Spatial Syntactic Analysis and Revitalization Strategies for Rural Settlements in Ethnic Minority Areas: A Case Study of Shuanglang Town, China

Abstract

Understanding the spatial morphological characteristics and driving factors of rural settlements in ethnic minority areas is crucial for the conservation and tourism development of ethnic villages. Accordingly, this study employs Shuanglang Town, China as a case study, adopting an integrated approach that combines spatial syntax analysis, the optimal parameter geodetector model, and GIS spatial analysis techniques. This comprehensive methodology systematically investigates the spatial morphological features, differentiation characteristics, and influencing factors of ethnic villages. The findings reveal the logical lineage and formation mechanisms underlying the overall layout, street network, and public spaces of the villages. Specifically, the results demonstrate (1) a discernible gradation in spatial configurations, transitioning from compact “back mountain villages” in the northeast to more dispersed “seaside villages” in the southwest, with notable disparities in accessibility among different villages; (2) topography, water distribution, and water quality as the dominant factors shaping village spatial patterns; (3) the interactive and heterogeneous effects of multiple natural and anthropogenic factors, including topography, water resources, agricultural practices, and ethnic cultural traditions, significantly influencing the spatial morphology of villages; and (4) common principles governing the site selection of different ethnic village typologies, reflecting the villagers’ understanding and intelligent utilization of the natural environment. This study contributes to comprehending the spatial characteristics of rural settlements in ethnic minority areas and provides a theoretical and practical foundation for advancing analogous rural revitalization initiatives. The findings offer insights into the spatial logic and formation processes of ethnic villages, informing conservation efforts and sustainable tourism development strategies.

1. Introduction

Rural settlements refer to an organic whole formed by multiple interdependent rural settlements with specific structures and functions within a certain region [1,2]. Villages and towns are the two basic forms, jointly reflecting the complex relationship between human activities and the natural environment [3,4,5]. In recent years, with the rapid development of the national economy, the proportion of the tertiary industry, especially tourism, in the gross domestic product and farmers’ income has been increasing [6,7]. Under the background of vigorously promoting the rural revitalization strategy, rural ecotourism has ushered in unprecedented development opportunities [8,9]. However, many tourism-oriented rural areas face numerous dilemmas in the development process: the destruction of the village environment, the erosion of the original spatial form of ancient villages, the excessive renovation of existing living spaces, and the collapse of ancient buildings due to disuse [10]. These problems seriously threaten the sustainable development of tourism-oriented rural areas. Therefore, it is crucial to study and protect rural settlements at the overall spatial planning level for the future development of rural tourism [11].

As spatial entities embodying multifaceted attributes such as rural socioeconomics, agricultural production, and traditional cultural customs, rural settlements have long been a core research focus in modern rural geography [12,13,14]. Scholars at home and abroad have conducted extensive and in-depth discussions on their evolution process, spatial pattern, and influencing mechanisms from different perspectives [15,16,17]. Early studies mainly focused on the shaping effect of the natural geographical environment on settlement morphology. With the development of the socio-economy, the influence of human factors has become increasingly prominent, and the influencing factors of settlement patterns have gradually extended to many aspects such as government policies, land systems, population migration, and urban–rural interaction [18,19,20,21]. Overall, research in Western countries is becoming increasingly diversified, and in-depth analysis of rural settlements combining theories from multiple disciplines has become mainstream. While related research in China started relatively late, in recent years, it has also achieved fruitful results in the study of settlement scale, evolution mechanism, spatial form, and spatial reconstruction [22,23,24].

Spatial syntax, as a quantitative analysis method to explore the relationship between spatial form and human social relations, changes the basic cognition of designers about space by transforming spatial associations into mathematical topological relations, providing a more scientific, rational, and universal basis for village spatial planning [25,26]. At present, spatial syntax is mainly applied at the urban scale, such as in describing urban spatial structure, formulating transportation planning, and optimizing public space design [27,28,29,30]. Research on rural areas is relatively scarce among international scholars. However, Chinese researchers have made significant contributions, particularly in the fields of village space cognition, morphological evolution, and structural characteristics [31,32,33]. However, overall, empirical analysis targeting tourism-oriented rural areas in ethnic minority areas is still very scarce. High-quality data are the foundation for conducting spatial perception research. Traditional methods such as questionnaire surveys and interviews have many limitations: small sample size, long cycle, high cost, difficulty supporting perception measurement in large areas and long time periods, and even more difficulty in reaching remote areas [34]. For example, this study compared traditional paper-based data collection methods with electronic methods in rural settings. It highlighted the low efficiency, high error rates, and logistical difficulties associated with paper-based methods [35]. Additionally, another study identified major challenges in conducting on-site questionnaire surveys in rural tourism areas, such as the limited number of tourists and the need for representative and valid data [36]. In recent years, with the development of new technologies such as 3S and big data, scholars have begun to use multi-source data, combined with spatial syntax, optimal parameter geographic detectors, and other models, in order to break through data bottlenecks and expand the breadth and depth of spatial perception research [37,38,39,40]. For instance, in the year 2024, one study has delved into the integration of social determinants with spatial analytical techniques to evaluate the optimal positioning of urban service amenities [41]. Concurrently, another study has ventured into an innovative methodology that amalgamates street view imagery, deep learning algorithms, and spatial syntax analysis to assess and juxtapose the perceptual quality and navigability of streets within the metropolitan landscapes of Shanghai and Chengdu [42].

This study focuses on the seven administrative villages in Shuanglang Town, Dali Bai Autonomous Prefecture, Yunnan Province. As a well-known scenic spot, Shuanglang Town has experienced a continuous rise in tourist numbers since the 1990s. However, due to uneven distribution of tourism resources across the villages, the allocation of tourist flows is severely imbalanced, underscoring the urgent need to explore optimization pathways from a spatial perspective. To this end, drawing on high-resolution remote sensing imagery, field investigations, and tourism and land use data provided by government agencies, this research employs spatial syntax theory and the optimal parameter geographic detector model to quantitatively interpret each village’s overall spatial form, road accessibility, and spatial synergy, and subsequently proposes targeted spatial optimization strategies to support the sustainable development of the tourism industry in the study area. The innovations of this study are (1) exploring and establishing a set of quantitative analysis methods for tourism-oriented rural spatial perception at the village scale, providing new research directions; (2) focusing on ethnic minority areas, deeply analyzing the evolution of rural spatial fabrics under specific cultural backgrounds, thereby enriching the research content of rural geography; and (3) proposing a village spatial optimization plan from a spatial perspective that considers local characteristics and industrial development, which may hold reference value for rural revitalization initiatives.

The structure of this paper is as follows: Section 2 introduces the overview of the research area and details the technical route, data sources, and analysis methods; Section 3 systematically explicates the calculation results of the spatial syntax parameters for each village in Shuanglang Town; Section 4 analyzes the key factors influencing spatial perception based on the optimal parameter geographic detector model; and Section 5 discusses the main findings and highlights the limitations and improvement directions of this research.

2. Materials and Methods

2.1. Study Area

This research focuses on Shuanglang Town, located in Dali City, Dali Bai Autonomous Prefecture, Yunnan Province, China. The town is situated on the northeast bank of Erhai Lake and has unique geographical and climatic characteristics. The terrain of Shuanglang Town decreases from west to east, presenting a plateau plain landform. It is adjacent to Erhai Lake in the west and borders the famous Buddhist holy land Jizu Mountain in the east, Wase Town in the south, and Shangguan Town and Huangping Town in Heqing County in the north. The administrative area of the town is approximately 218 square kilometers, with geographical coordinates of 99°58′ to 100°27′ east longitude and 25°25′ to 25°58′ north latitude [43], as shown in Figure 1.

Shuanglang Town belongs to the northern subtropical plateau monsoon climate zone. The geographic location, being surrounded by mountains, provides the town with a relatively closed climate environment, reducing the invasion of cold waves. The presence of Erhai Lake plays a natural role in regulating the temperature and humidity of the area, creating unique climatic conditions: winters are not extremely cold, summers are not scorching hot, and the seasons are distinct with moderate dryness and wetness. These climatic conditions promote the diversity of ecology and spatial morphology within the region. In addition, Shuanglang Town is also a multi-ethnic settlement area mainly inhabited by seven ethnic groups, including the Bai ethnic group. These groups bring rich ethnic cultures, customs, and regional deity beliefs, forming social and cultural activities and lifestyles with strong regional characteristics. The ethnic villages within the town’s jurisdiction showcase diverse cultural landscapes, becoming important windows for studying regional culture and social structure.

To further deepen the understanding of the multi-ethnic culture and economic activities in Shuanglang Town, this study conducted a detailed investigation of typical ethnic villages within the region.

Table 1. Key information of ethnic minority villages.

Village Name	Altitude	Economic Function	Ethnic Group	Distinctive Architecture/Landscape
Shuanglang Village	1970 m	Tourism, Business	Han, Bai	Zhengjue Temple, Kuixing Pavilion
Dajianpang Village	1960 m	Tourism, Business	Han, Bai	Yuji Nunnery, Feiyan Temple
Changyu Village	1968 m	Tourism, Business	Han, Bai	Wenchang Palace, Jinbang Temple
Qingshan Village	1962 m	Tourism, Business	Han, Bai	Qingshan Cliff Stone
Shikuai Village	1850 m	Agriculture (Sericulture)	Bai	Shiniu Dam
Wuxing Village	1831 m	Agriculture (Pomegranate)	Han, Bai, Dai, Naxi	Benzhu Temple
Huoshan Village	2540 m	Agriculture, Tourism	Bai	Pear Blossom Pond, Bai Peasant Painting Society

summarizes the key information of these villages, including their geographical locations (identified through satellite maps), altitudes, main economic functions, resident ethnic groups, and their distinctive architectures or attractions. Specifically, looking at the ethnic composition of the minority population in the research area, the Bai ethnicity accounts for the vast majority of the population. For example, in Huoshan Village, the Bai ethnicity constitutes 95% of the total population, while other ethnic minorities are less represented or not specifically mentioned. However, we have also checked the population data for other ethnic groups. According to the 2022 Dali Statistical Yearbook, Shuanglang Town has a total population of 19,815 people, including 17,443 Bai, 2028 Han, 121 Lisu, 99 Yi, 69 Dai, 18 Naxi, 10 Tibetan, and other ethnic groups with less than 10 people each. These data not only reveal the diversity of culture and economic activities in the Shuanglang Town area but also highlight the influence of regional characteristics and natural environment on community development [44,45].

These villages are known not only for their tourism and commercial activities but also for maintaining and developing specific agriculture and animal husbandry. Furthermore, the ethnic groups inhabiting them, including Han, Bai, Dai, Lisu, and Naxi, contribute rich cultural dimensions and distinctive architectures or attractions that are important components of the cultural heritage of the region.

To further explore the population structure, architectural characteristics, ethnic culture, and residents’ perceptions of their living space in each village within Shuanglang Town, this study conducted a comprehensive field survey in December 2023. During this survey, a total of 200 questionnaires were distributed, and 179 valid questionnaires were collected. Through the analysis of these data, the aim was to reveal the relationship between the spatial layout of the villages and the distribution of population vitality, and to explore how these factors jointly influence the daily life and cultural practices of the communities. To obtain accurate data on the spatial layout of the villages, the research also employed a combination of drone aerial photography and on-site observation, and then obtained precise information through image analysis and field verification. Based on the collected data, this study constructed a coordinate axis model to extract and analyze the spatial characteristic points of rural areas, further understanding the impact of different village spatial layouts on residents’ lives, as shown in

Table 2. Extraction of village spatial characteristic points (The photos are from the author’s on-site aerial photography, and have been calibrated by satellite maps).

Village Name	Type	Aerial View	Axis Line Drawing	Visual Drawing
Shuanglang Village	Linear
Dajianpang Village	Linear
Changyu Village	Linear
Qingshan Village	Linear
Shikuai Village	Scattered
Wuxing Village	Scattered
Huoshan Village	Scattered

.

2.2. Methods

2.2.1. Space Syntax

This study employs space syntax analysis as the main methodological tool to explore the intrinsic characteristics of village spatial structures. Space syntax is a widely applied and effective method in the field of urban and village spatial analysis, and its effectiveness has been fully verified through numerous case studies and empirical research [46,47]. This method can quantitatively describe and analyze the layout characteristics and internal logic of architectural spaces and public spaces in villages.

Firstly, this study uses AutoCAD software to draw the spatial model base map of the research object, and then employs Depthmap+ Beta 1.0 software for comprehensive space syntax analysis. According to previous research [48], this paper selects five core quantitative indicators: global integration, local integration, connectivity, synergy, and intelligibility, to reveal the multi-dimensional characteristics of village spatial structures and construct an analytical framework to explain the intrinsic relationships between these characteristics. Among them, integration, as the core indicator of space syntax analysis, measures the degree of connectivity of spatial units and can be further divided into global integration and local integration. Global integration reflects the centrality of a spatial unit in the overall spatial structure, while local integration quantifies the closeness of a spatial unit to its adjacent spaces within a specific topological step length. The level of integration values is represented by a color gradient from red to blue in the space syntax map. Formulas (1) and (2) provide the calculation method for integration:

$$I_i={\displaystyle \frac{n\left[{\log }_2\left(\frac{n+2}{3}-1\right)+1\right]}{\left(n-1\right)\left(M{D}_i-1\right)}},$$

(1)

$$D_i={\displaystyle \frac{{\Sigma }_{j=1}^n{d}_{ij}}{n-1}},$$

(2)

where $n$ represents the number of nodes in the spatial system, $d_{ij}$ is the distance between nodes $i$ and $j$, and $M{D}_i$ is the mean depth value of node $i$ [49,50].

Connectivity measures the degree of interconnection between a spatial unit and other units, reflecting the strength of interaction within the spatial network. Higher connectivity values indicate better accessibility, whereas lower values suggest poorer spatial permeability and increased difficulty in reaching certain areas. The calculation method for connectivity is shown in Formula (3):

$$c_i=\sum k,$$

(3)

where $k$ represents the number of spatial units directly connected to a specific spatial unit $i$.

Synergy, represented by $R^2$, indicates the level of agreement between global integration and local integration. In theory, higher synergy values facilitate a better comprehension of the overall spatial structure from local spatial information. The formula for calculating synergy is presented in Equation (4):

$$R^2={\displaystyle \frac{{\left[\sum \left(c_i-\stackrel{\text{-}}{c}\right)\left(I_i-\stackrel{\text{-}}{I}\right)\right]}^2}{\sum {\left(c_i-\stackrel{\text{-}}{c}\right)}^2{\left(I_i-\stackrel{\text{-}}{I}\right)}^2}},$$

(4)

0 ≤ R² < 0.5, 0.5 ≤ R² < 0.7, and 0.7 ≤ R² < 1 represent low, medium, and high spatial synergy, respectively [51,52,53].

Intelligibility refers to the correlation coefficient between the two syntactic variables of connectivity and integration, usually represented as a scatter plot in a Cartesian coordinate system. It measures the ease with which spatial users can infer the overall spatial layout by intuitively perceiving local spaces. The higher the degree of concentration of the scatter points around the 45° diagonal line, the higher the intelligibility of the space. When the correlation coefficient is greater than 0.45, the overall intelligibility of the street system is high [49].

2.2.2. Optimal Parameters-Based Geographical Detector

To quantitatively identify the diverse characteristics of village spatial patterns and uncover the underlying influencing mechanisms, this study introduces the optimal parameters-based geographical detector model (OPGD). This model excels in handling both quantitative and qualitative data, enabling the systematic identification of interactive explanatory factors affecting the target factor [54]. The OPGD model is composed of four functional modules: the factor detector, which identifies key influencing factors; the interaction detector, which explores interactions between factors; the risk detector, which assesses the risk levels of various factors; and the ecological detector, which examines the differences in impact of different ecological zones on dependent variables. Through the integrated analysis of these modules, the OPGD model can comprehensively reveal the formation mechanisms of the spatial differentiation patterns in villages. Specifically, the factor detector module assesses the influence of independent factors on the dependent variable, with its explanatory power represented by the q-statistic. The formula for this model is as follows:

$$\begin{gathered} q=1-{\displaystyle \frac{SSW}{SST}}=1-{\displaystyle \frac{\sum _{h=1}^L{N}_h{\sigma }_h^2}{N{\sigma }^2}} \\ SSW=\sum _{h=1}^L{N}_h{\sigma }_h^2,SST=N{\sigma }^2 \end{gathered}$$

(5)

The sum of variances within layers (sum of squares within layers, SSW) denotes the variability within each layer, whereas the total variance of the study area (total sum of squares, SST) signifies the overall variability. Here, h = 1, … L represents the stratification of the dependent factor Y or influencing factor X. N_h indicates the number of units in layer h, and N denotes the total number of units. ${\sigma }_h^2$ refers to the variance within each layer, while σ² indicates the overall variance. The q-value ranges from 0 to 1 and follows a non-central F distribution. A higher q-value implies a stronger explanatory power of factor X on the spatial distribution of villages, indicating the ability to explain 100% of the variance.

This study also utilizes the interaction detector module. This module assesses whether the interaction between two independent factors (X1 and X2) enhances or diminishes their respective explanatory power on the dependent variable (Y) [55]. By comparing the q-values of individual factors (q (X1) and q (X2)) with the q-value of the paired factors (q (X1 < X2)), the interactions can be categorized into five types, as illustrated in

Table 3. Interactive types between two factors.

Criteria	Interaction Type
q (X1∩X2) < Min (q (X1), q (X2))	Nonlinear, weakening
Min(q(X1), q (X2)) < q (X1∩X2) < Max (q (X1), q (X2))	Single variable, weakening
q (X1∩X2) > Max (q (X1), q (X2))	Double variable, enhancing
q (X1∩X2) = q (X1) + q (X2)	Independent
q (X1∩X2) > q (X1) + q (X2)	Nonlinear, enhancing

.

Based on the scope of the study area and referring to the research of relevant scholars [56] on spatial scale effects, a 500 m × 500 m fishnet grid is constructed. The selection of driving factors includes elevation, slope, and aspect. They are important factors affecting the spatial pattern differentiation of rural settlements, and can influence the distribution pattern of roads and buildings through affecting topography and land forms; precipitation and temperature conditions affect vegetation distribution, providing a good foundation for the quality of the living environment, thus influencing population concentration points; GDP and cultivated land area, which reflect the intensity of human activities, interact with the ecological environment and habitat quality, and also reflect the degree of population activity; railway, county road, and other road location conditions affect the degree of population concentration through transportation convenience; the distance to the town government, the distance to Erhai Lake, and the distance to the village committee affect the activity and concentration of villagers going to activity centers, thus influencing spatial accessibility. Considering the above factors and the availability of data, 12 driving factors, as shown in

Table 4. Independent variable factors affecting the degree of spatial population vitality.

Dimension	Factor	Resolution/m	Data Source
	Elevation X1	30 m	https://www.gscloud.cn/ (accessed on 22 June 2023)
	Slope X2	30 m
Natural Environment	Aspect X3	30 m
	Precipitation X4	1000 m	https://www.resdc.cn/ (accessed on 22 June 2023)
	Temperature X5	1000 m
Socio-economy	GDP X6	1000 m
	Cultivated Land Area X7	30 m
Location Factors	Distance to Railway X8	30 m	https://www.openstreetmap.org/ (accessed on 22 June 2023)
	Distance to County Road X9	30 m
	Distance to Town Government X10	30 m	https://www.tianditu.gov.cn/ (accessed on 22 June 2023)
Spatial Accessibility	Distance to Village Committee X11	30 m
	Distance to Erhai Lake X12	30 m

, are selected from four dimensions: natural environment, socio-economy, location conditions, and spatial accessibility, for the study of optimal parameters-based geographical detectors.

2.3. Research Framework

Based on field investigations and topological theory, this study constructs a research framework, as shown in Figure 2, which mainly includes three stages. The first stage comprises data preprocessing. Through field investigations and data integration, the field aerial maps and Google satellite maps are registered in Context Capture Center 64-bit and ArcGIS 10.8 software, and then the processed images are imported into AutoCAD 2020 to construct axial maps and Visual models, which are exported in dxf format to Depthmap + Beta 1.0 software for subsequent analysis. The second stage comprises the analysis of spatial morphological characteristics. Based on space syntax theory, the spatial morphology of villages is quantitatively analyzed from two dimensions: road network structure and overall space. In terms of road network structure, by calculating space syntax morphological analysis variable indicators such as global integration, local integration, choice, synergy, and intelligibility, the accessibility and reachability characteristics of the village road network are revealed; in terms of overall space; indicators such as visual integration, visual intelligibility, visual mean depth, and visual choice are used to explore the openness, synergy, and interactivity of the village space. In addition, the optimal parameters-based geographical detector method is used for quantitative identification and explanation of the driving forces of influencing factors on the spatial differentiation characteristics of villages. The third stage comprises the exploration of village revitalization strategies and mechanisms. According to the spatial characteristics of different types of villages in Shuanglang Town, corresponding revitalization strategies are proposed, such as optimizing road hierarchy and improving public space layout, to enhance the accessibility and livability of village spaces and reduce transportation carbon emissions. At the same time, the formation mechanism of village spatial heterogeneity is deeply explored to provide theoretical support and practical guidance for the sustainable development of rural areas.

In summary, this study systematically analyzes the spatial structure characteristics of rural settlements through field investigations, data processing, space syntax analysis, and optimal parameters-based geographical detectors, based on optimal parameters. Based on these analyses, targeted village revitalization strategies are proposed, which are crucial for advancing spatial optimization and sustainable development in rural areas.

3. Results

3.1. Axial Analysis of Space Morphology Characteristics

3.1.1. Global Integration Analysis

To analyze the spatial structure characteristics of different villages in Shuanglang Town, this study extracted the road axes of each village and calculated their global integration. The results showed that the global integration of the seven villages ranged from 0.208 to 0.820, with Changyu Village being the highest (0.820) and Huoshan Village being the lowest (0.208). Villages near Erhai Lake, such as Changyu Village, Shuanglang Village, and Qingshan Village, generally exhibited higher global integration (all higher than 0.5). This pattern was consistent with the field investigation results, indicating that these linear villages were the centers of socio-economic activities. In particular, the main commercial street of Shuanglang Ancient Town, due to its high node integration, became a key node in the village’s spatial structure. The buildings lining this main street are systematically arranged, forming three distinct core areas. These core areas align exactly with the principal streets of Shuanglang Town, specifically the ethnic cultural street of Shuanglang Ancient Town. This arrangement has facilitated the growth of commercial activities, a fact evidenced by the spatial intersection of villagers and tourists. Additionally, these lakeside villages usually have squares or activity centers with ethnic characteristics at the entrances, enhancing the sense of community belonging and attracting tourists’ interest. In contrast, the mountain villages dominated by traditional agriculture showed lower integration. The spatial layout of these villages was more scattered, deviating from the central axis, and unfolding along the irregular terrain, with relatively simple infrastructure, as shown in Figure 3 and

Table 5. Overview: Space syntax indicators of typical ethnic villages.

Village Name	No. of Axial Lines	Global Integration Value			Local Integration Value (R3)			Connectivity Value	Synergy Value	Intelligibility Value
		Max	Min	Average	Max	Min	Average	Average	Average	Average
Shuanglang Village	603	1.282	0.372	0.788	3.753	0.333	1.403	2.454	0.563	0.091
Dajianpang Village	601	0.692	0.210	0.421	2.719	0.333	1.091	2.180	0.180	0.049
Changyu Village	487	1.431	0.429	0.820	3.402	0.333	1.353	2.394	0.620	0.114
Qingshan Village	443	0.968	0.306	0.587	2.854	0.333	1.155	2.257	0.455	0.140
Shikuai Village	225	0.413	0.142	0.287	1.896	0.333	0.949	2.071	0.193	0.054
Wuxing Village	335	0.500	0.180	0.319	2.418	0.333	0.951	2.036	0.202	0.056
Huoshan Village	261	0.304	0.107	0.208	1.569	0.333	0.881	2.015	0.187	0.076

.

3.1.2. Connectivity Analysis

The average connectivity values of the seven villages ranged from 2.015 to 2.454, reflecting varying degrees of spatial connectivity. Shuanglang Village was characterized by the highest connectivity value (2.454), indicating that it had a well-organized and regular network structure that could effectively support high-volume traffic. The main street was not only highly integrated with this network but also had well-developed facilities at both ends to enhance its practicality for villagers and tourists. In contrast, Huoshan Village had the lowest connectivity value (2.015), indicating poor connectivity. The roads within the village mainly served individual households, were narrow (only 3 m wide) and randomly laid out, limiting accessibility and suitable only for pedestrian traffic. Additionally, the local activity center had been in a state of disrepair for years without renovation, leading to abandonment. This lack of infrastructure hindered villagers from participating in social activities outside their homes, resulting in low vitality and insufficient use of public spaces and roads.

3.1.3. Synergy Analysis

This study quantified the synergy of village layouts through the R² coefficient, which correlates global integration [HH] (X-axis) with local integration (Y-axis). Each road node is abstracted as a scatter point, and a two-dimensional scatter plot is generated through algorithmic analysis, with a fitted regression curve to illustrate the synergistic trend between different scales. The color of the scatter points transitions from blue to red, intuitively representing the change in overall integration from low to high. Changyu Village had the highest synergy (0.620), indicating a strong correlation between its global and local integration. This correlation helps intuitively understand the overall spatial layout of the village from a local perspective. The spatial structure of Changyu Village is stable, benefiting from its flat terrain near Erhai Lake and its location advantage adjacent to the lake-surrounding road. The residential buildings along the main streets are mostly constructed with stone materials and painted in traditional Bai colors such as red, green, and white. This layout is not only reflected in the axial map but also highlights the unique cultural characteristics of the village. A prominent archway with the characters “Changyu” is inscribed at the entrance, leading to the central square, which is the main venue for community interaction and important village activities. In contrast, Dajianpang Village recorded the lowest synergy score (0.180). As the village with the smallest administrative area, it has an irregularly branching road network that leads to poor connectivity between buildings. Field investigations revealed that most residences were self-built and unplanned, resulting in a disconnection between the overall layout and local spaces, which contributes to the observed lack of synergy (see Figure 4).

3.1.4. Intelligibility Analysis

The intelligibility of space syntax is quantified by the correlation coefficient between the two syntactic variables of connectivity and global integration and is represented in the form of a Cartesian scatter plot. Connectivity measures the number of other axial lines directly connected to a specific axial line, reflecting the local importance of that axial line in the entire system, while global integration assesses the overall accessibility of an axial line to all other axial lines in the system. A high correlation between these two indicators suggests that users can more easily understand the overall spatial configuration from local observations. Theoretically, a correlation coefficient greater than 0.45 (although rarely achieved in the real world) implies a highly intelligible street network, where the scatter points ideally form a 45° line on the graph, symbolizing optimal spatial coherence. Qingshan Village had the highest intelligibility (0.140), and the strong correlation highlighted a system where connectivity and integration were closely linked, aiding in tourist navigation and enhancing the touring experience.

In summary, the spatial morphology of ethnic minority villages mainly reflected the maintenance of socio-economic development and traditional culture. The cultural background and development patterns of these villages had a significant impact on their spatial structure. Ethnic minority villages are usually located in areas with sparse populations, complex terrain, backward transportation, and underdeveloped economies, presenting unique spatial challenges. Existing research primarily utilizes 3S technology for objective large-scale analysis and direct classification of village spatial morphology [57], with a focus on examining the relationship between village spatial distribution and population hotspots, but pays insufficient attention to the deep connection between village spatial structure and rural residents’ lives. This neglect overlooks the actual living conditions of villagers and the dissemination of their cultural heritage. Although space syntax effectively quantifies the internal relationships of village spaces and expresses them in a parameterized form, it still falls short in exploring the economic functions and temporal evolution of these spaces. Therefore, integrating space syntax with other methods is crucial for constructing a comprehensive quantitative research framework that considers the interplay of natural, cultural, and economic factors.

3.2. Visual Analysis of Space Morphology Characteristics

3.2.1. Visual Integration Analysis

Visual analysis is performed by dividing the space into grids of equal size. The visible area from a certain point in space is called the Visual. Visual grid analysis can reflect the spatial organization pattern through color changes. Visual analysis explores the visual relationship between different visible areas and further analyzes the visual impact of buildings on the overall space. Visual integration measures the likelihood of a person choosing a specific path at a certain node, while sight line analysis assesses the ease with which people observe and understand the space.

Redder colors and higher values indicate higher Visual integration, meaning a wider visible area in the space. Theoretically, this can attract more pedestrians and enhance the spatial vitality of the area. When people enter a new environment, they usually first focus on the places they are more likely to go. Huoshan Village, Wuxing Village, and Shikuai Village, located in mountainous areas, are the three villages with the highest Visual integration, with values of 13.42, 12.05, and 11.97, respectively. These villages are situated in mountainous areas with open views and fewer buildings and obstacles, theoretically leading to higher local population concentrations. However, due to the influence of terrain and transportation conditions, the spatial vitality of these villages is lower than that of lakeside villages, as shown in Figure 5.

Qingshan Village and Dajianpang Village have the lowest average Visual integration. Field investigations showed that the nature of most self-built houses in these villages, as well as the poor maintenance of many historic buildings, hindered their ability to attract crowds. This suggests that areas adjacent to historic buildings often have poor sight lines and reduced attractiveness. Therefore, when transforming villages, consideration should be given to enhancing spaces with more open views, distinctive features, and higher commercial value.

Taking the transformation of Dajianpang Village as an example, the analysis can start from the Nanzhao Style Island boat ticket office. Visual analysis identifies the village center as the area with the highest Visual integration, likely becoming the primary destination for tourists. It is beneficial to arrange functional spaces with high economic and cultural value (such as characteristic commercial areas) in the village center. This approach can provide a planning basis for future village transformation. Additionally, sight lines should be considered an important design factor in architecture and urban planning. Visual analysis can guide precise design choices and provide a scientific basis for village construction. Strategically arranging facilities such as guard booths, retail stores, playgrounds, and banks in areas with optimal Visual conditions can enhance functionality. Setting a certain flow of people at the entrance of the village and simulating the flow paths of people in each village and the impact of buildings on the flow can provide more references for future village design and transformation.

3.2.2. Visual Mean Depth Analysis

In spaces where the visual mean depth is smaller, the level of public exposure is essentially higher. The main tourist service center of Shuanglang Ancient Town is located in Shuanglang Village. Therefore, taking Shuanglang Village as an example, an analysis of Visual mean depth shows that the visual depth values of the main road on the east side and its intersections and open spaces are significantly lower than those of the west side’s Shuanglang Ethnic Culture Street, which has the highest global integration and is surrounded by shops and guesthouses. This indicates that Visual mean depth has a crucial impact on spatial perception and human behavior, effectively demonstrating that villagers and tourists are more likely to reach more open areas. Overall, the Visual mean depth of main roads is smaller than that of secondary roads, indicating that main roads have higher public visibility. The lowest values of average visual depth usually appear in some extended areas of space, suggesting that these areas have stronger public attributes, making them more likely to attract people to stay and engage in social activities. Visual mean depth effectively illustrates the number of turns and movement trajectories required for villagers or tourists to navigate within the village. Observing the darker areas in the Visual mean depth map indicates that these areas are the parts of the village most easily accessible to and frequently visited by the public, i.e., the areas with the lowest Visual mean depth values. As mentioned earlier, Visual mean depth does not represent physical distance but rather the degree of spatial connectivity and the ease of experiencing spatial transitions, as shown in Figure 6.

3.3. Comparative Analysis of Space Morphology Characteristic Indicators

3.3.1. Common Space Morphology Characteristics of Ethnic Minority Villages

Through comprehensive analysis, this study classifies Shuanglang Village, Dajianpang Village, Changyu Village, and Qingshan Village as “seaside villages”, and Shikuai Village, Wuxing Village, and Huoshan Village as “mountain-backed villages”, as shown in Figure 7. This classification is not arbitrary but deeply rooted in the geographical characteristics that influence settlement site selection: seaside villages strategically choose locations near water sources to fully utilize water resources, while mountain-backed villages choose locations within mountain landscapes to optimize available mountain resources. This site-specific settlement layout approach demonstrates the villagers’ profound understanding and ingenious use of the natural environment.

Furthermore, the cultural background of each settlement greatly shapes its architectural form and spatial organization. For example, the economic activities of seaside villages are mainly related to local tourism, so their spatial design and layout—with wide, scenic waterfronts and well-connected public spaces—are carefully planned to attract and accommodate tourists. In contrast, mountain-backed villages are dominated by traditional agriculture, and their spatial configuration aims to maximize agricultural production efficiency. Terraced fields and closely connected dwellings facilitate agricultural activities and rural community life, highlighting the complementary and symbiotic relationship between villagers’ livelihoods and unique topography.

It is worth noting that the connection between geographical environment and economic activities is accompanied by profound cultural influences, jointly determining the spatial and social structure of settlements. For example, in mountain-backed villages, public spaces are often centered around natural resources such as springs or communal farmlands, becoming important places for villagers to socialize, gather, and hold cultural celebrations, attesting to the traditional settlement construction wisdom of “depending on mountains when near mountains, and relying on water when near water”. This ancient adage succinctly summarizes how the natural environment shapes the economic livelihood patterns and social interactions of rural communities.

3.3.2. Different Space Morphology Characteristics of Ethnic Minority Villages

Located within the bustling Shuanglang Ancient Town Scenic Area, seaside villages, with their unique location advantages, excel in indicators such as spatial integration and synergy. These settlements are the concentration areas of local economic activities, with numerous commercial streets, tourist attractions, and homestays, jointly driving the booming development of the regional economy. Adjacent to administrative centers and with flat and accessible terrain, the vitality of seaside villages is further promoted, ensuring that they not only attract many tourists but also efficiently accommodate the flow of visitors. As tourists flock in pursuit of leisure vacations and cultural experiences, the scene of commercial prosperity in seaside villages becomes increasingly vibrant, with strong economic development momentum.

In stark contrast, mountain-backed villages face numerous difficulties, resulting in significant differences in their development status compared to seaside villages. Due to the rugged terrain and inconvenient transportation, the spatial integration and synergy levels of these settlements are significantly lower. The complex topography hinders external connections, making travel and access to basic public services extremely difficult, and limits the interaction between mountain-backed villages and the regional economic landscape. Therefore, the dominant industry in these areas remains traditional agriculture, with villagers’ livelihoods closely tied to the land and little outside influence and tourist interaction. Coupled with limited policy support, self-built dwellings and dilapidated historic buildings are common, further weakening the external attractiveness and accessibility of mountain-backed villages, exacerbating their isolated state.

The vastly different development trends of seaside villages and mountain-backed villages profoundly demonstrate the far-reaching impact of natural geographical endowments and cultural environment differences. Seaside villages rely on their superior location and infrastructure advantages to promote economic growth and social prosperity, while mountain-backed villages are constrained by unfavorable topographical conditions and policy environments, and highly dependent on self-sufficient agricultural production with extremely limited external participation. These differences not only affect economic development outcomes but also profoundly shape the social structure and cultural characteristics of rural communities, reflecting site-specific livelihood patterns and value orientations.

3.4. Factor Analysis of Spatial Population Vitality

3.4.1. Driving Factors of Spatial Population Vitality

In research on parameter optimization, the factor detector is used to calculate the Q-value for each continuous variable, determining the best parameters by considering various combinations of discretization methods and the number of bins. The factor detector computes the Q-values for each continuous variable related to geospatial data using different classification methods and the number of bins. The parameter combination that produces the highest Q-value is selected as the optimal discretization method. This chosen parameter combination effectively captures the heterogeneity of spatial stratification, with a particular focus on the importance of key continuous variables.

To discretize continuous variables in driving factors, a strategic approach is needed to ensure meaningful analysis. The optimal parameter-based geographical detector model is applied to explore various classification algorithms and determine the most effective discretization group number. Five methods are rigorously tested: quantile, sd, natural breaks, geometrical interval, and equal interval, to determine the most appropriate discretization technique for the specific dataset.

It was concluded that the natural breaks method is superior in optimizing the classification of continuous variables. This method excels at minimizing within-group variance while emphasizing between-group differences, thereby enhancing the clarity and reliability of spatial analysis results, as shown in Figure 8.

In detecting the driving factors of spatial population vitality differentiation, research data show that the magnitude of influence of each driving factor on spatial population vitality in the study area is as follows: distance to village committee (0.394) > distance to Erhai Lake (0.327) > slope (0.231) > distance to county road (0.2309) > annual average temperature (0.136) > distance to railway (0.131) > distance to town government (0.112) > GDP (0.102) > elevation (0.101) > cultivated land area (0.093) > annual average precipitation (0.080) > aspect (0.023). This result highlights the crucial role of spatial accessibility and location factors in shaping the spatial population vitality pattern, as shown in Figure 9.

Among all driving factors, the distance to the village committee has the most significant impact on population vitality, with a q value as high as 39.4%. In terms of location factors, the influence of distance to county roads is particularly prominent, reaching 23.1%, even surpassing the impact of distance to railway. Regarding natural factors, the influence of slope is the most critical, at 23.1%, followed by annual average temperature, with an influence of 13.6%. In comparison, the impact of socio-economic factors is relatively lower, with the influence of GDP and cultivated land area being 10.2% and 9.3%, respectively. Among the 12 influencing factors, aspect has the least impact, at only 2.3%, indicating its limited contribution to enhancing population vitality.

3.4.2. Interaction Factors of Spatial Population Vitality

In the interactive detection analysis of driving factors for spatial differentiation of population vitality, the research reveals that the interaction between dual factors mainly manifests in three types: nonlinear enhancement, linear enhancement, and linear weakening, as shown in Figure 10.

X1–X12 represent the names of driving factors, with specific naming shown in Table 3. The size of the circles represents the magnitude of interactive force, and the numbers in the circles represent the q value of interactive force.

Among all interaction records, the number of nonlinear enhancement instances is significantly higher than the sum of linear enhancement and linear weakening instances. Specifically, the interaction between distance to the village committee and distance to Erhai Lake has the greatest impact on the spatial differentiation of population vitality, exhibiting a linear enhancement relationship with a q value as high as 0.596. In contrast, the interaction between GDP and cultivated land area has the weakest impact on population vitality, although also showing linear enhancement, with a q value of only 0.153. Additionally, the interaction between aspect and distance to the county road shows a significant increase in influence, displaying a linear enhancement characteristic with a q value reaching 0.24. Overall, these intricate interactive relationships among driving factors, under the combined influence of natural conditions and socio-economic activities, significantly exacerbate the spatial differentiation of population vitality in the study area.

4. Discussion

4.1. Space Morphology Optimizing Strategies

Based on the synergy index, targeted strategies are proposed for optimizing the alley spaces of villages with a synergy lower than 0.2. By streamlining the rural alley network and strengthening alley network construction, the connectivity and accessibility of internal village spaces can be effectively improved. Specifically, the focus should be on optimizing the road network structure within the villages, rationally laying out main and secondary roads to form a clear hierarchy and smooth circulation of the alley network. At the same time, attention should be paid to the scale and proportion of alley spaces, creating a pleasant sense of scale, and enhancing the spatial quality of alleys. In addition, improving the pavement, greening, lighting, and rest facilities of alleys can create comfortable, safe, and vibrant alley spaces that promote villager communication and social interaction. The optimization of alley spaces can not only improve the internal spatial structure of villages and enhance residents’ quality of life but also provide important carriers for rural economic development and cultural inheritance.

Taking Dajianpang Village as an example, based on the existing street structure, damaged roads are streamlined and orderly connected with surrounding areas, and some missing routes are repaired. A convenient road traffic network is constructed to improve the convenience of villagers’ lives, clarify the flow of crowded and mixed alley spaces, classify road purposes, remove illegal self-built houses and structures, and repair the horizontal shape of crowded buildings on both sides of surrounding streets to maintain the circulation and accessibility of alleys. A new ecological walking path around Erhai Lake is added to provide villagers and tourists with better conditions for scenic walks closer to the lake, making the alley space more developed. We established an optimized alley space axial model for Dajianpang Village, imported the model into Depthmap+ Beta 1.0 software for axial analysis, and compared the quantitative results, as shown in Figure 11 and Figure 12. The results show that after optimization, the accessibility of the main streets on the east side of Dajianpang Village has greatly improved, and the synergy of the village has increased by 0.1, a significant improvement compared to before optimization.

4.2. Space Vitality Improving Strategies

Public spaces are an important component of rural settlements and play an indispensable role in villagers’ daily lives and social interactions. This study proposes optimization and activation strategies for the current problems of public spaces in rural settlements around Erhai Lake. Firstly, various public service facilities, such as village committees, health clinics, and cultural rooms, should be rationally laid out and improved to enhance their accessibility and service level. Secondly, the protection and renovation of traditional public spaces, such as ancestral halls, temples, and ancient woodlands, should be strengthened to sustain the historical context of the villages. Thirdly, diverse public activity spaces, such as village squares, recreational green spaces, and pastoral landscapes, should be created according to local conditions to enrich villagers’ public life. At the same time, villagers should be encouraged to participate in the creation and management of public spaces to enhance their sense of ownership and community belonging. The optimization and activation of public spaces can not only improve the living environment of rural settlements and enhance villagers’ quality of life but also stimulate the endogenous driving force for rural economic and social development.

4.3. Cultural Heritage Revitalizing Strategies

Rural settlements contain rich regional cultural resources and are important carriers for showcasing local characteristics and inheriting cultural heritage. This study proposes relevant strategies for the protection and activation of cultural spaces in rural settlements around Erhai Lake. Firstly, the protection and renovation of material cultural heritage, such as traditional dwellings and historic buildings, should be strengthened to sustain the historical features of rural settlements. Secondly, intangible cultural heritage, such as traditional festivals and folk arts, should be explored and inherited to enhance villagers’ cultural confidence and sense of identity. Thirdly, cultural industries should be developed, and distinctive cultural spaces, such as creative workshops, art galleries, and homestays, should be created to promote the integrated development of culture with tourism, creativity, and other industries. Villagers should be encouraged to participate in cultural activities and cultural inheritance to cultivate community building of rural culture. The optimization and activation of cultural spaces can not only protect and sustain the cultural heritage of villages and enhance the cultural charm of rural areas but also provide new momentum for the sustainable economic and social development of rural areas.

5. Conclusions

This study establishes a comprehensive quantitative analysis framework for assessing the spatial characteristics of tourism villages. By integrating space syntax theory and an optimal parameters-based geographical detector model, it systematically examines seven administrative villages in Shuanglang Town, Dali, Yunnan from multiple perspectives, including overall spatial form, road accessibility, and spatial synergy. The key conclusions are as follows:

(1)

The rural spatial pattern in this multi-ethnic settlement area, dominated by the Bai people, is deeply influenced by natural and human factors such as topography, water system distribution, agricultural production methods, and ethnic cultural traditions, presenting unique heterogeneous characteristics. This enriches the research content of rural geography.

(2)

Space syntax analysis reveals significant differences among the villages in indicators like global integration, local integration, and connectivity, reflecting the differentiation characteristics of spatial centrality, connectivity, and synergy. The spatial structures of ancient villages are relatively compact, while the layouts of newer villages are relatively loose.

(3)

The optimal parameters-based geographical detector analysis further identifies the key influencing factors and their interactive mechanisms affecting the spatial perception of rural settlements. Factors like topographic conditions, road accessibility, land use patterns, and tourism resource endowments have significant shaping effects, with certain nonlinear enhancement effects between them.

(4)

Based on the research findings, this paper proposes targeted rural spatial optimization strategies, including optimizing village spatial structure, improving tourism service facilities, promoting integrated industrial development, and inheriting ethnic cultural characteristics. These suggestions have reference value for regional rural revitalization and the integrated development of culture and tourism.

This study integrates advanced analytical methods like GIS, remote sensing, and big data mining, providing new ideas for overcoming the data limitations in traditional spatial perception research. However, there are still some issues regarding data timeliness and sampling representativeness that need to be further improved in future work. Extending the research to a larger geographical scale could also deepen the understanding of the general laws of rural settlement spatial evolution.

In summary, this research combines space syntax theory, optimal parameters-based geographical detectors, and other quantitative analysis techniques to systematically examine the spatial characteristics and influencing mechanisms of rural settlements in ethnic minority areas. The findings not only enrich the theoretical connotation of rural geography but also provide scientific support for the spatial optimization and sustainable development of tourism villages.

The innovations of this study lie in (1) establishing a comprehensive research framework integrating natural, cultural, and economic factors; (2) introducing cutting-edge analytical tools and multi-source data to break through the limitations of traditional spatial perception research; and (3) proposing targeted strategies for rural spatial optimization with practical guidance for the integrated development of rural tourism and cultural heritage protection.