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47-57 

47 
 

 

 

Article 

Examining the association between deteriorated 
urban fabric and socio-economic resilience in 
Tehran Metropolis  
Kooshiar Zebardast1*, Keramatollah Ziari2 
1Geography and Urban Planning, Kish International Campus, University of Tehran, Kish, Iran 
2Department of Geography and Urban Planning, Faculty of Geography, University of Tehran, Iran 

               A R T I C L E   I N F O 
 

Article history: 
Received 12 June 2025  
Received in revised form 
28 July 2025 
Accepted 13 August 2025 
 
Keywords:  
Urban deterioration, Socioeconomic resilience,  
Geographically weighted regression,  
Factor analysis  
 
*Corresponding author 
Email address: 
kooshiar.zebardas@ut.ac.ir 
 
  
DOI: 10.55670/fpll.fusus.3.4.5 
 

A B S T R A C T 
 

Deteriorated urban areas usually face social, economic, and environmental 
problems. They often struggle with issues like poverty, inadequate housing, 
poor public spaces, social isolation and a sense of hopelessness, limited 
business opportunities, and a lack of investment. These complex problems 
cause significant disaster resilience challenges for these areas. This article 
investigates the association between urban deteriorated fabric (UDF) rate and 
socioeconomic resilience (SER) in the neighborhoods of Tehran Metropolis. 
Fourteen SER variables are identified through a literature review. Exploratory 
factor analysis is used to transform them into fewer factors. Four factors are 
extracted and are labelled as economic, social, economic-demographic, and 
community capital resilience. Similar extracted factors are combined to obtain 
social and economic resilience subcomponents. Jenks' Natural Break 
classification method is used to classify the UDF rate into five categories. 
Ordinary least squares (OLS) and Geographically Weighted Regression (GWR) 
are used to examine the association between UDF rate (dependent variable) 
and SER subcomponents (independent variables). The findings of the study 
show that: (a) the GWR better captures spatial relationships between UDF rate 
and SER factors than the OLS method, (b) the relationship between DUFs and 
social and economic resilience is complex and not definitively one-sided, and 
(c) social and economic resilience can occur concurrently in DUFs, (d) 
neighborhoods with high UFD rates are clustered in the mid-southern parts of 
the Tehran city. Understanding the interplay between social and economic 
resilience in DUFs is crucial for developing effective strategies to promote 
recovery and long-term disaster resilience and sustainability.  

1. Introduction 
One of the challenges that many large cities worldwide 

face is managing natural disasters, necessitating the 
development of solutions to enhance the capacity and 
resilience of communities while decreasing the susceptibility 
of urban areas to such disasters [1].  Iran ranks as one of the 
most susceptible countries globally to natural disasters, 
particularly earthquakes and floods, owing to its climatic, 
geological, and socio-spatial developmental traits [2]. 
Statistics show that in recent years, on average, a destructive 
and damaging earthquake has occurred in some part of the 
country every five years, and Iran is currently at the top of the 

list of countries where earthquakes are associated with high 
casualties [3]. The city of Tehran sits at the base of the Alborz 
Mountains and is at risk from several active faults that pose a 
constant threat to the city [4]. As a result, a crucial aspect of 
development planning in Tehran city is to highlight and 
consider its susceptibility, particularly the deteriorated urban 
areas, to natural disasters. Urban deterioration in Iran is 
characterized by the decay of urban fabrics, particularly in 
older areas, due to factors such as physical decay, aging 
infrastructure, structural instability, inadequate 
infrastructure, lack of basic amenities, uneven development, 
inadequate planning, and a lack of comprehensive renovation 

Future Sustainability 

Open Access Journal 

https://doi.org/10.55670/fpll.fusus.3.4.5 

 

November 2025| Volume 03 | Issue 04 | Pages 47-57 

Journal homepage: https://fupubco.com/fusus 

 
ISSN 2995-0473 

mailto:kooshiar.zebardas@ut.ac.ir
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K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

48 
 

strategies [5-7]. This deterioration affects various aspects of 
city life, impacting residents' quality of life and posing 
challenges to sustainable urban development. The uneven 
distribution of resources and opportunities contributes to the 
concentration of deterioration in specific areas, exacerbating 
social and economic disparities. These areas frequently 
experience higher levels of poverty, unemployment, and 
social inequality, alongside limited access to essential 
services [8]. The High Council for Urban Planning and 
Architecture of Iran (HCUPAI) in a resolution issued in 2006, 
has defined Deteriorated Urban Fabric (DUF) as “areas within 
the legal boundaries of cities that are vulnerable due to 
physical deterioration, lack of proper access to vehicles, 
services, and urban infrastructure, that have low spatial, 
environmental, and economic value” [9]. To operationalize 
this definition, the HCUPAI [9] has introduced three 
indicators of (a) “fineness of the fabric”, defined as blocks 
where more than 50% of their parcels have an area of less 
than 200 square meters, (b) “structural instability” which 
refers to blocks where more than 50% of their buildings are 
unstable and lack a sound structural system, and (c) 
“impermeability” defined as blocks where more than 50% of 
their surrounding streets are less than 6 meters in width. 
Areas of the city that meet all three criteria are called DUFs. 
There are approximately 53,000 hectares of DUFs in Iran, 
about 20 percent of which belong to the country's 
Metropolises [10]. The Tehran City Renovation Organization 
(TCRNO), using the three HCUPAI indicators of DUFs, 
identifies the areas of the city that fall in the DUF category and 
updates them periodically. While the area of DUFs in Tehran 
city is increasing annually, its annual rate of renovation is 
considerably low [8]. This discrepancy indicates a challenge 
in effectively addressing urban decay and improving the 
living conditions in these areas of the city.   

Despite the presence and the increasing trends of DUF in 
many cities in Iran [11] and both in developing and developed 
countries [12], there is a lack of clear understanding of the 
association between DUF rates and the underlying socio-
economic resilience in these areas. Most current studies 
examine urban decay or resilience separately, and analyzing 
their bidirectional relationship in a fast-growing city like 
Tehran is scant. Additionally, few studies have combined 
spatial analysis (GIS), exploratory factor analysis (EFA), and 
spatial regression analysis (OLS and GWR) to assess these 
relationships quantitatively. This study aims to fill these gaps 
by examining the spatial association between the DUF rate 
and the SER domains at the neighborhood level in Tehran 
Metropolis, specifically addressing the following questions: 
(1) How does the rate of urban deterioration vary across 
Tehran’s neighborhoods, and (2) how does this variation 
correlate with socio-economic resilience? 

This article is organized as follows: after the 
introduction, the socioeconomic indicators selection process 
is explained. In the next section, the study area and 
methodological framework of the study are presented.  Then, 
the quantification of the SER and the examination of the 
relationship between DUFs and SER subcomponents are 
presented. In the latter parts of the paper, the results, 
discussion, and conclusions are presented. 

 
 

2. Socioeconomic resilience indicators 
In recent years, the concept of resilience has gained 

considerable attention because of the continued vulnerability 
of cities to adverse effects of growing urban population, 
climate change, increasing trends in natural disasters, and 
aging public infrastructure [13,14]. Bruneau et al. [15] 
quantify the disaster resilience of a community in the context 
of four specific dimensions of resilience: technical, 
organizational, social, and economic. Later research 
contributions, such as the disaster resilience of place (DROP) 
model by Cutter et al. [16], build upon these categorizations 
of multi-dimensional behavior by pinpointing particular sets 
of quantitative indicator variables that can be used to 
analytically represent the various traits of resilience across its 
multiple dimensions. Cutter's work builds on the four 
dimensions suggested by Bruneau et al. [15] and adds two 
new dimensions of ecological resilience and community 
competence [16]. Subsequent disaster resilience (DR) 
frameworks reviewed by Asadzadeh et al. [17] show that in 
all of the 36 DR frameworks reviewed, the social and 
economic dimensions of resilience are present. As Kumar and 
Mehany state, socioeconomic factors play a crucial role in 
building disaster resilience, as disaster occurrences impact 
the social and economic development of urban areas; the 
socioeconomic dimension “has been seen as a facilitator of 
disaster resilience” [18]. Despite the relative importance of 
the community SER, there have been few studies focused on 
it, and even fewer have tried to quantify it, highlighting a 
significant issue that requires attention. Of the several studies 
that have identified and used sets of indicators to assess 
socioeconomic resilience [18-26], the socioeconomic 
resilience capacity index (SERCI) proposed by Gatiso & 
Greenhalgh [23] is taken as a basis here and other relevant 
studies [18, 21, 24, 25, 27] are used to identify and adapt the 
appropriate indicators that could represent the 
socioeconomic resilience at the neighborhood level in Tehran 
city. After eliminating highly correlated variables, fourteen 
variables are used to measure the SER at the neighborhood 
level in Tehran city (Table 1).   

 
3. Methodology 

Tehran Metropolis, with a population of 8.6 million, is the 
capital and the most populous city in Iran. The city is 
composed of 22 districts and 354 neighborhoods with a total 
area of about 730 km2. The city is located in the northern part 
of the country (Figure 1). According to Tehran City Deputy 
Mayor for Urban Planning and Architecture, the DUFs in 
Tehran city cover an area of 4,400 hectares [29], which is 
about 6.1 percent of the total area of the Tehran Metropolis. 
The number of residents living in these DUFs is about 15 
percent of the total population of Tehran City. About 22 
percent of Tehran city’s parcels are located in DUFs [3]. The 
population density in the DUFs of Tehran city is about 395 
persons per hectare, which is more than two and a half times 
the population density of the entire city [10]. To achieve the 
mentioned objectives, the methodological framework of the 
study is presented in Figure 2. 

 
 
 
 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

49 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

4. Analysis 
In the analysis part of the study, the steps identified in 

the methodological section are undertaken. To quantify the 
socioeconomic resilience at the neighborhood level, 
exploratory factor analysis (EFA) is performed to extract the 
underlying dimensions of the SER. The extracted factors are 
then combined to obtain a theoretically coherent set of factors 
that represent the SER. To examine the relationship between 
urban deterioration rate and SER factors, spatial regression 
analyses (OLS and GWR) are performed. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

4.1 Exploratory factor analysis to extract dimensions of 
SER 
EFA is performed to extract factors from the fourteen 

SER variables. The Kaiser-Meyer-Olkin (KMO) measure of 
sampling adequacy of 0.755 and Bartlett’s sphericity test 
result (χ2 = 2691.92; df = 91; ρ= 0.0001) indicate the 
suitability of the performed EFA. A varimax rotation and 
Kaiser criteria (choosing factors with eigenvalues greater 
than one) are used to select a small number of factors that 
include important variables that have high factor loadings 

Table 1. Selected indicators to measure SER at the neighborhood level in Tehran Metropolis 

No Dim Indicators Acronym References 

1 Social Proportion of population with university 
diploma (%) HED Landry et al. [25] 2020 

2 Social Land use diversity LUD Hafsi et al. [21] 2023 

3 Social Population density DEN Landry et al. [25] 2020; Yin et al. [24] 2025  

4 Social Sense of belonging BEL Gatiso & Greenhalgh [23] 2025; Navidpour et al. 
[27] 2025 

5 Social Satisfaction with neighborhood relations REL Gatiso & Greenhalgh [23] 2025; Navidpour et al. 
[27] 2025 

6 Social Satisfaction with participation in 
neighborhood decisions PAR Gatiso & Greenhalgh [23] 2025; Navidpour et al. 

[27] 2025 

7 Social Proportion of population without a high-
school diploma (%) LIT  Landry et al. [25] 2020 

8 Economic  
Household income  INC Gatiso & Greenhalgh [23] 2025; Yin et al. [24] 

2025 
9 Economic Car ownership CAR Gatiso & Greenhalgh [23] 2025 

10 Economic Number of employed per household NEH Lau [28] 2013 

11 Economic Ratio of skilled labor to total workforce SKL  Yin et al. [24] 2025 

12 Economic Housing unit ownership OHS Landry et al. [25] 2020 

13 Economic Percent employed EMP Kumar & Mehany [18] 2022 

14 Economic Population dependency DEP Hafsi et al. [21] 2023 
 

 

 

Figure 1. Spatial distribution of DUF and its composing indicators in Tehran Metropolis 

 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

50 
 

while minimizing the factor loadings of the unimportant ones, 
thus making it easier to interpret and label the factors. Four 
factors are extracted which cumulatively explain about 
68.71% of the data variance. Based on the highlighted 
variables for each factor, the four extracted factors are labeled 
as “economic resilience”, “economic-demographic resilience”, 
“social resilience”, and “community-capital resilience” (Table 
2). 

Factor 1, which explains about 20.76% of the data 
variance, has high loadings with percent employed (0.836), 
number of employed per household (0.835), and housing unit 
ownership (-0.712). These indicators reflect a household's 
capacity to withstand economic shocks and stressors through 
income generation, employment stability, and asset 
ownership. This factor is labeled as economic resilience. 
Factor 2 accounts for 14.2% of the data set’s common 
variance and has a significant positive loading on the 
proportion of the population without a high-school diploma 
(0.771), car ownership (0.695), household income (0.686), 
and ratio of skilled labor to total workforce (0.682). It also has 
a negative loading with population dependency (-0.681). Car 
ownership and household income are indicators of financial 
resilience, while the ratio of skilled labor to the total 
workforce and population dependency reflects demographic 
resilience. This factor represents economic-demographic 
resilience. Accounting for 15.78% of the data variance, factor 
3 has high loadings with population density (0.836), the 
proportion of the population with a university diploma 
(0.723), and land use diversity (-0.717). It represents social 
resilience. 
Factor 4 explains 11.57% of the data variance and has a 
significant positive loading on satisfaction with neighborhood 
relations (0.848), sense of belonging (0.732), and satisfaction 
with participation in neighborhood decisions (0.444). These 
variables relate to the social cohesion and support systems 
that help individuals and communities withstand challenges 
and adapt to change. They contribute to the overall strength 
of social ties and a community's ability to cope with adversity. 
This factor, therefore, is labeled community-capital resilience. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
Figure 2. Methodological framework of the study 

4.2 Combining similar extracted SER factors into a single 
subcomponent 

In EFA, it is customary to combine similar extracted factors 
into a new composite subcomponent to simplify 
interpretation or enhance theoretical coherence [30], even if 
EFA initially separates them [31]. Similar factors one and two 
are combined to represent the economic subcomponent, and 
similar factors three and four are combined to represent the 
social subcomponent of the SER.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 2. Extracted factors, their corresponding variables, and labels 

Original variables Acronym Factors 
(F1) 

Economic 
Resilience 

 

(F2) 
Economic-

Demographic 
Resilience  

(F3) 
Social 

Resilience 

(F4) 
Community-

capital 
Resilience 

Percent employed EMP 0.836 -0.231 0.249 -0.075 
Number of employed per household NEH 0.835 -0.061 -0.160 -0.143 
Housing unit ownership OHS -0.712 0.336 -0.026 0.006 
Proportion of population without a 
high-school diploma (%) 

LIT -0.452 0.771 -0.001 0.158 

Car ownership CAR 0.119 0.695 -0.296 0.082 
Household income INC -0.406 0.686 -0.426 0.062 
Ratio of skilled labor to total workforce SKL -0.396 0.682 0.202 0.223 

Population dependency DEP 0.573 -0.681 0.044 -0.014 
Population density DEN 0.002 -0.194 0.836 0.094 
Proportion of population with 
university diploma (%) 

HED 0.015 -0.105 0.723 0.173 

Land use diversity LUD -0.015 -0.046 -0.717 0.173 
Satisfaction with neighborhood 
relations 

REL -0.236 -0.063 -0.100 0.848 

Sense of belonging BEL -0.179 0.397 0.005 0.732 
Satisfaction with participation in 
neighborhood decisions 

PAR 0.214 0.124 0.252 0.444 

Eigenvalues 4.98 2.22 1.35 1.07 
Percent variations explained 20.76 20.52 15.78 11.57 

 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

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To combine these factors to obtain the economic and 
social subcomponents of SER, their factor scores are first 
normalized using Equation (1) [32]: 

𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 =  (𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖−𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖)
(𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖−𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖)

             (1) 

Where, 𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 is the normalized factor score for factor i in 
neighborhood j, 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 is the factor score for factor i in 
neighborhood j, 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖  and 𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 are the minimum and 
maximum value of factor score for factor i, respectively.  
Then, the factor scores for the combined factors of SER are 
computed by way of equation (2) [33] wherein the variance 
explained by each factor is used as a measure of the 
importance of that factor: 

𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑁𝑁𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑘𝑘𝑖𝑖 =  
∑ (λ𝐹𝐹𝑖𝑖  ×𝑁𝑁𝐹𝐹𝐹𝐹𝑖𝑖𝑖𝑖)𝑚𝑚
𝑖𝑖=1

∑ λ𝐹𝐹𝑖𝑖  
𝑚𝑚
𝑖𝑖=1

          (2) 

Where, 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑘𝑘𝑖𝑖 is the combined resilience 
subcomponent k score in neighborhood j, λ𝐹𝐹𝑖𝑖  is the percent 
variance explained by factor i, 𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖 is the normalized factor 
score for factor i in neighborhood j, m is the number of factors 
to be combined to arrive at the combined resilience 
subcomponent k.  
The combined economic resilience subcomponent, which 
measures the neighborhood economic vitality, suggests that 
the economic resilience in DUF neighborhoods exhibits high 
percentages of employed, a higher number of employed per 
household, lower levels of educational equality, higher 
percentages of inhabitants with vehicle access and household 
income, and those with fewer housing ownership and also 
fewer population dependency.  
 

 

The second combined subcomponent measures the social 
capacity of the DUF neighborhoods. It shows that the social 
resilience in the DUF neighborhoods is accompanied by such 
characteristics as areas with higher population density, 
higher levels of educational equality, lower rate of land-use 
diversity, and higher levels of social capital. 

4.3 Examining the relationship between urban 
deterioration and socioeconomic resilience 
To examine the relationship between urban 

deterioration and SER, the UDF rate is taken as the dependent 
variable, and the two combined subcomponents of the SER, 
namely economic resilience and social resilience 
subcomponents, are used as the independent variables in the 
following OLS and GWR regression analyses. UDF rate is 
computed by dividing the UDF area of a neighborhood by its 
total area. It is categorized into five classes (very low, low, 
moderate, high, and very high UDF) using Jenks Natural Break 
in ArcGIS and is presented in Figure 3. The spatial distribution 
pattern of UDF rate at the neighborhood level (Figure 3) 
shows that neighborhoods with high UDF rates are clustered 
in the mid-southern parts of the city.  

4.4 Performing ordinary least squares (OLS) regression 
analysis 
The OLS is a type of global statistics that assumes a 

constant relationship over space; therefore, the parameters 
are estimated to be the same for all the study areas [34]. To 
examine the relationship between urban deterioration and 
socioeconomic resilience at the neighborhood level, first, an 
OLS method is applied.  

 
 

 
 Figure 3. UDF rate classification of the neighborhoods using the Jenks Natural Break method 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

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As shown in the methodological framework of the study 
(Figure 2), in the OLS regression analysis, the dependent 
variable is the UDF ratio, and the independent variables are 
the two SER factors, namely, the economic and social 
resilience. The results of the OLS regression analysis are 
shown in Table 3. The OLS regression result indicates that, 
with about 9.9% accuracy (adjusted R2= 0.0996) and for all 
the neighborhoods in the city, the urban deterioration is 
negatively significantly associated with economic resilience 
and positively significantly related with the social resilience 
subcomponents of the SER. 

4.5 Performing Geographically Weighted Regression 
(GWR) 
GWR is an extension of the traditional standard 

regression framework, which allows local rather than global 
parameters to be estimated [35]. It is a type of local statistics 
that produces a set of local parameter estimates that show 
how a relationship varies over space [34].  

To examine the possibility of applying a GWR, it's crucial 
to ensure that spatial autocorrelation is present in the OLS 
residuals and that the R2 of the GWR is greater than the R2 of 
the corresponding OLS regression [35]. Moran’s I is computed 
for controlling the presence of spatial autocorrelation in the 
OLS residuals. Moran’s I result indicates that the standardized 
residual of the OLS regression is spatially autocorrelated and 
is distributed in a clustered manner (Figure 4: Moran’s I = 
0.757, p-value = 0.000). The adjusted R2 of the applied GWR 
(0.648) is greater than that of the corresponding OLS (0.099) 
regression. Since the prerequisites of applying a GWR are met, 
the GWR results (Table 4) could be used for further analysis. 
The results of the GWR analysis in Table 4 and the spatial 
variation of the negative and positive coefficients of the 
independent variables from the GWR model are shown in 
Figure 5.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 4. The Moran’s Index for the standardized residual of the OLS 
regression 

Table 4 and Figure 5 show that the intercept coefficients and 
coefficients of both economic and social subcomponents of 
SER have, concurrently, a negative and positive relationship 
with the DUF rate, depending on their location:  
• The intercept coefficients in Figure 5 (a) show that positive 

coefficients (in about 58% of the neighborhoods) belong to 
the neighborhoods located in the southern and western 
parts of the city, and the negative coefficients (in about 
42% of the neighborhoods) are located in the north-
eastern parts of the Metropolis. This implies a generally 
higher DUF rate in the southern parts of the city.   

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Results of the OLS regression analysis 

SER subcomponents Coefficient Robust SE  Robust t Robust p VIF 

Intercept 0.169 0.107 1.581 0.115 -- 
Economic Resilience -0.691 0.162 -4.249 0.000 1.000 
Social Resilience 0.520 0.156 3.328 0.000 1.000 
Model diagnostics Multiple 𝐶𝐶2= 0.105; Adjusted 𝐶𝐶2= 0.0996; AICc= 46.52   

 

Table 4. The results of the GWR 

SER subcomponent GWR coefficients Directions of relationships in the GWR model 

Min Max Mean SD + (%) + sig. (%) - (%) - sig. (%) 

Intercept 
-0.860 1.253 0.126 0.393 

58.09 23.88 41.91 3.45 

Economic Resilience 
-2.163 1.632 -0.196 0.730 

44.64 10.57 56.36 30.26 

Social Resilience 
-0.757 2.221 0.262 0.539 70.23 26.34 29.77 14.56 

Model diagnostics Multiple 𝐶𝐶2= 0.725; Adjusted 𝐶𝐶2= 0.648; AICc= - 228.27 

 

 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

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• In 44.6% of the neighborhoods, the DUF rate is positively 
associated, and in the remaining 56.4% it is negatively 
associated with the economic resilience of the SER (Figure 
5 (b)).  

• In 70.2% of the neighborhoods, the DUF rate is positively 
associated, and in 29.8% of the neighborhoods, it is 
negatively associated with the social resilience of the SER 
(Figure 5 (c)). 

• Of the 75 neighborhoods that are categorized as 
neighborhoods with high and very high DUF rate: 
o  42 neighborhoods (56%) have a negative relationship 

(14 of them are statistically significant), and the 
remaining 33 neighborhoods have a positive 
relationship with economic resilience. 

o 38 neighborhoods (50.7%) have a positive relationship 
(14 of them statistically significant) and the remaining 
neighborhoods have a negative relationship (24% of 
them statistically significant) with social resilience. 

5. Results and discussion 
The findings of applying the OLS regression (Table 3) 

indicate that: 
• Both economic and social subcomponents representing 

SER have a statistically significant relationship (p=0.05) 
with the DUF rate. 

• The adjusted  𝐶𝐶2 of 0.099 of the OLS model indicates that 
about 9.9% of the variations in the DUF rate is explained by 
the two SER subcomponents. 

• The DUF rate has a negative significant relationship with 
the economic and a positive significant association with the 
social subcomponents of the SER. 

 
 

 
 
 
 

The OLS results suggest that across all neighborhoods, as the 
rate of DUF rises, the social resilience improves, but the 
economic subcomponents of SER decline. To explore 
potential local differences in how the DUF rate correlates with 
SER subcomponents, the GWR outcomes are compared with 
those from the OLS regression. The global 𝐶𝐶2 of GWR (0.725) 
in comparison with the 𝐶𝐶2 of corresponding OLS regression 
(0.105) shows a dramatic improvement in 𝐶𝐶2 of GWR over the 
OLS. The 𝐶𝐶2 values in GWR range from 0.0004 to 0.5058 
(Figure 6), which is indicative of a local variation in the 
relationship between DUF rate and SER subcomponents. The 
GWR indicates that, in contrast to the OLS regression, the 
relationship between the DUF rate and SER subcomponents 
varies across different areas, with the highest coefficient of 
determination (𝐶𝐶2) found in neighborhoods situated in the 
central parts of the city. The comparison between OLS and 
GWR analyses indicates that the GWR model is better at 
capturing the spatial relationships between urban 
deterioration and the SER subcomponents in the DUFs of the 
city. 

The spatial distribution of the significant coefficients of 
the intercept and the two SER subcomponents is presented in 
Figure 7. The findings of this part of the study (Table 4 and 
Figure 7) indicate that the association of the DUF rate with the 
economic resilience subcomponent of the SER is positively 
statistically significant in 10.57% and is negatively 
statistically significant in 30.26% of the city’s neighborhoods. 
This finding implies that DUFs concurrently have significant 
challenges and opportunities with economic resilience. This 
is contrary to the general consensus that DUFs face significant 
challenges in terms of economic resilience: they often 
struggle to attract investment [36], retain residents, and 

Figure 5. Spatial variation of the negative and positive coefficients of the intercept and the independent variables from the GWR model 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

54 
 

recover from economic shocks due to factors like reduced 
economic activity [36, 37], out-migration [37, 38], and 
decreased property values [38,39].  

On the social resilience subcomponents of the SER, the 
findings of the study indicate that, similar to the economic 
resilience, the DUF rate association with the social resilience 
subcomponent of the SER is positively statistically significant 
in 26.34% and is negatively statistically significant in 14.56% 
of the neighborhoods (Table 4). This finding is contrary to the 
findings of Taghvaei & Asadi [40] and Sarrafi & Razavian [41] 
that the DUFs are one-sidedly negatively related to social 
resilience. The findings of this study show that the 
relationship between DUFs and social and economic 
resilience is complex and not definitively one-sided.  The 
relationship between urban deterioration and SER 
subcomponents is mixed and varies across the city 
neighborhoods. In some neighborhoods of the city, the DUF 
rate is positively related, and in others it is negatively related 
to both social and economic subcomponents of the SER. This 
is true even in neighborhoods located in the “high and very 
high DUF rate” areas (Figure 7). This study suggests that 
social and economic resilience can occur concurrently in 
DUFs. This may be due to the interconnectedness of social and 
economic factors, and interventions may have positively or 
negatively impacted both social and economic resilience in 
the DUFs.  

 
 

 
 

Figure 6. The local 𝐶𝐶2 classification by Jenks' Natural Break method 

 

On the positive relationship between DUF rate and social 
and economic resilience, the findings of this study support the 
earlier assertions by Giacometti & Teräs [42] that a 
community with high social resilience may be better able to 
mobilize resources and support one another during economic 
downturns and conversely, social norms that emphasize 
adaptability or collective action can also contribute to 
economic resilience. On the negative relationship between 
DUF rate and social and economic resilience, this study 
supports the findings of  (a) Hassanvand et al. [43] that social 
inequalities such as unequal access to education, healthcare, 
or job opportunities, can make certain groups more 
vulnerable to economic shocks and less able to recover, even 
if the overall economy is resilient, and, (b) Hegazy et al. [22] 
that economic hardship can lead to social vulnerability and 
decreased community participation, hindering the 
development of social resilience and potentially leading to 
further economic decline. This study has a limitation that 
needs to be taken into consideration. The findings of this 
study are based on data gathered at the neighborhood level in 
Tehran Metropolis, Iran; therefore, the results cannot be 
generalized to other large cities or metropolises in Iran. 
Further analysis based on nationally representative data is 
required. But the methodological framework used in the 
study is innovative and could pave the way for similar studies 
in other countries.  

 
 

 
 
 
 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

55 
 

 
 
 
 
 

6. Conclusion 
This study examined the relationship between urban 

deterioration rate and fourteen variables depicting the SER. 
The results of this study show that (a) the UFD rates among 
the city’s neighborhoods is clustered in nature and 
neighborhoods with high and very high UDF rates are 
clustered in the mid-southern parts of the city, (b) the 
relationship between DUFs and socio-economic resilience is 
complex, not definitively one-sided, mixed and varies across 
different neighborhoods of the city, (c) that social and 
economic resilience may happen simultaneously in DUFs. The 
methodological framework used in the study, including EFA, 
combining similar extracted EFA factors to arrive at new 
composite SER subcomponents, global spatial 
autocorrelation (Moran’s I), Jenks Natural Break clustering, 
GWR and OLS regression analyses, can be applied to any other 
global location with similar datasets to contribute to the 
existing knowledge about urban decay and SER at the local 
scale. In the context of DUFs, understanding the interplay 
between social and economic resilience is crucial for 
developing effective strategies to promote recovery and long-
term sustainability. Targeted interventions that address both 
social and economic vulnerabilities, while also fostering 
positive interactions between them, are likely to be more 
successful in building overall socioeconomic resilience. This 
includes taking proactive steps to improve infrastructure, 
boost collaboration across various sectors, utilize technology 
effectively, and encourage community involvement.  

 

 

 

 

The ultimate aim is to enable cities to absorb, recover from, 
and adapt to shocks while fostering sustainable development. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 
be available upon request from the corresponding author. 

Conflict of interest 
The authors declare no potential conflict of interest. 

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Dimensions and Components of an appropriate 

Figure 7. The spatial distribution of the significant coefficients of the intercept and the two SER subcomponents 



K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

56 
 

pattern of Earthquake Disaster Management in 
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K. Zebardast & K. Ziari /Future Sustainability                                                                  November 2025| Volume 03 | Issue 04 | Pages 47-57 

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	1. Introduction
	One of the challenges that many large cities worldwide face is managing natural disasters, necessitating the development of solutions to enhance the capacity and resilience of communities while decreasing the susceptibility of urban areas to such disa...
	Despite the presence and the increasing trends of DUF in many cities in Iran [11] and both in developing and developed countries [12], there is a lack of clear understanding of the association between DUF rates and the underlying socio-economic resili...
	This article is organized as follows: after the introduction, the socioeconomic indicators selection process is explained. In the next section, the study area and methodological framework of the study are presented.  Then, the quantification of the SE...
	2. Socioeconomic resilience indicators
	In recent years, the concept of resilience has gained considerable attention because of the continued vulnerability of cities to adverse effects of growing urban population, climate change, increasing trends in natural disasters, and aging public infr...
	3. Methodology
	Tehran Metropolis, with a population of 8.6 million, is the capital and the most populous city in Iran. The city is composed of 22 districts and 354 neighborhoods with a total area of about 730 km2. The city is located in the northern part of the coun...
	4. Analysis
	In the analysis part of the study, the steps identified in the methodological section are undertaken. To quantify the socioeconomic resilience at the neighborhood level, exploratory factor analysis (EFA) is performed to extract the underlying dimensio...
	4.1 Exploratory factor analysis to extract dimensions of SER
	EFA is performed to extract factors from the fourteen SER variables. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy of 0.755 and Bartlett’s sphericity test result (χ2 = 2691.92; df = 91; ρ= 0.0001) indicate the suitability of the performed ...
	Factor 1, which explains about 20.76% of the data variance, has high loadings with percent employed (0.836), number of employed per household (0.835), and housing unit ownership (-0.712). These indicators reflect a household's capacity to withstand ec...
	Factor 2 accounts for 14.2% of the data set’s common variance and has a significant positive loading on the proportion of the population without a high-school diploma (0.771), car ownership (0.695), household income (0.686), and ratio of skilled labor...
	Factor 4 explains 11.57% of the data variance and has a significant positive loading on satisfaction with neighborhood relations (0.848), sense of belonging (0.732), and satisfaction with participation in neighborhood decisions (0.444). These variable...
	4.2 Combining similar extracted SER factors into a single subcomponent
	In EFA, it is customary to combine similar extracted factors into a new composite subcomponent to simplify interpretation or enhance theoretical coherence [30], even if EFA initially separates them [31]. Similar factors one and two are combined to rep...
	To combine these factors to obtain the economic and social subcomponents of SER, their factor scores are first normalized using Equation (1) [32]:
	,𝑁𝐹𝑆-𝑖𝑗.= ,(,𝐹𝑆-𝑖𝑗.−,𝐹𝑆-𝑖𝑀𝑖𝑛.)-(,𝐹𝑆-𝑖𝑀𝑎𝑥.−,𝐹𝑆-𝑖𝑀𝑖𝑛.).             (1)
	Where, ,𝑁𝐹𝑆-𝑖𝑗. is the normalized factor score for factor i in neighborhood j, ,𝐹𝑆-𝑖𝑗. is the factor score for factor i in neighborhood j, ,𝐹𝑆-𝑖𝑀𝑖𝑛.  and ,𝐹𝑆-𝑖𝑀𝑎𝑥. are the minimum and maximum value of factor score for factor i, re...
	Then, the factor scores for the combined factors of SER are computed by way of equation (2) [33] wherein the variance explained by each factor is used as a measure of the importance of that factor:
	,𝐶𝑜𝑚𝑏𝑅𝑒𝑠𝑆𝑢𝑏𝑐𝑜𝑚𝑝-𝑘𝑗.= ,,𝑖=1-𝑚-(,λ-,𝐹-𝑖.. ×,𝑁𝐹𝑆-𝑖𝑗.).-,𝑖=1-𝑚-,λ-,𝐹-𝑖.. ..          (2)
	Where, ,𝐶𝑜𝑚𝑏𝑅𝑒𝑠𝐶𝑜𝑚𝑝-𝑘𝑗. is the combined resilience subcomponent k score in neighborhood j, ,λ-,𝐹-𝑖.. is the percent variance explained by factor i, ,𝑁𝐹𝑆-𝑖𝑗. is the normalized factor score for factor i in neighborhood j, m is the nu...
	The combined economic resilience subcomponent, which measures the neighborhood economic vitality, suggests that the economic resilience in DUF neighborhoods exhibits high percentages of employed, a higher number of employed per household, lower levels...
	The second combined subcomponent measures the social capacity of the DUF neighborhoods. It shows that the social resilience in the DUF neighborhoods is accompanied by such characteristics as areas with higher population density, higher levels of educa...
	4.3 Examining the relationship between urban deterioration and socioeconomic resilience
	To examine the relationship between urban deterioration and SER, the UDF rate is taken as the dependent variable, and the two combined subcomponents of the SER, namely economic resilience and social resilience subcomponents, are used as the independen...
	4.4 Performing ordinary least squares (OLS) regression analysis
	The OLS is a type of global statistics that assumes a constant relationship over space; therefore, the parameters are estimated to be the same for all the study areas [34]. To examine the relationship between urban deterioration and socioeconomic resi...
	As shown in the methodological framework of the study (Figure 2), in the OLS regression analysis, the dependent variable is the UDF ratio, and the independent variables are the two SER factors, namely, the economic and social resilience. The results o...
	4.5 Performing Geographically Weighted Regression (GWR)
	GWR is an extension of the traditional standard regression framework, which allows local rather than global parameters to be estimated [35]. It is a type of local statistics that produces a set of local parameter estimates that show how a relationship...
	To examine the possibility of applying a GWR, it's crucial to ensure that spatial autocorrelation is present in the OLS residuals and that the R2 of the GWR is greater than the R2 of the corresponding OLS regression [35]. Moran’s I is computed for con...
	Figure 4. The Moran’s Index for the standardized residual of the OLS regression
	Table 4 and Figure 5 show that the intercept coefficients and coefficients of both economic and social subcomponents of SER have, concurrently, a negative and positive relationship with the DUF rate, depending on their location:
	5. Results and discussion
	The findings of applying the OLS regression (Table 3) indicate that:
	The spatial distribution of the significant coefficients of the intercept and the two SER subcomponents is presented in Figure 7. The findings of this part of the study (Table 4 and Figure 7) indicate that the association of the DUF rate with the econ...
	On the social resilience subcomponents of the SER, the findings of the study indicate that, similar to the economic resilience, the DUF rate association with the social resilience subcomponent of the SER is positively statistically significant in 26.3...
	On the positive relationship between DUF rate and social and economic resilience, the findings of this study support the earlier assertions by Giacometti & Teräs [42] that a community with high social resilience may be better able to mobilize resource...
	6. Conclusion
	This study examined the relationship between urban deterioration rate and fourteen variables depicting the SER. The results of this study show that (a) the UFD rates among the city’s neighborhoods is clustered in nature and neighborhoods with high and...
	The ultimate aim is to enable cities to absorb, recover from, and adapt to shocks while fostering sustainable development.
	Ethical issue
	The manuscript contains all the data. However, more data will be available upon request from the corresponding author.
	Conflict of interest
	The authors declare no potential conflict of interest.
	References
	[1]     M. Pelling, The vulnerability of cities: natural disasters and social resilience. Routledge, 2012. https://doi.org/10.4324/9781849773379
	[2]  Z. Beheshti, A. Gharagozlou, M. Monavari, and M. K. Zarkesh, "Landslides behavior spatial modeling by using evidential belief function model, Promethean II model, and index of entropy in Tabriz, Iran," Arabian Journal of Geosciences, vol. 14, no....
	[3]  K. Nozari and M. Rafiyan, "Explanation the Dimensions and Components of an appropriate pattern of Earthquake Disaster Management in Deteriorated Urban Areas in Tehran city," Iranian Islamic city studies, vol. 3, no. 43, p. 25, 2021.
	[4]  F. Kamranzad, H. Memarian, and M. Zare. “Earthquake risk assessment for Tehran, Iran,” ISPRS International Journal of Geo-Information, vol. 9, no. 7, pp. 430, 2020.
	[5]  H. Sarvari, A. Mehrabi, D. W. Chan, and M. Cristofaro, "Evaluating urban housing development patterns in developing countries: Case study of Worn-out Urban Fabrics in Iran," Sustainable Cities and Society, vol. 70, p. 102941, 2021.
	[6]  M. Mohammad Ebrahimi and A. Hoseinianrad, "Evaluating the components of residential quality in the Worn-out Texture of Jahrom city," Geographical Planning of Space, vol. 13, no. 2, pp. 151-165, 2023.
	[7]  V. R. Anaraki Mohammadi, M. R. Zandmoghaddam, and S. Kamyabi, "Participation in the Improvement and Renovation of Deteriorated Urban Fabrics (Case Study: District 12 of Tehran)," Journal of Industrial and Systems Engineering, vol. 17, no. 1, pp. ...
	[8]  A. Andalib, "Pathology of Revitalizing Deteriorated Urban Fabrics in Iran from the Perspective of Balanced Renovation theory," Journal of Revitalization School, vol. 1, no. 1, pp. 44-51, 2024.
	[9]  HCUPAI -The High Council for Urban Planning and Architecture of Iran, The Resolution for identifying Deteriorated Urban Fabrics. Ministry of Roads and Urban Development, Tehran, 2006.
	[10]  Urban Development and Regeneration Company-UDRC, Statistics on the area and population of Deteriorated Urban Fabrics identified in the country's cities. Tehran, 2017.
	[11]  M. Haghpanah, B. Karimi, and J. E. D. Mahdi Nejad, "Effects of Physical and Social Factors on the Participatory Improvement of Worn-out Textures; Case Study: Nader Kazemi Neighborhood of Shiraz," Armanshahr Architecture & Urban Development, vol....
	[12]  G. Liu, Z. Yi, X. Zhang, A. Shrestha, I. Martek, and L. Wei, "An evaluation of urban renewal policies of Shenzhen, China," Sustainability, vol. 9, no. 6, p. 1001, 2017.
	[13]  S. M. Rezvani, M. J. Falcão, D. Komljenovic, and N.M. de Almeida, "A systematic literature review on urban resilience enabled with asset and disaster risk management approaches and GIS-based decision support tools," Applied Sciences, vol. 13, no...
	[14]  X. Zeng, Y. Yu, S. Yang, Y. Lv, and M.N.I. Sarker, "Urban resilience for urban sustainability: Concepts, dimensions, and perspectives," Sustainability, vol. 14, no. 5, p. 2481, 2022.
	[15]  M. Bruneau, S. E. Chang, R.T. Eguchi, G.C. Lee, T.D. O'Rourke, A.M. Reinhorn, ... and D. Von Winterfeldt, "A framework to quantitatively assess and enhance the seismic resilience of communities," Earthquake spectra, vol. 19, no. 4, pp. 733-752, ...
	[16]  S. L. Cutter, L. Barnes, M. Berry, C. Burton, E. Evans, E. Tate, and J. Webb, "A place-based model for understanding community resilience to natural disasters," Global environmental change, vol. 18, no. 4, pp. 598-606, 2008.
	[17]  A. Asadzadeh, P. Salehi, and J. Birkmann, "Operationalizing a concept: The systematic review of composite indicator building for measuring community disaster resilience," International journal of disaster risk reduction, vol. 25, pp. 147-162, 2017.
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