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Article

Exploring the Impact of Public Spaces on Social Cohesion in Resettlement Communities from the Perspective of Experiential Value: A Case Study of Fuzhou, China

by
Yafeng Lai
1,
Pohsun Wang
1,* and
Kuohsun Wen
2
1
Faculty of Innovation and Design, City University of Macau, Macau 999078, China
2
School of Design, Fujian University of Technology, Fuzhou 350118, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(10), 3141; https://doi.org/10.3390/buildings14103141
Submission received: 24 August 2024 / Revised: 24 September 2024 / Accepted: 29 September 2024 / Published: 1 October 2024
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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Abstract

:
With the rapid pace of global urbanization, the urbanization of resettlement communities in China has garnered increasing attention from scholars. This study, grounded in experiential value theory, delves into the relationship between public spaces in resettlement communities and their social cohesion. Focusing on resettlement communities in the central urban area of Fuzhou, this study employs a mixed-method approach to analyze the functional characteristics of public spaces using geospatial data, including their green coverage ratio, spatial accessibility, facility configuration, and neighborhood density. A correlation analysis and multiple linear regression were employed to identify the key elements influencing social cohesion. The results indicate significant disparities in the green coverage, accessibility, facility configuration, and neighborhood density of public spaces. These differences are evident in the quantitative metrics used and also reflect imbalances in spatial layout and resource distribution, highlighting potential pathways for optimizing the quality of public spaces. Further data analyses revealed that both emotional value (β = 0.602, p < 0.01) and functional value (β = 0.136, p < 0.01) have significant positive impacts on social cohesion, with emotional value being particularly influential. This study offers insights for urban planners and policymakers by providing scientific evidence for the optimization of public space design in resettlement communities, with implications for community governance and urban sustainability.

1. Introduction

“Resettlement communities” exist in various forms across the globe and play a crucial role in advancing urban sustainability strategies. Internationally, resettlement communities often emerge from UNHCR-led relocation programs designed to help resettled refugees adapt to new countries, with such programs implemented in Western nations, including in the United States, Australia, and European countries [1,2]. In China, however, the concept primarily refers to government-led rural-to-urban resettlement initiatives, which create unique communities driven by large-scale urbanization policies [3]. These resettlement communities adhere to the urban community development model, constructed according to specific standards and regulations [4]. As of 2023, China’s urbanization rate had risen to 65.22% [5], making resettlement communities a significant focal point for studying the social structure and function of cities. Unlike traditional communities, resettlement communities are deeply integrated into urban society in terms of their physical space, living environment, and infrastructure, while simultaneously preserving certain rural traditions, making them a vital component influencing urban development. Thus, the healthy development and construction of resettlement communities in China are of great significance for urban sustainability.
Research over the past few decades has demonstrated that the built environment plays a key role in enhancing social interaction and cohesion within different communities [6]. As part of the built environment, public spaces serve as crucial venues for residents to engage in daily recreational activities and as important platforms for promoting social interaction [7]. This paper focuses on two aspects of public spaces in resettlement communities: first, the design of physical spaces that facilitate public interactions, such as green spaces, squares, and community parks and, second, the impact of these spaces on residents’ social behaviors, such as playing chess, dancing in public squares, and taking walks. Existing research has shown that the spatial characteristics of public spaces significantly influence residents’ perceptions of their environment. Kaplan and Kaplan’s Environmental Preference Theory and Ulrich’s Stress Recovery Theory both emphasize the positive effects of natural environments on people’s psychological states and behavior. Research indicates that green spaces and other natural environments contribute to increased environmental satisfaction and mental well-being among residents [8,9,10]. In the context of resettlement communities in China, these theories provide valuable insights into how optimizing public space design can enhance residents’ mental health and social connections. However, current research has not sufficiently explored how physical space design and behavioral characteristics specifically promote social cohesion. While numerous empirical studies have investigated different aspects such as residents’ safety, health, and well-being [11,12,13,14,15], the specific application of these findings to resettlement communities requires further investigation.
This paper aims to explore the impact of the design of public spaces in resettlement communities on social cohesion from the perspective of experiential value. “Experiential value” is theorized and measured as “the intrinsic and extrinsic value individuals derive from interactions within a space” [16]. As an extension of customer value theory, experiential value can be understood as the perception and relative preference for a product or service attributes generated through environmental interactions that either aid or hinder the achievement of individual goals [17]. Consequently, experiential value is closely linked to the environment of public spaces [18].
To comprehensively explore the impact of experiential value on the social cohesion in communities, this study specifically focuses on two of its aspects: emotional value and functional value. Emotional value pertains to how public spaces fulfill individuals’ emotional needs, thereby enhancing their sense of belonging and satisfaction. Functional value, on the other hand, addresses the practicality of public spaces and their effect on community interactions and social connections. Therefore, this paper will examine the contribution of experiential value to the development and maintenance of social cohesion within resettlement communities, a topic that has been insufficiently explored in current research. While previous studies have focused on health, safety, and well-being in these communities, the link between public space design and social cohesion remains under-researched. This study addresses this gap by investigating how environmental interactions provide value to individuals, fostering connections between them and strengthening social cohesion. Sustainable design strategies for public spaces are proposed, guided by the theoretical relationship between experiential value and social cohesion in resettlement communities.
Through this study, we seek to address the following three research questions: (1) How are public spaces in resettlement communities related to experiential value theory, particularly in terms of their functional and emotional value? (2) How do public spaces in resettlement communities contain emotional value? (3) How do the functional and emotional values of public spaces influence their relationship to social cohesion in resettlement communities? This study draws on survey data and geospatial data collected from resettlement communities in the central urban area of Fuzhou. It uses multiple linear regression to statistically analyze the questionnaire data and propose a design strategy, based on experiential value theory, that impacts the social cohesion felt in public spaces in resettlement communities.
The remainder of this paper is organized as follows: Section 2 outlines the concepts of a resettlement community, experiential value, and social cohesion and presents the research hypotheses. Section 3 details the steps of the research methodology. Section 4 presents the results of the analyses conducted. Section 5 discusses the findings and limitations of this study. Finally, Section 6 offers concluding remarks and suggests directions for future research directions (Figure 1).

2. Theoretical Background and Hypothesis Development

2.1. Theoretical Background

2.1.1. The Relationship between Public Spaces in Resettlement Communities and Experiential Value

Public spaces serve as venues for public interaction and social activities [19]. In this study, “public spaces” refer to physical spaces within a 15 min walk (1000 m) of the center of a resettlement community where gatherings and interactions occur. These spaces specifically include community parks, squares, children’s playgrounds, and street-side shops and exclude community roads and inaccessible green areas. With the development of the experience economy, the concept of “experience” has been widely studied and applied in fields such as tourism, hospitality, and dining [20,21,22]. Based on Holbrook’s concept of “value”, in this study, “experiential value” is defined as the interactive relativistic experience of a preference that residents derive from interacting with public spaces [23,24].
Research by Wu et al. highlights the critical role public spaces play in shaping individual experiences, noting that well-designed public spaces can significantly enhance experiential value [25]. Sui and Yang emphasize the importance of physical spaces in enabling the healthy lifestyles of elderly residents, linking the diversity of community spaces and the quality of recreational landscapes to residents’ perceptions of their space’s quality [26]. Chowdhury et al. further propose that spatial and design factors in residential environments are closely related to users’ cognitive perceptions and physical responses to that space [27]. Zheng et al. found that exposure to green spaces within a community influences the frequency with which residents partake in community activities and their satisfaction with their experiences [28]. These findings suggest that the experiential value of public spaces in resettlement communities is closely related to their physical characteristics and promotion of residents’ well-being, sense of belonging, social participation, and access to equitable opportunities.

2.1.2. The Relationship between Public Spaces in Resettlement Communities and Social Cohesion

Social cohesion is multidimensional and has a significant positive impact on social development [29]. It can be measured by the “level of connection and solidarity within a group”, which includes factors such as neighborhood familiarity, mutual assistance, and trust [30,31]. Studies have shown that public spaces play a critical role in developing and maintaining social relationships between residents [25]. Research by Mazumdar et al. indicates that the accessibility of these places is closely linked to their enhancement of social cohesion, supporting findings by Kwon et al. and Leyden [32,33,34].
However, the relationship between neighborhood density and social relationships is more complex. Dempsey et al. [35] found that a higher neighborhood density promotes the use of local services and facilities; however, the authors of other studies suggest that a high neighborhood density may be associated with lower levels of social cohesion [36,37]. Additionally, the results of studies by Maryna and Andrii, as well as that of Ludin et al., show that public spaces and infrastructure, such as parks, recreational facilities, educational institutions, and healthcare facilities, significantly impact social cohesion [38,39]. Wan et al.’s literature review further emphasizes how the physical characteristics and usage patterns of green spaces influence social cohesion [40]. Overall, the physical characteristics of public spaces are crucial in promoting interactions and trust among residents, thereby enhancing social cohesion.

2.1.3. The Impact of Experiential Value on Social Cohesion

Han et al. propose that indirect individual interactions significantly influence all dimensions of experiential value, which may also enhance residents’ perceptions of community and social cohesion [41]. Although the dimensional structure and scales of experiential value have been validated through numerous empirical studies [42,43,44,45], the role of experiential value in promoting social cohesion has not been fully explored. Research indicates that an increasing neighborhood density may reduce social cohesion [6]. Although high-density communities may enable more social interactions, this does not necessarily translate into higher levels of neighborhood cohesion [46]. High-density environments may increase social contact but could also lead to a decline in social cohesion [36,37,47]. This finding suggests that while urban development enhances the quality of living environments, if these spaces’ experiential value is not fully realized, social cohesion may be negatively impacted.

2.2. Hypothesis Development

2.2.1. Construction of the Indicator System

Experiential value is the intrinsic and extrinsic value individuals gain through interactions with spaces. Drawing on the experiential value structure proposed by Meng Qiuli, we categorize experiential value into five dimensions: functional, contextual, emotional, social, and economic [48]. Given the characteristics of resettlement communities, this study focuses on two core dimensions: functional value and emotional value, which represent the practicality of the physical environment and the emotional experiences of residents to it, respectively.
Functional value primarily concerns the practicality of public spaces and contains four subcategories: the green coverage ratio, spatial accessibility, the configuration of facilities, and neighborhood density. The green coverage ratio reflects the appearance of the community’s landscape and is measured by the proportion of green spaces and vegetation within a 15 min walk. Spatial accessibility is evaluated in terms of the spaces residents can reach on foot, indicating the convenience of public spaces. The configuration of facilities reflects the diversity and quality of public spaces’ functions and services, including the total number of facilities available for dining, shopping, finance, education, culture, life services, sports, recreation, and healthcare. Neighborhood density measures the residential density of resettlement communities, which is calculated by the number of residential points within a 15 min walk, reflecting population density and the space’s utilization.
Emotional value focuses on residents’ emotional experiences and includes five subcategories: attractiveness, pleasure, escapism, friendliness, and sense of belonging. These indicators provide a comprehensive framework for studying how public spaces in resettlement communities influence social cohesion, laying a solid foundation for subsequent empirical analyses.

2.2.2. Hypothesis Development and Model Construction

We aim to provide empirical evidence of the relationship between public spaces in resettlement communities and social cohesion based on experiential value theory and explore how design and planning can enhance community cohesion. By constructing the theoretical model shown in Figure 2, the following research hypotheses are proposed:
(1) Public spaces in resettlement communities can significantly enhance social cohesion.
(2) A greater functional value of the public spaces in resettlement communities can significantly enhance social cohesion.
(3) A greater emotional value related to the public spaces in resettlement communities can significantly enhance social cohesion.
By testing these hypotheses, this study aims to provide scientific evidence as to how public space design can effectively enhance social cohesion and offer specific recommendations for the sustainable development of resettlement communities.

3. Materials and Methods

3.1. Definition of Research Scope and Sample Selection

This study focuses on Fuzhou, the capital city of Fujian Province and the core city of the Fuzhou Metropolitan Area in China. The selection of Fuzhou as the study area was influenced by two main factors: First, Fuzhou’s rich and diverse cultural background, which includes prevalent folk beliefs and profound influences from Buddhism, Taoism, and Confucianism, which are tightly interwoven with local customs. Second, Fuzhou’s comprehensive level of development serves as a microcosm of the economic growth seen in China’s second-tier cities. Thus, Fuzhou was chosen as the research site not only for its representativeness but also for its unique research value. According to data from the Fuzhou Real Estate Registration and Transaction Center, as of December 2023, there were 503 registered resettlement communities in Fuzhou (Figure 3), which primarily follow on-site and off-site resettlement models. To ensure the representativeness and scientific rigor of our sample, we employed statistical principles to randomly distribute surveys across selected study units. Information about the sample is presented in Table 1.

3.2. Public Spaces’ Experiential Value and the Scale of Their Social Cohesion

3.2.1. Questionnaire Design and Participants

This study focuses on resettled adults in the main urban area of Fuzhou, employing experiential value theory to investigate the relationship between public spaces and social cohesion. The questionnaire is divided into three sections: basic information about the participants, the experiential value attached to public spaces, and the scale of social cohesion.
(1) Demographic Information: The first section collects basic demographic information, including gender, age, presence of children, education level, occupation, and duration of residence. Duration of residence is considered a potential factor influencing neighborhood social cohesion [49] and is measured on a scale from “less than one year” to “more than three years”. These demographic variables serve as control variables with a potential impact on social cohesion.
(2) Experiential Value: The second section assesses the experiential value residents attach to public spaces in resettlement communities. This section uses a scale developed by Meng Qiuli [48], which distinguishes between functional value (measured by 4 items) and emotional value (measured by 5 items). The functional value questions are designed to evaluate aspects such as the utility and usability of public spaces, while the emotional value questions explore residents’ feelings about and psychological responses to these spaces. Both types of value serve as variables with an influence on social cohesion.
(3) Social Cohesion: The third section employs the social cohesion scale developed by Latham et al. [31] to evaluate three key aspects of community cohesion: (a) residents’ knowledge of each other, (b) their willingness to help each other, and (c) the level of trust among residents. Each item is rated on a Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). This scale directly measures social cohesion outcomes and aligns with this study’s objective, examining how a public space’s experiential value influences social cohesion.
By explicitly linking each section of the questionnaire to the research objectives, this design ensures a comprehensive assessment of how functional and emotional values of public spaces impact social cohesion.

3.2.2. Data Sources

A pilot survey was conducted in Jin’an District, Fuzhou, in February 2024, yielding 48 valid responses. The questionnaire was adjusted based on these results. The formal survey was conducted between March and June, using a convenience sampling method to distribute questionnaires to residents in 10 selected study units. These communities were in different locations and contained various types of public spaces. The questionnaire was distributed both online (via community homeowner groups) and offline, resulting in 287 responses. After data cleaning, 267 valid questionnaires were obtained, with an effective response rate of 93%. To ensure the validity of the analysis, we ensured that the sample size used was sufficient for statistical reliability. According to Memon et al., a sample size within this range is generally deemed adequate for ensuring the robustness of statistical analyses [50]. In this study, we did not analyze experiential value or social cohesion using single-variable analyses, as our goal was to explore the relationship between public spaces and social cohesion through multiple linear regression models that consider social demographic variables. Multiple linear regression was chosen because it can assess the relationships between multiple independent variables and a single dependent variable simultaneously. This method is particularly suitable for our analysis, as it allows for the control of various confounding factors and quantifies the strength and direction of the relationship between public space characteristics and social cohesion.

3.3. Geographic Data Collection and Processing

The original POI data included a wide variety of categories that contained overlapping and redundant data. The data were reclassified according to the “Research on POI Classification Standards” [51]. We also used road network data (including attributes such as road names and lengths) and green space distribution data to analyze the green coverage ratio, spatial accessibility, facility configuration, and neighborhood density of the public spaces in resettlement communities. To avoid errors caused by coordinate discrepancies, the data were standardized using the WGS_1984 coordinate system, resulting in the POI data used in this study.

4. Results

4.1. Analysis of the Functional Characteristics of Public Spaces in Resettlement Communities

We utilized Geographic Information Systems (GISs) to import and analyze the collected POI data, focusing on four aspects within a 15 min walk of each community—the green coverage ratio, spatial accessibility, facility configuration, and neighborhood density—to reveal the functional characteristics of public spaces in resettlement communities (Table 2). The results indicate significant differences in the characteristics of public spaces across different communities. These differences are not only reflected in the quantitative metrics of green coverage and the configuration of facilities, but also in the imbalances in spatial accessibility and the distribution of neighborhood points.
In terms of the green coverage ratio (Figure 4), the surface area covered by green spaces in the public spaces within each survey unit was calculated using green space distribution data. The analysis revealed spatial disparities in the green levels of public spaces in resettlement communities, with insufficient equity in green space services. Specifically, Bai Ma River New Village, Shiou Wangzhuang District Ten, and Jinjian Community had the lowest green coverage levels. In terms of spatial accessibility and facility configuration (Figure 5 and Figure 6), the quality of these areas’ services was assessed based on the number of facilities accessible within a 15 min walk. The analysis identified that Helin Xincheng, Chengxiangfang Community, and Yuefeng Xincheng had inadequate services and failed to provide residents with high-quality spaces for the community to gather and participate in activities. Regarding neighborhood density (Figure 7), the distribution of residential densities within the living circles of various communities was examined. The results indicated that, apart from Shiou Wangzhuang District Ten and Wenquan Dongtang Community, the distribution of residential density in the communities was relatively balanced.
This study highlights the unequal distribution of public spaces in resettlement communities in terms of their green coverage, accessibility, facilities, and neighborhood density and identifies potential directions for improving the quality of the public spaces in these communities.

4.2. Survey Data Results and Analysis

4.2.1. Validity Testing of the Questionnaire

In this study, we utilized SPSS 25.0 to perform KMO and Bartlett’s tests of the valid questionnaire data, assessing the suitability of the variables for a factor analysis. The results demonstrated a high level of internal consistency. The KMO value was 0.901 (a reference value > 0.7), and Bartlett’s test of sphericity showed a significance value (sig) of 0.000 (Sig. < 0.05), indicating that the variables were appropriate for a factor analysis (Table 3). The Cronbach’s alpha coefficients were all above the acceptable standard of 0.7, with the functional value α = 0.87, emotional value α = 0.861, and social cohesion α = 0.848, indicating the good internal consistency of the variables (Table 4). Additionally, the composite reliability (CR) coefficients for each dimension were all greater than 0.7, suggesting that the observed variations of each latent variable have internal consistency. In addition, all variable fitting indicators meet these standards (Table 5), indicating that the model has good adaptability.

4.2.2. Correlation Analysis

To verify our research hypotheses, a bivariate Pearson correlation analysis was conducted to examine the relationships between the functional and emotional value of public spaces in resettlement communities and carry out a subjective assessment of social cohesion. The correlation results are presented in Table 6. From the results presented in the table above, the following points can be derived:
(1) Correlation Analysis: This analysis reveals significant relationships between the functional and emotional value of public spaces in resettlement communities and the various dimensions of social cohesion.
(2) The Functional Value of Public Spaces: The green coverage ratio exhibits moderate correlations with all dimensions of social cohesion. Spatial accessibility is also moderately correlated with all dimensions of social cohesion and has the highest correlation with overall social cohesion. Facility configuration demonstrates stronger correlations, particularly with participants’ understanding of their neighborhood and overall social cohesion. Neighborhood density shows moderate correlations, with the highest correlation being with overall social cohesion.
(3) The Emotional Value of Public Spaces: Attractiveness displays strong correlations with the different dimensions of social cohesion, and especially with overall social cohesion. Pleasure, escapism, and friendliness all exhibit strong correlations, with friendliness showing the highest correlation with overall social cohesion. The sense of belonging displays the strongest correlations across all dimensions, and particularly with overall social cohesion.

4.2.3. Linear Regression and the Analysis of Its Results

To verify which elements of the experiential value of public spaces in resettlement communities influence social cohesion, and their significance, a multiple linear regression analysis was conducted. The dependent variable was social cohesion (including neighborhood familiarity, mutual assistance, and trust), while the independent variables were the functional and emotional value of public spaces in resettlement communities. The results of the regression model (Table 7) and the data displayed in the coefficients table (Table 8) were obtained through this analysis.
From the results of the multiple linear regression models shown in Table 7, the following conclusions can be drawn:
(1) All eleven models passed the F-test, indicating that each is statistically valid. They demonstrate a linear relationship with the dependent variable and are successfully established. Model 4 exhibited the strongest explanatory power, with an R2 value of 0.6. Its adjusted R2 value of 0.585 suggests that, after accounting for sample size and the number of variables present, the model maintains substantial explanatory strength.
(2) The R2 and adjusted R2 values for Models 1 to 3 and Models 5 to 11 range between 0.285 and 0.519, indicating that these models are less able to explain the social cohesion seen, even though they are still statistically significant.
(3) The Durbin–Watson statistic is close to 2, indicating that there is no autocorrelation among the residuals, thus satisfying the assumptions of the regression model. Most models have Durbin–Watson values near 2, particularly Models 1 and 4, which both equal 2.
From the data presented in Table 8 regarding the multiple linear regression models, the following insights were obtained:
(1)
The Overall Impact of Public Spaces on Social Cohesion
Public spaces in resettlement communities significantly enhance social cohesion (β = 0.584, p < 0.01). Both their functional value (β = 0.136, p < 0.01) and emotional value (β = 0.602, p < 0.01) positively impact social cohesion. Enhancing both types of value can strengthen social cohesion.
(2)
The Impact of Functional Value
The green coverage ratio (β = 0.264, p < 0.01), facility configuration (β = 0.215, p < 0.01), attractiveness (β = 0.262, p < 0.01), friendliness (β = 0.26, p < 0.01), and a sense of belonging (β = 0.331, p < 0.01) positively impact residents’ understanding of their neighborhood. Hypothesis 2 is partially supported (H2a and H2c are supported; H2b and H2d are not). The green coverage ratio (β = 0.306, p < 0.01), a sense of belonging (β = 0.233, p < 0.01), and the length of residence (β = 0.174, p < 0.01) positively impact mutual assistance. The frequency of the use of public spaces (β = −0.263, p < 0.05) and the presence of children (β = −0.175, p < 0.05) have negative impacts. Hypothesis 3 is partially supported (H3a and H3c are supported; H3b and H3d are not). The green coverage ratio (β = 0.272, p < 0.01) and friendliness (β = 0.295, p < 0.01) positively impact neighborhood trust. Hypothesis 4 is partially supported (H4a and H4c are supported; H4b and H4d are not).
(3)
The Impact of Emotional Value
Emotional value contributes to an understanding of the neighborhood, supporting Hypothesis 5 (H5a, H5b, H5d, and H5e are supported). Emotional value significantly impacts mutual assistance; Hypothesis 6 is supported (H6a, H6d, and H6e are supported). Emotional value significantly influences neighborhood trust, supporting Hypothesis 7 (H7c, H7d, and H7e are supported).

5. Discussion

This study explores how the functional and emotional value of public spaces in resettlement communities influences social cohesion from the perspective of experiential value theory. Through geographic spatial data and survey analyses, our findings provide visual and quantitative methods for analyzing the functional characteristics of public spaces, offering strategies to enhance social cohesion among residents. The use of ArcGIS for visualizing the functional characteristics of public spaces revealed significant differences across different types of resettlement communities in terms of their green coverage ratio, spatial accessibility, facility configuration, and neighborhood density. Additionally, the survey analysis indicated that emotional value (β = 0.602, p < 0.01) and functional value (β = 0.136, p < 0.01) both have significant positive impacts on social cohesion, with emotional value showing a more pronounced effect.
The results of previous studies have demonstrated the importance of the quality and accessibility of public spaces in enhancing community interactions and social cohesion [33,34,52]. This research further supports these findings, particularly highlighting the significant impact of emotional value on this cohesion, which confirms the applicability of experiential value theory within the context of resettlement communities. The results also revealed substantial differences in public space characteristics across communities, which align with the global phenomenon of unequal resource distribution in the urbanization process [53,54,55,56].
Additionally, to further explore the role of public spaces in fostering social cohesion, this study incorporates multidimensional perspectives, including infrastructure, transportation, and relational mobility, emphasizing the impact of these factors on the perception of public spaces. Research indicates that shared mobility systems, as part of smart cities, not only enhance the accessibility of public spaces but also provide key opportunities for urban competitiveness [57]. By improving transportation connectivity, these systems promote social interaction among residents, thereby contributing to increased social cohesion. Moreover, changes in transportation and population mobility also influence the usage patterns of public spaces. During the urban–rural transformation process, city expansion has affected migration patterns and frequency, particularly in resettlement communities. Understanding public spaces from the perspective of relational mobility offers deeper insights into their role in fostering resident interactions. In these communities, where population mobility is frequent, public spaces serve not only as venues for residents to meet and interact but also facilitate interactions among unfamiliar residents through thoughtful design and layout. This mobility and the accessibility of public spaces have a profound impact on social cohesion among residents, helping to alleviate the social isolation caused by migration and enabling residents to build stronger social networks [58].
Furthermore, Pantić et al. have highlighted the significant impact of changes in the methods used for public participation in activities on urban planning and public space design. The shift from traditional to virtual participation offers potential benefits, and this change affects both the design and use of public spaces [3,59], while also underscoring the importance of incorporating diverse community perspectives into the design process to effectively meet varying needs. By integrating these participatory approaches into the design of public spaces, we can better adapt to the evolving expectations of residents, thereby enhancing the relevance and effectiveness of urban planning strategies.

5.1. Practical Implications

These findings contribute significantly to our understanding of public space design and social cohesion in resettlement communities. First, this study shows that increasing green coverage and improving the configuration of facilities can significantly enhance social cohesion, providing empirical evidence of this for urban planners. In practical terms, this implies that design strategies should focus more on enhancing emotional value by increasing the number of interactive spaces and green areas to boost residents’ sense of belonging and satisfaction. Additionally, the improvement in functional value, such as through the configuration of facilities, has also been proven to positively impact social cohesion, suggesting that designs should consider the reasonable layout and accessibility of facilities to ensure that all groups have access to high-quality public spaces.

5.2. Limitations

Despite the in-depth understanding provided by the multiple linear regression and correlation analyses of the relationship between public spaces in resettlement communities and social cohesion, some limitations to this study exist:
(1) Firstly, we primarily focused on resettlement communities in Fuzhou, which may limit the generalizability of our results. Future research should expand this work to other regions or countries to verify the broader applicability of these findings.
(2) Secondly, in this study, we did not fully consider the impact of socioeconomic factors, such as income level and educational background, on social cohesion, which may be significant. Future studies could include more control variables to minimize the potential biases in these results. Furthermore, this study relies on self-reported survey data, which may be subject to biases such as respondent bias and inaccuracies in self-reported measures. These factors could affect the reliability of the findings related to public space usage and social cohesion.
(3) GIS-Based Analysis Limitations: While the use of a GIS-based kernel density analysis adds depth to this study, it also has inherent limitations. For instance, the resolution of the data and the assumptions underlying the analysis could influence the interpretation of the spatial patterns seen.
By acknowledging these limitations, we aim to provide a more nuanced understanding of this study’s findings and to highlight areas for future research. Addressing these limitations will be crucial for refining this methodology in subsequent studies and for achieving more reliable outcomes.

6. Conclusions

6.1. Conclusions from This Research

Based on prior research into the impact of the urban built environment on social cohesion, this study provides new insights into the relationship between public spaces in resettlement communities and social cohesion. By empirically testing and validating the proposed research hypotheses, this study confirms that both the functional and emotional values of public spaces play a critical role in enhancing social cohesion within resettlement communities. Grounded in experiential value theory, this research explores the complex relationship between public spaces and social cohesion. Furthermore, this study thoroughly considers the complex conditions under which the transformation of public spaces occurs during the transition of people from rural to resettlement communities, analyzing the “space–cognition–development” concept in depth. Utilizing current public space construction standards, this study completes and optimizes its research results based on the emotional value of these spaces, as perceived by residents, thus establishing relevant design strategies for real-world scenarios:
(1) Enhancing the Functional Value of Public Spaces
Urban planners should prioritize the development of green spaces and the rational configuration of public facilities. Specific policies could include incentives for creating green infrastructure and guidelines for ensuring the accessibility and adequacy of facilities in public spaces. Implementing regular assessments and community feedback mechanisms can further ensure that public spaces meet residents’ needs.
(2) Improving the Emotional Value of Public Spaces
Policy initiatives should support the incorporation of creative landscapes and artistic installations in public space design. This could involve funding programs for public art and generate landscape design competitions. Additionally, planners should promote the creation of multifunctional spaces that foster community interactions, supported by guidelines for designing inclusive and engaging environments.
(3) Promoting Public Participation and the Application of Technology in Public Spaces
To align community designs with actual needs, policymakers should establish frameworks for involving residents in the design and planning processes. Developing best practices for integrating technological tools into planning processes can enhance the effectiveness of the design of community spaces.

6.2. Future Research Directions

It is important to note that planning and constructing public spaces in resettlement communities is a complex and ongoing process influenced by various factors, including national policies, construction costs, and socioeconomic conditions. In this context, future research should explore the specific links between public spaces in resettlement communities and social cohesion further. By combining quantitative and qualitative research methods, an in-depth analysis of the influence of various factors on residents’ social cohesion and community participation can be conducted. Furthermore, special attention should be paid to the diverse needs of residents, especially in urban environments with significant cultural and social differences. Through field studies and data analyses, the key elements of public spaces affecting social cohesion can be identified and practical design recommendations can be made. This will not only help enhance the functional and emotional value of public spaces but also provide empirical support for policymakers, promoting more inclusive community-building strategies. Ultimately, implementing these strategies will contribute to the creation of a more harmonious and sustainable urban environment, improving residents’ quality of life and social cohesion.

Author Contributions

Conceptualization and methodology, P.W. and K.W.; conceptualization, methodology, investigation, and writing—original draft, Y.L. and K.W.; investigation and writing—review and editing, Y.L. and P.W.; investigation, Y.L., P.W. and K.W.; formal analysis, P.W. and K.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Science and Technology Development Fund of the Macao Special Administrative Region (Project No.: 0036/2022/A).

Data Availability Statement

The data are not publicly available. The data used in this study are government data that cannot be openly shared. Readers may apply to the relevant government agencies for access to these data.

Acknowledgments

We would like to express our sincere gratitude to everyone who contributed to this research. Additionally, we also acknowledge the financial support provided by the Science and Technology Development Fund of the Macao Special Administrative Region (Project No.: 0036/2022/A), which made this study possible.

Conflicts of Interest

The authors declare no conflicts of interest.

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Buildings 14 03141 g001
Figure 1. Research framework.
Figure 1. Research framework.
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Figure 2. Model diagram of research hypothesis.
Figure 2. Model diagram of research hypothesis.
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Figure 3. Map of the study area and distribution of resettlement communities.
Figure 3. Map of the study area and distribution of resettlement communities.
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Figure 4. Analysis of the coverage of green spaces within the public spaces in this study’s sample.
Figure 4. Analysis of the coverage of green spaces within the public spaces in this study’s sample.
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Figure 5. Analysis of accessibility of public spaces within this study’s sample.
Figure 5. Analysis of accessibility of public spaces within this study’s sample.
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Figure 6. Analysis of the configuration of facilities within the public spaces in this study’s sample.
Figure 6. Analysis of the configuration of facilities within the public spaces in this study’s sample.
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Figure 7. Analysis of neighborhood density in the public spaces within this study’s sample.
Figure 7. Analysis of neighborhood density in the public spaces within this study’s sample.
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Table 1. Sample information.
Table 1. Sample information.
NumberCommunity NameAdministrative DivisionCompletion Time/YearResettlement ModeCommunity Size
1Xiajing New CityCang Shan2020In situ resettlement, off-site resettlement11 buildings, 1416 units
2Jinxiang GardenTai Jiang2016In situ resettlement9 buildings, 1938 units
3Shiou Wangzhuang Tenth DistrictJin An2013In situ resettlement3 buildings, 760 units
4Yuefeng New CityJin An2010In situ resettlement11 buildings, 2271 units
5Chengxiang Fang CommunityGu Lou2010Off-site resettlement13 buildings, 2315 units
6Pushang CommunityCang Shan2009In situ resettlement20 buildings, 790 units
7Helin New CityJin An2008Off-site resettlement, in situ resettlement34 buildings, 5378 units
8Jinjian CommunityCang Shan2006In situ resettlement48 buildings, 890 units
9Wenquan Dongtang CommunityGu Lou2003In situ resettlement9 buildings, 906 units
10Baimahe New VillageTai Jiang1996In situ resettlement19 buildings, 346 units
Table 2. Statistical results of the sample’s public space data.
Table 2. Statistical results of the sample’s public space data.
Community NameGreen Coverage Ratio/%Spatial Accessibility Area/km2Facility Configuration/NumberNeighborhood Residential Points/Number
Xiajing New City7%1.87 257862
Jinxiang Garden16.4%1.71 67469
Shi Ou Wang Zhuang Tenth District5.7%0.87 1200100
Yuefeng New City9.3% 1.51 24529
Chengxiang Fang Community22.9% 1.53 16822
Pushang Community15.4%1.95174873
Helin New City11%1.096713
Jinjian Community6.4%1.7146035
Wenquan Dongtang Community13.3%1.281914157
Baimahe New Village5.6%1.08172584
Table 3. KMO and Bartlett’s tests.
Table 3. KMO and Bartlett’s tests.
KMO Value0.901
Bartlett’s Test of SphericityApproximate Chi-Square2959.51
Df190
p-Value 0
Table 4. Questionnaire’s validity and reliability test results.
Table 4. Questionnaire’s validity and reliability test results.
Questionnaire CompositionFunctional ValueEmotional ValueSocial Cohesion
Cronbach’s alpha0.870.8610.848
Table 5. Model fit index test.
Table 5. Model fit index test.
IndexCMIN/DFRMRGFIAGFINFIIFITLICFIRMSEA
optimal index<3<0.05>0.9>0.9>0.9>0.9>0.9>0.9<0.08
Measurement results2.0040.0250.9460.9170.9360.9670.9570.9670.061
Table 6. Summary of information about variables.
Table 6. Summary of information about variables.
VariableTheoretical Dimensions of Social Cohesion
Neighborly UnderstandingNeighborly HelpNeighborly TrustSocial Cohesion
Functional valueGreening coverage0.229 **0.277 **0.273 **0.294 **
Spatial accessibility0.305 **0.355 **0.297 **0.362 **
Facility configuration0.426 **0.411 **0.388 **0.466 **
Neighborhood density0.278 **0.283 **0.295 **0.324 **
Emotional valueAttractiveness0.553 **0.512 **0.519 **0.603 **
Happiness0.447 **0.517 **0.529 **0.564 **
Escapism0.451 **0.452 **0.483 **0.525 **
Friendliness0.566 **0.534 **0.594 **0.643 **
Sense of belonging0.604 **0.565 **0.560 **0.658 **
Note: ** p < 0.01.
Table 7. Results of the multiple linear regression models.
Table 7. Results of the multiple linear regression models.
ModelRR2Adjusted R2Std. Error of the EstimateDurbin–WatsonFp-Value
10.6880.4740.4530.6514223.0480.000
20.6910.4780.4580.567281.91323.4520.000
30.6890.4750.4540.551782.03123.1460.000
40.7750.60.5850.452081.9738.4790.000
50.7150.5120.4940.498881.89929.9110.000
60.5710.3260.2940.740121.96810.2370.000
70.6180.3820.3530.619531.9213.1050.000
80.5340.2850.2510.646282.0098.4440.000
90.720.5190.4940.626411.93621.0060.000
100.6810.4630.4360.578691.92716.7970.000
110.6960.4840.4570.55032.02718.2370.000
Table 8. Data from the multiple linear regression models.
Table 8. Data from the multiple linear regression models.
VariableNeighborly UnderstandingNeighborly HelpNeighborly TrustSocial Cohesion
Model 1Model 6Model 9Model 2Model 7Model 10Model 3Model 8Model 11Model 4Model 5
Gender−0.0590.01−0.0850.0070.061−0.032−0.0730.006−0.096 *−0.0480.014
Age−0.113−0.202−0.11−0.101−0.167−0.11−0.068−0.152−0.066−0.108−0.149
Children status−0.046−0.14 *−0.034−0.1−0.175 **−0.11−0.031−0.136−0.044−0.067−0.111
Education level−0.152−0.195 *−0.134−0.125−0.181−0.099−0.06−0.097−0.038−0.131−0.157 *
Occupation−0.084−0.118−0.0980.0450.0060.029−0.008−0.041−0.028−0.022−0.034
Duration of residence0.012−0.04−0.001−0.06−0.105−0.0540.115 *0.0530.121 *0.024−0.017
Frequency of space participation−0.015−0.14 *−0.01−0.153 *−0.263 **−0.133 *−0.074−0.216 **−0.056−0.088−0.171 **
Duration of space use0.1040.193 **0.115 *0.10.174 **0.093−0.0120.094−0.0030.0760.139 **
Greening coverage 0.264 ** 0.306 ** 0.272 **
Spatial accessibility 0.013 0.071 −0.022
Facility configuration 0.215 ** 0.15* 0.197 **
Neighborhood density 0.04 0.047 0.087
Attractiveness 0.262 ** 0.141 * 0.122
Happiness −0.143 * 0.065 0.097
Escapism 0.028 0.078 0.127 *
Friendliness 0.26 ** 0.164 * 0.295 **
Sense of belonging 0.331 ** 0.233 ** 0.156 *
Functional value0.105 0.191 ** 0.062 0.136 **
Emotional value0.539 ** 0.44 ** 0.609 ** 0.602 **
Public space of resettlement community 0.584 **
Note: The β-value indicates the extent of the influence of the independent variable on the dependent variable. * p < 0.05 ** p < 0.01.
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Lai, Y.; Wang, P.; Wen, K. Exploring the Impact of Public Spaces on Social Cohesion in Resettlement Communities from the Perspective of Experiential Value: A Case Study of Fuzhou, China. Buildings 2024, 14, 3141. https://doi.org/10.3390/buildings14103141

AMA Style

Lai Y, Wang P, Wen K. Exploring the Impact of Public Spaces on Social Cohesion in Resettlement Communities from the Perspective of Experiential Value: A Case Study of Fuzhou, China. Buildings. 2024; 14(10):3141. https://doi.org/10.3390/buildings14103141

Chicago/Turabian Style

Lai, Yafeng, Pohsun Wang, and Kuohsun Wen. 2024. "Exploring the Impact of Public Spaces on Social Cohesion in Resettlement Communities from the Perspective of Experiential Value: A Case Study of Fuzhou, China" Buildings 14, no. 10: 3141. https://doi.org/10.3390/buildings14103141

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