1,763
Views
2
CrossRef citations to date
0
Altmetric
Research Article

A statistical methodology for the socio-spatial assessment of neighborhood life quality

, & | (Reviewing editor)
Article: 1528702 | Received 14 Jul 2017, Accepted 20 Sep 2018, Published online: 15 Oct 2018

Abstract

Many housing projects suffer from striking dissonance between the physical built environment and their social life. Accordingly, there is a significant need to deduce socio-spatial assessment tools that can predict the resulting community satisfaction based on realizing their psychological and social needs (nonphysical aspects) in terms of the components forming the built environment (physical aspects; architectural, urban design, and planning) to provide an acceptable level of social life quality. From this view, this paper presents a statistical methodology for predicting these qualitative aspects through conducting a questionnaire on four neighborhoods of intermediate social housing level, in Egypt.

PUBLIC INTEREST STATEMENT

Life quality, which reflected on human stability in built environments and sustained communities, was a difficult assessment because of its tangle with many fields such as environment, economic, decision-making, culture, … etc. This perspective article deduces socio-spatial assessment tools that can predict resulting life quality in neighborhoods based on realizing residents psychological and social needs. It was found that the models presented a method for calculation of either the psychological or the social aspects or the total of both of them within the given values of one or all of the neighborhood’s built environment components. The research was studied for the types of intermediate social housing level. The findings imply the decision makers and governmental authorities should use the deduced socio-spatial assessment tools as a significant guide for setting their priorities and orienting investments within defined milestones that target community satisfaction in the first place.

1. Introduction

The concept of “urban life quality” emerged during the late 1960 s and early 1970 s when the environmental crisis became a major global issue. Since that time, the term continues to be used by behavioral scientists, government personnel, design practitioners, and others. It always refers to whether people live well or poorly (Proshansky & Fabian, Citation1986). Urban life represents more than integrating a variety of physical configurations; it incorporates a social life that implies the necessity of satisfying psychological, and social community needs.

In order to investigate the vitality of the psychological, and social neighborhood – nonphysical – aspects (qualitative and subjective dimensions) in relation to its physical ones (quantitative and objective dimensions) and the significance of their correlation on the resulting life quality that is interpreted through the residents’ satisfaction and to test this through statistical measurable methods, it is essential to elaborate on the basic concepts of the neighborhood as an urban and a social entity and shed light on its assessment tools. The research consecutively includes the two sets of variables, and then tests the validity of these variables to be included within the statistical model that digitally deduces the effect of the physical aspects as independent variables on the nonphysical ones as dependent variables.

The model is tested on two types of intermediate social housing levels: the first is referred to as the free housing type and the second is the institutional one. In the former, the local authority of the city provides land lots’ subdivisions with infrastructure and public services, ready to be sold for residents. In the latter, an institution such as a bank or a social organization establishes all the neighborhood (house buildings, public services, infrastructure, …etc.) and puts up for sale the residential units for inhabitants.

2. Literature review: neighborhood quality of life’s aspects

Neighborhood formerly embodied the smallest unit of the city from as early as 1920. For the social and urban experts, this concept expressed the total territory of life in the local level (Mann, Citation1970). The definition of the neighborhood was consecutively addressed in many fields:

In the psychological science, “Ruth Glass” described it as a distinct territorial group of people representing specific physical characteristics for its area and other specific social ones for the inhabitants (Smith, Citation2010), aiming at satisfying main human needs (Mann, Citation1970), with intensive face-to-face social interaction. (Glass, Citation1948; Smith, Citation2010; Suttles, Citation1972). Moreover, “Ross & Reimer” added that it should accommodate a chance for relaxation and stress relief. (Mann, Citation1970).

As for the social science: it is rather viewed as a simple built environment (incorporating houses, school(s), playground(s), shopping center(s), and transportation station(s)) in which a mother feels her children are safe (Gallion & Eisner, Citation1963; Girling & Kellett, Citation2005). In addition, its size is relevant to motivate neighbors’ interaction (Deng, Citation2011), and enhance good communication among them for a healthy social and physical life (Gallion & Eisner, Citation1963).

In the urban environment management science: it is conceived as the smallest local geographic area in the city with homogeneous characteristics (Deng, Citation2011; Frey, Citation2005; Gallion & Eisner, Citation1963), possessing a census tract, ZIP Code, physical boundary, and with almost the same demographic characteristics (National Institute of Justice (NIJ), Citation2009).

Generally, all definitions of the neighborhood concentrated on physical built environment and human social life as explained by the theorists, such as Hall who mentioned than the visual and aesthetic harmony in built environment reflects on the emergence of a harmonious social order (Hall, Citation2002), while Lefebvre confirms that the society’s spatial refers to the physical manifestation of areas (Lefebvre, Citation1991), whereas Gehl classified human activities according to opportunities in the built environment (Gehl, Citation1987). From this view the quality assessment of neighborhood as built environment mainly has social aspects.

The neighborhood quality assessment, planners and environmentalists began to investigate this issue in the 21st century, in an attempt to achieve sustainability, with its focus on three main aspects (Litman, Citation2008):

First, environmental aspects: including natural environment, pollution, climate change, and bio-diversity. Second, economic aspects: including economic efficiency, productivity, business, employment, and taxes. And finally, social aspects: including social welfare, justice, human health, cultural and historical identity, and participation.

Other neighborhood sustainability assessment tools were investigated in the United States with the National Environmental Policy Act (NEPA) in 1969, a project for environmental impact assessment. It was mainly developed to test the effect of the economic and social transformations of the twentieth century on human environment (Costanza et al., Citation2007). Most of these relevant tools prioritize the environmental aspects, with less focus on social ones; concerned with achieving justice, and social and psychological community satisfaction.

Many researchers studied the quality assessment of neighborhood life with focus on the built environment and people, which is named quality of urban environment life (QOUL). QOUL is related to linking the objective dimensions of the urban environment and people’s subjective evaluations of the urban environment (McCrea, Marans, Stimson, & Western, Citation2011a). The objective dimensions include the characteristics of the urban environment while the subjective ones focus on the notion of satisfaction with place or where one lives in the urban environment (McCrea, Stimson, & Marans., Citation2011b).

Objective dimensions refer to the physical features of the neighborhood. It can be mainly measured by units such as distances, and densities. According to Roderick Peter McCrea, it is related to distances from services and facilities; distances to rural and semirural land; and distances from the coast; population, housing and road densities. Thus, they include land use and planning transport infrastructure in the form of three main sets: first, accessibility measured by the straight-line distance (in meters) between the respondent’s residence and the closest facility of type, with four variables: secondary school distance, regional shopping center distance, sporting facility distance (including parks, swimming centers, bowling centers, golf courses, rifle ranges, soccer fields, and tennis courts), and hospital distance. Second, objective density was measured with two variables: Population density (per square kilometer), Dwelling density (per square kilometer). Third, cost of housing was measured with two variables related to the cost of renting: the cost of purchasing dwellings in the resident’s Census Collection District, the cost of renting, and the percentage of rented dwellings (McCrea, Shyy, & Stimson, Citation2006). Whereas, the objective social dimensions are related to household structure; socioeconomic environments; disadvantaged environments; and ethnic environments (McCrea, Citation2007).

As for the subjective dimensions that represent people satisfaction in urban features, they included three groups. (Sirgy, Rahtz, Cicic, & Underwood, Citation2000). First, business-related services (e.g., banks, restaurants, hotels, radio stations, television stations, private schools, etc.). Second, government-related services (e.g., law enforcement, fire department, rescue squad, public schools, public hospitals, social services, etc.), and third, nonprofit services (e.g., religious organizations, and services supporting the vulnerable segments of the community such as children, the poor, etc.). Sirgy and Cornwell introduced subjective dimensions into three major categories: physical, social, and economic (Sirgy & Cornwell, Citation2002). The physical ones include: upkeep of homes and yards, landscape in the neighborhood, the street lighting in the neighborhood, crowding and noise level, nearness of neighborhood to facilities needed, and quality of the environment in the community. Social include: social interactions with neighbors, the outdoor play space, living in the neighborhood, relations within the community, rate of crimes, racism, and privacy satisfaction. And finally, economic: home value in the neighborhood, cost of living in the community, socio-economic status of neighborhood, and neighborhood improvement. Other categorization was introduced by Diener, et al. including: Pleasant effect; for example, joy, elation, contentment, or indeed happiness as a feeling. Unpleasant effect: for example, shame, sadness, and anxiety, and finally life satisfaction, either overall life satisfaction or satisfaction in particular life domains (Diener, Citation1984; Diener, Suh, Lucas, & Smith, Citation1999). Michalos and Zumbo defined a five-group subjective: housing, recreational activity, transportation, government services, and residential area (Michalos & Zumbo, Citation1999), while McCrea and his colleagues have examined different geographic levels of subjectivity including regional services; such as health and education, the cost of living, neighborhood crime, and public facilities (parks, libraries, etc.) (McCrea, Stimson, & Western, Citation2005).

Turksever and Atalik used seven indicators as subjective dimensions: health, climate, crowding, sporting, housing conditions, travel to work, and environmental pollution (Turksever & Atalik, Citation2001). Whorton and Moore studied six dimensions related to the community development, leadership development, and industrialization, including concern for: crime, the availability of jobs, access to adequate health care, available housing, satisfaction with public education, and satisfaction with community (Whorton & Moore, Citation1984).

The measurement of QOUL depends on subjective and objective urban environment indicator evaluations. McCrea has examined them at a broad level by investigating the relationships between objective density and subjective overloading where he presented a composite measure content pollution, loss of natural areas, traffic congestion, and cost of housing as urban problems (McCrea, Citation2007).

The studies which focus on objective QOUL typically include many objective characteristics of the urban environment, often through combining or weighting objective indicators by using index for ranking places. Other studies also emphasized the trade-off between positive and negative aspects of urban living (McCrea et al., Citation2011b). For example, Blomquist, et al have modeled the trade-off between housing costs, wages and amenity. Hedonic wage and rent equations were used to derive implicit prices for various urban amenities (Blomquist, Berger, & Hoehn, Citation1988). Cicerchia et al. have theorized the trade-off between city effect and urban load. City effect relates to “access to superior urban functions, opportunities and services” available by virtue of a city’s size. Urban load relates to a number of negative consequences of urban growth, for example, congestion and environmental degradation (Cicerchia, Citation1999).

Subjective involves both feelings and subjective judgments, they relate to evaluations of satisfaction with various aspects of life in psychological processes (McCrea et al., Citation2011a). For example: Sirgy et al. divided subjective measures into two major groups: global and facet based (Sirgy, Widgery, Lee, & Grace, Citation2010). Global subjective measures of community well-being focus on global satisfaction with one’s community, perception of community quality of life, and perceived community quality of life, while facet-based measures of community well-being can be categorized in terms of deductive versus inductive. Deductive facet-based measures of community well-being are formative measures in which the dimensions involved in the measure are theory driven. In contrast, inductive measures are based on a review of past research or the judgment of a panel of experts.

Generally subjective and objective dimensions relate with each other in evaluations, via the satisfaction of the social needs in urban environment, such as favorable neighborly relations and a sense of community (Davidson & Cotter, Citation1991, Citation1991; Farrell, Aubry, & Coulombe, Citation2004; Farrell et al., Citation2004; Sirgy & Cornwell, Citation2002, Citation2002). On the other hand, the evaluation of the QOUL (subjective and objective dimensions), was done through using index for the chosen subjective indicators depending on people satisfaction through evaluations, whereas objective indicators are quantitatively measured (McCrea et al., Citation2005; Michalos & Zumbo, Citation1999; Sirgy & Cornwell, Citation2001; Sirgy et al., Citation2000; Turksever & Atalik, Citation2001). Another method was examined using GIS, which requires geometric location information for mapping and analytical purposes, to link objective information about the urban environment with people satisfaction subjective evaluations (McCrea et al., Citation2006).

Generally, the prior studies focused primarily on the subjective evaluation of QOUL and have found that people’s subjective evaluations of many aspects of the urban environment correlate with their overall life satisfaction and specifically about urban living (McCrea et al., Citation2011b). Moreover, differences between residents’ preferences and their consequent selection for their accommodation may weaken the relationships found between proximity to natural environments and subjective evaluations of the urban environment (McCrea et al., Citation2011b). Subjective evaluations have been shown to be more significant in predicting neighborhood satisfaction than objective measures (Gant & Alaimo, Citation2006).

Accordingly, most of the previous studies have investigated the subjective assessment of residents for the physical/objective aspects in view of their satisfaction, neglecting the subjective measurement for their perception of its existence, and its significance in the built environment. In other means, the existence of a certain element (e.g. landmarks) may not prove to be significant or even perceived for some residents in unplanned areas although it might meet their satisfaction if existing. On the other hand, establishing a space for urban farming as a secondary economic revenue or a social gathering node between buildings would be much more significant. This is crucial especially in unplanned areas in developing countries with limited resources, where quality of life possesses different means of satisfaction.

From this view, the research will accordingly present a socio-spatial assessment tool for the neighborhood in the form of statistical models, to reach a formula correlating the degree of social satisfaction quantitatively represented by the nonphysical aspects (immaterial), and which are related to human feelings: happiness, safety, sense of belonging, and psychological satisfaction), as a function of physical aspects (that are not typically measured but rather interpreted from the subjective assessment of residents unlike prior studies), and which are related to the components of built environment on the three design scales: architectural design, urban design, and urban planning through statistical method in an attempt to present an adequate guide for decision makers to prioritize the physical aspects that strongly affect the residents’ satisfaction.

3. Methodology

The authors have developed an action-ethnographic research based on measuring the physical and nonphysical (objective and subjective) variables using the concept of ‘weighed satisfaction’ to cross reference the qualitative data with the quantitative ones. This method has been selected in response to the fact that life quality assessment, is considered one of the essential attributes in the built environment for the neighborhood.

3.1. Study context and data collection

In order to deduce the statistical models previously referred to, that incorporate the physical and nonphysical (immaterial) aspects of the neighborhood, the empirical study would thus focus on:

- Deducing the variables representing the physical aspects from previous literature review and theories, together with other relevant sustainability assessment tools on the neighborhood scale, together with those representing the nonphysical aspects from human needs, and urban sociology theories.

- Determining how to measure the above variables depending on the concept of ‘weighed satisfaction’ as will be explained in the following sections.

3.2. Predicting variables

3.2.1. Physical aspects

Incorporate the three components of the built environment with all its design scales:

- Urban planning component: includes the variables representing the general living conditions of the neighborhood, such as: land uses; site planning characteristics, streets’ network, amenities, … etc.

- Urban design component: includes the variables representing the urban form incorporating the visual composition of the urban environment of buildings and spaces, and their elements (Sert, Citation1956).

- Architectural design component: that focuses on the building as a single unit (Moore, Citation1979). This incorporates variables decomposing the exterior and interior design elements of the housing buildings.

All these previous components are adequately divided into subcomponents incorporating measurable variables, that are deduced based on many previous studies including: “Clarence Perry,” “N. Carpenter,” “Duany Plater-Zyberk,” “Clarence Stein & Henry Wrighit,” “Wales Charles,” “Peter Calthorpe,” “Green Neighborhoods,” “Gated Communities,” and “Liveable Neighbourhoods” (Aditjandra, Mulley, & Nelson, Citation2013; Atkinson & Blandy, Citation2006; Calthorpe, Citation1993; Duany & Plater-Zyberk, Citation1994; Frey, Citation2005; Furuseth, Citation1997; Girling & Kellett, Citation2005; Mann, Citation1970; Neal, Citation2003; Russ, Citation2009; Towers, Citation2005; Walters, Citation2007); together with other relevant tools of sustainability assessment at the neighborhood scale such as; Leadership in Energy & Environmental Design for Neighborhood Development (LEED-ND), Building Research Establishment Environmental Assessment Methodology for communities (BREEAM Communities), The Japanese Comprehensive Assessment System for Building Environmental Efficiency for Urban Design (CASBEE-UD), Building for Life, Pearl Community Rating System, Sustainable Renovation of Buildings for Sustainable Neighbourhoods (HQE2R), The Sustainable Project Assessment Routine (SPAR), Neighbourhood Sustainability Framework (NSF) in New Zealand, and One Planet Living (OPL). (LEED, Citation2009; for neighborhood development, 2013; BREEAM Communities, technical manual, code for a sustainable built environment SD202-0.0:Citation2012, www.breeam.org BREEAM for Communities, 2008; www.breeam.org; www.buildingforlife.org; Dall’O’, Galante, Sanna, & Miller, Citation2013; Giordano, Citation2010; Castaneda, Citation2013; Deng, Citation2011; Commission for Architecture and the Built Environment (CABE), Citation2001).

3.2.2. Nonphysical (immaterial) aspects

Representing the residents’ psychological and social satisfaction. It incorporates two main components:

- Psychological component including self-initiatives, motives, affecting the human behavior and that can be translated into a set of personal impressions and feeling; happiness, satisfaction, optimism, … etc.

- Social component including social interactions such as: friendships, enhancing the sense of belonging, cooperation, safety, … etc.

The nonphysical aspects are deduced from different human needs’ theories, with their different categories and levels such as: physiological satisfaction, safety, self-esteem, self-fulfillment needs “Maslow, A. (1943–1954)” (Layne, Citation2009); others such as feeling the joy of achievement, authority, affiliation, and motivation (McClelland, Citation1961), or existence, relatedness, and maturity. “Alderfer, Citation1969” (http://www.yourcoach.be/en/employee-motivation-ebook). Moreover, Findley pointed out the physical, psychological, social, and self-fulfillment needs. Human needs’ matrix incorporated: being, having, doing, interacting, together with subsistence, protection, affection, understanding, participation, leisure, creation, identity, and freedom (Costanza et al., Citation2007; Max-Neef, Citation1991). Therefore, it is evidently clear that they all incorporated two main dimensions: the psychological and social ones, and these two components constituted the nonphysical aspects analyzed in this research, whose subcomponents are proposed from these theories and literature review.

Accordingly, the research deduces the subcomponents of the physical and nonphysical aspects as shown in Tables and with total of 71 and 12 respectively and can be summarized as follows:

Table 1. The components of the physical aspects

Table 2. The components of nonphysical aspect

The subcomponents of physical aspects are:

First: The Urban planning component constituting six subcomponents: site, neighborhood size, street network, land use, the property, and services and facilities.

Second: The urban design component constituting four subcomponents: general form, urban blocks, open spaces, and street facilities.

Third: Architectural design component constituting three subcomponents: optimal performance of the buildings, interior design of housing units, exterior design of housing buildings.

The subcomponents of nonphysical (immaterial) aspects are:

First: Thepsychological’ subcomponent including six elements (variables): a comfortable shelter, life privacy, appropriate social level (comfortable and healthy environment), friendly places, creativity and artistic expression, and feeling the spatial ingenuity. These variables can rather be explained as follows:

- A comfortable shelter: means having a safe shelter enhancing relaxation feelings.

- Life privacy: quiet and saving from external intrusion.

- Appropriate social level (comfortable and healthy environment): having suitable income and adequate standard of living.

- Friendly places: feeling the intimacy of places.

- Creativity and artistic expression: enhancing the sense of uniqueness, and creativity.

- Feeling the ingenuity of the place: enhancing a sense of being distinguished by living in this place.

Second: The social subcomponent including six elements (variables): friendship, security inside the neighborhood, security inside the dwelling, sense of justice among members of society, self-independence, and participation in society and public life. These variables can rather be explained as follows:

- Friendship: enhancing friendship making, sense of belonging, interaction, and cooperation.

- Security inside the neighborhood, and the dwelling: feeling safe from danger, threat, risk, or injury inside the house and the neighborhood.

- Sense of justice among members of society: represented by inhabitants being equal in rights.

- Self-realization and self-independence: enhancing trust, respect and admiration.

- Participation in society and public life: social communication through participation in community and public life.

3.2.3. Measuring the variables (physical and nonphysical) depending on the concept of “weighed satisfaction”

Since the life quality assessment, considered as one of the essential attributes in the built environment for the neighborhood, includes various physical and nonphysical components, so measured in a quantitative way would not be sufficient to yield precise results for assessing their effects on the residents’ satisfaction. This is attributed to the fact that every resident possesses his/her own preferences based on his/her cultural and social background. Accordingly, measuring the satisfaction degree for an element would not yield reliable results unless they are correlated with its degree of importance for that resident.

Consecutively, in this research, the neighborhood quality assessment will be performed using the method of Raphal’s weighed equivalence tool, which is used in measuring the quality of life profile-adolescent version to ensure the accuracy of results. The quality of life profile-adolescent version contains three components (Raphael, Rukholm, Brown, Hill-Bailey, & Donato, Citation1996)

- Being: involving “physical, psychological, spiritual” attributes to reflect how a person is.

- Belonging: involving “urban, social, community” attributes that represent the relevance of the person to the environment.

- Becoming: “practical, entertainment, growth” attributes that refer to the activities carried out in everyday life.

Each component is divided into three subcomponents: each of them includes six elements, with total of 54 elements. This scale provides importance and satisfaction ratings along a 5-point Likert for each element. The quality scores are calculated as follows: (the quality = (importance score/3) * (satisfaction score −3)). The scores ranging from −3.33 (not at all satisfied with extremely important for the resident) to 3.33 (extremely satisfied and extremely important for the person) (Smith & Briers, Citation2001).

According to the research’s variables, it incorporates two main groups (physical and nonphysical). Each is measured along a 5-point Likert, but differ in assessment levels. In the physical aspects, each element is estimated through two levels (degree of importance, and degree of satisfaction if it exists). This aspect includes three components as previously indicated (architectural design, urban design, and urban planning); each is divided into 3, 4, and 6 subcomponents, including 15, 24, and 32 variables, respectively. In nonphysical aspects, each element is estimated through one level determining the degree of residents’ satisfaction about each element in the neighborhood. This aspect constitutes two components (psychological and social); each is divided into six variables.

3.3. Case study

In order to measure the previously deduced variables, a questionnaire has been designed to incorporate all these indicators, and to be applied in Sheikh Zayed and 6th of October cities, representing one of the Egyptian new cities. They were established in 1979. According to 2016 census statistics, the city achieved 53.43% of its full-targeted capacity in 2000 (General Authority for Urban Planning [GAUP], Citation2018).

These case studies are selected mainly because they represent one of the most important satellite cities lying to the west of Cairo, that were mainly constructed to accommodate the over population in unplanned areas in Cairo, yet they failed to attract the targeted population as indicated despite their satisfaction for many life quality aspects.

Therefore, applying the proposed methodology for assessing the socio-spatial life quality on such areas would be significant. In addition, the intermediate housing was selected as it represents the main housing category established by the government through two methods: the free housing type and the institutional housing one, which represent the only existing housing typologies for that housing level. The high and low level in these cities incorporate many market and uncontrollable aspects that would affect the final deduced model, and thus were excluded.

The research incorporates two types of intermediate social housing level: the free housing type and the institutional housing one, as previously indicated. The same size of sample was taken for each type of housing level as two neighborhoods for each type, for an easy comparison between them. The first sample representing the first type are taken from two residential neighborhoods in 6th of October, whereas, the second sample representing the second type are selected from two residential neighborhoods in Sheikh Zayed.Footnote1

This is based on a uniform distribution of neighborhoods only representing these types under test after excluding the under-construction neighborhoods as a systematic random sampling. Sample size; to ensure the accuracy of the questionnaire results, 30 inhabitants at least have to be questioned (Charles & Mertler, Citation2002; Creswell, Citation2002; Gall, Borg, & Gall, Citation1996; Gay & Airasian, Citation2003; McMillan & Schumacher, Citation2001), so the final sample size taken in this research is 50 inhabitants for each neighborhood with different ages ranging from 20- to 60-year-old males and females that are randomly selected to ensure incorporating different rational perspectives for residents. Accordingly, the total number of samples is 200. The data were collected by personal interview with each individual to complete the questionnaire.

3.4. Questionnaire

The questionnaire form constitutes three parts: the first part incorporates the general information about the participants and their residences (profession, age, gender, current and previous addresses, the area and number of rooms in the residence, … etc.).

The second part includes measuring the physical features of the neighborhood depending on residents’ opinions, incorporating three components (architectural, urban design, and urban planning) with their subcomponents and elements with a total of 71. Each element is measured in respect to its importance, and degrees of satisfaction from the residents’ points of view.

The third part includes measuring the nonphysical aspects representing the residents’ feelings about their neighborhood, incorporating two components (psychological and social) with their subcomponents and elements with a total of 12. Each element is measured in respect to its degree of satisfaction from the residents’ point of view. These two parts constitute five degrees’ scaled measurement for each question representing the Likert scale. (Appendix A shows the questionnaire)

3.5. Data analysis

Since the main aim of the research is to deduce a model for the neighborhood life quality assessment using qualitative measurable methods, the two categories of components and their integrated variables are analyzed as follows:

First, the physical aspects’ group is analyzed based on the weighed satisfaction score method previously explained for each variable, then followed by calculating the sum for each component (architectural, urban design, and urban planning) (Appendix B shows the physical attributes’ photos of the case study), as indicated in these five steps:

I. Finding ‘weighed satisfaction score’ for each variable according to this equation:

weight. s.x1 = (I.x1/3)*(S.x1-3)

Where: weight.s.x1 = weighed satisfaction for variable x1, I.x1 = the importance of variable x1, S.x1 = the degree of resident satisfaction with variable x1.

II. Finding adjusted standard score for the variable: this incorporates converting the negative values of the weighed satisfaction scores to positive values; in accordance with Rapharl’s law where the values of each variable may be positive or negative, to ensure having an accurate result when calculating the sum of the weighed satisfaction scores for each component.

• The standard score can be calculated as follows: (Carey et al., Citation2007; Kurpius & Stafford, Citation2006).

Z = (X-μ)/σ

Where: z = standard score, x = original degree, μ = arithmetic mean, σ = standard deviation

Then: Zweight.s.x1 = (weight.s.x1—μ)/σ

Where: Zweight.s.x1 = standard value for variable x1, weight.s.x1 = weighting satisfaction for variable x1

• Adjusted standard score (Carey et al., Citation2007; Kurpius & Stafford, Citation2006)

T-scores = (z * 10) + 50

Where: T-scores = adjusted standard score, Z = standard score

Then: standard.w.s.x1 = (Zweight.s.x1 *10) + 50

Where: standard.w.s.x1 = adjusted standard score for variable x1

III. Finding the relative importance of each variable:

This step is mainly intended to deduce the relative importance of the variable to represent its calculated weight in relation to the importance of the other variables, since each variable differs in its importance, and this is done through the following formula.

Weight. I.x1 = I.x1/total. I.x

Where: weight.I.x1 = relative importance of variable x1, I.x1 = the importance of variable x1, total I.x = the sum of the important values of all variables included in the component containing variable x1.

IV. Calculating the specific gravity for each variable, which means the value of the variable with respect to the other variables in its component.

Weight. IS.x = weight.I.x1* standard.w.s.x1

Where: Weight. IS.x = specific gravity for variable x1, weight.I.x1 = relative importance of variable x1, standard.w.s.x1 = adjusted standard score of variable x1.

V. Calculating the total value of each component:

Total.IS.y = Σ Weight.IS.x (x = 1→ k)

Where: total.IS.y = the total value of component y

Second, the nonphysical aspects’ group is analyzed by repeating the same previous steps as follows:

The total value of each component (psychological and social) is calculated by adding the elements of each one.

Total.y = Σ y1 (y = 1→ 6) for the psychological component

Total.c = Σ c1 (c = 1→ 6) for the social component

The total value of the nonphysical aspects, which represent the residents’ feeling towards their neighborhood (measuring the neighborhood social life quality), is calculated by adding the total values of the psychological and the social components.

Total.y.c = total.y + total.c

Where: total.y.c = values of the nonphysical aspects’ two components.

3.6. Statistical analysis

All the above calculated variables are further statistically analyzed using the statistical package for social sciences (SPSS) version 18, in an attempt to deduce a statistical model where the social life quality (nonphysical) variables represent dependent variables and the other physical aspects’ variables represent the independent (predicting) ones; and therefore having a precise tool for the assessment of the social life quality of the neighborhood. To achieve these aims, three steps are performed:

First: Testing the validity and reliability of the used scale for the nonphysical aspects’ components’ measurement by calculating the correlation between the average of each of the psychological and social component and its integrated variables using Spearman’s rank order, and the reliability is calculated for each of them by using Cronbach’s alpha.

Second: Testing the correlation between the nonphysical aspects (and its components) and each of the components of the physical aspects (architectural, urban design, and urban planning), by using Spearman’s Rank Order Correlation (rho) to calculate the relationship between the variables of nonparametric type.

Third: If the correlation test is significant, that validates the use of the previously deduced variables as dependent and independent variables, in multiple regressions. Stepwise regression method that is used to determine how well a set of variables is able to predict a particular outcome (Pallant, Citation2005), therefore, the multiple regression is performed between:

- Each component of the nonphysical aspects as dependent variables and each of the components of the physical aspects, as independent variables.

- The total of the nonphysical aspects as dependent variables and each of components of the physical aspects, as independent variables.

4. Results

The results of the statistical analysis indicated above can be explained as follows:

First: the results of step one:

The validity test, illustrating the correlation between the average of the psychological component and its integrated variables ranging from 0.536 to 0.854 with significance at the 0.01 level, and between the average of social component and its integrated variables ranging from 0.542 to 0.755 with significance at the 0.01 level, which means all these variables are valid.

The reliability test; the Cronbach’s alpha for the psychological component equals 0.837, and for the social component equals 0.761. if the values of the Cronbach’s alpha are 0.7 or higher it will be enough to accept the internal consistency reliability (Pallant, Citation2005). This means all results are reliable.

Second: the results of step two indicating the correlation between all components are shown in Table for the two tested areas.

Table 3. Correlation value between nonphysical and physical aspects’ components

From these tables, it is evidently clear that all correlation tests have resulted in significant values, consequently, all nonphysical aspects’ components and its integrated variables (representing the neighborhood social life quality) can be predicted through the physical aspects’ components using the multiple regression method.

Third: the results of step three: where multiple regression is used to deduce the targeted statistical models using stepwise method. It is worth mentioning that in most of the resulting models in this step, two formulas are accepted where the value of ANOVA test is less than 0.0005, and the final resulting model is selected based on the higher percentage of which the independent variables can predict the dependent variable. All final mathematical formulas are shown in Table for the two tested areas.

Table 4. Multiple regression resulting formulas between nonphysical and physical aspects’ components

5. Discussion of findings

The above-illustrated results reveal the following facts:

I. Since the social aspects of the neighborhoods represented by the human needs’ social and physiological satisfaction constitute an important attribute in the factors affecting sustainability and life quality of the neighborhood, however, it is very hard to scientifically measure these qualitative aspects, in addition to their deviated variances among residents as they are relevant to their preferences from one side and occupy different degrees of vitality or importance for each resident from another side. Accordingly, the research has initially succeeded in the deduction of measurable quantitative variables that are represented by the psychological and the social components and which take into consideration residents’ preferences by using the ‘weighed scale’ method, and these sets of components were rather tested to ensure their validity and reliability through statistical tests.

II. Despite the general conception of the vitality of giving important consideration to the social sustainability attributes of neighborhoods, very few researches have studied the break down of the components of the neighborhood as a social entity versus these components as a physical entity comprising the architectural, urban design and urban planning components, to deduce the strength of the correlations existing between each one of them on the social sustainability of the neighborhood and to test their variance according to the level of housing and whether they differ among the same level with its different housing types, as this would ultimately influence the investment plans oriented to each of the physical components.

According to the statistical results, the following findings can be discussed as follows:

I. There is a medium and strong positive correlation between the nonphysical aspects (and its components) and the physical aspects’ components in the tested intermediate social housing level, for the free housing type and the institutional housing one, as shown in Table for 6th of October and Sheikh Zayed respectively, as they have near values ranging between 0.4 and 0.6, implying the capability of deducing a precise statistical model in which the nonphysical aspects can be predicted by the physical ones.

From here, it is essential to discuss the implications of these correlations. First, the architectural component appears to achieve the highest correlation values with the psychological component in each of the intermediate social housing level types, in general, having higher values in 6th October (free housing) than Sheikh Zayed (institutional housing), holding the values of 0.68, and 0.52 respectively, due to the fact that in the former every resident is totally free to design his/her unit compared to the prototype design of the latter.

As for the urban component, it achieves the highest correlation value with the social component in the institutional housing type in Zayed as compared to the free one in 6th of October, holding the values of 0.66, and 0.44 respectively. This is attributed to the fact that urban design features were completely neglected in the free type where many of its streets are not paved, and where some urban spaces and greenery areas were relatively better in the instuitional one. Therefore, the architectural component still holds the strongest correlation in the free type as the residents compensated the mess in urban features by satisfying their architectural desires.

As for the planning component, it is evidently clear that it did not achieve higher values than either of the architectural or the urban design component in the two tested study areas due to the dominance of the architectural component in the free type and the urban design component in the institutional type.

Consequently, the nonphysical aspects as a whole (social and psychological components) are deduced to be highly correlated with the urban design component in the institutional housing type, and the architectural component in the free housing one. This implies the necessity of having an efficient architectural design for the housing units that make use of areas and satisfies the needed number of rooms for the targeted families, or else all psychological and social needs will not be satisfied and the housing type will either be subject to deformation or complete abundance. Consequently, governmental authorities should pay attention to the investments extensively oriented for streets’ network without providing an efficient architectural design or vice versa as this would result in collective residents’ rejection and waste of resources.

This can be easily interpreted in the predicting models giving precise deduction for the resulting nonphysical aspects that are rather hard to predict upon the design of any neighborhood except after establishment, and this is considered to be the added value of this research.

II. To further highlight the resulting equations, it is worth mentioning that all the models presented in the above section illustrate a method for calculation of either the psychological or the social aspects or the total of both of them within the given values of one or all of the physical components (architectural, urban design, and urban planning) using regression methods that normally ignore independent variables that are strongly correlated with each other, and keep the most influential variables that can best predict the dependent variable. This fact justifies the absence of some components form the final equations illustrated in the previous section of results.

III. The above findings would certainly guide decision makers during their design of neighborhoods to make justified priorities for the implementation of the different physical components especially if there are few available investments to overcome the common problems in developing countries of investing in noneffective physical components without focusing on important architectural or urban design details that most affect the social satisfaction of targeted residents, the fact that leads to either of total abundance for the whole neighborhood. As the case in many new settlements constructed in the new cities to accommodate millions of population transferred from informal areas whether inside or surrounding the central business districts in cities and metropolitan areas especially in Egypt, for example there are many governmental economic districts and youths’ accommodation areas constructed by the central government, or the other situation where the community find appropriating solutions to satisfy their social needs. The latter result is evidently clear in the case of Zeinhom in Cairo, that despite the great investments done for the regeneration and upgrading of this informal area on the physical scale, none of the social aspects of the population were satisfied especially those concerned with their social daily life habits that were not totally considered during regeneration processes. For example, they created their own semipublic spaces in front of their doors in the streets, which were not initially designed by the government leading to undesirable visual and urban forms informally created by the community. Thus, all governmental plans should be subject to more dynamic and flexible solutions rather than creating prototype housing projects and getting external funds for their establishment without a profound socio-spatial analysis to guarantee wise investments’ allocation together with community satisfaction and providing sustainable solutions for the urban sprawl and informalities’ invasion.

6. Conclusion

Creating healthy and livable communities has become a priority in establishing sustainable neighborhoods especially in new cities that aim to reduce the urban sprawl in cites and accommodate the relocated population in informal settlements. Despite the sustainability that incorporates urban, economic, environmental, and social dimensions, the first three of these have frequently been dealt with in many researches as they can be easily transformed into quantitative variables that can be easily measured and thus controlled. Yet, the social ones have been rarely addressed within a precise measurable tool that can predict the effect of the built environment including all the physical aspects on the resulting community satisfaction for their psychological and social needs such as enhancing the sense of belonging, safety, social interaction, fostering creativity, … etc. Moreover, such social dimensions are always difficult to measure as they vary according to resident’s social and cultural background from one side and according to personal preferences from another side. Therefore, there is a crucial need for socio-spatial analysis and assessment tools to precisely measure the effect of physical aspects on the nonphysical ones.

Consequently, from this view, and within the focus of the paper to emphasize the concept of neighborhood as an interactive social moderator between the physical environment spatially and their daily life practice, the paper has initially deduced the variables representing the physical aspects, within three main components of the built environment in the neighborhood: architectural design, urban design, and urban planning, with a total of 71, and those representing the nonphysical ones that are related to immaterial human beings’ requirements within two main components: psychological and social with a total of 12 variables. The latter were measured using the concept of the ‘weighed satisfaction’ to overcome the personal preferences issues.

In the empirical study, these variables were measured through conducting a questionnaire on two types of intermediate social housing level (free housing type and institutional housing one) in 6th of October and Sheikh Zayed city respectively in Egypt, with a total of 100 in each. These measurements were analyzed using statistical methods that first started with proving the validity and the reliability of the deduced variables in the two sets, and then proceeded to measure the correlation existing between each of the components comprising each set of variables, and finally ended with deducing mathematical formulas that can predict values of social and psychological community satisfaction, as dependent variables, in terms of physical attributes of the built environment, as independent variables, to act as a socio-spatial assessment tool.

The results yielded medium and strong positive correlations between each of the three components of the physical aspects and that of the nonphysical ones together with their cumulative variable in each of the housing types, with the dominance of the architectural component in the free housing type and that of urban design in the institutional type, whose housing units are prototypes with relatively better developed urban spaces. These correlations act as a guide to design the models for socio-spatial assessment by using multiple regression-stepwise method. The paper has finally presented three models for each type: illustrating the mathematical formula between the physical aspects’ components, and each of the psychological, and social component representing the nonphysical ones, and then with their cumulative effect together.

The research findings imply the strong recommendation for the decision makers and governmental authorities to use the deduced socio-spatial assessment tools as a significant guide for setting their priorities and orienting investments within defined milestones that target community satisfaction in the first place rather than the ambitious motivation to finish the establishment of the built environment and infrastructure that don’t meet community expectations leading at the end to either deformation or complete abundance with ultimate waste of resources without providing any sustainable solutions for relieving the urban sprawl in cities or relocation from informal settlements. Further, future researches should test this deduced tool on other levels and types of housing.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Riam Mohamed Elsagher Mahmod ElMorshedy

Riam Mohamed Elsagher Mahmod Elmorshedy is a lecturer at the department of architecture, Faculty of engineering, South Valley University, Egypt. Her doctoral study was focused on assessing the urban built environments from their resident’s viewpoints, as a tool for enhancing the quality of life in neighborhoods by depending on the social sustainability. She interests with design residential areas as a social life supporting sustained and stabled community life. She published some researches in the field such as:

  • Assessment of Neighborhoods Theoretical Models According to Comparative Measur-ement Tool.

  • Statistical Assessment for The Role of Neighborhood Facades in Social Satisfac-tion: Case Of 6th October City.

  • The Concept of Social Sustainability in The Context of Neighboring Residential Design.

This research is a new step in her scientific work towards social sustainability in built environment.

Notes

1. It is worth mentioning that the selected two neighborhoods in Sheikh Zayed city are the only ones representing the typology in this city, until now.

References

  • Aditjandra, P. T., Mulley, C., & Nelson, J. D. (2013). The influence of neighbourhood design on travel behaviour: Empirical evidence from North East England. Transport Policy, 26, 54–65. doi.org/10.1016/j.tranpol.2012.05.011
  • Alderfer, C. P. (1969). An empirical test of a new theory of human needs. Organizational Behavior & Human Performance, 4(2), 142-175.
  • Atkinson, R., & Blandy, S. (2006). Gated communities social sustainability in contemporary and historical gated development. London and New York: Routledge.
  • Blomquist, G. C., Berger, M. C., & Hoehn, J. P. (1988). New estimates of quality of life in urban areas. Journal of the American Economic Review, 78(1), 89–107.
  • BREEAM Communities, technical manual, code for a sustainable built environment SD202-0.0:2012, www.breeam.org BREEAM for Communities, (2008). (In) S. Giordano (Ed.) (2010). The Environmental Sustainability Evaluation of Logistic Settlements ( PhD. Thesis). Technological Innovation for Built, Retrieved April 9, 2015, from http://www.breeam.org/page.jsp?id=537
  • Calthorpe, P. (1993). The next American Metropolis: Ecology, community, and the American dream. New York, New York: Princeton Architectural Press.
  • Carey, J. J., Delaney, M. F., Love, T. E., Richmond, B. J., Cromer, B. A., Miller, P. D., … Licata, A. A. (2007). DXA-generated Z-scores and T-scores may differ substantially and significantly in young adults. Journal of Clinical Densitometry, 10(4), 351–358. doi:10.1016/j.jocd.2007.06.001
  • Castaneda, R. C. (2013). The quest for sustainable communities: the role of performance monitoring in the planning, design, and certification of sustainable neighborhoods ( MSc. Thesis). The Graduate Faculty of The University of Texas at San Antonio.
  • Charles, C. M., & Mertler, C. A. (2002). Introduction to educational research (4th ed.). Boston, MA: Allyn & Bacon.
  • Cicerchia, A. (1999). Measures of optimal centrality: Indicators of city effect and urban overloading. Journal of Social Indicators Research, 46(3), 276–299.
  • Commission for Architecture and the Built Environment (CABE). (2001). The value of urban design, to examine the value added by good urban design. Great Britain: Thomas Telford Ltd.
  • Costanza, R., Fisher, B., Ali, S., Beer, C., Bond, L., Boumans, R. L., … Snapp, R. (2007). Analysis: Quality of life: An approach integrating opportunities, human needs, and subjective well-being. Ecological Economics, 61, 267–276. doi:10.1016/j.ecolecon.2006.02.023
  • Creswell, J. W. (2002). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Upper Saddle River, NJ: Pearson Education.
  • Dall’O’, G., Galante, A., Sanna, N., & Miller, K. (2013). On the integration of Leadership in Energy and Environmental Design (LEED)® ND protocol with the energy planning and management tools in Italy: Strengths and weaknesses. Energies, 6, 5990–6015. doi:10.3390/en6115990
  • Davidson, W. B., & Cotter, P. R. (1991). The relationship between sense of community and subjective well-being: A first look. Journal of Community Psychology, 19(3), 246–253. doi:10.1002/(ISSN)1520-6629
  • Deng, W. (2011). Improving sustainability decision-making at neighbourhood level (A New Framework for Performance Assessment Based on China’s Small Residential District) ( PhD. thesis). The University of New South Wales, NSW, Australia.
  • Diener, E. (1984). Subjective well-being. Journal of Psychological Bulletin, 95(3), 542–575.
  • Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Journal of Psychological Bulletin, 125(2), 276–302. doi:10.1037/0033-2909.125.2.276
  • Duany, A., & Plater-Zyberk, E. (1994). The neighborhood, the district and the corridor. In P. Katz (Eds.), The new urbanism: Toward an architecture of community (pp. xvii- xx). New York City: McGraw-Hill Education.
  • Farrell, S. J., Aubry, T., & Coulombe, D. (2004). Neighborhoods and neighbors: Do they contribute to personal well-being? Journal of Community Psychology, 32(1), 9–25. doi:10.1002/(ISSN)1520-6629
  • Frey, H. (2005). Designing the city towards a more sustainable urban form. London and New York: Routledge.
  • Furuseth, O. J. (1997). Neotraditional planning: A new strategy for building neighborhoods? Land Use Policy, 14(3), 201–213. doi:10.1016/S0264-8377(97)00002-1
  • Gall, M. D., Borg, W. R., & Gall, J. P. (1996). Educational research: An introductio (6th ed.). White Plains, NY: Longman.
  • Gallion, B. A., & Eisner, S. (1963). The land use plan. The urban pattern: City planning and design (2nd) (pp. 223- 237). Toronto: Van Nostrand.
  • Gant, M. L., & Alaimo, K. (2006). Predictors of neighborhood satisfaction. Journal of Community Practice, 14, 27–50. doi:10.1300/J125v14n04_03
  • Gay, L. R., & Airasian, P. (2003). Educational research: Competencies for analysis and application (7th ed.). Upper Saddle River, NJ: Pearson Education.
  • Gehl, J. (1987). Life between buildings: Using public space. New York: Van Nostrand Reinhold.
  • General Authority for Urban Planning (GAUP). (2018). Ministry of Housing, Utilities and Urban communities (MHUUC). Cairo, Egypt: General Authority for Urban Planning. Retrieved June 10, 2014, from Egypt.www.buildingforlife.org
  • Giordano, S. (2010). The Environmental Sustainability Evaluation of Logistic Settlements ( PhD. Thesis). Technological Innovation for Built Environment, Politechnico Di Torino, Facolta Di Architettura, Italy.
  • Girling, C., & Kellett, R. (2005). Skinny streets and green neighborhoods design for environment and community. Washington, Covelo, & London: Oisland press.
  • Glass, R. (1948). The social background of a plan: A Study OF Middlesbrough. London: Routledge and Kegan Paul.
  • Hall, P. (2002). Cities of tomorrow. Oxford: Blackwell.
  • Kurpius, S. E. R., & Stafford, M. E. (2006). Chapter 5 (standardized scores- do you measure up?). In S. E. R. Kurpius & M. E. Stafford (Eds.), Testing and measurement (pp. 71-79). London, Thousand Oaks and New Delhi: SAGE publication, Inc.
  • Layne, M. R. (2009). Supporting intergenerational interaction: Affordance of urban public space. USA: The Graduate Faculty of North Carolina State University.
  • LEED. (2009). For neighborhood development, congress for the new urbanism, natural resources defense council, and the U.S. green building council, 2013. USA.
  • Lefebvre, H. (1991). The production of space. Oxford, OX, UK: Blackwell.
  • Litman, T. (2008). Well measured: Developing indicators for comprehensive and sustainable transport planning. British Columbia: Victoria Transport Policy Institute.
  • Mann, P. H. (1970). The neighborhood. In R. Gutman & D. Popenoe (Eds.), Neighborhood, city, and metropolis: An integrated reader in urban sociology (pp. 568-582). New York: Random House.
  • Max-Neef, M. A. (1991). Human scale development conception, application and further reflections. New York and London: The Apex Press.
  • McClelland, D. C. (1961). The achieving society. Princeton: D. Van Nostrand Company, Inc.
  • McCrea, R. (2007). Urban quality of life: Linking objective dimensions and subjective evaluations of the urban environment ( PhD. Thesis). The University of Queensland, Brisbane.
  • McCrea, R., Marans, R. W., Stimson, R., & Western, J. (2011a). Subjective measurement of quality of life using primary data collection and the analysis of survey data. In investigating quality of urban life (pp. 55–75). Berlin, Germany: Springer.
  • McCrea, R., Shyy, T.-K., & Stimson, R. (2006). What is the strength of the link between objective and subjective indicators of urban quality of life? Journal of Applied Research in Quality of Life, 1, 79–96. doi:10.1007/s11482-006-9002-2
  • McCrea, R., Stimson, R., & Marans., R. W. (2011b). The evolution of integrative approaches to the analysis of quality of urban life. In investigating quality of urban life (pp. 77–104). Berlin, Germany: Springer.
  • McCrea, R., Stimson, R., & Western, J. (2005). Testing a moderated model of satisfaction with urban living using data for Brisbane-South East Queensland, Australia. Journal of Social Indicators Research, 72, 121–152. doi:10.1007/s11205-004-2211-x
  • McMillan, J. H., & Schumacher, S. (2001). Research in education: A conceptual introduction (5th ed.). New York: Longman.
  • Michalos, A. C., & Zumbo, B. D. (1999). Public services and the quality of life. Journal of Social Indicators Research, 48(2), 125–156. doi:10.1023/A:1006893225196
  • Moore, G. T. (1979, September). Architecture and human behavior: The place of environment-behavior studies in architecture. Wisconsin Architect, 18–21.
  • National Institute of Justice (NIJ). (2009). Why neighborhoods matter: The importance of geographic composition. Geography & Public Safety, 2(2), 1-2.
  • Neal, P. (2003). Urban villages and the making of communities. London and New York: Spon Press.
  • Pallant, J. (2005). SPSS survival manual: A step by step guide to data analysis using SPSS for windows (Version 12). Sydney: Allen & Unwin.
  • Proshansky, H. M., & Fabian, A. K. (1986). Psychological aspects of the quality of urban life. In D. Frick (Eds.), The quality of urban life: Social, psychological, and physical conditions (pp. 19-30). Berlin, Germany: Walter de Gruyter.
  • Raphael, D., Rukholm, E., Brown, I., Hill-Bailey, P., & Donato, E. (1996). The quality of life profile-adolescent version: Background, description, and initial validation. Journal of Adolescent Health, 19, 366–375. doi:10.1016/S1054-139X(96)00080-8
  • Russ, T. H. (2009). Site planning and design handbook (2nd ed.). New York city: The McGraw-Hill Companies.
  • Sert, J. L. (1956, August). Urban design. Progressive Architecture, 37, 97–112.
  • Sirgy, M. J., & Cornwell, T. (2001). Further validation of the Sirgy et al.’s measure of community quality of life. Journal of Social Indicators Research, 56(2), 125–143. doi:10.1023/A:1012254826324
  • Sirgy, M. J., & Cornwell, T. (2002). How neighborhood features affect quality of life. Journal of Social Indicators Research, 59, 79–114. doi:10.1023/A:1016021108513
  • Sirgy, M. J., Rahtz, D. R., Cicic, M., & Underwood, R. (2000). A method for assessing residents’ satisfaction with community-based services: A quality-of-life perspective. Journal of Social Indicators Research, 49(3), 279–316. doi:10.1023/A:1006990718673
  • Sirgy, M. J., Widgery, R. N., Lee, D.-J., & Grace, B. Y. (2010). Developing a measure of community well-being based on perceptions of impact in various life domains. Journal of Social Indicators Research, 96, 295–311. doi:10.1007/s11205-009-9479-9
  • Smith, J. H., & Briers, G. E. (2001). Quality of life of scholarship recipients- Introduction and theoretical framework. Journal of Southern Agricultural Education Research, 51(1), 114–123.
  • Smith, M. E. (2010). The archaeological study of neighborhoods and districts in ancient cities. Journal of Anthropological Archaeology, 29, 137–154. doi:10.1016/j.jaa.2010.01.001
  • Suttles, G. D. (1972). The social construction of communities. Chicago: University of Chicago Press.
  • Towers, G. (2005). An introduction to urban housing design: At home in the city. Bound, Great Britain: Elsevier.
  • Turksever, A. N. E., & Atalik, G. (2001). Possibilities and limitations for the measurement of the quality of life in urban areas. Journal of Social Indicators Research, 53(2), 163–187. doi:10.1023/A:1026512732318
  • Walters, D. (2007). Designing community “Charrettes, master plans and form-based codes:”. Bound, Great Britain: Elsevier.
  • Whorton, J. W., & Moore, A. B. (1984). Summative scales for measuring community satisfaction. Journal of Social Indicators Research, 15, 297–307. doi:10.1007/BF00668676

Appendix A.

The questionnaire

The first part: The general information about the participants and their residences, The name: ………………. (optional)

The second part: the physical features of the neighborhood depending on residents’ opinions

The third part: the nonphysical aspects representing the residents’ feelings about their neighborhood

Appendix B.

The physical attributes’ photos of case study

Table B1. Urban planning attributes in case study

Table B2. Urban design attributes in case study

Table B3. Urban architectural attributes in case study