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Volume 37, Issue 3 (2022)
GeoRes 2022, 37(3): 361-368 |
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Received: 2022/06/12 | Accepted: 2022/08/10 | Published: 2022/07/1
Received: 2022/06/12 | Accepted: 2022/08/10 | Published: 2022/07/1
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Doughaei M, Mahdinia M, Daneshvar M. Identifying the Factors Affecting the Variations of Urban Settlement patterns Based on Social Ecology in Different Dimensions by Content Analysis Method. GeoRes 2022; 37 (3) :361-368
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1- Department of Urban Planning & Design, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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Introduction
Cities, due to their distinctive economic and social characteristics, have always attracted population. This geographic mobility, however, is not limited to migrants; social groups residing within cities also seek to relocate to areas with higher living standards. This phenomenon initiates continuous socio-spatial transformations and changes in residential patterns. A lack of sufficient understanding of the factors shaping these transformations leads to a poor grasp of their complexity, ultimately hindering effective urban planning.
In this context, Robert Park and Ernest Burgess address this issue based on the principles of natural ecology, introducing concepts such as the social process and succession. According to Park, the social process includes four stages: competition, conflict, accommodation, and assimilation (Park, 1925). Similarly, Ron Johnston describes this phenomenon as ecological replacement, consisting of four stages: invasion, succession, stabilization, and densification (Johnston, 1971). Knox and Pinch, however, argue that this is a socio-spatial dialectic, forming an important aspect of urban morphology, driven by households’ mobility, socio-economic advancement, and lifestyle changes (Knox & Pinch, 2010).
Mike Davis posits that individuals consider factors such as housing costs, ownership, shelter quality, proximity to work, and social security when making residential relocation decisions (Davis, 2006). Meanwhile, Louis Wirth identifies factors such as density, land prices, rents, access to services, health, aesthetics, and avoidance of noise and pollution as criteria influencing residential choice (Wirth, 1938). Clearly, the common thread in these discussions is the concept of competition (Park, 1925), which, alongside power, leads to the formation of social classes and socio-spatial segregation. As David Harvey notes, the dominant class in society shapes social hierarchies, which in turn affect urban structures (Harvey, 2003). David Sibley further contends that powerful social groups engage in exclusionary practices against weaker groups, resulting in spatial deprivation within the city and limiting weaker groups’ access to certain urban spaces (Sibley, 1988).
Manuel Castells offers a different perspective, emphasizing the role of information and new communication technologies in the emergence of global cities and increasing social inequalities (Castells, 2004). In terms of socio-spatial patterns and residential differentiation, Buechley highlights social status, degree of urbanization, and segregation as effective indicators for identifying distinct social domains within cities (Buechley, 1956). Building on Buechley’s ideas, Duncan Timms applies factors such as social status, family characteristics, migration status, and ethnicity to develop the “urban mosaic” model (Timms, 1971).
While Sibley emphasizes economic and cultural status, ethnicity, and race (Sibley, 1988) and Wirth identifies ethnicity, religion, language, and customs as primary reasons for socio-spatial isolation of minorities (Wirth, 1927), Susan Smith points to ideology, sentiment, and political processes as drivers of social segregation, which she considers the outcome of government policies, institutional practices, and individual and social actions (Smith, 1989; Massey, 1993). Winchester and White similarly identify economic standards, social norms, and legal codes as drivers of socio-spatial isolation (Winchester & White, 1988). Jane Jacobs, from another perspective, considers residential density, population density, land-use planning, urban spatial patterns, and diverse social life as key factors influencing the socio-spatial organization of cities (Jacobs, 1961).
A review of prior research shows that most studies have either focused solely on spatial analysis of residential patterns in cities, neglecting social ecology, or examined the effects of one or a few specific concepts from social ecology. Most of these studies are descriptive and confined to specific urban areas. Even studies that are closest in topic and methodology to the present research have addressed only a small portion of the theoretical scope. Given the significance and applicability of the social ecology approach in understanding the multidimensional complexities of urban spaces, the most notable gap in this field is the lack of a comprehensive framework addressing approaches and factors influencing transformations in residential patterns. Accordingly, the aim of this study was to fill this important research gap
Methodology
The present study was conducted using a content analysis method with a quantitative approach in 2021. The research stages were based on the process proposed by White and Marsh (White & Marsh, 2006). In the first step, the statistical population (content) of the study was selected based on criteria such as type and thematic scope, accessibility, language, and year of publication. Accordingly, 18 primary studies from English-language articles and books by experts in the field of social ecology, whose electronic versions were accessible and published from 1925 (the establishment of the Chicago School) to the present, were considered as the statistical population. It should be noted that sources merely describing urban social ecology concepts, without relevance to residential transformations, were excluded.
In the second step, data collection was performed through document observation (line-by-line review) and text summarization. In the third step, content analysis was conducted inductively, focusing on the manifest content (apparent meaning) of the texts, in two coding stages. In the first stage, initial or open coding was used, aiming to break down large qualitative data into discrete segments, examine and compare them to identify similarities and differences for better organization (Saldana, 2015). Thus, in this stage, concepts related to residential patterns were extracted from the broader topic of social ecology. Considering the first objective of the study, which was to identify dimensions affecting residential patterns, the recording units (concepts) were manually coded and categorized into six dimensions: social, economic, cultural, spatial, physical, and political-legal.
In the second stage, axial coding was applied. Axial codes serve as secondary labels assigned to data after the primary code has been determined, enriching the extracted codes (Saldana, 2015). Accordingly, based on the study’s second objective, which was to identify factors influencing residential patterns, categories and then factors were constructed and organized according to the primary codes (concepts). Overall, 169 recording units (concepts), 39 categories, and 10 factors were extracted, explained, and categorized.
In the fourth step, the recording units were counted and their frequencies reported and interpreted according to dimensions and factors. To determine the relationships among factors and their influence on urban residential patterns, correlation analysis was conducted, and due to the nominal nature of the variables, Cramer’s V coefficient was used.
In the fifth step, inter-coder reliability was assessed using Scott’s Pi coefficient, a nominal agreement measure between two coders, indicating the reliability of the coding process (Scott, 1955). For calculating Scott’s Pi, 20% of the recording units were randomly selected, and the coding process was repeated by a third author. Then, the observed agreement and the expected agreement between the two codings were calculated for each factor. Finally, the results were inserted into the formula to compute the reliability coefficient, which was reported at 87%, indicating an acceptable level of coding reliability.
Findings
As previously mentioned, the content analysis was conducted in two coding stages. In the first stage, all 169 recording units were categorized into six primary codes, corresponding to different dimensions. After initial coding and extraction of recording units, axial coding was applied to construct the categories and factors of the study.
The primary coding revealed that concepts related to social ecology and residential patterns could be organized across multiple dimensions of social, cultural, economic, spatial, physical, and political-legal depending on the theoretical perspective of each scholar. For instance, Burgess’s concentric zone theory included concepts such as invasion-succession, geographic mobility, natural and artificial features, historical areas, and social resistance, which were grouped under social, spatial, and physical dimensions. Park’s theories on social distance and natural environments highlighted racial-class awareness, marginalization, prejudices, social position, and social homogeneity, categorized into social, cultural, economic, and spatial dimensions. Wirth’s urban lifestyle and ghetto theories, Johnston’s urban settlement patterns, and the urban mosaic and socio-spatial segregation concepts proposed by Timms, Sibley, and others, similarly encompassed multiple dimensions, capturing social, cultural, economic, spatial, physical, managerial, and political-legal aspects (Park, 1925; Wirth, 1927, 1938; Johnston, 1971; Timms, 1971; Sibley, 1988; Castells, 2004; Jacobs, 1961; Harvey, 2003; Davis, 2006).
Axial coding allowed for the construction of 39 categories and 10 overarching factors influencing residential patterns. These factors included socio-spatial mobility, social homogeneity, social distance, social status, housing supply and demand, economic conditions, segregation, systemic deprivation, urban development patterns, and the network society. Each factor consisted of multiple categories, which were further broken down into recording units, reflecting the multifaceted nature of residential transformation in urban areas.
A quantitative analysis of the data was then performed. Counting the recording units according to dimensions revealed that the social dimension appeared 71 times, the cultural dimension 44 times, the economic dimension 41 times, the spatial dimension 35 times, the political-legal dimension 24 times, and the physical dimension 11 times. Frequencies of the 10 main factors affecting residential patterns were also calculated: social distance appeared 8 times, socio-spatial mobility 7 times, segregation 5 times, social homogeneity and social status 4 times each, the network society 3 times, and other factors twice each.
To examine how the 10 factors influenced each other and, ultimately, residential patterns, correlation analysis was conducted. Since the variables were nominal, Cramer’s V coefficient was used to measure the strength of association between nominal variables with multiple categories. The coefficient ranges from 0 to 1, with values closer to 1 indicating a stronger relationship. To interpret the intensity of relationships, the following ranges were applied: a coefficient of 1 indicates perfect correlation; 0.700–0.999 indicates strong correlation; 0.400–0.699 indicates moderate correlation; 0.050–0.399 indicates weak correlation; and below 0.050 indicates no correlation. Pairwise correlations among the factors were calculated to understand their interrelationships and combined influence on urban residential patterns.
Discussion
The main objective of this study was to develop a comprehensive and integrative framework regarding the dimensions and factors influencing transformations in residential patterns through the lens of urban social ecology. Achieving this goal required a broader and deeper perspective on the issue compared to previous studies. Accordingly, the results of similar research were compared with the findings of this article, distinguishing between the dimensions and factors affecting residential patterns.
In this context, various studies have examined the dimensions influencing changes in residential patterns, with some focusing solely on the roles of economic, social, cultural, political, and managerial dimensions (Alalhesabi & Karani, 2012; Malekzade et al., 2020; Wu et al., 2014). Others have not only highlighted multiple dimensions but also have assessed their frequency and ranking, finding that social and economic approaches are the most recurrent, while the physical dimension appeares less frequently (Jalilisadrabad & Hashemi, 2020; Pourjafar et al., 2019). The findings of the present study support these results, attributing the highest frequency to social, cultural, and economic approaches and the lowest frequency to the physical approach. Unlike other studies, however, this study also considered the spatial approach separately in coding, placing it at a mid-level frequency.
Regarding factors influencing residential patterns, although several studies exist, only a limited number have analyzed residential transformations from the perspective of social ecology. For example, Wu et al. in Nanjing, China, have highlighted factors such as government policies, market performance, elites, and technology in residential pattern transformations, which align closely with the categories identified in this study (Wu et al., 2014). Similarly, Alalhesabi and Karani have recognized culture, identity, economy, technology, social relations, and policy as influential factors in housing transformation, largely corresponding to the categories in this research (Alalhesabi & Karani, 2012). Other studies focus on factors that match the recording units extracted in this study, including the influence of migrants on land use, migrant population, duration of residence, education level, social interactions, proximity to relatives, income, occupation, access to services, land value, roles of governmental institutions, unequal land-use distribution, ethnicity, class gaps, discriminatory policies, culture, religion, language, aspirations for a better life, zoning policies, rehabilitation and renovation, and land ownership (Pourjafar et al., 2019; Mostofi Al-Mamalaki et al., 2019; Boterman et al., 2021).
A notable observation is that previous studies typically focused on only one concept associated with social ecology, such as residential segregation, spatial separation, cultural mixing, socio-spatial transformations, or changes in urban spatial structure. In contrast, the present study encompassed all of these concepts from a holistic perspective. Nevertheless, the main limitation of this study was the accessibility of electronic versions of certain sources and the required content.
It is recommended that the results derived from the correlation analyses could be formulated as multiple hypotheses and empirically tested
Conclusion
The results indicated that the factors of social distance and socio-spatial mobility had the highest frequency. In the correlation analysis among the ten factors, it was found that social homogeneity exhibited strong correlations with social status, housing supply and demand, segregation, systemic deprivation, urban development patterns, and the network society. Additionally, socio-spatial mobility also showed a strong correlation with the network society. Based on the correlation findings, it was revealed that social homogeneity, due to its direct connections and strong correlations with six other factors, had the greatest influence on urban residential patterns.
Overall, the study concludes that from the perspective of urban social ecology, the ten factors examined influence residential patterns through bidirectional relationships, forming a network of interactions that collectively shape and transform urban settlement structures
Acknowledgments: Not applicable.
Ethical Permission: Not applicable.
Conflict of Interest: This article is derived from the doctoral dissertation of the first author, titled “Explaining the Housing Planning Pattern in Underprivileged Areas of Mashhad Based on Urban Social Ecology.”
Author Contributions: Mehrad Doghaei M (First Author), Main Researcher (50%)/Mahdnia MH (Second Author), Assistant Researcher (25%)/Daneshvar (Third Author), Methodologist (25%)
Funding: The study was funded by the first author.
Cities, due to their distinctive economic and social characteristics, have always attracted population. This geographic mobility, however, is not limited to migrants; social groups residing within cities also seek to relocate to areas with higher living standards. This phenomenon initiates continuous socio-spatial transformations and changes in residential patterns. A lack of sufficient understanding of the factors shaping these transformations leads to a poor grasp of their complexity, ultimately hindering effective urban planning.
In this context, Robert Park and Ernest Burgess address this issue based on the principles of natural ecology, introducing concepts such as the social process and succession. According to Park, the social process includes four stages: competition, conflict, accommodation, and assimilation (Park, 1925). Similarly, Ron Johnston describes this phenomenon as ecological replacement, consisting of four stages: invasion, succession, stabilization, and densification (Johnston, 1971). Knox and Pinch, however, argue that this is a socio-spatial dialectic, forming an important aspect of urban morphology, driven by households’ mobility, socio-economic advancement, and lifestyle changes (Knox & Pinch, 2010).
Mike Davis posits that individuals consider factors such as housing costs, ownership, shelter quality, proximity to work, and social security when making residential relocation decisions (Davis, 2006). Meanwhile, Louis Wirth identifies factors such as density, land prices, rents, access to services, health, aesthetics, and avoidance of noise and pollution as criteria influencing residential choice (Wirth, 1938). Clearly, the common thread in these discussions is the concept of competition (Park, 1925), which, alongside power, leads to the formation of social classes and socio-spatial segregation. As David Harvey notes, the dominant class in society shapes social hierarchies, which in turn affect urban structures (Harvey, 2003). David Sibley further contends that powerful social groups engage in exclusionary practices against weaker groups, resulting in spatial deprivation within the city and limiting weaker groups’ access to certain urban spaces (Sibley, 1988).
Manuel Castells offers a different perspective, emphasizing the role of information and new communication technologies in the emergence of global cities and increasing social inequalities (Castells, 2004). In terms of socio-spatial patterns and residential differentiation, Buechley highlights social status, degree of urbanization, and segregation as effective indicators for identifying distinct social domains within cities (Buechley, 1956). Building on Buechley’s ideas, Duncan Timms applies factors such as social status, family characteristics, migration status, and ethnicity to develop the “urban mosaic” model (Timms, 1971).
While Sibley emphasizes economic and cultural status, ethnicity, and race (Sibley, 1988) and Wirth identifies ethnicity, religion, language, and customs as primary reasons for socio-spatial isolation of minorities (Wirth, 1927), Susan Smith points to ideology, sentiment, and political processes as drivers of social segregation, which she considers the outcome of government policies, institutional practices, and individual and social actions (Smith, 1989; Massey, 1993). Winchester and White similarly identify economic standards, social norms, and legal codes as drivers of socio-spatial isolation (Winchester & White, 1988). Jane Jacobs, from another perspective, considers residential density, population density, land-use planning, urban spatial patterns, and diverse social life as key factors influencing the socio-spatial organization of cities (Jacobs, 1961).
A review of prior research shows that most studies have either focused solely on spatial analysis of residential patterns in cities, neglecting social ecology, or examined the effects of one or a few specific concepts from social ecology. Most of these studies are descriptive and confined to specific urban areas. Even studies that are closest in topic and methodology to the present research have addressed only a small portion of the theoretical scope. Given the significance and applicability of the social ecology approach in understanding the multidimensional complexities of urban spaces, the most notable gap in this field is the lack of a comprehensive framework addressing approaches and factors influencing transformations in residential patterns. Accordingly, the aim of this study was to fill this important research gap
Methodology
The present study was conducted using a content analysis method with a quantitative approach in 2021. The research stages were based on the process proposed by White and Marsh (White & Marsh, 2006). In the first step, the statistical population (content) of the study was selected based on criteria such as type and thematic scope, accessibility, language, and year of publication. Accordingly, 18 primary studies from English-language articles and books by experts in the field of social ecology, whose electronic versions were accessible and published from 1925 (the establishment of the Chicago School) to the present, were considered as the statistical population. It should be noted that sources merely describing urban social ecology concepts, without relevance to residential transformations, were excluded.
In the second step, data collection was performed through document observation (line-by-line review) and text summarization. In the third step, content analysis was conducted inductively, focusing on the manifest content (apparent meaning) of the texts, in two coding stages. In the first stage, initial or open coding was used, aiming to break down large qualitative data into discrete segments, examine and compare them to identify similarities and differences for better organization (Saldana, 2015). Thus, in this stage, concepts related to residential patterns were extracted from the broader topic of social ecology. Considering the first objective of the study, which was to identify dimensions affecting residential patterns, the recording units (concepts) were manually coded and categorized into six dimensions: social, economic, cultural, spatial, physical, and political-legal.
In the second stage, axial coding was applied. Axial codes serve as secondary labels assigned to data after the primary code has been determined, enriching the extracted codes (Saldana, 2015). Accordingly, based on the study’s second objective, which was to identify factors influencing residential patterns, categories and then factors were constructed and organized according to the primary codes (concepts). Overall, 169 recording units (concepts), 39 categories, and 10 factors were extracted, explained, and categorized.
In the fourth step, the recording units were counted and their frequencies reported and interpreted according to dimensions and factors. To determine the relationships among factors and their influence on urban residential patterns, correlation analysis was conducted, and due to the nominal nature of the variables, Cramer’s V coefficient was used.
In the fifth step, inter-coder reliability was assessed using Scott’s Pi coefficient, a nominal agreement measure between two coders, indicating the reliability of the coding process (Scott, 1955). For calculating Scott’s Pi, 20% of the recording units were randomly selected, and the coding process was repeated by a third author. Then, the observed agreement and the expected agreement between the two codings were calculated for each factor. Finally, the results were inserted into the formula to compute the reliability coefficient, which was reported at 87%, indicating an acceptable level of coding reliability.
Findings
As previously mentioned, the content analysis was conducted in two coding stages. In the first stage, all 169 recording units were categorized into six primary codes, corresponding to different dimensions. After initial coding and extraction of recording units, axial coding was applied to construct the categories and factors of the study.
The primary coding revealed that concepts related to social ecology and residential patterns could be organized across multiple dimensions of social, cultural, economic, spatial, physical, and political-legal depending on the theoretical perspective of each scholar. For instance, Burgess’s concentric zone theory included concepts such as invasion-succession, geographic mobility, natural and artificial features, historical areas, and social resistance, which were grouped under social, spatial, and physical dimensions. Park’s theories on social distance and natural environments highlighted racial-class awareness, marginalization, prejudices, social position, and social homogeneity, categorized into social, cultural, economic, and spatial dimensions. Wirth’s urban lifestyle and ghetto theories, Johnston’s urban settlement patterns, and the urban mosaic and socio-spatial segregation concepts proposed by Timms, Sibley, and others, similarly encompassed multiple dimensions, capturing social, cultural, economic, spatial, physical, managerial, and political-legal aspects (Park, 1925; Wirth, 1927, 1938; Johnston, 1971; Timms, 1971; Sibley, 1988; Castells, 2004; Jacobs, 1961; Harvey, 2003; Davis, 2006).
Axial coding allowed for the construction of 39 categories and 10 overarching factors influencing residential patterns. These factors included socio-spatial mobility, social homogeneity, social distance, social status, housing supply and demand, economic conditions, segregation, systemic deprivation, urban development patterns, and the network society. Each factor consisted of multiple categories, which were further broken down into recording units, reflecting the multifaceted nature of residential transformation in urban areas.
A quantitative analysis of the data was then performed. Counting the recording units according to dimensions revealed that the social dimension appeared 71 times, the cultural dimension 44 times, the economic dimension 41 times, the spatial dimension 35 times, the political-legal dimension 24 times, and the physical dimension 11 times. Frequencies of the 10 main factors affecting residential patterns were also calculated: social distance appeared 8 times, socio-spatial mobility 7 times, segregation 5 times, social homogeneity and social status 4 times each, the network society 3 times, and other factors twice each.
To examine how the 10 factors influenced each other and, ultimately, residential patterns, correlation analysis was conducted. Since the variables were nominal, Cramer’s V coefficient was used to measure the strength of association between nominal variables with multiple categories. The coefficient ranges from 0 to 1, with values closer to 1 indicating a stronger relationship. To interpret the intensity of relationships, the following ranges were applied: a coefficient of 1 indicates perfect correlation; 0.700–0.999 indicates strong correlation; 0.400–0.699 indicates moderate correlation; 0.050–0.399 indicates weak correlation; and below 0.050 indicates no correlation. Pairwise correlations among the factors were calculated to understand their interrelationships and combined influence on urban residential patterns.
Discussion
The main objective of this study was to develop a comprehensive and integrative framework regarding the dimensions and factors influencing transformations in residential patterns through the lens of urban social ecology. Achieving this goal required a broader and deeper perspective on the issue compared to previous studies. Accordingly, the results of similar research were compared with the findings of this article, distinguishing between the dimensions and factors affecting residential patterns.
In this context, various studies have examined the dimensions influencing changes in residential patterns, with some focusing solely on the roles of economic, social, cultural, political, and managerial dimensions (Alalhesabi & Karani, 2012; Malekzade et al., 2020; Wu et al., 2014). Others have not only highlighted multiple dimensions but also have assessed their frequency and ranking, finding that social and economic approaches are the most recurrent, while the physical dimension appeares less frequently (Jalilisadrabad & Hashemi, 2020; Pourjafar et al., 2019). The findings of the present study support these results, attributing the highest frequency to social, cultural, and economic approaches and the lowest frequency to the physical approach. Unlike other studies, however, this study also considered the spatial approach separately in coding, placing it at a mid-level frequency.
Regarding factors influencing residential patterns, although several studies exist, only a limited number have analyzed residential transformations from the perspective of social ecology. For example, Wu et al. in Nanjing, China, have highlighted factors such as government policies, market performance, elites, and technology in residential pattern transformations, which align closely with the categories identified in this study (Wu et al., 2014). Similarly, Alalhesabi and Karani have recognized culture, identity, economy, technology, social relations, and policy as influential factors in housing transformation, largely corresponding to the categories in this research (Alalhesabi & Karani, 2012). Other studies focus on factors that match the recording units extracted in this study, including the influence of migrants on land use, migrant population, duration of residence, education level, social interactions, proximity to relatives, income, occupation, access to services, land value, roles of governmental institutions, unequal land-use distribution, ethnicity, class gaps, discriminatory policies, culture, religion, language, aspirations for a better life, zoning policies, rehabilitation and renovation, and land ownership (Pourjafar et al., 2019; Mostofi Al-Mamalaki et al., 2019; Boterman et al., 2021).
A notable observation is that previous studies typically focused on only one concept associated with social ecology, such as residential segregation, spatial separation, cultural mixing, socio-spatial transformations, or changes in urban spatial structure. In contrast, the present study encompassed all of these concepts from a holistic perspective. Nevertheless, the main limitation of this study was the accessibility of electronic versions of certain sources and the required content.
It is recommended that the results derived from the correlation analyses could be formulated as multiple hypotheses and empirically tested
Conclusion
The results indicated that the factors of social distance and socio-spatial mobility had the highest frequency. In the correlation analysis among the ten factors, it was found that social homogeneity exhibited strong correlations with social status, housing supply and demand, segregation, systemic deprivation, urban development patterns, and the network society. Additionally, socio-spatial mobility also showed a strong correlation with the network society. Based on the correlation findings, it was revealed that social homogeneity, due to its direct connections and strong correlations with six other factors, had the greatest influence on urban residential patterns.
Overall, the study concludes that from the perspective of urban social ecology, the ten factors examined influence residential patterns through bidirectional relationships, forming a network of interactions that collectively shape and transform urban settlement structures
Acknowledgments: Not applicable.
Ethical Permission: Not applicable.
Conflict of Interest: This article is derived from the doctoral dissertation of the first author, titled “Explaining the Housing Planning Pattern in Underprivileged Areas of Mashhad Based on Urban Social Ecology.”
Author Contributions: Mehrad Doghaei M (First Author), Main Researcher (50%)/Mahdnia MH (Second Author), Assistant Researcher (25%)/Daneshvar (Third Author), Methodologist (25%)
Funding: The study was funded by the first author.
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