsante, 2016), this research employed the Laddering research technique to weight the indicators based on neighborhood residents’ perception.

By incorporating residents’ perceptions into the development and weighting of urban indicators for assessing urban quality of life, this study aims to reduce reliance on secondary data (PMBH, 1996; Sposati, 2000; IPPUC, 1996). The method’s independence from secondary data sources, such as those of the Brazilian Institute of Geography and Statistics (IBGE), enhances its potential for systemic use by urban planning institutions. Considering residents’ perceptions, which are often neglected in urban quality of life assessments, allows for adapting these assessments to the concept of equity through the involvement and participation of local communities in shaping spaces that enhance urban quality of life.

Finally, in the Assessment Stage (C), the proposed method was evaluated based on its “applicability” and “usefulness according to public officials and researchers. During this stage, several videoconference meetings and semi-structured interviews provided feedback on the method. The officials’ participation helped confirm that the method met the objectives of applicability (ease of use and understanding of the results) and usefulness (method and results’ relevance for decision-making).

This paper offers contributions that address challenges identified in the literature regarding the selection of indicators for urban quality of life and the perceptions of the officials involved. Key contributions include:

1. weighting of indicators based on residents’ perceptions;

2. providing flexibility in the calculation and conceptual model, allowing adaptation to various urban contexts; and

3. presenting and disseminating results through visual tools for public managers and researchers.

Additionally, this study advances the existing literature (IPPUC, 1996; Socco et al., 2003; Sposati, 2000; Scussel, 2007; Delsante, 2016) by improving the methods for presenting and disseminating results, with a focus on clarity and usability. It introduces the use of free online tools, such as Visual Dashboards, to share, edit, and update information interactively. Urban quality of life is a complex, multidimensional phenomenon, and further research on assessment methods in urban planning and management remains an important knowledge gap.

While the research has made important contributions, some limitations should be noted, including the complexity of applying the methodology at the neighborhood scale and the subjective nature of weighting urban indicators based on residents’ perceptions. To address these limitations, future research could apply the evaluation method in other neighborhoods to adapt it to different urban contexts, refine the method (conceptual framework, questionnaire, data collection protocol, processing, and result presentation), and improve the dissemination of results through visual dashboards and other data-sharing tools.

Acknowledgements

The authors gratefully acknowledge PROPUR/UFRGS and CAPES.

References

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