How Does the Built Environment Shape Place Attachment in Chinese Rural Communities?

Abstract

In the course of rural spatial transformation in China, the vicissitudes of the traditional built environment have given rise to the deconstruction of locality, whereas place attachment emerges as the crux for addressing this issue. Considering that current research on how the built environment influences place attachment remains deficient in constructing a multi-dimensional and composite analytical framework from a rural perspective, this paper constructs a ”community–individual” nested analytical framework and establishes a five-dimensional system of rural built environment elements covering roads, boundaries, regions, nodes, and landmarks. On this basis, this paper takes 15 village cases in Leiling Town, Guangdong Province, China, as the research object, using a hierarchical linear model (HLM) to systematically analyze the impact of rural built environment elements on residents’ place attachment. The study finds that 1. At the individual level, the average score of place attachment is 0.61, with females showing significantly higher levels than males, and age and length of residence being positively correlated with place attachment. 2. At the community level, the built environment explains about 15% of the variance in attachment, with the distance from villages to town centers being negatively correlated and building compactness, environmental tidiness, and cultural landmark density being positively correlated. 3. Node–landmark elements have a significantly stronger impact on place attachment than road-boundary and functional-area elements. 4. The influence mechanism follows the identity cycle of “memory identity—place identity—social identity”.

1. Introduction

Chinese society is characterized by profound rural characteristics [1], and the homeland holds an indispensable position in the hearts of Chinese people, which constitutes the emotional foundation of rural residents’ place attachment in China. However, the source of this emotional foundation is now facing drastic changes. At present, rural areas in China are undergoing the largest and fastest spatial transformation in human history. Driven by the strategies of Rural Revitalization and New Rural Construction, the modernization of rural infrastructure, public service facilities, and living environments has accelerated. However, early construction overlooked the main needs of residents, leading to the replacement of traditional dwellings, ancestral halls, and street textures by standardized and homogenized modern buildings. This has caused the erosion of rural landscapes, putting rural locality at risk of deconstruction, and consequently dissolving residents’ local sense of belonging and identity [2]. To this end, the Chinese government has proposed the concept that “effective governance is the foundation of rural revitalization”, emphasizing the dominant role of villagers and relevant organizations in rural construction and committing to reshape the local emotional foundation of modern rural governance.

Place attachment is a positive emotional bond formed between individuals and places [3], and its formation is deeply influenced by individuals’ personal experiences and the social culture of the place. The academic community has conducted extensive statistical analyses on the level, characteristics, and formation process of place attachment. Xu [4] found that gender, household registration, and length of stay were significantly correlated with place attachment. Rashid [5] revealed that the causes of neighborhood attachment largely stem from close neighborhood relationships. Brown [6] explored the pathways through which individual sociodemographic and family economic attributes influence place attachment, discovering that individual socioeconomic attributes significantly affect the intensity of place attachment. Existing research indicates that residents’ individual socioeconomic attributes and physical-mental states are key factors influencing the degree of place attachment, a variable relationship that cannot be ignored when exploring the impact of other factors on place attachment.

When exploring the key factors influencing place attachment, the built environment, as the material carrier of the human–land relationship, has gained widespread academic recognition for its core role [7,8]. From urban spatial layout to neighborhood facility configuration and the completeness of basic service facilities, various elements of the built environment profoundly influence residents’ emotional experiences and identity construction by affecting their perception of places. Existing studies have found through extensive empirical analyses that multiple elements of the built environment are significantly associated with residents’ level of place attachment. For example, the tidiness of urban living environments [9], the morphological features of buildings [10], the diversity of neighborhood facilities [11], and the accessibility of public spaces [12] all have varying degrees of impact on residents’ place attachment. Additionally, built environment spaces with regional characteristics that meet daily functional needs and promote social interaction among residents [13], such as historical and cultural heritage and urban landmarks, can further strengthen the emotional bond between people and places, effectively enhancing residents’ level of place attachment. When exploring the formation mechanism of place attachment, research has further confirmed that “time investment” is a key variable [14]. As residents’ length of stay increases, the intensity of place attachment gradually strengthens through the continuous accumulation of memories and emotional precipitation. This dynamic process reveals that place attachment is not static but has the characteristics of gradual accumulation and continuous deepening.

Although the impact mechanism of the built environment on place attachment has accumulated relatively rich research, existing achievements still have significant limitations in terms of research fields and methodology. From a spatial dimension, existing studies mainly focus on urban scenarios such as urban communities, parks, and schools, with an obvious research gap in rural contexts. Most existing rural-related studies remain at the level of single environmental elements, such as public spaces, or fragmented analysis at the individual dimension, failing to construct a composite multi-dimensional analytical framework and lacking a systematic exploration of the impact mechanism of “rural built environment–place attachment”. Moreover, most studies tend to be qualitative, with obvious insufficient empirical data support.

Place attachment is a core concept in the theory of human–land relationships [15]. Carrying out research on the correlation between the rural built environment and residents’ place attachment can not only promote the paradigm shift of rural human settlement environment construction from “designer-led” to “user needs-oriented” but also provide a scientific basis for reshaping the emotional connection between humans and land in rural areas. Based on this, this study is rooted in the rural regional context of China, takes rural residents as the research object, and systematically explores the action characteristics and formation mechanism of rural built environment elements and residents’ place attachment by constructing a “community–individual” nested analytical framework and using statistical methods such as the multilevel linear model (HLM). The study aims to reveal the transmission path of built environment–place attachment, provide a scientific basis for “people-oriented” rural planning practices and theoretical innovations, and help coordinate the development of human–land relationships under the Rural Revitalization Strategy.

2. Research Design

2.1. Research Framework on the Influence of the Built Environment on Place Attachment

Place attachment is the connection of human and local emotions. To fully explore the pathways through which the rural built environment influences residents’ place attachment, this paper constructs a systematic technical pathway comprising four steps: framework construction, data collection, variable measurement, and correlation analysis, as shown in Figure 1.

Step 1: Construct a community-individual nested analytical framework for the impact of the rural built environment on residents’ place attachment. The framework integrates three categories of core elements and two spatial scales. Among them, the three types of elements include the characteristics of the built environment in rural areas, the social characteristics of residents, and the place attachment of residents. The three types of elements are all quantitatively expressed through different indicators or scales; the two scales are the community scale and the individual scale.

Step 2: Carry out multi-source data collection and processing. On the one hand, technical means such as OSM from GaodeMap and POI from GaodeMap are used, combined with government open data platforms, to systematically obtain data on the spatial structure, road network, architectural features, and population distribution of each rural sample, constructing a rural built environment database. On the other hand, through field visits, questionnaires, and other methods, specific evaluations of residents’ place attachment are collected, covering psychological dimensions such as place identity and place dependence, to ensure the authenticity and richness of the data. Meanwhile, the collected data are cleaned and verified, and outliers are removed to lay a solid foundation for subsequent analysis.

Step 3: Implement the variable measurements and data standardization. For built environment data, key characteristic parameters are extracted according to the preset indicator system for quantitative analysis; for resident questionnaire data, individual attribute information is systematically sorted. To eliminate the impact of dimensional differences in data, a normalization method is used to transform all variables into a unified dimension, making the data comparable.

Step 4: Analyze the influencing factors of the built environment on place attachment. A hierarchical linear model is established to identify the direct and indirect effects of community-scale built environment variables on place attachment while controlling for individual factors and to explore the role of individual characteristics in the relationship between the two. Through parameter estimation and significance testing of the model, the direction and intensity of the influence of each factor are clarified, ultimately revealing the internal action pathway through which the rural built environment affects residents’ place attachment, providing a scientific basis for rural planning and construction.

2.2. Variable Description

This study takes exploring the impact of the rural built environment on place attachment as its core objective. Accordingly, place attachment is set as the dependent variable, and rural built environment elements are taken as the independent variables. Through a systematic review and analysis of the key literature in related fields at home and abroad, the specific measurement methods and theoretical basis for the dependent and independent variables are now described as follows.

2.2.1. Methods for Measuring Place Attachment

Place attachment expresses an individual’s psychological state of feeling happy, comfortable, and secure towards a place [16], and its intensity indicates the level of satisfaction with the place and the extent to which personal needs are met [17]. Williams’ two-dimensional structural framework of place attachment has been widely applied. He divides place attachment into two dimensions—place identity and place dependence [18]. Place identity refers to the emotional connection between people and places, including attitudes, values, beliefs, and meanings [19]. Hammitt [20] believes that the significance of a place to an individual lies in its promotion of people’s social connections and the cultivation of “group belonging”. Place dependence refers to the functional dependence on the countryside. Antonio [21], through exploration, revealed the significant influence of the quality of urban life on place attachment and linked the quality of life with self-confidence, happiness, and behavior. Therefore, this paper draws on Williams’ Place Attachment Scale and makes adaptive modifications based on the content of the classic scale designed by Williams and Jerry Wassk in 2003 [22], taking into account the environmental characteristics of rural areas. Eventually, the Place Attachment Scale (

Table 1. Place Attachment Assessment Scale.

Num.	Place Identity	Place Dependence
1	This village is very special to me.	This village better meets my residential needs than other places.
2	I am extremely satisfied with the living environment in this village.	The living environment in this village cannot be replaced by other places.
3	I strongly identify as a member of this village.	The feeling I get here cannot be obtained in other places.
4	I deeply identify the culture and spirit of this village.	Many things I can do in this village cannot be done elsewhere.
5	I consider this village a part of my life.	When I go to other places, I feel more satisfied.
6	If possible, I would like to live here long-term.	What I do in other places feels the same as what I do in this village.
7	I am happy to share my hometown (this village) with others.	In my free time, I prefer to stay in this village rather than go elsewhere.
8	I would feel very reluctant to leave this village.	I would rather do what I want to do in this village than go elsewhere.

) is formed, and the Likert 5-level scale is used for evaluation. The place attachment of rural residents in this paper is obtained by adding the scores of place identity and place dependence, followed by range-standardization processing, and then summing up the results. The value range is from 0 to 1.

2.2.2. Explanation of Variables Affecting Place Attachment

This study constructs an analytical framework from the dual dimensions of community and individual: the community dimension focuses on the built environment index system at the rural scale, covering spatial elements such as settlement morphology and infrastructure; the individual dimension emphasizes the investigation of residents’ individual characteristics, including demographic attributes, residential experience, and other variables.

The core indicator of the community dimension in this study is the rural built environment system. Given that the study aims to construct a composite multi-dimensional index framework to systematically evaluate the correlation mechanism between the built environment and rural residents’ place attachment—and that the formation and maintenance of place attachment are highly related to individuals’ image cognition of places—this paper draws on Kevin Lynch’s urban image theory to establish a rural built environment measurement system comprising five dimensions: path, edge, district, node, and landmark.

The path dimension primarily includes elements related to accessibility and travel costs. Koohsari [23] found a significant positive correlation between place attachment and travel time to destinations, while Williams [24] confirmed the spatial heterogeneity of place attachment through cross-scale analysis, suggesting that traffic accessibility indirectly strengthens place attachment by reducing travel costs and optimizing usage experience. This study selects two common indicators in rural accessibility research, distance to the town center and road density, as analytical metrics.

The edge dimension focuses on elements related to rural boundaries. Li [25] revealed a positive correlation between street community integration and place attachment, indicating that different building layouts and spatial compactness influence residents’ willingness to participate in public activities by affecting convenience. Common elements here include settlement status index and rural agglomeration degree. Furthermore, considering that the regional natural environment is the original attraction of place attachment in rural areas and landscape boundaries like mountains and waters play a crucial role, this paper includes the comfort level of landscape boundaries as an indicator in the boundary dimension.

The district dimension incorporates characteristics of the regional scope, including population density, per capita farmland, and tidiness. Studies by Li [25] and Felonneau [9] have confirmed the close correlation between place attachment and both population density and regional tidiness. Additionally, referencing related research on rural built environments and considering the significant impact of rural environmental factors, like farmland, water bodies, and mountains, on residents’ production and lifestyle [26], this paper includes per capita farmland in the indicator system.

Node and landmark are combined into one dimension in this paper. Because in the rural context, both node and landmark refer to supporting service facilities, with node leaning toward public service facilities and landmark toward cultural facilities. Hou [27] found that high-quality public spaces and facility configurations significantly influence place attachment, while Cai [28] confirmed the close relationship between rural public cultural spaces and residents’ place attachment. Zahnow [29] revealed that community groups’ activity participation in specific places can strengthen place attachment, and the impact of different space types varies significantly. Material cultural elements such as rural morphology, dwellings, ancestral halls, and ancient trees directly present community historical imprints, promoting positive spatial perception and evaluation, and even serving as symbolic bonds of rural nostalgia beyond time and space [30,31]. This study selects service facility density, service facility diversity, and landmark richness as indicators.

The individual factors influencing the place attachment of rural residents include gender [32,33], age [34], education [35], and length of residence [36]. The above factors have been proven by existing studies to have a significant impact on place attachment [37]. By integrating the variables of both the community and individual dimensions, the variables of the individual–community nested dimension formed are shown in

Table 2. Community-Individual Nested Variables Affecting Residents’ Place Attachment.

Level	Variables	Indicator Description
Community scale	Path
	Distance to town left	Distance from village left to town left (km).
	Road density	Calculated as $y={\displaystyle \frac{{\sum }_i^N{l}_i}{S}}$, where li is the length of each road in the village, and S is the village area [38].
	Edge
	Rural shape index	Calculated as $s={\displaystyle \frac{P}{(1.5\lambda -\sqrt{\lambda }+1.5)}}\sqrt{{\displaystyle \frac{\lambda }{A\pi }}}$ [38], where λ is the aspect ratio of the village boundary, P is the perimeter, and A is the area.
	Rural agglomeration degree	Calculated as $C=S/\pi R^2$, where S is the area of the study object, and πR2 is the area of its minimum bounding circle.
	Landscape boundary comfort	Residents rate the comfort of rural landscape boundaries (1–5 points, where 1 = least comfortable, 5 = most comfortable); scores are standardized and averaged.
	District
	Population density	Rural permanent population/rural administrative area (persons/mu).
	Per capita cultivated land	Rural cultivated land area/rural administrative area (mu/person).
	Environmental tidiness	Residents rate environmental tidiness (1–5 points, where 1 = least tidy, 5 = most tidy); scores are standardized and averaged.
	Node and Landmark
	Service facility density	Number of supporting facilities in the village/rural administrative area (units/1000 mu).
	Service facility diversity	Calculated as $A_d$=${\displaystyle \frac{-\sum k\left. [\left(p\right)\left(lnp\right)\right]}{lnk}}$, where Ad is service facility diversity, k is the number of service facility types, and p is the proportion of each facility type in total spatial activities.
	Richness of landmark elements	Number of landmark elements (sacrificial buildings, sacrificial squares, traditional gatehouses, ancient bridges, ancient trees)/rural administrative area.
Individual scale	Gender	Male = 1; female = 0.
	Age	Continuous variable (years).
	Education	None = 1, primary school = 2, junior high school = 3, senior high school/vocational school = 4, and university degree or above = 5.
	Residence duration	<5 years = 1, (5,10] years = 2, (10,20] years = 3, (20,40] years = 4, and >40 years = 5
	Current residence	Within the survey area = 1; outside the survey area = 0.

.

2.3. Case Area and Data Foundation

2.3.1. Overview of the Case Area

This paper takes Leiling Town, Chaonan District, Shantou City, Guangdong Province, China, as the research area (see Figure 2). Leiling Town is located at the southern foot of Danan Mountain at the junction of Chaozhou City, Puning City, and Huilai County in eastern Guangdong. Covering an area of 63.19 square kilometers, it has a permanent resident population of approximately 38,000 and governs over 1 community and 14 administrative villages. In 2019, it was included in the ninth batch of national “One Village, One Product” demonstration towns and villages in China. As a significant high-quality lychee production area in China, it is renowned as the “Hometown of Litchi”. The overall terrain is predominantly characterized by hilly landscapes. Among the abundant mountain ranges, the deep and fertile red soil provides favorable conditions for lychee planting, making it an economic pillar for the local residents. In addition, the local area retains a strong sacrificial culture. For this reason, each village has built a sacrificial temple building. The unique and rich production and living style, as well as the distinctive production and living environment, have greatly affected the place attachment of rural residents, providing a better research platform for this study to explore the impact of the built environment in rural areas on the place attachment of rural residents.

2.3.2. Data Sources

This paper uses three types of data: 1. POI data based on the GaodeMap application programming interface (API) (https://ditu.amap.com (accessed on 3 March 2025)); 2. environmental attribute data, including administrative boundaries at all levels, relevant road networks, and building information of Leiling Town, Shantou City, Guangdong Province, China, obtained from OpenStreetMap (https://www.openstreetmap.org (accessed on 5 March 2025)) and GaodeMap; and 3. social, demographic, and economic data from the 2022 “China County Statistical Yearbook (Township Volume)” released by the National Bureau of Statistics and the Leiling Town Government Information Disclosure Platform (http://www.chaonan.gov.cn/stcnllzf/gkmlpt/index (accessed on 3 March 2025)).

Place attachment and personal attributes are derived from a 10-day social survey of villages in Leiling Town from 5 to 15 January 2025. A strip survey method was used to randomly select farmers for the questionnaire survey. The questionnaire includes three parts: personal information of respondents, rural natural background, and rural attachment. Among them, personal information includes gender, age, education level, and length of residence; rural natural background includes factors such as the comfort of mountain–water boundaries and rural cleanliness. Rural attachment is assessed using the Place Attachment Assessment Scale (see Table 1). The survey obtained 489 questionnaires, of which 448 were valid, with an efficiency of 91.62%. The average number of farmer questionnaires per village is 29.87, meeting the requirements for multilevel model analysis [39]. Reliability was tested using Cronbach’s alpha on the SPSS 22.0 platform, yielding a coefficient of 0.896, indicating good reliability.

2.4. Research Methods

This study uses a multi-level model to explore the impact of the built environment on residents’ place attachment. The multilevel model is developed to address the limitations of traditional regression analysis in dealing with multi-layer nested data. The factors that affect place attachment include both individual factors and environmental factors, which have a nested structure. Therefore, a multilevel model is used to identify the factors that affect residents’ level of place attachment [40]. The multilevel model in this paper contains two levels: the first level is individual characteristics, and the second level is built environment characteristics. This paper primarily focuses on the direct impact of high-level built environment variables on low-level individual place attachment and includes individual-level variables as control variables in the model [41]. After controlling for individual-level variables, it is believed that the slope of the impact of built environment factors on residents’ place attachment in each village is consistent, and the difference is only reflected in the intercept. Therefore, only the random intercept model is considered, as specified below.

$${PA}_{ij}=({\beta }_0+{\beta }_1{B}_{0j}+{\beta }_2{N}_{0j}+{\beta }_3{S}_{iJ})+({\mathsf{\mu }}_{0j}+{\mathsf{\epsilon }}_{ij})$$

where ${PA}_{ij}$ is the place attachment score of individual resident i residing in rural settlement j, resident i (from 1 to 448) is nested in settlement j (from 1 to 15); $B_{0j}$ is the built environment variable for the j-th rural settlement; $N_{0j}$ is the control variable for the j-th rural settlement; $S_{iJ}$ represents the socioeconomic attribute variable of individual resident i residing in rural settlement j; and β is the regression coefficient. The formula inside the second set of parentheses represents the random effects of the model, reflecting the magnitude of variation between and within groups.

3. Results Analysis

3.1. Overall Characteristics of Rural Residents’ Place Attachment

The scores of the identity dimension and dependence dimension of the place attachment of the rural residents in Leiling Town are shown in Figure 3. The scores of rural residents in both dimensions range from 8 to 40, with the mean of the former being 28.8326 (S.D. = 4.731) and the latter being 26.3147 (S.D. = 4.792). The Shapiro–Wilk test is conducted separately for both variables to assess normality. The W values are 0.9467 (p < 0.001) and 0.9432 (p < 0.001), respectively, indicating that both variables are normally distributed among rural residents (see Figure 3). Both variables exhibit skewness values less than 0, with place identity showing a greater degree of left skew than place dependence. From the perspective of the normal distribution, the score of place dependence was more concentrated; the results of the correlation analysis of the two show that the correlation coefficient of the two exceeds 0.8 and is two-tailed significant at the 0.01 level, which indicates that the two have a high degree of synergy.

The standardized mean of rural residents’ place attachment is 0.612. Based on the questionnaire data, the descriptive statistics of the rural residents’ place attachment levels are shown in

Table 3. The level of place attachment among rural residents.

Personal Attributes	Classification	Sample Size	Mean of Place Attachment
Gender	Male	240	0.607
	Female	208	0.617
Age	<30	89	0.606
	(30,40]	102	0.608
	(40,50]	113	0.600
	(50,60]	107	0.618
	(>60]	37	0.644
Education	None	126	0.612
	Primary school	64	0.620
	Junior high school	153	0.614
	Senior high school/vocational school	29	0.601
	University degree or above	76	0.603
Residence duration	<5	15	0.604
	(5, 10]	27	0.578
	(10, 20]	75	0.613
	(20, 40]	132	0.621
	>40	199	0.610
Current residence	Within the survey area	319	0.611
	Outside the survey area	128	0.619

. In terms of gender, the degree of place attachment of men is slightly lower than that of women, which is basically consistent with other studies; in terms of age, the degree of place attachment of rural residents of all ages is similar. In comparison, the mean of place attachment of people over 60 years old is higher, which is related to the lower social pressure of people in this age group; in terms of education level, the residents with junior high school education or below have a higher degree of attachment than those with high school education or above; in terms of residence time, the longer the residence time, the higher the place attachment score.

3.2. Overall Characteristics of the Built Environment in Leiling Town

As shown in

Table 4. The descriptive statistics of the built environment in Leiling Town.

Indicator	Mean Value	Standard Deviation Value	Unit
Distance to town center	2.497	1.291	km
Road density	2.096	2.202	m/km2
Rural shape index	2.066	0.643	-
Rural agglomeration degree	0.271	0.133	-
Landscape boundary comfort	4.000	0.349	-
Population density	0.388	0.156	person/mu
Per capita cultivated land	0.184	0.051	mu/person
Environmental tidiness	4.036	0.351	-
Service facility density	4.330	9.270	unit/mu
Service facility diversity	3.586	2.171	-
Richness of landmark elements	0.390	0.338	unit/mu

, the descriptive statistics of the built environment in Leiling Town reveal the following: the average distance from villages to the town center is 2.497 km, with a road density of 2.096 m/km², a rural shape index of 2.066, a rural agglomeration degree of 0.271, a landscape boundary comfort level of 4.000, a population density of 0.388 persons/km², per capita cultivated land of 0.184 mu/person, environmental tidiness of 4.036, a service facility density of 4.330, a service facility diversity of 3.586, and a landmark richness of 0.390. In terms of standard deviation, the service facility density shows the most significant variation among villages, followed by notable differences in service facility diversity, road density, and road diversity. By contrast, the per capita farmland indicator exhibits the smallest degree of dispersion.

3.3. Results of Multi-Level Model Analysis

The empty model in the multi-level model is introduced, and the intraclass correlation coefficient (ICC) of the empty model is calculated to be 14.56%, which is a medium-strength correlation. It is believed that about 14.56% of the variation in residents’ place attachment comes from the village level, and the rest comes from the individual level. It can be seen that the impact of village-level variables on residents’ place attachment cannot be ignored. The multicollinearity test is conducted on the data, and it is found that the variance inflation factor (VIF) < 5, indicating that the correlation between the data would not cause a significant deviation in parameter estimation. This paper constructs five models. Model 1 is a basic model that includes all built environment elements. Models 2 to 5 are type models formed by separately analyzing four types of environmental elements in the built environment, namely roads, boundaries, regions, nodes, and signs, to reveal the differences in the impact of different types of built environments on the place attachment of rural residents. HLM6.0 is selected as the operating platform to construct and run the random intercept model. The results are shown in

Table 5. Results of multilevel linear regression model.

Indicator		Model 1		Model 2		Model 3		Model 4		Model 5
		Estimate	S.E.	Estimate	S.E.	Estimate	S.E.	Estimate	S.E.	Estimate	S.E.
Community scale	Path
	Distance to town center	−0.1174 *	0.1658	−0.0166 *	0.1132
	Road density	0.0365	0.1314	0.2107	0.1122
	Edge
	Rural shape index	−0.1504	0.0612			−0.1503	0.1013
	Rural agglomeration degree	0.1451 *	0.081			0.1448 *	0.0576
	Landscape boundary comfort	0.1227	0.1174			−0.0092	0.0862
	District
	Population density	−0.1473	0.1505					−0.0851	0.1129
	Per capita cultivated land	0.1585	0.1023					0.1435	0.0941
	Environmental tidiness	0.0937 *	0.1647					−0.128 *	0.1003
	Node and Landmark
	Service facility density	0.1065	0.1489							0.2401 *	0.0781
	Service facility diversity	−0.018	0.0986							−0.0929	0.0915
	Richness of landmark elements	0.1464 *	0.1178							0.0767 **	0.0989
Individual scale	Gender	−0.2191 **	0.1017	−0.2233 *	0.1013	−0.2084 **	0.1013	−0.2267 **	0.102	−0.2404 **	0.1018
	Age	0.0629 *	0.0368	0.0646 *	0.0367	0.0651 *	0.0367	0.058	0.0369	0.0575 *	0.0368
	Education	0.0038	0.0366	0.0007	0.0385	0.0008	0.0365	0.0009	0.0368	−0.0027	0.0367
	Residence duration	0.0939 **	0.0434	0.0959 *	0.0431	0.0838 **	0.0432	0.1064 **	0.0429	0.1084 **	0.0429
	Current residence	−0.0427	0.0933	−0.0456	0.0912	−0.0502	0.0915	−0.0209	0.0923	−0.0298	0.0923
	Constant	16.46385	1.235	17.385	1.362	19.043	1.167	18.902	1.736	20.446	1.924
Log likelihood		−1264.754		−832.479		−834.675		−894.416		−946.2735
ICC/%				6.61		6.94		7.32		9.89

Note: ** indicates significance at the 0.01 level, and * indicates significance at the 0.05 level.

.

The regression results of Model 1 show that after controlling for individual socioeconomic attributes, factors significantly associated with place attachment include distance from the town center, rural concentration, clean environment, and richness of cultural elements. The distance from the town center is significantly negatively correlated with villagers’ place attachment. In addition, none of the other built environment variables involved in Model 1 passed the significance test.

Models 2 to 5, respectively, conduct regression analysis on roads, boundaries, regions, nodes, and signs in the built environment. It is found that different types of built environment elements have different degrees of influence on place attachment, and the degree of influence of nodes and signs on place attachment is greater than that of the other three types of elements. The road level explains 6.61% of the variation in place attachment, but the two selected factors do not pass the significance test; the boundary level explains 6.94% of the variation in place attachment; the regional level explains 7.32% of the variation in place attachment. Among them, per capita cultivated land and a clean environment have a significant impact on place attachment; the node and sign level explain 9.89% of the variation in place attachment, among which the density of service facilities and the richness of cultural elements have a significant impact on place attachment. A horizontal comparison between models is conducted. Due to the interaction between variables, the cumulative contribution rate of the four types of environmental variables to place attachment (22.82%) is greater than the total contribution rate of all variables (14.56%).

From the perspective of individual socioeconomic attributes, gender, age, and length of residence have significant effects in all five models, while education and whether or not to work away from home have no significant effect on place attachment in all models. Gender has the highest degree of influence, which is significantly correlated in all five models, and its influence exceeds that of other individual socioeconomic attributes. Length of residence is also significantly correlated in all five models, while age is correlated in models 1, 2, 3, and 5.

4. Discussion

The first objective of this study is to investigate the degree of matching between personal place attachment and environmental characteristics of rural residents based on a multi-dimensional conceptual model of embodied cognition and social space integration. Generally speaking, multiple sets of regression analyses indicate that facility convenience, social connection, and environmental experience can greatly affect residents’ embodied experience of use, thereby influencing their place dependence and further having a huge impact on their identification and attachment.

The results of this study are consistent with those of this study. Firstly, the distance from the town center shows a significant negative correlation with villagers’ place attachment. That is, the place identity of residents in the villages around the town center of Leiling Town is higher than that in other areas. Based on interviews and on-site investigations, it is found that these areas have experienced faster village development compared to other regions, offering convenient facilities and a wealth of amenities for daily life. Additionally, transportation is relatively convenient, with access to public buses and highway interchanges. In contrast, rural residents in other areas need to travel through or first reach the town center, which increases the living costs for the locals and makes their attachment to the area less strong. Secondly, the degree of rural agglomeration showcases a significant positive correlation with place attachment. This is because the more compact and concentrated the countryside is, the stronger the social connection it can provide for residents. Existing studies indicate that the stronger the social ties an individual forms in their place of residence, the higher the degree of place attachment they exhibit [42]. Furthermore, environmental cleanliness shows a significant positive correlation with villagers’ place attachment, which is basically consistent with other studies [43]. It is generally believed that if there are uncomfortable odors, environments, and garbage disposal in the countryside, the residents’ degree of place identity and dependence will be relatively low, and they may even think that this place is not worth identifying with. Secondly, the richness of cultural elements, such as traditional buildings, sacrificial buildings, and sacrificial cultural squares, shows a significant positive correlation with residents’ place attachment. Their richness is closely related to the frequency of traditional activities held in rural areas. Studies show that the places where people engage in rituals, prayers, and other communal activities foster stronger connections with others, leading to a heightened sense of place attachment [29]. Traditional activities can not only effectively promote the dynamic emotional connection between people and places but also have a positive impact on the generation and maintenance of place attachment as they continue to develop over time [44]. During the research, it is found that Leiling Town retains a considerable amount of sacrificial culture. Every year, worship activities are held in cultural buildings. The landmark buildings represented by the temple hold a high status in the hearts of Leiling Town. Interviews with residents reveal that the sacrificial activities in Leiling Town are an important way to maintain and enhance the sense of rural identity.

Residents shape their memory and identification with the village through social interaction and environmental perception within it. They shape their place identity through inheriting folk culture, perceiving the unique built environment, and conducting a large number of social interactions. Furthermore, they form their own identity in a strong sense of belonging and identity. The influence of the built environment on place attachment follows this cycle of “memory identity—place identity—identity” and plays a positive role in the cycle.

This also explains why, among the various elements in the construction phase, nodes and symbolic elements have a greater impact on place attachment compared to the other three types of elements. Design optimization of nodes and symbols can more effectively enhance the level of place attachment among residents and strengthen the connections between people.

The second objective of this study is to explore the differences in place attachment among different individuals and the reasons behind such differences. Generally speaking, factors such as gender, age, length of residence, educational level, and whether one has traveled away significantly influence the degree of place attachment. This is because the formation of residents’ place attachment stems from their embodied practices carried out in the countryside [45] and is related to their emotional experiences and expressions, as well as the duration of emotional experiences.

Studies show that the physical and mental differences brought about by gender differences lead to significant differences between men and women in terms of place cognition, place experience, and expression. In this study, the significant differences in place attachment between males and females are attributed, on one hand, to differences in emotional expression between genders, and on the other hand, to the division of labor within the family and varying cultural expectations for men and women. It is found in the research that the division of labor and cultural expectations for women in Leiling Town make women more inclined than men to build social support networks within the countryside. The frequency and intensity of neighborhood interaction among women are higher than those among men, which makes the tie between women and the local area deeper. Furthermore, this study finds that age and the duration of residence are correlated with the degree of place attachment, among which the duration of residence shows a significant correlation. This is because the longer one resides in a place, the more pronounced their social interactions become and the tighter their social networks are. Interpersonal relationships and social networks are important components of place attachment. However, it is not the case that the longer one lives there, the higher the level of place attachment one has. Conversely, residents who have lived there for 20 to 40 years actually exhibit a greater degree of attachment compared to those with other residence durations. Interviews with local residents reveal that, firstly, those who have lived there for over 40 years are often older and have difficulty moving around, making it impossible for them to engage in lychee production or more cultural activities. This has led to a gradual decline in their embodied experiences and prevented them from receiving more support in the countryside. Secondly, as one grows older, familiar companions leave one after another, the personal social network in the countryside gradually declines, and the pleasure gained in the countryside gradually fades away.

5. Research Conclusions and Outlook

5.1. Conclusions

Against the backdrop of the deepening implementation of the Rural Revitalization Strategy, strengthening the emotional bond between people and place and constructing a built environment that meets the production and living needs of rural subjects have become core propositions for breaking the impasse of rural sustainable development. As a key indicator for measuring the strength of the emotional connection between residents and rural spaces, a thorough exploration of the influencing factors of place attachment can not only provide a scientific basis for the reconstruction of rural spaces but also inject theoretical impetus into new rural construction, possessing both academic value and practical significance.

Based on this, this study has constructed a systematic technical path of framework construction, data acquisition, data quantification, and model verification. Innovatively, from the five-dimensional perspective of imagery, it systematically deconstructs and reconstructs the elements of the rural built environment. Moreover, by using a multilevel linear regression model, it deeply analyzes the differential influence mechanisms of various elements of the rural built environment on residents’ place attachment. Through rigorous empirical analysis, the following core conclusions are drawn:

(1)

The mean value of place attachment of rural residents in Leiling Town is 0.612; gender, age, and length of residence are the main individual variables affecting the place attachment of rural residents.

(2)

14.56% of the variation in rural residents’ place attachment can be attributed to the built environment. Our findings indicate that the distance from the urban center, rural concentration, rural cleanliness, and iconic cultural buildings are the main built environment variables influencing place attachment, which is basically consistent with the results of related studies. The analysis reveals that enhancing the compactness of village building layout, environmental cleanliness, and construction of traditional cultural venues can boost rural residents’ place attachment.

(3)

Nodes and landmark elements have a greater impact on the place attachment of rural residents than roads, boundaries, and regional elements. Optimizing the design and experience of nodes and landmark elements is the main way to improve the place attachment of rural residents in the study area.

(4)

The formation of rural residents’ place attachment depends on their continuous embodied practice in the countryside and the sharing of meaning in social interactions. The impact of the built environment on attachment follows the identity cycle of “memory identity—place identity—identity”.

5.2. Outlook

The research findings show that there are differentiated conclusions in the case study regarding the relationship between the two factors of going out and education and place attachment. Among them, the place attachment of the uneducated group and the primary and junior high school groups is higher than that of the high school and university groups, resulting in insignificant regression results. These results show that the impact of socioeconomic variables on place attachment is not robust. Subsequent studies can carry out more targeted research design and analysis on this point. In addition, it should be pointed out that the sample area selected in this study belongs to the category of southern rural areas, and its built environment has significant structural regional differences with other regions. Subsequent studies will include villages in other regions for further comparative studies.