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Article

Walkable Urban Environments: An Ergonomic Approach of Evaluation

by
Letizia Appolloni
1,*,
Alberto Giretti
2,
Maria Vittoria Corazza
1 and
Daniela D’Alessandro
1
1
Department of Civil, Building and Environmental Engineering, Sapienza University of Rome, 00184 Rome, Italy
2
Department of Architecture, Constructions and Structures, Polytechnic University of Marche, 60121 Ancona, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(20), 8347; https://doi.org/10.3390/su12208347
Submission received: 2 September 2020 / Revised: 28 September 2020 / Accepted: 6 October 2020 / Published: 10 October 2020
(This article belongs to the Special Issue Sustainable Development Goals in Healthy Cities)
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Abstract

:
Background. The salutogenicity of urban environments is significantly affected by their ergonomics, i.e., by the quality of the interactions between citizens and the elements of the built environment. Measuring and modelling urban ergonomics is thus a key issue to provide urban policy makers with planning solutions to increase the well-being, usability and safety of the urban environment. However, this is a difficult task due to the complexity of the interrelations between the urban environment and human activities. The paper contributes to the definition of a generalized model of urban ergonomics and salutogenicity, focusing on walkability, by discussing the relevant parameters from the large and variegated sets proposed in the literature, by discussing the emerging model structure from a data mining process, by considering the background of the relevant functional dependency already established in the literature, and by providing evidence of the solutions’ effectiveness. The methodology is developed for a case study in central Italy, with a focus on the mobility issue, which is a catalyst to generate more salutogenic and sustainable behaviors.

1. Introduction

The quality of urban life depends on the ability to combine the natural environment, the built environment and mobility, so that social and functional needs of the inhabitants are fully satisfied, and the long-term negative impact of health risk factors is minimized. One of the main objectives of the Global Action Plan on Physical Activity (GAPPA) 2018–2030 of the World Health Organization (WHO), is to create urban environments that promote and safeguard the rights of all people, of all ages, to have equitable access to safe places and spaces to perform regular physical activity, according to their needs [1,2]. Salutogenicity is a concept that captures in a word this complex statement [3]. The WHO has identified a set of affordable and cost effective interventions to achieve good levels of salutogenicity, providing the greatest possible impact on health by reducing illness, disability and premature death from non-communicable diseases (NCDs) [4]. In particular, increasing all forms of physical activity could provide considerable benefits on different diseases, like type-2 diabetes, dementia, cerebrovascular diseases, breast cancer, colorectal cancer, depression, ischemic heart disease. Jarrett et al. [5] estimates that, in the UK context, a widespread physical activity, such as walking 1.6 km or cycling 3.4 km, would save approximately 17 billion GBP/year in 20 years (1% of the National Health Fund).
By emphasizing the conditioning aspects that the urban structure has on the behaviour of its inhabitants, the WHO objectives establish a direct relationship between salutogenicity and urban ergonomics. In particular, the literature largely supports the WHO perspective, by emphasizing the relevant relations occurring between mobility (i.e., walkable urban environments) and health [6,7,8,9,10,11,12,13]. Sallis et al. [14] observe a direct relationship between physical activity and residential density, density of the intersections, density of public transport, number of parks and green spaces within a radius of one km from the house. Similar results have been observed in other studies concerning the relationship between urban spaces and physical activity [15], between the built environment and physical activity [16,17,18,19,20,21,22,23], and between the built environment and several chronic diseases [24,25,26,27], highlighting some of the main risk factors [28,29,30]. Other consistent evidence concerns the relationship between environmental exposures (e.g., air pollution, noise, etc.), cardiovascular disease and mortality [24,25]. Only few studies, mainly qualitative, correlate the built environment with inhabitants’ perceptions of the outdoor environment (e.g., chromatic and olfactory features) [31]. The wide range of health benefits for all age groups caused by the presence of green elements, which complements the improvement of the ecological–climatic conditions of the cities, is investigated in [32]. In general, the proximity to the parks and recreational environments is associated with reduction in stress, improvement of mental health, stimulated social cohesion, increased physical activity, reduced cardiovascular diseases and diabetes, reduced exposure to air pollution, reduced exposure to excessive noise, and improved comfort [9,33,34,35,36]. However, more studies are required to qualify to a better degree the influences of the size, the distance and of other features of green areas, on the level of physical activity achieved by the population [36]. Even within the transportation and mobility governance domains, the role of walking and cycling is no longer considered just ancillary to motorized modes, with several examples in literature and practice acknowledging physical activity as crucial for sustainable transportation planning [37,38,39]. Moreover, research on innovation for transit facilities strongly relies on ergonomics as an element of strength to increase the attractiveness of public transport and create new urban landmarks [40,41].
Modelling urban ergonomics is a complex issue since it involves a number of different domains, from the citizens’ behaviour to urban planning and building technology. The main problem in modelling systems with a high degree of complexity concerns the achievement of a sufficient level of generality in the representation, so that reliable predictions can be formulated. In the urban ergonomics case, for example, the unsystematic components of citizens’ behaviour, affected by hardly quantifiable socio-cultural aspects, can cause significant variations, from case to case, both in the model’s structure and in the relevance of its parameters. In such situations, a combined probabilistic and case-based reasoning approach can be effective [42]. Case-Based Reasoning (CBR) [43] is a form of exemplar-based analogical reasoning, which provides support to problem solving whenever general domain theories are missing. In CBR, past solutions are reused to solve problems in similar contexts. The practical problem of CBR is that reusing past solutions in new contexts usually requires a portion of general knowledge that is usually not available in the CBR systems. On the other side, probabilistic reasoning, and more specifically Bayesian Networks (BNets), can be used to derive general models that are shared by the elements of a training case set [44]. For example, in transport planning BNets are long used to develop activity-based models, with further applications and case studies on a vast array of fields (from environmental safeguarding, to road safety, to policy decisions, etc.), involving all modes. However, one of the problems arising in using BNets in modelling complex domains is that a general statistic can be hard to achieve because the training set is usually not homogenous within the boundaries of the knowledge modelling set. For example, in urban ergonomics, similar neighbourhoods may have totally different influence degrees of green areas on the general performance parameters. This often happens because the relevant elements fall outside the knowledge boundary of the modelling effort, and, consequently, are not represented. On the other hand, the knowledge modelling effort is always constrained by a number of practical limits, not least, the manageability of the parameter set.
Two main issues emerge at this point of the discussion as the main focus of this paper. The first concerns the identification and qualification of a distinctive set of measurable indicators that reliably captures the influence of the urban ergonomics on the salutogenicity degree of an urban context. The sets of core indicators proposed in the literature [16,29,45,46,47,48,49,50,51,52,53,54] are scarcely integrated, sometimes unmanageable (i.e., the number of indicators is too high) and sometimes inconsistently shaped (i.e., unbalanced in specific arguments). An in-depth analysis of the indicators proposed in the literature has already been accomplished by the authors [55,56] showing flaws, including data completeness, the difficulty and the cost of accessing the database and the lack of standardization (there is no common standardized method of measurement or cataloguing). Hence, a careful selection of available indicators and their arrangement in a sound, complete and effective set must be developed. The second point concerns the modelling approach. A modelling strategy that is able to overcome the limits of CBR and BNet, by highlighting contextual regularities that would have been otherwise hidden by the domain complexity, must be developed.
The paper structure is as follows. In Section 2, the urban ergonomics indicators that have been used to investigate the salutogenicity of the downtown of the Rieti city, a midsized Italian town close to Rome, are introduced. In Section 3, the paper details the modelling approach and structure of the Bayesian Network model. Analytical results are presented and elaborated in Section 4, and some areas for further improvements are elaborated in Section 5; eventually, concluding remarks are provided in Section 6.

2. The Human–Environment Interface to Link Ergonomics and Sustainability

The urban space’s salutogenicity, as defined above, can be read in terms of ergonomics. More specifically, ergonomics can be defined as: “scientific discipline concerned with the understanding of the interactions among human and other elements of a system, and the profession that applies theory, principles, data and methods to design in order to optimize human well-being and overall system performance” [57]. Consistently, ergonomics is multidisciplinary as it focuses on the Human–Environment Interface (HEI) and searches for a concrete and rational answer to such a complex relationship [58,59]. As such, ergonomics is applicable to systems of urban areas. In fact, urban spaces shape the system that structures a city by linking people, places and functions. The vision of the city as a complex system was pioneered by Christopher Alexander in 1965, and epitomized by his statement: “the city is not a tree” [60]. The HEI was central in this approach although, nowadays, a revision placing major emphasis on the sustainability issues is needed. Since the urban environment is, above all, a human environment, the HEI represents the whole set of interactions between the complex systems of urban spaces and the people (or users) who inhabit them and walk within. Quality HEI can be claimed when an urban space’s performance fully meets people’s requirements, starting from Safety and Well-being as primary needs. The meeting of Safety and Well-being requirements implies that the urban environment is designed and structured as a set of spaces where people experience and perceive freedom from whatever sort of risks, danger or losses. In turn, Sustainability can be interpreted as the ability of the users to maintain such status with no (or minimal) long-term negative effects on the environment they experience and perceive. All of the above is complemented by an additional requirement: Usability, i.e., the possibility to perform appropriate functions, complying with Safety and Well-being requirements, within a sustainable approach.

2.1. An Approach to Urban Ergonomics Indicators

Within this vision, ergonomics by managing HEI, can be applied to any phases of planning, design, implementation, evaluation, regeneration, maintenance and improvement of the system of urban areas/spaces, to generate an effective requirement/performance equilibrium. In Italy, the requirement/performance approach was initially developed by UNI (Ente Nazionale Italiano di Unificazione, i.e the national board for standardisation) and applied to standardize a wide range of products, components, services, operations and functions, hence its full transferability to this study, as well.
Measuring or assessing a performance, as the efficiency with which each urban space component fulfils its intended purpose, is the main task within walkability tools. The quality of the performance assessment relies on the accuracy in the investigation and surveys of places, their constitutive elements and the activities they generate. Coherently with the HEI approach, the results from such investigation can be further developed in two ways:
  • The conventional walkability exercise: a walkability list is designed to assess a set of performances according to the perception of the urban spaces walked by the exercise participants (surveyors, interviewees); each item in the list is weighted and scored, so as to identify a rank of performance which may enable/hinder people walk the surveyed areas (typically a very specific origin/destination path, e.g., home-to-school) and decide whether they are walkable, and to what extent. To progress beyond this practice, walkability lists can be associated with weighted indicators, for which a range of performance targets are set. Performance targets, in turn, are reported according to threshold and optimal values. Surveyors can score the listed indicators and success can be claimed only when the performance targets are achieved.
  • The study of walkability variables: after the detailed survey and the creation of the walkability list, the indicators are interpreted in terms of variables describing the dynamic relationships between the characteristics of the surveyed areas and the behaviour of their inhabitants. The variables can be disassembled and classified in terms of requirement/performance analysis within a BNets process. This gives rise to a structured system of variables classified in terms of needs, requirements and performance.
Each way complements the other, with scientific literature abounding for the former. For the latter, a classification system focused on the Usability, Well-being and Safety requirements, as defined by the UNI standards [61], has been developed and applied to the case study described in Section 4, to highlight the role of each within the ergonomics of urban spaces/areas. More specifically, each Usability, Well-being and Safety need gives rise to several requirements that the urban areas’ performance is designed to meet [62]. The reference for the definition of these needs and the related requirements was based on previous studies [63,64] and synthesized in Table 1.
It must be noted that Usability includes the concept of Accessibility, according to its broader definition as the “the ease with which exchange opportunities can be accessed” [65]. Consequently, within HEI, Accessibility is considered as a prerequisite that all the requirements already comply with.
The BNet approach might provide an effective response and a reliable solution to the research question about the complexity in the management of urban phenomena, as it discloses all the interrelations among the HEI components.
According to Table 1, to each need several requirements correspond, and the urban space performance is designed to meet all of them [56].
To quantify the performance levels, a set of 29 measurable performance domains has been defined, according to 5 categories: Natural elements, Built environment, Mobility, Urban furniture and Perceived environment. Each set of performance includes a variable amount of indicators for a total of 67, each of them described according to the measurement unit, threshold and optimal values (with reference to presently existing regulations on this matter), achievable score and weight (Table 2, Table 3, Table 4 and Table 5). Some of these indicators have been selected from other sets already in use in the literature [56,63,64].
Weights for evaluation categories and indicators were developed by a multidisciplinary panel of 10 experts. The preliminary literature review analysis on neighborhood environmental factors affecting walking and, therefore, ergonomics was the basis to define the above-mentioned indicators, evaluation categories and criteria for scores and weights, which were presented to the experts, from different fields: public health, urban planning, transportation and urban policy makers. They were asked to assess the overall set of urban ergonomics indicators and define weights for each indicator. After this first round, the panel of experts, representing the involved fields of study, agreed to participate in a discussion to finalize the weights. The weights reported in Table 2, Table 3, Table 4 and Table 5 are the shared result of this discussion [63].
The performance’s value (e.g., A1. Vegetation, etc.) is obtained by adding the scores of the indicators (e.g., A1.1, A1.2, A1.3, etc.), weighted by their relative weight (e.g., wA1.1, wA1.2, wA1.3, etc.), normalized in the interval 0–10, according to the following Equation (1):
Pp = [(a*w1) + (b*w2) + (c*w3) + ……..]/10
where
  • Pp = performance score (e.g., A1);
  • a,b,c = scores of the individual indicators calculated by the surveyors (e.g., A1.1, A1.2, A1.3);
  • w1, w2, w3 = weights attributed to each indicator (e.g., wA1.1, wA1.2, wA1.3).
The Natural elements category in Table 2 includes both man-made components that translate into green public areas, tree lined streets, fountains and waterworks, and the availability of bodies of water, whose effects on the behaviors and habits of the inhabitants are largely recognized in the scientific literature, due to their capacity to mitigate temperature and sun exposure [33,36,66].
The Built environment category (Table 3) relies on indicators applicable to describe the major features of the urban structure, from morphology, to configuration, density, texture and quality and its functions. All highlight crucial features for evaluating the ergonomics of the urban spaces [16,29,63,67,68,69,70,71,72].
In the Mobility category (Table 4), ten indicators specifically describe the infrastructure specifically associated with the capability to increase and facilitate active and sustainable mobility [73,74,75,76,77] as “part of the daily life of all the inhabitants, by means of transportation, free time and workplace activities…” [78]. The Natural elements and Built environment categories describe the urban fabric features where walkability takes place, whereas Mobility assesses the quality of connections to generate walkable conditions. Together, they describe the walkability of the public realm.
The Urban furniture category (Table 5) relies on five indicators to assess comfort and perceived safety. They describe the characteristics of a given urban space needed to increase social interactions and support walking [66,71,79]. To conclude, Perceived environment (Table 5) includes four indicators related to the users’ perception of neatness of the urban space [50,54,73,80]. Both categories represent added values to the walkability of the public realm.

2.1.1. The Performance–Requirement Matrix

The selection of indicators is often a challenging task in planning and evaluation practice. Indicators should be SMART—Specific, Measurable, Attainable, Realistic and Tangible [81] and, possibly, comparable. Issues of uncertainty and difficulties in the measurements are commonly addressed. For example, accessibility is a broad concept (as synthesized in [82]), measurable in different ways and with different indicators. A control tool is then required to check whether a specific requirement is actually met by a given performance. This could be a control matrix, like the one presented in Table 6, where the requirements from Table 1 are matched with the performance listed in Table 2, Table 3, Table 4 and Table 5.
This control matrix makes it possible to check that each performance of the environment is meeting at least one requirement, from the users’ side. Each single indicator may be used for more than one requirement/performance relationship, which creates a multiscope assessment of the walkability features.

2.2. The Case Study

The set of walkability indicators was tested on a case study where a previous analysis focused on the walkability of the urban areas around the historic center of Rieti, a middle-size province town, 80-km far from Rome, in Central Italy, with just less than 50,000 inhabitants. The former analysis was designed to test and validate a composite indicator, the Walking Suitability Index of the Territory—T-WSI [63]. Unlike other walkability tools, T-WSI was not designed to assess the walking-friendliness of given origin-destination’s paths or trips, but of the whole walking environment of an area, in this case 9 out of 15 neighbourhoods in Rieti. T-WSI can be thus considered the “prequel” of the current HEI approach which, in turn, includes a larger array of indicators (those in Table 2, Table 3, Table 4 and Table 5) and covers a vaster area (approximately 60% of the built area of the city as in Figure 1).
Increasing the number of indicators obviously improves the accuracy of the study and great attention was attached to their SMART characteristics. Within the HEI approach, however, one more advance is represented by the quality of the analysed districts. Areas 1 to 9 in Figure 1 and Table 7 were developed between the 1920s and 1990s, under different planning criteria and visions of the city, but under the same need to provide the 1900s’ expanding population with housing and basic services (schools, churches, etc.). Much different is the city center (Area 10 in Figure 1 and Table 7), which has always been a pedestrian area since it was developed in 1149 as a papal seat. The quality of the built environment is high with narrow alleys, squares and landmarks such as the medieval walls and cultural heritage buildings (with an enforced Limited Traffic Zone—LTZ helping to preserve all of that). Usually, historic centers are not included in the walkability analysis since, by definition, they are fully walkable. In this case, contemplating the city center in the HEI analysis makes it possible to introduce an unusual but significant term of comparison (a reference) among the more modern districts. Moreover, it introduces the research question about the assumed attractiveness of historic areas as a catalyst to attract pedestrians.
To describe the differences between these districts, an abacus of photographs associating local environments with HEI categories is provided in Table 8.

2.3. The Data Collection

The first step in the analysis was the data collection to “feed” the indicators. A trained researcher was in charge of collecting data for every link of the road network at each considered district. This required surveys, calculations and/or measurements according to the type of indicator to develop. A spreadsheet was specifically designed to collect data for each indicator’s category. Once data were entered, a further data assessment was carried out by a different trained researcher, who performed quality control on a random sample of records, independently re-collecting the same data under the same experimental conditions. Finally, the validated set of entered data was processed and results are presented in Section 3.

3. The Bayesian Network Model

A BNet probabilistic model has been built to seek out and analyse the complex relationships occurring among the different performances, which become “variables” in the BNet model. The BNet model was built in two phases:
  • Structural modelling: consisting of the definition of the model structure, which was carried out by determining the conditional independency among the variables;
  • Dependency modelling: by calculating of the strength of conditional dependency among the variables.
Both phases were based either on data, through machine learning algorithms (i.e., structural learning and EM-learning), or on first principles implemented by manual coding physical laws, well established rules, or expert knowledge depending on their availability (as further reported). The final model was composed of seven elementary networks arranged in three levels. The first level includes five elementary networks (Figure 2, Figure 3, Figure 4, Figure 5 and Figure 6) that represent the basic parameters used to quantify the main features of the urban space ergonomics. Manual coding has been mainly used in first-level networks, since there is a clear functional dependency among the measured parameters and the ergonomic features. Just a small number of inter-parameter dependencies have been derived by data only when a well-grounded explanation could have been formulated.
The 29 output variables of these networks are the input nodes of the second level network. The second level is made of a single network (Figure 7—top) that arranges the 29 urban ergonomic indicators calculated by the level 1 networks by establishing the conditional dependencies occurring among them. This network has been derived essentially from data, but a careful and systematic analysis of each derived relation has been carried out, to assess the statistical analysis on a semantic basis.
The third level is made of a single network as well, and it is aimed at eliciting and coding experts’ assessment about the performance level of the different neighbourhoods. As for the weights, a group of 10 urban planners and designers, both from professional and academic fields, was involved to assess the quality of the outdoor spaces of each neighbourhood. To this end, the experts have been provided with a specific assessment template where the general performance parameters were detailed (for example, usability is itemized in terms of easiness to equip, transitability, flexibility, accessibility, identifiability and conditions of use). The experts’ assessment outcome has been finally integrated in the overall model using standard BNet learning algorithms. In this way, statistical dependencies have been calculated among the 29 observable neighbourhoods’ ergonomic features and the expert assessments (Figure 7).
Finally, all the variables in the three networks have been connected to the Neighbourhood node, containing just the neighbourhood identifiers, so as to have a case-based reasoning (CBR) support to urban design. By observing or providing evidence to one or more neighbourhood identifiers in the Neighbourhood node, the network shows the statistics of a single case or a combination of cases, respectively. In fact, this inference resembles the case selection in CBR. Through this simple expedient, the network gives the possibilities to compare the models of different neighbourhoods’ cases, pointing out similarities and differences. Furthermore, the neighbourhood identifier can be used as a key to access unstructured information sources (e.g., drawings, photos, tabled data, etc.) stored in complementary data environments, so that the analysis can be easily extended beyond the representation boundary of the statistical model. On the other hand, by selecting a single feature or performance node, the network points out the cases (i.e., the neighbourhoods) that share the selected value and the statistics of the other performance and feature nodes. Hence, by combining observations in the different network levels, a number of interesting inferences can be implemented. In general, the BNet model provides support to preliminary urban design through what-if analysis, by means of forward propagation of the observations of level 1 nodes, as well as support to urban regeneration policies through diagnostic reasoning, by means of backward propagation of observations of level 2 or level 3 nodes. The following section details the networks’ levels.

3.1. Level 1 Networks

Five level 1 networks have been defined according to as many domain analyses: Built Environment, Natural Elements, Mobility, Urban Furniture and Perceived Environment. Direct dependencies among input and output nodes have been manually coded, the output (thick boundary nodes) being weighted sums of the inputs. Occasionally, parallel data analysis showed some strong dependencies between input nodes. These dependencies were included only when they were understood in terms of general theories, hence preserving the highest possible generality degree of the model.
Figure 2 shows the network concerning the Built Environment domain analysis. Data analysis pointed out some dependency relations among features: the sky view factor (sWf) affects the albedo (Al), as it is geometrically explainable, while the quality of the margins (Qm) is affected by the mix of land use (LUM), by the quality of services (Qs) and by the sky view factor (sWf), which can be figured out as well.
Figure 3 describes the network related to the Natural Elements domain analysis of the Water, Green and Biodiversity nodes. Some inter-parameter links occur in this domain as well. The quality of green areas (Qg) affects the quality of the water elements (Qw), and the percentage of permeable surfaces (PERp) affects the proximity of the water elements (Pw). In both cases, this evidence is explicable on a clear conceptual basis, hence, they have been included in the model.
The Mobility domain analysis is represented in Figure 4 where the features of the urban space that enhance sustainable and active mobility are highlighted.
A number of dependencies among urban features, having a plausible ground in theory, have been strongly suggested by the recorded data, and therefore added to this network. These include, the relation between the length of the sidewalks (PERl) and the presence of cycling paths (PERc), the relation between the proximity of the pedestrian areas (Ppa) and the width of sidewalks (Ws), the relation between the proximity of the LTZ (P and the proximity to parking lots (Pp), the relations among the proximity (Ppa) and the quality (Qpa) of the pedestrian areas with the flow of public transport means (Fl).
Figure 5 represents the network of the Urban Furniture domain analysis. Dependency relations among features concern the luminous flux (Lux) and therefore the brightness of an urban space, which influences the quality and affects, therefore, the readability of information signs (Qse). In fact, the quality of these elements is measured by analysing their visibility and maintenance status.
Finally, in the network representing the Perceived Environment domain analysis (Figure 6), the road cleaning (Frc) and emptying waste containers (FW) conceivably influence night noise levels (Nn).

3.2. Level 2 and 3 Networks

Figure 7 shows the final BNet model resulting from the integration of the output nodes of the seven networks (Level 2—top) with the network generated by experts (Level 3—bottom). The relations occurring among nodes at level 2 have been derived from data by selecting the ones that provided strong semantic evidence. In fact, the BNet machine learning algorithms supplied more than one model for the given data set. Among them, the ones that were the most plausible on a semantic basis were selected, excluding ordinary or counterfactual correlations, and correlations that appeared to be scarcely generalizable. The results show the relevant complexity of the real situation, deriving from the high degree of interconnectedness of the urban performance parameters. Each of these links is semantically dense, thus calling for additional multidisciplinary interpretations, to be addressed in forthcoming studies.
The level 3 network captures the general knowledge about urban ergonomics. Our approach, to include directly a significant number of expert assessments, is an attempt to capture and quantify the different perspectives on a statistical basis. The noteworthy point is that level 3 coding has been carried out by experts on a general set of parameters (e.g., intermodality, inclusiveness, accessibility, etc.), which was not directly connected with the performance variables part of level 2.
A final issue concerns the relationships occurring among level 2 and level 3 nodes. In this research, the case-based approach implemented by the Neighborhood node provided an initial solution to the problem, acting in principle as a sort of naïve Bayesian classifier. Then, a number of direct relations have been implemented, as in the previous levels, when the data provided strong insights about semantically well-grounded correlations.

4. Results Analysis

Following the layered model structure, the analysis is arranged by levels. According to the HEI approach, level 1 network analysis provides an overview of the general urban ergonomic indicators for each neighborhood and a quantitative assessment of the indicators’ scores to highlight drivers and barriers for walkability. Level 2 and 3 network analysis provides an integrated and more systemic evaluation of ergonomics according to the emerged Bayesian Network structure.

4.1. General Urban Ergonomic Indicators—Level 1 Network Analysis

The availability of a large test area, which amounts to 10 neighbourhoods, ensures a good soundness of results and the emergence of performance gaps and critical factors affecting walkability in real built environments.
Table 9 shows the scores of each indicator and the average scores per performance obtained in the investigated neighborhoods. The table also shows, for each indicator, the percentage of neighborhoods with unsatisfactory scores and, for each district, the percentage of indicators with unsatisfactory scores. In this preliminary study, the values ≥6/10 have been arbitrarily considered satisfactory.
The analysis on the neighbourhoods stresses how the city centre is the most walkable, as expected. Likewise, the second and third ranked districts (Borgo and Fiume Dei Nobili) supply a walkable environment due to low-rise building stock, pedestrian-oriented management of local mobility, and clear hierarchy and functional mix, with residence as the prevailing use. According to the scores associated with the different performance levels, the perception of safety seems rather critical (for example, in 90% of the neighbourhoods, crossings signs and markings are not adequate; in 80% of the areas, the sidewalks are not present or are inadequate). The unsuitability of some environmental factors (especially vegetation and waters, density of intersections, density of public transport, the presence of cycling and pedestrian paths, sidewalks, etc.) is clearly detrimental to the salutogenicity of the neighbourhoods [3]; these, along with the poor enforcement of LTZs (missing in 80% of the areas), and pedestrianized areas (unavailable for 60%), no cycle paths or seats (everywhere in the case study), are additional barriers to support behavioural changes in favour of more physical activity levels and more active environments [56,66].
A focus on the Mobility performance might help to explain some of the barriers mentioned above. This is the bulkiest category, which describes the quality of connections and, in turn, is strongly affected by the urban fabric. It is not surprising, then, that Mobility highlights contrasting results. On the one hand, areas such as the city centre (District n. 10) are fully preserved by traffic phenomena by the enforcement of LTZ (although vehicular flows are not negligible, as surveyed in [63]), supplied with parking areas to avoid on-street parking, and pedestrianized to recreate the pristine HEI and enhance the cultural heritage. On the other, in areas where monofunctional land use (residence) is associated with modest or poor building quality, the Mobility features are consistently similar, with no walking-enhancing solutions: no LTZ and pedestrianization, basic transit supply, on-street parking, double lines and spill over. The paucity of Urban furniture and Natural elements are contributing factors in both cases, although with different outcomes. In the city centre, seating, public lighting, signs and marking are designed to highlight the premium built environment, which is so significant per se that natural elements are reduced to waterworks and sparse vegetation. In the contemporary districts, urban furniture is mostly reduced public lighting and road signs, mostly to prevent unsafe events. Vegetation, although richer, relies on small public parks and gardens, tree-lined streets and private areas, which help to increase the quality of the urban environment but not enough to trigger salutogenic behaviours.

4.2. The Overall Systemic Assessment—Levels 2 and 3 Analysis

The average value of ergonomics of Rieti obtained through the Bayesian model, expressed in percentage, is 44.2%, and Table 9 illustrates the score obtained by each neighbourhood. About 80% of the neighbourhoods have a low ergonomic level, which, therefore represent a widespread situation in the city of Rieti. Through a backward analysis (i.e., by observing the lower value on the Ergonomics node—see Figure 7), it is possible to identify the causes of the overall poor performance for the Rieti town. The analysis of the individual indicators (Table 8) highlights significant deficiencies, especially in terms of public transport, cycle paths and street furniture; the presence of green areas and the cleanliness of the urban space are also lacking. All these factors can be easily improved through a review of local land use planning policies [83,84]. At present, however, the lack of the aforementioned environmental factors determines the critical issues in terms of indicators related to the ability to walk and more generally to physical activity [3,63].
A further analysis can be conducted, selectively observing different values of the neighbourhood node. In that way, it is possible to compare the statistics of the different neighbourhoods, to highlight specific deficiencies and to identify possible improvements. As we have already pointed out, the explanations of complex states of affairs can affect the representation of boundaries of the probabilistic model.
For example, on one side, the Centro Storico shows the highest value of the ergonomics score (57.1%—Table 10). This can be explained in terms of several interventions already realized by the public administration in that area to increase its ability to attract people and to improve the economy. On the other side, however, the same neighbourhood shows poor performance in some inner nodes that can be explained only through the topology of the network. In fact, the lack of public transport shows, in the Centro Storico case, a strong correlation to the olfactory perception nodes and to the presence of traffic (Figure 7), providing a clear hint of the unpleasant smells generated by unbalanced traffic patterns.
A second noteworthy example is the Villa Reatina neighbourhood, where the worst situation is observed (36.6%—Table 9). Villa Reatina is considered a working-class neighbourhood whose first urbanization dates back to the fascist period and expanded in the following years. It is mainly a residential area, consisting of one or two-family buildings (built from 1920 to 1940) and multi-family buildings (built from 1960 to 1990), most of which fall within economic and popular building projects [54.6%]. It is a neighbourhood where social inequalities are particularly evident due to both the origin of the neighbourhood and its urban conformation. The Bayesian Network model shows that the low level of ergonomics is determined by equally scarce values of usability, well-being and safety (worst value). Proceeding backwardly on the BNet model, it is possible to identify the causes that are the prime factors determining such a low level of ergonomics. In the neighbourhood, at present, there are several shortcomings, mainly related to the urban structure that compromise the aesthetic quality of the neighbourhood and its liveability (lack of identity and recognizability). They include lack of hierarchical relations among spaces, both formal and dimensional, poorly defined urban margin and connectivity between the various parts of the neighbourhood, etc. As for the historic centre, shortcomings are identified in public transport, in cycling networks, in pedestrian crossings and, more generally, in the design of urban space dedicated to pedestrians (lack of seats, lighting, etc.).

4.3. General Trends

Finally, it is worth annotating some general trends concerning salutogenicity that can be identified from this level of analysis.

4.3.1. Environmental Factors

Environments with poor environmental and aesthetic quality could greatly influence the lifestyle of the population and be perceived as unsafe. The lack of environmental factors that support physical activity pushes Villa Reatina to the worst ergonomics rank and to the fifth to last in walkable neighbourhood among those studied. In fact, urban and indoor environments represent one of the major health determinants, and a clear and updated regulatory system is a key factor to ensure not only Public Health safeguarding [84], but also more sustainable mobility patterns.

4.3.2. The Safety Issues

Table 9 shows the ergonomics scores, stratified by their principal components (Usability, Well-Being and Safety). According to previous studies [12,24], Safety obtains the lowest scores in all the neighbourhoods. Results achieved for Safety, as “the set of conditions related to the safety of users, as well as to the defence and prevention of damage caused by accidental factors, in the operation of the urban outdoor space system” [85], highlight once more the relevance of Mobility in promoting sustainable travel habits. In particular, the factors determining the low safety score are mainly related (Figure 7) to the lack of public transport and cycle paths (both with insufficient scores in 100% of the districts), crossings (insufficient in 90% of districts), pedestrian areas and limited traffic areas (both insufficient in 80% of districts). These aspects, addressed within local general mobility policies [86,87], should be taken into account and solved into the Urban Traffic Plans (UTPs) and Sustainable Urban Mobility Plans (SUMPs), by means of specific actions, like those recently recommended by the WHO [2,88,89], as further discussed in Section 5.
The Safety issue introduces one more element to consider: the relevance of maintenance of paths and urban furniture to ensure proper and favourable walking conditions and to avoid accidents and falls. Several studies on areas similar to those in the case study [64,90,91] stressed how maintenance, if neglected, generates inappropriate walking behaviours among the pedestrians, thus reducing the overall walkability of an area. This means that walkability needs an integrated management (land use policies, mobility regulations, maintenance plans), which the BNet applications might help to foster by highlighting the several interrelations between the HEI other variables.
The use of the BNets provides a complete framework of the current situation in the neighborhoods, highlighting the causes that determine the above-mentioned ergonomic results in the various areas.

4.3.3. The Well-Being Issue

The factors involved in the definition of the districts’ wellbeing also obtained insufficient scores. Well-being, as defined by the WHO (“a state of total physical, mental and social well-being” and not simply “absence of disease or infirmity” [87]), in the study was intended primarily as “the set of conditions relating to states of the open urban spaces system adequate to life, health and the performance of the inhabitants’ activities” [40].
The conditions of well-being, as stated in the norm [88], refer to the positive sensory perception of the environment by the user, to health and hygiene situations, to the absence of pathogenic conditions and to the safety of users. If the user’s satisfaction represents the level of comfort that is perceived in the performance of daily activities in an open space, the concept of well-being can be conditioned, since it is connected (Figure 7) to the factors that define the use and safety of the spaces in carrying out the activities. Several variables negatively affect the Well-being scores; some of them still due to Mobility issues (the lack of public transport, of bicycle paths, limited traffic areas, crossing). In addition, the inadequate management and maintenance of public roads (with insufficient scores in 90% of the districts), cleaning of outdoor spaces (insufficient in 100% of the districts), as well as approximate street furniture (lack of seats and suitable signs in 100% of the districts) are equally lacking.

4.3.4. Mobility

Mobility has a central role in generating salutogenic cities according to the HEI approach. Both the results from the indicators and the BNet analyses evidence how unbalanced traffic patterns in favour of private traffic are detrimental to Safety in general and in some specific areas within Well-being and Usability. Hence, an appropriate Mobility governance should be adopted, starting from the enforcement of its sustainability-oriented regulatory tools. The usual regulatory tools are those to manage traffic flows and to develop sustainable mobility patterns (fully described in the literature [89] and largely enforced across Europe).
As shown by scores achieved in the city centre and in the other neighbourhoods in the case study, pedestrianization, LTZs and Zone 30s can be considered unavoidable interventions to safeguard and rehabilitate premium value environments in central historic areas and, in general, to enhance walking even in areas where the built environment may lack distinct qualities.

5. Discussing Possible Advances

The methodology described in Section 2 relies on the reported array of measurable indicators to assess the salutogenicity of a given urban context. However, cities are complex, each with different local features and living conditions. Such specificities introduce three issues to address as possible areas of advancement in the proposed methodology: i) once more, the relevance of selecting SMART indicators, but also the possibility of creating flexible tools, adaptable to the most different of urban contexts by adding more parameters; and ii) the possibility of enlarging the analysis by contemplating non-physical aspects like the human or social environments, and iii) the need to include different categories of surveyors in the analysis.
For what concerns the first issue, the HEI approach makes it possible to freely add to, or remove from, the selected categories more indicators than those used for this case study, if need be, thus tailoring the assessment and further fine-tuning the overall analysis to any case in hand. The evaluation of land use as an element affecting the walkability of a given urban environment is a case in point. In the literature, the assessment of land use is often assumed in terms of dominant functions, thus replicating the approach usually applied in urban planning. Consequently, an area can be assessed in terms of land use mix, by measuring the availability of its main uses: residential, commercial, etc. This is certainly a major determinant, as, in the literature, it has been long-acknowledged that monofunctional uses are detrimental to livability, based on specific indicators like, for example, land use entropy [92]. However, these types of indicators. although accurate, call for extensive data process [82,92], are area-based and do not consider local effects at the ground level generated by bustling storefronts, street activities, and outdoors-based behaviors [67,79]. These contribute to urbanity, i.e., those specific characteristics of urban life, and placing an emphasis on indicators measuring the vibrancy of the city life at the ground level results in an added-value to the walkability assessment. Some urbanity indicators describing the sidewalks’ levels of service (width, types of equipment) to accommodate the street activities were already developed for this case study and fully described in [61], but, in the literature, more can be found associated, for example, with: Trip-Generating Clusters (i.e., the capacity of given urban activities and facilities to generate the displacement of pedestrians); buildings’ variety of formal features as elements to support walking (mostly space syntax-based, such as: Mean Convex Space; Mean Number of Entrances per Convex Space; Percentage of Blind Convex Spaces, etc.) [93]; landscape and environmental metrics (based on the quality of vegetation and pollutants’ concentrations) [94]; participation in public life (as revealed preferences) [95].
The relevance of participation introduces the second issue, i.e., the possibility of highlighting how the human and social environments affect this type of study. Additionally, in this case, more indicators can be easily introduced than those used for the case in hand, which were derived by the local situation: Rieti is a typical middle-size European provincial town, with modest crime rates, no social conflicts, poor-quality but still livable outskirts, and welfare problems rarely experienced. Thus, the difference in this matter between the city center and the surrounding districts relies mostly on the former’s higher quality of built environment over the latter’s, and its natural vocation as a walking environment is also preserved by the enforcement of the local LTZ. Here, a consolidated land use based mostly on a mix of commercial facilities at the ground level and residential use on the upper stories, which attracts locals and visitors from the surrounding cities for most of the day, also helps to keep the area alive and avoid decadence. On the contrary, in urban areas with marked social inequalities among the city center and the other neighbourhoods (the former being either too exclusive with high-value properties and tourism-gentrified, or too dilapidated due to subpar building stock, security and safety problems, and inhabited by less and less affluent residents and businesses), more indicators and additional analyses are certainly needed. Again, in the literature, indicators to assess the role of the social environment that are consistent with the HEI approach abound and can cover all the contemplated categories. For example, indicators assessing social costs of transport, public perception and awareness, and safety levels [96,97] are appropriate to highlight the relationships between social inclusion and mobility. The approach in [98] seems to be particularly germane to assess how inequalities can be generated by natural and built environment features (by considering indicators assessing vegetation planting, access to green and recreational areas, on the one hand, and the effect of foreclosure, homeownership or vacancy rates on the other). Eventually, surveys based on Revealed and Stated Preferences can give rise to accurate assessment of how citizens perceive the environments they inhabit and the quality of the supplied services and equipment.
As described in Section 2.1.1, both the survey and the data process for the case study were carried out by researchers, so to achieve an expert assessment as a research goal. However, consistently with the HEI approach and the easiness of scoring the performance via the lists presented in Table 1, Table 2, Table 3, Table 4 and Table 5, the assessment can be extended to the real users, i.e., the neighborhoods’ inhabitants. This opportunity, currently under study, calls for the voluntary involvement of different users’ categories (according to age, gender, special needs). Perceptions vary not only with physical status but with familiarity with the urban context, trip purposes, weather conditions. All of the above will be considered to design accurate survey conditions and ensure comparability of the prospective results with those already available. Involving citizens represents a two-fold added-value as it complements the expert assessment and provides specific insights according to the actual abilities of the participants.

6. Concluding Remarks

This paper has discussed the salutogenicity issue of urban environments and its relationship with urban ergonomics, focusing on mobility and walkability. The paper has proposed a methodology to define generalized statistical models of urban ergonomics and salutogenicity, has introduced and discussed the relevant parameters derived from the large sets proposed in the literature, has discussed the emerging structure of a multi-level Bayesian network model, and has provided evidence of the solution’s effectiveness through an in depth discussion of the modelling result. The methodology has been applied to the city of Rieti, a middle-sized town in central Italy.
Although the model has been able to provide consistent snapshots of the actual state of affairs of the case study, and has supported interpretations and diagnoses of the emerging performance gaps, further work is still necessary to extend this methodology to provide a more generalised approach and consider the possible advances highlighted in Section 5. Moreover, a preliminary first principle modelling approach, developed exclusively using equations and parameters derived from the literature, shown, in our case study, scarce correlations among the various parameters, making the resulting model relatively unusable. On the other side, a second, purely data-driven approach gave a perfect image of the state of the matter, which, possibly due to overfitting, was hardly generalizable to other cases. The mixed data-driven plus first principle approach presented in this paper provided the best trade-off between accuracy and generality. However, further studies are still necessary to consolidate the methodology and the possibility of including more indicators, as stressed in Section 5, will contribute to mitigating the elements of uncertainty.

Author Contributions

Conceptualization, L.A., D.D., and M.V.C.; Data curation, L.A. and A.G.; Supervision, D.D. and A.G.; Writing—original draft, L.A. and A.G.; Writing—review and editing, D.D., M.V.C., and L.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 12 08347 g001 550
Figure 1. The case study areas in Rieti, central Italy.
Figure 1. The case study areas in Rieti, central Italy.
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Figure 2. I level: Bayesian elementary network, Built Environment.
Figure 2. I level: Bayesian elementary network, Built Environment.
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Figure 3. I level: Bayesian elementary network, Natural Elements.
Figure 3. I level: Bayesian elementary network, Natural Elements.
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Figure 4. I level: Bayesian elementary network, Mobility.
Figure 4. I level: Bayesian elementary network, Mobility.
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Figure 5. I level: Bayesian elementary network, Urban Furniture.
Figure 5. I level: Bayesian elementary network, Urban Furniture.
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Figure 6. I level: Bayesian elementary network—Perceived Environment.
Figure 6. I level: Bayesian elementary network—Perceived Environment.
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Figure 7. The final BNet model representing the ergonomics of urban spaces.
Figure 7. The final BNet model representing the ergonomics of urban spaces.
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Table
Table 1. Main needs and requirements within the Human–Environment Interface.
Table 1. Main needs and requirements within the Human–Environment Interface.
NeedsWell-BeingSafetyUsability
RequirementsSunshine exposureConflicts with motorized usersArchitectural barriers’ removal
Winter exposure (>8 h)Sidewalk on one side of the streetNo grades to access buildings
Summer exposure (<10 h)Sidewalk on both sides of the streetCrossing equipped with ramp+grade
VentilationNon-slippery surfacesRest facilities/equipment on hilly links
Canyon effectSignaled crossingsNo grade between driveways and sidewalks
TemperatureUnsignaled crossingsAccessible bus stops
Vegetation, tree-lined linkObstaclesTactile-plantar route for visually impaired users
Brick/stone surfacesDumpstersNatural directions for visually impaired users
Impervious surfaces for road and sidewalksAds markersRumble strips
HVAC units along the streetSurfaces’ evennessEquipment for hearing impaired
Relative humidityAvoidance of chinks and potholesMaintenance
Availability of natural surfaces (vegetation, bodies of water, etc.)Avoidance of distressesDistress density < 50% of the surface
NoiseSlippery factorsCleanness and Sanitation
Traffic noiseLeavesAvailability of dumpster and bins
Noise from working activities WaterRoad cleaning
Public lightingIceGraffiti removal
Light poles only Adaptability to events
Neon lightsAvailability of route for emergency rescue
GlarePossibility to enforce car-free areas
Reflecting surfacesPossibility to remove urban furniture
Level of service (LoS)
Suitability of (LoS) for sidewalks
Suitability of urban furniture
Sidewalk evenness
Availability of universal design furniture
Table
Table 2. Performance Indicators within the Human–Environment Interface, Natural elements.
Table 2. Performance Indicators within the Human–Environment Interface, Natural elements.
CategoryPerformanceIndicatorsMeasure UnitLimit ValueOptimal ValueScoreWeight
Max/Min
A–Natural elements A1. VegetationA1.1—Green area/inhabitantm2/inhabitant9200/1060
A1.2—Proximity to green aream≤300≤30020
A1.3—Quality of green areabad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent20
A2. Bodies of waterA2.1—Proximity of elementsm≤300≤3000/1060
A2.2—Quality of elementsbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
A3. Protection of biodiversityA3.1—Proximity to green aream≤300≤3000/1040
A3.2—Percentage of permeable surfaces %≥30≥3040
A3.3—Proximity to green corridorm1000 > A3.3 ≥ 750A3.3 < 60020
Table
Table 3. Performance Indicators within the Human–Environment Interface, Built environment.
Table 3. Performance Indicators within the Human–Environment Interface, Built environment.
CategoryPerformanceIndicatorsMeasure UnitLimit ValueOptimal ValueScoreWeight
Max/Min
B—Built environmentB1—Functional mixB1.1—Land use mixB1.1—Entropy equation≥0.510/1040
B1.2—Proximity to businessm≤300≤30030
B1.3—Quality of elements bad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent30
B2—Hierarchy B2.1—Hierarchical relationshipYes/noYesYes0/1040
B2.2—Dimensional relationshipYes/noYesYes30
B2.3—Formal relationshipYes/noYesYes30
B3—LandmarksB3.1—Cultural and natural landmarksYes/no≥1 in the reference area 1 everywhere0/1060
B3.2—Quality of landmarksBad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
B4—MarginsB4.1—Strong margins% compared to the edge length≥70≥70 0/1050
B4.2—Dimensional relationship H/Wm/m0.5 ≤ B4.2 < 1110
B4.3—Percentage of sky view% of sky view≥70≥7030
B4.4—Quality of marginsbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent10
B5—NodesB5.1—Density of intersectionsn/Km250 < B5.1 ≤ 70 ≥1300/10100
B6—SurfacesB6.1—Percentage of permeable surfaces%≥30≥300/1020
B6.2—Reflective powerAlbedo>0.3 at least 50% of the surface>0.3 at least 50% of the surface10
B6.3—Technical adequacyYes/noYesYes30
B6.4—Quality of surfacesbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
B7—Temporary usesB7.1—Amount of time dedicated to temporary functions [Hours spent for temporary uses/day] *100≤10% ≤10% 0/100100
Table
Table 4. Performance Indicators within the Human–Environment Interface, Mobility.
Table 4. Performance Indicators within the Human–Environment Interface, Mobility.
CategoryPerformanceIndicatorsMeasure UnitLimit ValueOptimal ValueScoreWeight
Max/Min
C—MobilityC1—SidewalksC1.1—Length of sidewalk% of the total viability≥50≥750/10040
C1.2—Width of sidewalkm≥1.503.00 ≤ C1.2≤ 5.0020
C1.3—Quality of sidewalkbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
C2- ParkingC2.1—Proximity to parking aream≤300≤3000/10060
C2.2—Quality of parking areabad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
C3—Transit supplyC3.1—Frequency and flow of public transportationn/h≥ 4≥ 60/10060
C3.2—Quality of public transportationbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
C4—Transit stopsC4.1—Proximity to stopsm≤300≤3000/10060
C4.2—Quality of stopsbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
C5—Pedestrian crossingsC5.1—Presence of crossingsYes/no –No every 100 m1>10/10050
C5.2—Width of crossingsm≥2.50 ≥4.00 50
C6—Road (vehicle) trafficC6.1—Traffic flown/h600 ≥ C6.1 > 300≤3000/100100
C7—Bicycle paths (intermodality)C7.1—Percentage of bicycle paths% of the total road network ≥50≥500/10030
C7.2—Proximity to pathsm≤300≤30050
C7.3—Quality of bicycle pathsbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent20
C8—Pedestrianization (intermodality)C8.1—Percentage of pedestrian zones % of the surface urban spaces≥50≥500/10030
C8.2—Proximity to pedestrian zonesm≤300≤30050
C8.3—Quality of pedestrian zonesbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent20
C9—Limited traffic zones—LTZC9.1—Percentage of limited traffic zones% of the surface urban spaces≥40≥40 50
C9.2—Proximity to limited traffic zones m≤300≤30030
C9.3—Quality of limited traffic zonesbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent20
C10—Public RoadsC10.1—Percentage of arterial roads% of the surface urban spaces≤10≤100/10060
C10.2—Quality of road networkbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
Table
Table 5. Performance Indicators within the Human–Environment Interface, Urban furniture and Perceived environment.
Table 5. Performance Indicators within the Human–Environment Interface, Urban furniture and Perceived environment.
CategoryPerformanceIndicatorsMeasure UnitLimit ValueOptimal ValueScoreWeight
Max/Min
D–Urban furnitureD1—SeatsD1.1–Length of seatingm/m2 every 3 m2 of space 0.15 < D1.1 < 0.30≥0.300/10060
D1.2–Quality of the seatingbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
D2—Public LightingD2.1–Light flowLux15 < D2.1 ≤ 1815 < D2.1 ≤ 180/10060
D2.2–Quality of the light fixturesbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
D3–SignsD3.1–Minimum distance from danger signm ≥50 ≥500/10020
D3.2–Presence of information signsNo for each intersection≥1≥140
D3.3–Quality of signsbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
D4–Waste dumpD4.1–Distance between dumpstersm≤ 150≤ 1500/10060
D4.2–Quality of waste containersbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent40
D5–ShadowsD5.1–Percentage of shaded areas% compared to open space>30 >500/10050
D5.2–Transmission coefficient%25 ≥ D5.2 > 15 at least 50% of surface15 ≥ D5.2 ≥ 0 at least 90% of surface15
D5.3–AlbedoAlbedo>0.3>0.315
D5.4–Quality of sun protection elementsbad/poor/mediocre/
sufficient/good/excellent		sufficientexcellent20
E–Perceived environmentE1–Chromatic feature–colorsE1.1–Chromatic harmonybad//mediocre/
good/excellent		goodexcellent0/100100
E2–Olfactory feature–smellsE2.1–Presence of olfactory sourceYes/no–m>300 0/100100
E3–External environment noiseE3.1–Daytime noise leveldB(A)≤57 0/10050
E3.2–Night-time noise leveldB(A)≤4750
E4–NeatnessE4.1–Frequency of road cleaningn/week2 < E4.1 ≤ 4>70/10060
E4.2–Frequency of waste collectionn/day≥3≥340
Table
Table 6. The Requirement–Performance Control Matrix.
Table 6. The Requirement–Performance Control Matrix.
HEI Human—Environment Interface
HumanEnvironment
Performance
Conditions and functions to meet users’ need
Walkability of the public realm
Urban fabricConnectivityAdded values
Natural elementBuilt environmentMobilityUrban FurniturePerception
NeedRequirementA1A2A3B1B2B3B4B5B6B7C1C2C3C4C5C6C7C8C9C10D1D2D3D4D5E1E2E3E4
Conditions to bemaintained or a desired state to be achieved in the field of:To make explicit users’ need, specifically for:VegetationWaterBiodiversityFunctional mix HierarchyLandmarksMarginsNodesSurfacesTemporary UsesSidewalksParkingTransit supplyTransit StopsPedestrian crossings Road (vehicle) trafficBicycle pathsPedestrianizationLTZPublic RoadsSeatsPublic LightingSignsWaste dumpShadowsColorosSmellsNoiseNeatness
Well-beingHygienic-environmental managementSunshine/sunlightA.11
A13		 B42
B43		 A.11
A13
D51
D52
D53
Air qualityA.11A.21 C61 C71C81
C82
C83		C71C91
C92
C93
Ventilation A.21 B42
Hygrometric controlTemperatureA.11 A32 B42 A32 D51
D52
D53
Relative humidity A.21
Visual suitabilityLighting D21
D22		 D21
D22
No glare A32
B62		 D21
D22		 D51
D52
D53
Auditory suitabilityNoise level B12 B42 C61 C101
C102		 C61
E31
E32
EaseLevels of Service B21
B22
C22		 B51C11C21
C22		C31
C41
C42		B22
B51
C32		C52 C72
C72		B51B51 D11
D12
SafetyRisk preventionAvoidance of conflicts with motorized users C12 C31C31
C32		C51
C52		 A33
C71
C72
C73		C81
C82
C83		C91
C92
C93		 D21
D22		D31
Obstacle/barrier-free sidewalks C11
C12
C13		 D11
D12
Even sidewalk C13
No-slippery sidewalk C13
UsabilityMaintenanceCleanness and sanitation B64 C13C22C22C22
C32		 D41
D42		 E11B12
C61
D41
D42
E11
E12
E21		B12
C61		C61
D41
D42
E11
E12
E21
E 41
E42
FlexibilityAdaptation to events A.12
A.13
A.31
A.33		 A12
A21
B11
B12		C12A12
A22
B13		 B51 A12
A22
A13
B32		C12 C31
C32		 C81
C82
C83		C91
C92
C93		C101
C102		D11
D12
RelationshipsPrivacy B23 C13
CommunicationA12 A12A12 B51 C12 C31 D11
D12		 D31
D32
D33		 D12
Table
Table 7. Characteristics of the districts in the study area.
Table 7. Characteristics of the districts in the study area.
FEATURESDISTRICTSURFACE (m2)POPULATION
(unit)		POPULATION DENSITY
(Inhabitants/km2)
1100s–1920s original settlement of the city with mixed land use including several landmarks10Historic center259,34117716828.8
1920s–1990s residential, working class area, mix of building types: one- or two-family houses, two or three-storey apartment buildings (some within social housing projects)9Villa Reatina282,32023038157.4
2Fassini307,98524377912.7
1950s–1980s residential middle-class areas, mix of detached and terraced houses, little villas, low-rise apartment blocks4Fiume dei Nobili119,4008246901.2
5Città Giardino124,408137511,052.3
8Borgo210,38318498788.7
6Regina Pacis175,874239013,589.3
1960s–1990s suburban residential areas, mix of prevalently low-rise apartment blocks and terraced houses1Micioccoli571,39535626233.9
7Piazza Tevere273,87917656444.5
1970s–2000s mainly residential upper middle class area, consisting of single-family villas, little villas, terraced houses, small apartment blocks.3Zona Residenziale355,8118652431.1
Total2,680,79619,141
Table
Table 8. Local environments and HEI.
Table 8. Local environments and HEI.
HEI CATEGORIES
NEIGHBORHOODS		NATURAL ELEMENTSBUILT ENVIRONMENTMOBILITYURBAN FURNITUREPERCEIVED
ENVIRONMENT
10 Centro Storico
Sustainability 12 08347 i001body of water,
vegetation		landmarks
availability, strong margins, paving quality		pedestrianized areasquality seating, availability of lighting,chromatic
harmony
9 Villa Reatina
Sustainability 12 08347 i002modest vegetationpoor presence of strong margins, low quality of marginslow availability of pedestrian crossing, lack of pedestrian areas, bicycle paths and LTZlow availability of urban furniture
4 Fiume dei Nobili
Sustainability 12 08347 i003modest vegetationlack of strong margins, good quality of marginslow availability of pedestrian crossing, low availability of sidewalk, lack of pedestrian areas, bicycle pathslow availability of urban furniturechromatic
harmony
1—Miccioccoli
Sustainability 12 08347 i004 no strong margins, no landmarkslow availability of pedestrian areas and bicycle pathslow availability of urban furniture
2 Fassini
Sustainability 12 08347 i005 no strong margins, no landmarkslow availability of pedestrian crossing, lack of pedestrian areas, bicycle paths and LTZlow availability of urban furniture
7 Piazza Tevere
Sustainability 12 08347 i006modest vegetationno landmarkslack of pedestrian areas, bicycle paths and LTZmodest availability of urban furniture
8 Borgo
Sustainability 12 08347 i007vegetationfunctional mix, landmarks, strong marginslow availability of pedestrian crossingslow availability of urban furniture
6 Regina Pacis
Sustainability 12 08347 i008 low availability of functional mix, no landmarkspoor quality of sidewalk, low availability of pedestrian areas and bicycle pathslow availability of seatingnoise level (daytime)
3 Zona Residenziale;
Sustainability 12 08347 i009availability of vegetation no functional mixlow availability of pedestrian crossings; lack of pedestrian areas, bicycle paths and LTZlow availability of urban furniturechromatic
harmony
5 Città Giardino
Sustainability 12 08347 i010 no landmarkslow availability and poor quality of sidewalk; low availability of bicycle pathsavailability of lighting for pedestrian
low availability of seating		noise level (daytime)
Table
Table 9. Scores of each indicator for each neighbourhood in the case study.
Table 9. Scores of each indicator for each neighbourhood in the case study.
Neighbourhoods1.Micioccoli2.Fassini3.Zona Residential4.Fiume Dei Nobili5.Città Giardino6.Regina Pacis7.Piazza Tevere8.Borgo9.Villa Reatina10.Centro StoricoAverage Scores Per PerformanceInsufficient Score < 6 (%) *
Natural ElementsVegetation5.66.2636.3666.244.65.440
Water 3.25.20.34.83.64.44.36.446.44.380
Protection of biodiversity101010888108888.80
Built EnvironmentFunctional mix 75.62.488.15.78.68.15.57.86.740
Hierarchy4447774100105.750
Landmarks00000006.869.22.270
Margin 4.24.34.78.36.25.93.96.13.88.35.650
Nodes333103333384.280
Paving quality77.376.56.36.37.2676.16.70
Change of use2.57.507.52.502.56.305.63.470
MobilityAvailability of Parking 9.16.96.87.77.77.26.47.47.177.30
Public transportation3.42.22.43.32.52.32.62.72.53.32.7100
Public transportation stops6,84,26,16,56,96,56,56,67,16,56.410
Pedestrian crossings 6.23.10.45.53.24.24.43.31.83.33.590
Road (vehicle) traffic6.68.88.94.37.56.97.15.97.17.77.120
Sidewalks 7.14.21.84.54.542.46.423.64.080
Availability of bicycle paths0.12.903.24.52.70.35.503.52.3100
Availability of pedestrian zones5.525.46.96.225.57.93.885.360
Availability of LTZ00031.52.90.87.608.92.580
Pres. public roads22.12.32.22.42.12.22.11.78.12.790
Urban furnitureAvailability of Seating3.232.42.73.432.73.12.73.12.9100
Availability of lighting6.56.37.25.86.55.66.56.55.55.76.240
Availability of Sign 4.94.84.85.25.44.44.64.54.15.44.8100
Availability of dumpsters 6.16.23.67.17.67.36.587.56.96.710
Pres. Protection00000000000:0100
Perceived environmentColors 4.45.16.85.36.84.545.34.56.45.370
Odors 10661066666107.20
Noise6.47.17.85.387.17.17.287.47.110
Neatness 3.22.92.84.63.63.43.64.34.24.73.7100
Insufficient score < 6 (%)58.665.565.558.648.365.562.137.965.537.9
*:% of neighborhoods with indicator score <6; % of indicators with a score <6 for each neighborhood.
Table
Table 10. Neighbourhoods’ ergonomics obtained using a discrete Bayesian network and level of ergonomics of each component in the investigated neighborhoods.
Table 10. Neighbourhoods’ ergonomics obtained using a discrete Bayesian network and level of ergonomics of each component in the investigated neighborhoods.
NeighbourhoodsErgonomics Value (%)Usability
(%)		Well-Being
(%)		Safety
(%)
1.Micioccoli42.045.142.240.1
2.Fassini40.942.842.438.3
3.Zona Residenziale45.946.049.344.9
4.Fiume dei Nobili45.849.047.742.9
5.Città Giardino48.650.451.646.8
6.Regina Pacis39.742.341.035.9
7.Piazza Tevere40.443.140.738.0
8.Borgo45.147.347.343.1
9.Villa Reatina36.638.538.432.0
10.Centro Storico57.161.85954.9

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Appolloni, L.; Giretti, A.; Corazza, M.V.; D’Alessandro, D. Walkable Urban Environments: An Ergonomic Approach of Evaluation. Sustainability 2020, 12, 8347. https://doi.org/10.3390/su12208347

AMA Style

Appolloni L, Giretti A, Corazza MV, D’Alessandro D. Walkable Urban Environments: An Ergonomic Approach of Evaluation. Sustainability. 2020; 12(20):8347. https://doi.org/10.3390/su12208347

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Appolloni, Letizia, Alberto Giretti, Maria Vittoria Corazza, and Daniela D’Alessandro. 2020. "Walkable Urban Environments: An Ergonomic Approach of Evaluation" Sustainability 12, no. 20: 8347. https://doi.org/10.3390/su12208347

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