Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS

1.1 Research background

The spatiotemporal evolution of rural construction areas is a core topic in geography, urban and rural planning, and socioeconomics [1]. In developed countries such as Europe and Japan, rural population decline, the decline of traditional agriculture, and the protection and development of cultural heritage are becoming increasingly prominent [2]. Studies have shown that spatial changes in rural areas are not only affected by natural conditions, but also driven by socioeconomic changes and policy interventions [3]. However, the path of rural development around the world is not uniform, especially in developing countries, where the driving factors of rural construction and evolution are more complex [4]. Rapid urbanization has led to large-scale population outflows, land abandonment, and industrial monoculture in rural areas, resulting in the gradual disappearance of traditional villages [5,6]. In this context, how to promote economic development while preserving rural culture and ecological characteristics has become one of the key challenges in global rural planning [7]. Therefore, analyzing the spatiotemporal evolution of China’s rural areas, especially areas rich in cultural and natural resources such as the Nanxi River Basin, will bring new perspectives and experiences to global rural construction and protection research.

China has a large rural population, and the rural construction area accounts for a large proportion of the total land area [8]. The data from China’s third land survey indicates that as of the end of 2019, established villages nationwide occupied 405.9741 million mu of land, which is 5.18 times more than urban land [9]. At present, China’s rural construction areas are undergoing structural transformation and reorganization, with problems such as prominent structural contradictions, rural population migration to cities, and the shift from urban–rural binary opposition to multiple oppositions becoming increasingly prominent [10]. To make the best use of rural land resources and promote long-term growth in the region, researchers are now looking into how the layout of rural construction areas changes over time and space, as well as the regular mechanisms that control these changes [11]. Therefore, it is of considerable research significance and theoretical value to accurately analyze the spatiotemporal distribution characteristics of rural construction areas within the basin and understand the influencing factors of their formation. Since China proposed the rural revitalization strategy in 2018, rural regional construction has ushered in new opportunities and challenges [12]. The strategy aims to promote agricultural and rural modernization, improve farmers’ living standards, improve rural infrastructure, and strengthen ecological and environmental protection [13,14]. The Nanxi River Basin, an important river basin in Zhejiang Province, has unique natural and cultural resources and is one of the key areas in rural revitalization [15]. The ancient villages on both sides of the middle reaches of the Nanxi River are outstanding representatives of Wenzhou’s ancient dwellings. At present, there are still quite complete village appearances and cultural customs from the Song Dynasty (A.D.960–1279), as well as a large number of ancient buildings from the Ming and Qing Dynasties (A.D.1368–1912). In 2001, it was included in the UNESCO World Heritage Center’s preliminary list of China’s World Heritage. The villages in the basin have traditional vernacular architecture and a rich cultural heritage, but they also face issues such as rural population outflow, traditional agricultural decline, and insufficient modern infrastructure. The Nanxi River Basin’s regional distribution of rural construction clearly demonstrates its proximity to mountains and rivers, with topography and hydrological conditions profoundly influencing the layout of villages and architectural forms. Due to this unique natural geographical environment, the spatial layout of rural construction in this area exhibits strong local characteristics [16]. However, modern economic activities and infrastructure construction have gradually impacted traditional villages in the basin, leading to drastic changes in the spatial pattern [17]. Therefore, exploring the spatiotemporal evolution of rural construction in the Nanxi River Basin and its driving factors will help provide a scientific basis for rural planning and design and provide important theoretical support for achieving the coordination of economic development and cultural and ecological protection.

Despite numerous studies on the spatiotemporal evolution and influencing mechanisms of settlements, the Jiangnan River Basin in China lacks comprehensive research on the regional studies of settlement construction and influencing factors. Compared with existing studies, this study has the following innovations: first, a multi-dimensional factor interaction model. This study employs the geographic detector model to examine the interactive relationship between the three major factors of culture, nature, and economy. It reveals how these factors jointly influence the spatiotemporal evolution of rural construction in the Nanxi River Basin. This study addresses the issue that the analysis of the influencing factors of the spatiotemporal evolution of the conventional buffer zone on its distribution cannot reveal the interaction between the factors and the primary and secondary connections. The existing literature rarely discusses this multi-dimensional interaction analysis. Second, this study not only conducted a case analysis of the rural construction area in the Nanxi River Basin but also proposed a new spatiotemporal evolution model that combines the protection of cultural heritage with economic development, making up for the shortcomings of previous studies that only focused on the analysis of natural or economic factors. In addition, most of the studies conducted in the Nanxi River Basin are environmental engineering and hydraulic studies, such as water resources and environmental governance, lacking a discussion of the complex causes of spatial heterogeneity and rural formation. Third, although the research object is the Nanxi River Basin, the theoretical framework proposed in this study is also applicable to other rural areas rich in cultural and natural resources, providing a new theoretical perspective and empirical reference for rural development and cultural heritage protection around the world.

Therefore, this study aims to construct a comprehensive analytical framework to explore the spatiotemporal evolution of rural construction in the Nanxi River Basin and analyze the multidimensional driving factors behind it. Using the Nanxi River Basin as an example, this study begins by examining the spatiotemporal pattern and influencing factors of the regional evolution of rural construction. It then goes on to summarize and refine the evolution mechanism of rural built-up areas. We want to do three main things with this study: (1) look at how the pattern of rural construction changes over time in the Nanxi River Basin; (2) find out how different factors, like natural geography, socioeconomic development, and protecting cultural heritage, affect the changes in the basin’s rural areas; and (3) use this information to help China’s rural revitalization strategy work and show other rural areas around the world how to find a balance between protecting culture, developing the environment sustainably, and becoming more economically modern. This study employs innovative multi-factor interaction analysis to offer a universal theoretical framework that transcends a single case, making it applicable to rural revitalization policies worldwide. Simultaneously, the study introduces a novel model that integrates cultural preservation and economic growth, offering fresh perspectives for those involved in rural planning and policy formulation, particularly in managing the interplay between cultural heritage preservation and modernization initiatives.

2.1 Research progress on rural construction regions

The term rural construction area refers to the specific geographical scope of various infrastructure construction, housing construction, public service facility construction, and environmental governance activities in rural areas. Research on rural construction areas primarily focuses on four key aspects.

First, the change and evolution mechanism of rural spatial pattern. Existing literature mostly uses a combination of quantitative and qualitative methods to study the spatiotemporal evolution of rural construction areas, especially the study of land use and landscape pattern changes. Scholars Hailu et al. analyzed the information on land use and land cover (LULC) changes in Jimma Geneti District, western Ethiopia, from 1973 to 2019 and the driving forces behind such modifications to understand the dynamic trend of LULC changes [18]. This type of research demonstrates the effective application of spatiotemporal evolution models in different regions, but its limitations in comprehensive multi-factor interaction analysis are worth noting. On the contrary, the distribution evolution and influencing factors of rural construction’s regional space are studied for administrative regions. Chinese researcher Yang [19] used land use data from the Taihang Mountains in Hebei Province from 1990 to 2020 to create 10 landscape-level indicators to study the dynamics of LULC. He also used the Integrated Assessment of Ecosystem Services and Trade-offs (InVEST) model to evaluate HQ and the geographically weighted regression (GWR) model to quantify and analyze the effect of land use change on HQ [19]. However, Yang did not fully combine the effects of social and cultural factors. Therefore, while these studies have been innovative in revealing the laws of spatiotemporal changes, they have overlooked the impact of cultural heritage protection on rural construction areas.

Second, rural economic development relies heavily on this driving force and model. Many studies have explored the role of infrastructure such as transportation and industry in promoting rural construction. In 2020, researcher Alvarez-Diaz et al. used a spatial gravity model to look at how tourists move through rural tourist spots in Spain’s provinces. This showed how relevant factors affect the growth of rural construction in the region [20]. Chinese scholar Huang et al. [21] created a dynamic gravity model using the maximum entropy (MaxEnt) theory to figure out and keep an eye on the strength of connections and changing features of two-way relationships. This model shows how the Belt and Road Initiative (BRI) is changing over time and space [21]. From 1995 to 2015, Chinese scholar Haiyue Fu looked at how the Central Plains Urban–Rural Agglomeration’s (ZURA’s) transportation network and economic network interacted with each other. He used a space syntax model to figure out how the transportation network was structured and an improved gravity model to figure out how the economic network was structured. They conducted bivariate spatial autocorrelation analysis to study the relationship between transportation and economic networks. They pointed out that the “multi-center, unbalanced” growth model of the economic network and the “multi-core, multi-directional” structure of the transportation network are more likely to promote the sustainability of the urban–rural system [22]. A lot of research has used spatial weight matrices to look at how the focus of rural construction, regional development, and complex network relationships change over time. These relationships are based on economic construction infrastructure data from areas like transportation and industry. While we cannot ignore the role of economic factors in promoting the construction area, most current studies treat it as a single dimension, neglecting its interaction with cultural and natural factors.

The third is ecological environmental protection and sustainable development, which is mainly based on sustainable natural resource management, spatial coupling of three-life ecosystems, and environmental, climate, and agricultural sustainability assessment frameworks. Scholars Streimikis et al. provide an agricultural sustainability assessment framework that links rural policy goals with sustainable development, climate change mitigation, and environmental policy goals through systematic tracking of indicators [23]. Chinese scholar Yang et al. took the urban and rural space of Chongqing as an example and used the coupled coordination evaluation model (CCD) and geographic information systems (GIS) spatial methods to analyze the correlation, coupling, and coordination. She believes that four sustainable development models, namely the ecological industry development model, the cultural tourism integration model, the industrial-city integration model, and the life service model, can promote the dynamic development of rural and urban areas [24]. Chinese scholars Fu et al. reviewed the ecological restoration of China’s rural sustainable development and proposed a “landscape pattern-ecosystem services-sustainable development” co-evolutionary framework to describe the ecological restoration at the landscape scale and its impact on landscape pattern and ecological processes, and the impact of ecosystem services on human well-being, sustainable livelihoods, and socioeconomic development [25]. These studies highlight that the sustainable development of rural areas requires the construction of a comprehensive framework that coordinates regional space, culture, and ecology so that settlement research is no longer a linear data fragment connection, adding more scientific and ecological index evaluation and ecological concepts, and strengthening the development of land use sustainability and settlement adaptability.

The fourth aspect is the safeguarding and application of rural cultural heritage. The study highlighted the indispensability of protecting and utilizing cultural heritage in rural regional construction. On the one hand, in terms of material space, by constructing a heritage style evaluation system, the importance of the traditional construction area style in the rural construction area is ranked, and corresponding regional solutions and optimization plans are provided. Jiang et al. Chinese scholars used the historical urban landscape method to look at the urban morphology and urban landscape characteristics of Suzhou’s ancient construction area before 1949, between 1949 and 1978, and after 1978. They also came up with ways to protect local heritage and promote sustainable urban and rural development [26]. Based on the spatial gene theory, Jiang et al. studied the self-generation law of the spatial morphology of Xiaoxi Village in Western Hunan, China. They used the CityEngine parametric platform and digital technology to look into its application possibilities, which effectively supported the preservation and integration of rural spatial landscape resources [27]. On the other hand, Jiang et al. employed the cultural ecology perspective to examine the local landscape heritage and protection strategies in the rural construction area, focusing on cultural formation. Scholar Tzortzi et al. used the southern edge of Milan (urban–rural transition edge) as a research object to investigate the role that green infrastructure and nature-based solutions (NBS) can play in redefining suburban areas and linking urban and rural landscapes. They transform into geographical indications and NBS networks, incorporating cultural heritage and multicultural aspects, from the perspective of ecosystem service enhancement [28]. Scholar Sardaro et al. used masserias, a type of historical building in rural Puglia in southern Italy, as a case study to investigate the attitudes of stakeholders toward its protection. The study discovered that densely populated and underdeveloped rural areas pose a serious threat to the protection of such architectural heritage. In addition, the community has a general interest in the protection and use of these structures [29]. In summary, although the impact of cultural heritage on rural construction has been preliminary discussed in existing research, an effective comprehensive analysis framework has not yet been formed, especially when exploring its interaction with natural and economic factors. The development of settlement construction areas is inseparable from the joint effects of “cultural communication – natural environment – social economy.” Therefore, it is very necessary research content to substitute the influence of three different dimensions into the study of settlement construction areas. It can provide a multi-dimensional perspective to determine the causes of the development of settlement construction areas and their accompanying causal derivative relationships, and thus better offer advice and help for regional construction and urban and rural planning.

2.2 Technologies for the spatiotemporal evolution of rural construction areas

When reviewing existing research on the spatiotemporal evolution of rural construction areas, there are four main academic focuses. First, remote sensing and GIS reveal spatial patterns’ spatiotemporal evolution by focusing on changes in rural spatial morphology, land use structure, village layout, and so on. Researchers Alijani et al. measured changes in land use in the Chalous study area in Iran found out how the geophysical and socioeconomic conditions were changing, and looked at how these changes might affect other places in the region with similar conditions [30]. Chinese scholars Li et al. simulated and predicted the evolution trend of rural construction areas in Jizhou District in 2030, based on the CLUE-S model of spatial land use conversion and its impact on small areas within rural construction areas in Jizhou District from 1962 to 2015 [31]. Furthermore, Chinese scholars Liu et al. utilized the traditional villages of Jiaxian and Linxian in Jinshanxia, Yellow River Basin, as their research subjects. They employed the GIS geographic information system through spatiotemporal data analysis, landscape pattern index, and other methods, with a focus on three distinct dimensions: spatial scale (macro, medium, and micro), time scale (past, present, and future), and variable factors (cultural, social, and natural) [32]. Chinese scholars Wen et al. utilized the comprehensive index method, ArcGIS, and FRAGSTATS landscape pattern analysis to construct the rural residential land transition conceptual model. They also analyzed the characteristics of the change range, change trend, change form, and change intensity of rural residential land in the Beijing–Tianjin–Hebei region from 1980 to 2018 [33].

The second is social network analysis, which focuses on the study of rural socioeconomic transformation. It can reveal the social dynamic mechanism of rural construction by analyzing the relationship between villagers, floating population, land transfer, and so on [34]. Scholar Wilson et al. analyzed the resilience of Hu Village (Sichuan, China) based on the conceptual framework of assessing community resilience. It is important to note that rural areas have changed the resilience level benchmark of the original space a lot in response to complex social structures and fast human interventions, especially the effects of globalization. They have also made high levels of self-adaptation and elastic adjustments in response to industrialization, de-agriculturalization, anti-urbanization, and changing stakeholder expectations [35]. On the contrary, it focuses on the role of social interactions in the village, such as interpersonal relationships, land use rights, and economic connections, in spatial evolution. Chinese researchers Kong et al. came up with a multi-objective spatial reconstruction model of rural construction areas based on a particle swarm optimization algorithm. This model built social connections between villages while taking into account land use quantity, spatial compactness, land use suitability, and relocation distance as reconstruction variables. They conducted an empirical study on Chenggui Township, Hubei Province, in Central China and proposed five rural construction area reconstruction modeling scenarios [36]. Similarly, Chinese scholars Zhou et al. used the Angou Basin in the hilly gully area of the Loess Plateau as their research object. They constructed a research framework of “gravity identification-network evaluation-spatial optimization” to analyze the characteristics of the network as a whole, region, and node. They then used social network analysis to reveal the spatial pattern of the rural construction area in the basin [37].

The third method involves constructing a quantitative model and conducting a spatial statistical analysis. Researchers construct quantitative models of research samples, such as gravity models, regression analysis, and spatial autocorrelation models, to quantify the temporal and spatial trends of rural construction. Zhang et al., Chinese scholars, used a spatial autocorrelation model to look at how environmental pollution sources differ and group together in 338 prefecture-level or municipal administrative units in China. They did this by looking at rural environmental pollutant emissions and socioeconomic cross-sectional data, which showed the regional spatial pattern of China’s green development and rural environmental governance [38]. Chinese scholar Jiang et al. drew on the conceptual framework of ecological risk assessment and landscape ecology theory to develop a methodology for deriving the spatiotemporal pattern of land use conflicts in China from 2001 to 2017. Then, they used a multilevel regression model to identify the driving factors of land use conflicts at different levels. The study pointed out that DEM has a greater impact on conflict intensity than socioeconomic factors [39]. In the same way, Kim et al. used machine learning to model land use change, taking into account nonlinear relationships in land development. They did this by training their model on 9 million data samples from Florida between 1900 and 2019 [40]. In 2023, Zhang et al. from China studied how cultivated land changed over time and space in the Weibei Arid Plateau region from 1995 to 2020. They looked at the changes over time using the center of gravity transfer and land use transfer matrix and connected geographic detection models to find out what was driving the changes in cultivated land [41].

Fourth, the comprehensive method of qualitative research, such as field surveys, in-depth interviews, historical documents, and other qualitative research, reveals the cultural, social background, and historical development logic of rural construction. In Charqoli Village, Iran, scholars Abadi and Khakzand investigated the different dimensions of agricultural tourism in promoting regional development of sustainable rural construction using grounded theory. The study discovered a relationship between agricultural tourism and six general dimensions: socio-cultural, economic, agricultural, environmental, physical, and planning [42]. Through focus groups, interviews, oral history, personal transformation, participatory ethnographic observation, and logical framework analysis (LFA), Ancuța and Jucu did a case study on the Hărman Commune in Brasov County, Romania, and thought about how to use local cultural heritage to promote sustainable rural development [43]. Specifically in the protection of vernacular architecture and the revitalization of traditional villages, qualitative research can provide an in-depth understanding of local knowledge and residents’ lifestyles [44,45].

From this, we can see that the current spatiotemporal evolution analysis mainly uses a quantitative and qualitative approach to statistically analyze spatial data and complex causal results. However, there are several challenges, including the difficulty in obtaining spatial statistical data, the lack of in-depth analysis of evolutionary influencing factors’ mechanisms, the insufficient multi-scale comprehensive analysis, and the absence of quantitative research on dynamic-coherent mechanisms. This needs further exploration by subsequent scholars.

To get around the usual one-dimensional analysis model, this study suggests a three-way interactive model of “cultural genes, geographical environment, and economic potential.” Based on the geographic detector model, this framework shows how multidimensional factors interact in a way that is not linear. It has three main dimensions: The first dimension is the cultural gene dimension, which is achieved through spatial quantitative analysis of cultural landscape genes, such as the remains of the Yongjia School. The second dimension is the physical geography dimension, which integrates spatial environmental indicators like terrain slope and visual field. The third dimension is the economic momentum dimension, which covers social and economic factors like urban–rural distance and educational resources. This theoretical framework provides methodological support for the analysis of the spatiotemporal evolution mechanism in the following text. Adding the institutional transformation of cultural capital to the spatial analysis system shows how innovative it is. This creates a model for analysis that can be used in similar areas around the world.

3.1 Location and methods

3.1.1 Geographical scope

The Nanxi River Basin is located in the southeast of Zhejiang Province, with a geographical range of 120°19′ to 120°59′ east longitude and 28°00′ to 28°34′ north latitude. The Nanxi River originates from Daqinggang at the junction of Xianju County and Yongjia County, passes through Xikou, Yantou, Shatou, and Shangtang Town in Yongjia County, and finally merges into the Ou River at Oubei Town on the opposite bank of Wenzhou. This river is the largest tributary on the north side of the lower reaches of the Ou River, and its basin area covers Yongjia County and several surrounding administrative regions. Its total basin area is 2,436 km² (see Table 1 for detailed basin area data), of which Yongjia County occupies 2,223 km², accounting for 91.2% of the total area. The mainstream of the Nanxi River extends from north to south, with a total length of 142 km. As a closed basin and independent economic zone, the Nanxi River Basin covers the entire Yongjia County (Figure 1). Despite numerous changes in Yongjia County’s seat and administrative divisions, the Nanxi River Basin has remained a stable administrative area since the Tang Dynasty (A.D.618–907), thereby forming a unique cultural and dialect area. As a result, the Nanxi River is an important representative in the construction of river basin villages and regional space research.

Table 1

Watershed area within Nanxi River counties (cities and districts)

Counties (cities, districts)	Yongjia County	Jinyun County	Huangyan District	Yueqing City	Qingtian County	Total
Area within the Nanxi River Basin (km2)	2,223	96	48	46	23	2,436

Source: Author’s statistics.

Figure 1

Study area of the Nanxi River Basin.

Therefore, the main basin of the Nanxi River serves as the primary distribution area for its rural construction planning area, with tributaries like Xiaonanxi, Heshengxi, and Yantanxi supplementing it (Table 2). Different topographical and economic factors distinguish the overall distribution characteristics, which are linear and water-adjacent, in the upper, middle, and lower reaches. They are: (1) The upper basin (west side of Luoyangling-Nayantanxi in Xikou Village) is scattered, short, and feather-like, with only scattered terraces and valleys along the river. (2) The rural construction area in the middle basin (Xikou Village-Shatou Village) is relatively intact, with the largest number of construction areas and a large number of original village residents. The construction area scales and textures of the villages are three different combinations of “scattered villages – collective villages – township villages,” which are distributed in the large alluvial plains and river valley basins on both sides of the Nanxi River Basin and along the banks of the Xiaonanxi River Basin. (3) Industrialization and urbanization are more common in the rural construction areas of the lower reaches of the basin (Shatou Village-Oubei Town). A large number of rural construction areas are organized into construction area spaces with towns as units in the form of collective villages, distributed in Nancheng, Huangtian, and other streets (the plain area in the west of the lower reaches of the basin), and formed the earliest urban–rural integration space with a mixed symbiosis of township streets and rural construction areas in the basin.

Table 2

Tributaries of the Nanxi River Basin

Tributary	Catchment area (km2)	River length (km)	Drop (m)	Slope (‰)
Yantanxi Creek	267.3	31.4	830	10.8
Zhangxi Creek	138.2	29.6	1,090	14.8
Heshengxi Creek	321.9	40.0	700	9.7
Huatanxi Creek	102.0	28.9	730	7.3
Xiaonanxi Creek	640.6	73.3	1,110	5.6
Doumenxi Creek	94.0	19.8	480	13.8

Source: Author’s statistics.

This study is based on the regional patch data of rural construction in the entire Nanxi River Basin, with an area of 2,436 km² from 1990 to 2020. It uses the landscape pattern index, standard deviation ellipse, and kernel density method to analyze the evolution characteristics of rural construction areas and uses geographic detectors to analyze the detection and interaction of influencing factors affecting the spatiotemporal changes of rural construction areas (Figure 2).

Figure 2

Research framework.

3.2 Data source

There are four sources of basic research data for this paper: (1) The rural construction area data in 1990, 2000, 2010, and 2020 are from the China High-Resolution Land Cover Dataset (CLCD) [54]. Ten years is a reasonable time span that can reflect long-term spatial evolution trends while avoiding the impact of short-term fluctuations. The research data source (CLCD) is updated every 10 years to ensure data accessibility and consistency. Many policy adjustments, economic development, and social changes usually have significant impacts within a 10-year cycle, which meets research needs. The researchers selected the impervious category, removed the boundaries of urban built-up areas (http://data.ess.tsinghua.edu.cn/), and combined semi-supervised visual recognition detection to determine the patches of rural construction areas. (2) The Wenzhou Natural Resources and Planning Bureau (https://zhejiang.tianditu.gov.cn/wenzhou/#/history) compiled the Nanxi River Basin Map, which provided the basin boundary and main river vector data for this study. After comparing the “Nanxi River Water System and Station Location Schematic Diagram” provided by the Yongjia County Water Conservancy Bureau, the researchers collected the basin boundary and main river vector data after georeferencing the map in ArcMap software. (3) The digital elevation model (DEM) data used in the study comes from the Japanese ALOS Earth Observation Satellite (https://search.asf.alaska.edu/). (4) The Wenzhou City Data Open Platform (https://data.wenzhou.gov.cn/jdop_front/index.do), Yongjia County Natural Resources and Planning Bureau, and Yongjia County Statistical Yearbook provide data on the regional impact factors of rural construction.

4.1 Changes in the spatiotemporal distribution of rural construction areas

4.1.2 Patch increase and decrease in rural construction areas: Spatiotemporal evolution

This study uses GIS spatial analysis methods (such as landscape index analysis, standard deviation ellipse method, kernel density analysis, etc.) to look at the patterns of how patches grow and shrink in rural construction areas from 1990 to 2020. GIS technology is used to identify the changing trends of patches in different time periods, calculate the spatial aggregation and migration paths of construction areas, and combine geographic detectors to analyze the core factors driving the changes. ArcGIS software performed a spatial overlay analysis of landscape patches in the Nanxi River Basin’s rural construction area over the past 30 years, generating a spatial change map from 1990 to 2020. From the perspective of the research period, the rural construction area in the Nanxi River Basin has undergone three major processes: (1) overall cluster increase; (2) dots and blocks increase and strips decrease to dots; and (3) small block increase and partial strip decrease. From 1990 to 2000, the number of patches in the rural construction area decreased, and the total area increased. The traditional village tourism area in the middle reaches (Yantou, Fenglin, Shatou Town) and the urban–rural development area in the lower reaches (Nancheng, Dongcheng, Huangtian, Jiangbei Street) accounted for a significant portion of this increase (Figure 3a). The development of the Nanxi River Scenic Tourism Area and the expansion of land for urbanization in the southern part of Yongjia County have an impact. From 2000 to 2010, the number of patches in the rural construction area increased and decreased. It shows that the strip-like reduction areas are mainly concentrated around the middle reaches of the Nanxi River (Shatou Town) in the study area, which are affected by the water conservancy management of the basin (the impact side of the meander of the Lingxia Village-Taipingyan section), and a small amount is distributed along the tributaries of the Xiaonanxi River and around the town seats in the upper reaches (Da Ruo yan, Feng lin Town); the block-like increase areas are scattered in other township seats and emerging tourism and characteristic industry clusters (Figure 3b). Between 2010 and 2020, there was a decrease in the frequency of demolition and construction in rural construction areas. This led to the formation of the urban pattern in the downstream Yongjia urban area and a gradual stabilization of the distribution of rural construction areas. Upstream and downstream areas concentrate on the areas with reduced construction patches in a strip-like and dot-like pattern. The rural construction areas in Sanjiang Street and Nancheng Street have increased significantly in clusters (Figure 3c).

Figure 3

Regional changes in rural construction in the Nanxi River Basin, 1990–2020. (a) Changes in settlement patches from 1990 to 2000, with magnified areas of increase and enhanced variations. (b) Changes in settlement patches from 2000 to 2010, with magnified areas of increase and enhanced variations. (c) Changes in settlement patches from 2010 to 2020, with magnified areas of increase and enhanced variations.

4.2 Evolution of the spatial pattern in rural construction areas

To elucidate the changes in the spatial patterns of geographical elements in rural construction areas, this study employs the standard deviation ellipse analysis method. It measures the evolution characteristics of the spatial distribution center of the rural construction area in the study area using formulas (1) to (5) and then draws the standard deviation ellipse of the spatial distribution of patches in the rural construction area (Table 5, Figure 4). The lengths of the x- and y-axes of the standard deviation ellipse show how concentrated and directional the rural construction areas are in space. The x-axis (average 0.1541 km) represents the spatial expansion range in the north–south direction, while the y-axis (average 0.1722 km) represents the east–west direction. The longer y-axis indicates that the distribution of rural construction areas in the east–west direction is more dispersed. The center coordinate core exhibits a greater offset in the horizontal direction. The rotation angle deviation decreases year by year, with an average rotation angle of 160.11°, and the deviation range of the main parameters is less than 13%. Further, the researchers found that: (1) From the perspective of the overall spatial pattern of the construction area, the basic spatial pattern of the rural construction area in the Nanxi River Basin tends to be stable. The study area is located in a mountain valley on the southeast plain’s coastal edge. The terrain is high in the northwest and low in the southeast. The basin’s central part is an impact plain with flat terrain, while the eastern part is undulating. (2) In the spatial distribution of rural construction areas, the roads and watersheds are relatively obvious, and the Hangzhou-Wenzhou Railway, S26 Zhuyong Expressway, and the Nanxi River Basin pass through from north to south. From a regional perspective, the study area primarily experiences economic growth along the north–south axis, with areas such as Huangtian, Wuniu, and Sanjiang Street exhibiting higher economic levels, concentrated in the southern end of the basin’s lower reaches. This influences the basic spatial pattern of rural construction areas, which distribute and develop from south to north along the basin.

Figure 4

The standard deviation ellipse diagram illustrates the rural construction areas in the Nanxi River Basin from 1990 to 2020.

The center of the standard deviation ellipse of the rural construction area in the Nanxi River Basin is the center of gravity of the rural construction area. To investigate the spatial evolution of the rural construction area’s center of gravity in the study region, we utilized the 1990 construction area’s center of gravity as the starting point for measuring the dynamic shifts in the migration of the rural construction area’s center of gravity between 1990 and 2020 (Figure 4). During the study period, the average annual migration rate of the rural construction area’s center of gravity in the Nanxi River Basin was 41.77 m/a. Among them, the annual migration rate from 2010 to 2020 reached a high of 82.2 m/a, surpassing the annual rates from 1990 to 2000 by 55.7 and 65.6 m/a, respectively. Upon examining the development over the past 30 years, the annual migration rate of the center of gravity demonstrated a linear pattern of “slowing down-increasing speed,” while the overall spatial structure change of the rural construction area remained relatively stable. From the perspective of direction, the regional center of rural construction in the study area experienced a “southwest–northeast” direction conversion process from 1990 to 2020, and the regional center of construction moved to the north to a large extent from 2010 to 2020. Overall, the regional center of construction is located at the junction of the administrative boundaries of Daruoyan Town and Yantou Town, slowly moving toward Yantou Town without a sharp shift, and the regional spatial pattern of rural construction shows a balanced development trend.

4.3 Changes in the distribution density of rural construction areas

To learn more about how the distribution of rural construction areas is grouped in space, this study used ArcMap 10.8 to find the center of the patches where rural construction areas changed. It then used the kernel density analysis method to figure out how densely the rural construction areas changed in the Nanxi River Basin from 1990 to 2020 were spread out in space (Figure 5). The research findings demonstrate that: (1) From 1990 to 2020, the majority of the rural construction areas in the Nanxi River Basin were distributed along the river, while the high-density change areas were primarily located in Huangtian, Sanjiang, and Nancheng Streets in the southern part of the downstream, and in Shatou and Yantou Town in the middle reaches. From 1990 to 2000, the Nanxi River Scenic Area underwent significant planning and governance changes. Rural construction areas in the downstream basin took advantage of the rural revitalization policy’s transportation advantages, population agglomeration, and development dividends. The development of commercial and trade towns and industrial manufacturing industries bolstered the regional economy, while the vigorous development of downstream light industrial industries contributed to the spatial aggregation characteristics of the rural construction areas in the lower reaches of the Nanxi River. At the same time, in order to focus on the construction of the historical style area of the Nanxi River Scenic Area, the ancient villages of Cangpo, Furong, Daitou, and other ancient villages were used as anchors in the middle reaches of the Nanxi River for repair and construction of supporting facilities around the scenic area. From 2000 to 2010, the Nanxi River Basin demonstrated a high-density change feature, characterized by a continuous concentrated distribution, with the central urban area and established towns serving as the core. Additionally, the spatial aggregation and density intensity were generally further enhanced. From 2010 to 2020, the development of urban–rural integration and the optimization of land resource structure in the Nanxi River Basin led to a significant reduction in the density change area of Yantou Town and Shatou Town. This resulted in the differentiation and disconnection of local high-density areas, and the majority of the study area transitioned into medium- and low-density areas. The construction of rural construction areas began to diverge to the upstream area, but due to the small number of upstream construction areas, poor village economy, low development level, and weak planning rigor, it has never been further developed and governed. (3) Looking at the evolution of the nuclear density of the construction area over the past 30 years, the rural construction area in the study area has gone from a period of rapid fluctuation to a period of point-by-point divergence and then to a period of stable development, showing an overall coordinated development trend.

Figure 5

The regional nuclear density distribution of rural construction in the Nanxi River Basin from 1990 to 2020: (a) 1990–2000, (a) 2000–2010, and (c) 2010–2020.

4.4 Factors influence the regional distribution of rural construction

4.4.1 Factor detection analysis

We obtain the detection results of factors affecting the regional distribution of rural construction in the Nanxi River Basin through the GeoDetector factor detector. The p values in the factor detection results are all 0, indicating that the data effect is significant. This study analyzed the results and found that different influencing factors have different impacts and complexity on the regional distribution of rural construction from 1990 to 2020, and the index of various factors is not in a stable and continuous upward trend as the years grow.

Figure 6a illustrates that, when considering the development dimensions of the three primary factors from 1990 to 2020, cultural factors outweigh economic factors and natural factors. The total q value of economic factors and cultural factors is relatively high and has formed a coupled spiral upward trend in the 30 years of development. Among them, cultural factors have always had the highest total q value in the past 30 years and have strong cultural guidance for the rural construction area in the Nanxi River Basin. Figure 6b ranks the influence value of the sub-item influencing factors as follows: cultivated land distance ranks higher than educational resources, cultural heritage, elevation, urban distance, water distance, visual range, population density, and slope. As shown in Table 6, the factors with the largest changes in the past 30 years are: population density (fluctuation of 0.019, 0.154–0.147–0.135–0.142), visual field (fluctuation of 0.026, 0.203–0.215–0.229–0.210), elevation (fluctuation of 0.029, 0.482–0.497–0.489–0.468), slope (fluctuation of 0.032, 0.109–0.119–0.103–0.087), and distance to water area (fluctuation of 0.05 6, 0.286–0.333–0.299–0.277) have a small change range, while the distance to town (fluctuation is 0.164, 0.274–0.388–0.329–0.438), educational resources (fluctuation is 0.138, 0.619–0.621–0.560–0.436), cultural heritage (fluctuation is 0.134, 0.366–0.382–0.560–0.452), and farmland distance (fluctuation is 0.117, 0.738–0.720–0.689–0.621). In addition, while there is a positive correlation between the distance to town and the year, the correlation between other influencing factors gradually weakens over time. This shows that the distance between the rural construction area and the urban center is slowly but surely becoming a bigger economic factor. The main thing that is stopping the growth of the rural construction area in the Nanxi River Basin is how well transportation connects the rural and urban areas and when they happen.

Figure 6

A trend chart of impact factors and factors from 1990 to 2020 is presented: (a) analysis of influencing factors and (b) impact factor analysis.

Table 6

Factor detector analysis results from 1990 to 2020

Year	Numerical value	Natural factors			Economic factors			Cultural factors
		Elevation	Slope	Visual analysis	Population density	Distance from cultivated land	Distance from town	Educational resources	Cultural heritage	Distance from water area
		X1	X2	X3	X4	X5	X6	X7	X8	X9
1990	q statistic	0.482	0.109	0.203	0.154	0.738	0.274	0.619	0.366	0.286
	p value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	Total	0.794			1.165			1.271
2000	q statistic	0.497	0.119	0.215	0.147	0.720	0.388	0.621	0.382	0.333
	p value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	Total	0.831			1.254			1.336
2010	q statistic	0.489	0.103	0.229	0.135	0.689	0.329	0.560	0.560	0.299
	p value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	Total	0.821			1.153			1.418
2020	q statistic	0.468	0.087	0.210	0.142	0.621	0.438	0.436	0.452	0.277
	p value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
	Total	0.831			1.254			1.336

Source: Author’s statistics.

The main influencing factors that dominate the spatial distribution of the rural construction area in the Nanxi River Basin are cultivated land distance ( q mean is 0.692), educational resources ( q mean is 0.559), elevation (q mean is 0.484), cultural heritage ( q mean is 0.440), and town distance ( q mean is 0.357). Among them, cultivated land distance, educational resources, elevation, and cultural heritage are the most important influencing factors affecting the spatial distribution of the construction area in the Nanxi River Basin. Because the rural construction area in the Nanxi River Basin has cultural and historical heritage such as the Yongjia School and the inheritance of farming and reading, the overall county planning has focused on “farming and studying” as the two major life goals of residents in the construction area since the Tang and Song Dynasties (A.D.618–1279). In addition, the planning and protection of the Nanxi River Scenic Area and its unique landscape and geographical attributes make the elevation, cultural heritage, cultivated land production, and educational resources quite suitable to become the cultural landscape genes of the local characteristic “farming and reading.”

4.4.3 Two-factor interaction detector analysis

The researchers used the interaction detector analysis method to look at changes in rural regional construction in the Nanxi River Basin from 1990 to 2020. Table 7 shows the factors that affected these changes and how they interacted with each other. This interactive detection analysis uses the q value to find out how well each factor and its combination can explain changes in regional construction. It also looks at how different natural and social environmental factors have affected the layout of rural areas at different times.

The 1990 interactive detection analysis reveals that socio-economic factors, particularly population density, cultural and educational layout, and cultivated land density, primarily influenced rural regional construction during this period. Early stages of rural development closely linked residents’ residential choices to cultural and educational resources and agricultural production. Specifically, the interaction q value of cultivated land distance and cultural heritage and educational site layout was high ( q = 0.826, q = 0.779), and the two-factor interaction values related to cultivated land factors were relatively high, indicating that agricultural production was still the main driving force for rural construction during this period, and villages were mostly located in areas rich in agricultural resources to facilitate production activities and the convenience of population life (Table 8).

Table 8

Two-factor detection analysis results

Xn ∩ Xn	q-value	Interaction Results	Xn ∩ Xn	q-value	Interaction results
Results of the interaction factor detection analysis in 1990
Elevation (X1) 1990 ∩ Slope (X2) 1990	0.494	Two-factor enhancement	Visual analysis (X3) 1990 ∩ Educational resources (X7) 1990	0.650	Two-factor enhancement
Elevation (X1) 1990 ∩ Visual analysis (X3) 1990	0.591	Two-factor enhancement	Visual analysis (X3) 1990 ∩ Cultural heritage (X8) 1990	0.458	Two-factor enhancement
Elevation (X1) 1990 ∩ Population density (X4) 1990	0.531	Two-factor enhancement	Visual analysis (X3) 1990 ∩ Distance from water area (X9) 1990	0.418	Two-factor enhancement
Elevation (X1) 1990 ∩ Distance from cultivated land (X5) 1990	0.773	Two-factor enhancement	Population density (X4) 1990 ∩ Distance from cultivated land (X5) 1990	0.742	Two-factor enhancement
Elevation (X1) 1990 ∩ Distance from town (X6) 1990	0.553	Two-factor enhancement	Population density (X4) 1990 ∩ Distance from town (X6) 1990	0.352	Two-factor enhancement
Elevation (X1) 1990 ∩ Educational resources (X7) 1990	0.724	Two-factor enhancement	Population density (X4) 1990 ∩ Educational resources (X7) 1990	0.674	Two-factor enhancement
Elevation (X1) 1990 ∩ Cultural heritage (X8) 1990	0.578	Two-factor enhancement	Population density (X4) 1990 ∩ Cultural heritage (X8) 1990	0.431	Two-factor enhancement
Elevation (X1) 1990 ∩ Distance from water area (X9) 1990	0.502	Two-factor enhancement	Population density (X4) 1990 ∩ Distance from water area (X9) 1990	0.377	Two-factor enhancement
Slope (X2) 1990 ∩ Visual analysis (X3) 1990	0.330	Nonlinear Enhancement	Distance from cultivated land (X5) 1990 ∩ Distance from town (X6) 1990	0.759	Two-factor enhancement
Slope (X2) 1990 ∩ Population density (X4) 1990	0.226	Two-factor enhancement	Distance from cultivated land (X5) 1990 ∩ Educational resources (X7) 1990	0.826	Two-factor enhancement
Slope (X2) 1990 ∩ Distance from cultivated land (X5) 1990	0.742	Two-factor enhancement	Distance from cultivated land (X5) 1990 ∩ Cultural heritage (X8) 1990	0.779	Two-factor enhancement
Slope (X2) 1990 ∩ Distance from town (X6) 1990	0.344	Two-factor enhancement	Distance from cultivated land (X5) 1990 ∩ Distance from water area (X9) 1990	0.756	Two-factor enhancement
Slope (X2) 1990 ∩ Educational resources (X7) 1990	0.632	Two-factor enhancement	Distance from town (X6) 1990 ∩ Educational resources (X7) 1990	0.720	Two-factor enhancement
Slope (X2) 1990 ∩ Cultural heritage (X8) 1990	0.416	Two-factor enhancement	Distance from town (X6) 1990 ∩ Cultural heritage (X8) 1990	0.448	Two-factor enhancement
Slope (X2) 1990 ∩ Distance from water area (X9) 1990	0.329	Two-factor enhancement	Distance from town (X6) 1990 ∩ Distance from water area (X9) 1990	0.451	Two-factor enhancement
Visual analysis (X3) 1990 ∩ Population density (X4) 1990	0.321	Two-factor enhancement	Educational resources (X7) 1990 ∩ Cultural heritage (X8) 1990	0.655	Two-factor enhancement
Visual analysis (X3) 1990 ∩ Distance from cultivated land (X5) 1990	0.750	Two-factor enhancement	Educational resources (X7) 1990 ∩ Distance from water area (X9) 1990	0.664	Two-factor enhancement
Visual analysis (X3) 1990 ∩ Distance from town (X6) 1990	0.384	Two-factor enhancement	Cultural heritage (X8) 1990 ∩ Distance from water area (X9) 1990	0.482	Two-factor enhancement
Results of the interaction factor detection analysis in 2020
Elevation (X1) 2000 ∩ Slope (X2) 2000	0.511	Two-factor enhancement	Visual analysis (X3) 2000 ∩ Educational resources (X7) 2000	0.652	Two-factor enhancement
Elevation (X1) 2000 ∩ Visual analysis (X3) 2000	0.605	Two-factor enhancement	Visual analysis (X3) 2000 ∩ Cultural heritage (X8) 2000	0.470	Two-factor enhancement
Elevation (X1) 2000 ∩ Population density(X4) 2000	0.535	Two-factor enhancement	Visual analysis (X3) 2000 ∩ Distance from water area (X9) 2000	0.449	Two-factor enhancement
Elevation (X1) 2000 ∩ Distance from cultivated land (X5) 2000	0.769	Two-factor enhancement	Population density (X4) 2000 ∩ Distance from cultivated land (X5) 2000	0.725	Two-factor enhancement
Elevation (X1) 2000 ∩ Distance from town (X6) 2000	0.601	Two-factor enhancement	Population density (X4) 2000 ∩ Distance from town (X6) 2000	0.428	Two-factor enhancement
Elevation (X1) 2000 ∩ Educational resources (X7) 2000	0.731	Two-factor enhancement	Population density (X4) 2000 ∩ Educational resources (X7) 2000	0.648	Two-factor enhancement
Elevation (X1) 2000 ∩ Cultural heritage (X8) 2000	0.600	Two-factor enhancement	Population density (X4) 2000 ∩ Cultural heritage (X8) 2000	0.453	Two-factor enhancement
Elevation (X1) 2000 ∩ Distance from water area (X9) 2000	0.546	Two-factor enhancement	Population density (X4) 2000 ∩ Distance from water area (X9) 2000	0.401	Two-factor enhancement
Slope (X2) 2000 ∩ Visual analysis (X3) 2000	0.350	Nonlinear Enhancement	Distance from cultivated land (X5) 2000 ∩ Distance from town (X6) 2000	0.742	Two-factor enhancement
Slope (X2) 2000 ∩ Population density (X4) 2000	0.226	Two-factor enhancement	Distance from cultivated land (X5) 2000 ∩ Educational resources (X7) 2000	0.814	Two-factor enhancement
Slope (X2) 2000 ∩ Distance from cultivated land (X5) 2000	0.725	Two-factor enhancement	Distance from cultivated land (X5) 2000 ∩ Cultural heritage (X8) 2000	0.782	Two-factor enhancement
Slope (X2) 2000 ∩ Distance from town (X6) 2000	0.435	Two-factor enhancement	Distance from cultivated land (X5) 2000 ∩ Distance from water area (X9) 2000	0.748	Two-factor enhancement
Slope (X2) 2000 ∩ Educational resources (X7) 2000	0.639	Two-factor enhancement	Distance from town (X6) 2000 ∩ Educational resources (X7) 2000	0.764	Two-factor enhancement
Slope (X2) 2000 ∩ Cultural heritage (X8) 2000	0.434	Two-factor enhancement	Distance from town (X6) 2000 ∩ Cultural heritage (X8) 2000	0.603	Two-factor enhancement
Slope (X2) 2000 ∩ Distance from water area (X9) 2000	0.364	Two-factor enhancement	Distance from town (X6) 2000 ∩ Distance from water area (X9) 2000	0.539	Two-factor enhancement
Visual analysis (X3) 2000 ∩ Population density (X4) 2000	0.334	Two-factor enhancement	Educational resources (X7) 2000 ∩ Cultural heritage (X8) 2000	0.646	Two-factor enhancement
Visual analysis (X3) 2000 ∩ Distance from cultivated land (X5) 2000	0.737	Two-factor enhancement	Educational resources (X7) 2000 ∩ Distance from water area (X9) 2000	0.673	Two-factor enhancement
Visual analysis (X3) 2000 ∩ Distance from town (X6) 2000	0.479	Two-factor enhancement	Cultural heritage (X8) 2000 ∩ Distance from water area (X9) 2000	0.533	Two-factor enhancement
Results of the interaction factor detection analysis in 2010
Elevation (X1) 2010 ∩ Slope (X2) 2010	0.499	Two-factor enhancement	Visual analysis (X3) 2010 ∩ Educational resources (X7) 2010	0.603	Two-factor enhancement
Elevation (X1) 2010 ∩ Visual analysis (X3) 2010	0.612	Two-factor enhancement	Visual analysis (X3) 2010 ∩ Cultural heritage (X8) 2010	0.603	Two-factor enhancement
Elevation (X1) 2010 ∩ Population density (X4) 2010	0.561	Two-factor enhancement	Visual analysis (X3) 2010 ∩ Distance from water area (X9) 2010	0.432	Two-factor enhancement
Elevation (X1) 2010 ∩ Distance from cultivated land (X5) 2010	0.738	Two-factor enhancement	Population density (X4) 2010 ∩ Distance from cultivated land (X5) 2010	0.704	Two-factor enhancement
Elevation (X1) 2010 ∩ Distance from town (X6) 2010	0.572	Two-factor enhancement	Population density (X4) 2010 ∩ Distance from town (X6) 2010	0.393	Two-factor enhancement
Elevation (X1) 2010 ∩ Educational resources (X7) 2010	0.693	Two-factor enhancement	Population density (X4) 2010 ∩ Educational resources (X7) 2010	0.594	Two-factor enhancement
Elevation (X1) 2010 ∩ Cultural heritage (X8) 2010	0.693	Two-factor enhancement	Population density (X4) 2010 ∩ Cultural heritage (X8) 2010	0.594	Two-factor enhancement
Elevation (X1) 2010 ∩ Distance from water area (X9) 2010	0.528	Two-factor enhancement	Population density (X4) 2010 ∩ Distance from water area (X9) 2010	0.362	Two-factor enhancement
Slope (X2) 2010 ∩ Visual analysis (X3) 2010	0.349	Nonlinear Enhancement	Distance from cultivated land (X5) 2010 ∩ Distance from town (X6) 2010	0.732	Two-factor enhancement
Slope (X2) 2010 ∩ Population density(X4) 2010	0.208	Two-factor enhancement	Distance from cultivated land (X5) 2010 ∩ Educational resources (X7) 2010	0.776	Two-factor enhancement
Slope (X2) 2010 ∩ Distance from cultivated land (X5) 2010	0.693	Two-factor enhancement	Distance from cultivated land (X5) 2010 ∩ Cultural heritage (X8) 2010	0.776	Two-factor enhancement
Slope (X2) 2010 ∩ Distance from town (X6) 2010	0.377	Two-factor enhancement	Distance from cultivated land (X5) 2010 ∩ Distance from water area (X9) 2010	0.717	Two-factor enhancement
Slope (X2) 2010 ∩ Educational resources (X7) 2010	0.579	Two-factor enhancement	Distance from town (X6) 2010 ∩ Educational resources (X7) 2010	0.722	Two-factor enhancement
Slope (X2) 2010 ∩ Cultural heritage (X8) 2010	0.579	Two-factor enhancement	Distance from town (X6) 2010 ∩ Cultural heritage (X8) 2010	0.722	Two-factor enhancement
Slope (X2) 2010 ∩ Distance from water area (X9) 2010	0.325	Two-factor enhancement	Distance from town (X6) 2010 ∩ Distance from water area (X9) 2010	0.483	Two-factor enhancement
Visual analysis (X3) 2010 ∩ Population density (X4) 2010	0.332	Two-factor enhancement	Educational resources (X7) 2010 ∩ Cultural heritage (X8) 2010	0.560	Two-factor enhancement
Visual analysis (X3) 2010 ∩ Distance from cultivated land (X5) 2010	0.715	Two-factor enhancement	Educational resources (X7) 2010 ∩ Distance from water area (X9) 2010	0.623	Two-factor enhancement
Visual analysis (X3) 2010 ∩ Distance from town (X6) 2010	0.442	Two-factor enhancement	Cultural heritage (X8) 2010 ∩ Distance from water area (X9) 2010	0.623	Two-factor enhancement
Results of the interaction factor detection analysis in 2020
Elevation (X1) 2020 ∩ Slope (X2) 2020	0.476	Two-factor enhancement	Visual analysis (X3) 2020 ∩ Educational resources (X7) 2020	0.467	Two-factor enhancement
Elevation (X1) 2020 ∩ Visual analysis (X3) 2020	0.590	Two-factor enhancement	Visual analysis (X3) 2020 ∩ Cultural heritage (X8) 2020	0.472	Two-factor enhancement
Elevation (X1) 2020 ∩ Population density (X4) 2020	0.488	Two-factor enhancement	Visual analysis (X3) 2020 ∩ Distance from water area (X9) 2020	0.298	Two-factor enhancement
Elevation (X1) 2020 ∩ Distance from cultivated land (X5) 2020	0.692	Two-factor enhancement	Population density (X4) 2020 ∩ Distance from cultivated land (X5) 2020	0.642	Two-factor enhancement
Elevation (X1) 2020 ∩ Distance from town (X6) 2020	0.591	Two-factor enhancement	Population density (X4) 2020 ∩ Distance from town (X6) 2020	0.475	Two-factor enhancement
Elevation (X1) 2020 ∩ Educational resources (X7) 2020	0.620	Two-factor enhancement	Population density (X4) 2020 ∩ Educational resources (X7) 2020	0.486	Two-factor enhancement
Elevation (X1) 2020 ∩ Cultural heritage (X8) 2020	0.593	Two-factor enhancement	Population density (X4) 2020 ∩ Cultural heritage (X8) 2020	0.480	Two-factor enhancement
Elevation (X1) 2020 ∩ Distance from water area (X9) 2020	0.507	Two-factor enhancement	Population density (X4) 2020 ∩ Distance from water area (X9) 2020	0.355	Two-factor enhancement
Slope (X2) 2020 ∩ Visual analysis (X3) 2020	0.312	Nonlinear Enhancement	Distance from cultivated land (X5) 2020 ∩ Distance from town (X6) 2020	0.753	Two-factor enhancement
Slope (X2) 2020 ∩ Population density (X4) 2020	0.210	Two-factor enhancement	Distance from cultivated land (X5) 2020 ∩ Educational resources (X7) 2020	0.702	Two-factor enhancement
Slope (X2) 2020 ∩ Distance from cultivated land (X5) 2020	0.625	Two-factor enhancement	Distance from cultivated land (X5) 2020 ∩ Cultural heritage (X8) 2020	0.706	Two-factor enhancement
Slope (X2) 2020 ∩ Distance from town (X6) 2020	0.473	Two-factor enhancement	Distance from cultivated land (X5) 2020 ∩ Distance from water area (X9) 2020	0.655	Two-factor enhancement
Slope (X2) 2020 ∩ Educational resources (X7) 2020	0.467	Two-factor enhancement	Distance from town (X6) 2020 ∩ Educational resources (X7) 2020	0.646	Two-factor enhancement
Slope (X2) 2020 ∩ Cultural heritage (X8) 2020	0.472	Two-factor enhancement	Distance from town (X6) 2020 ∩ Cultural heritage (X8) 2020	0.597	Two-factor enhancement
Slope (X2) 2020 ∩ Distance from water area (X9) 2020	0.298	Two-factor enhancement	Distance from town (X6) 2020 ∩ Distance from water area (X9) 2020	0.556	Two-factor enhancement
Visual analysis (X3) 2020 ∩ Population density (X4) 2020	0.295	Two-factor enhancement	Educational resources (X7) 2020 ∩ Cultural heritage (X8) 2020	0.577	Two-factor enhancement
Visual analysis (X3) 2020 ∩ Distance from cultivated land (X5) 2020	0.652	Two-factor enhancement	Educational resources (X7) 2020 ∩ Distance from water area (X9) 2020	0.536	Two-factor enhancement
Visual analysis (X3) 2020 ∩ Distance from town (X6) 2020	0.473	Two-factor enhancement	Cultural heritage (X8) 2020 ∩ Distance from water area (X9) 2020	0.545	Two-factor enhancement

Source: Author’s statistics.

Entering the twenty-first century, the construction of rural areas in the Nanxi River Basin has gradually shown a trend of joint influence from economic factors and cultural factors. In the analysis in 2000, the interaction between cultivated land density and cultural and educational facilities ( q = 0.814) is still significant, indicating that the reasonable distribution of cultivated land and the optimized layout of cultural and educational facilities have exerted a great impetus on rural construction and also reflected the Nanxi since ancient times, the river basin has been influenced by the environmental settlement concept and village design that emphasizes “farming and reading poetry and books.” On the contrary, the natural factor of terrain slope gradually shows its restrictive effect on village layout. For instance, the interaction between terrain slope and population density has a q value of 0.514, indicating that complex terrain conditions suppress population density growth and restrict village layout. At this stage, construction gradually becomes integrated, and planning needs to consider the balance between social needs and natural constraints.

By 2010, the role of economic and cultural factors in rural regional construction became more prominent, especially the factors such as cultivated land distance, cultural heritage, and educational resources, which restricted the layout of rural regional construction more and more obviously. In addition, the connection between elevation factors and construction conditions such as terrain construction and construction area boundaries has gradually increased. Specifically, the interaction q values of cultivated land distance and other factors are all above 0.69, the interaction q values of educational resources and other factors are all above 0.56, the interaction q values of cultural heritage and other factors are all above 0.56, and the interaction q values of elevation and other factors are all above 0.5. These four factors have strong interactions and dominant relationships. Further analysis reveals that large slopes restrict the use of cultivated land resources, leading to a relatively small construction scale in these areas. In addition, education, cultural heritage, and cultivated land have formed a rural dwelling model of learning, belief, and labor, which has a strong guiding role in the rural construction area of the Nanxi River Basin.

The interaction detection analysis in 2020 revealed more complex multi-factor interactions, especially the increasingly close interaction between economic, cultural, and natural environmental factors. The q value of the interaction between cultivated land distance and urban distance is 0.75, indicating that the availability of cultivated land has become an important factor in determining village expansion in rural areas with developed transportation and close to towns. Consequently, the urban–rural binary opposition gradually dissolves, leading to an increase in the population in urban areas. The number of relocations has also gradually increased, and future considerations and the radiation of urban areas have become more important construction considerations in the current period. With limited land resources, rural construction needs to pay more attention to intensification and rational planning. The interaction between elevation and visual conditions ( q = 0.59) shows that the potential of natural landscapes for rural construction and tourism development has increased, especially in areas with rich ecological resources and broad vision. This interaction is particularly significant. In addition, the interaction between terrain slope and the layout of cultural and educational facilities ( q = 0.620) reflects the challenges faced in the construction process in areas with complex terrain. In these areas, the rational layout of cultural and educational resources must overcome the adverse factors of terrain.

Table 7 reveals that all interaction factors exhibit a “double factor enhancement” detection interaction result, with the exception of the slope and visual analysis interaction from 1990 to 2020, which displays a “non-linear enhancement.” This means that when the two factors are combined, there is no obvious linear enhancement effect. Conversely, their interaction may reveal a more intricate nonlinear relationship, typically indicating that these factors exert varying degrees of influence on rural construction under specific conditions, without merely superimposing their effects. For example, the nonlinear enhancement of slope and visual conditions may indicate that in areas with complex terrain, despite excellent visual conditions, the limitation of slope may hinder the expansion of villages.

From 1990 to 2020, the construction of rural areas in the Nanxi River Basin underwent a gradual shift from early dominance by socioeconomic factors to a comprehensive consideration of the complex interaction of social, economic, and natural environmental factors. Farming distance, urban distance, educational resources, and cultural heritage are the main factors affecting the formation of rural construction areas in the Nanxi River Basin (Figure 7). The coupling relationship between the interaction of these factors and the distribution of rural construction areas shows a double-factor enhancement relationship. In addition, factors such as population density, cultivated land density, educational sites, cultural heritage, terrain slope, etc., have significant differences in their impact on village construction at different stages. The interaction between cultural and economic factors has significantly enhanced the impact of rural construction. The combination of cultural heritage and rural tourism not only promotes cultural protection, but it also promotes economic development, forming a typical pastoral cultural landscape of “farming, reading, and inheriting the family.” The interaction between slope and visibility presents a nonlinear enhancement relationship to the distribution of rural construction areas, increasing the complexity and diversity of rural construction. The planning process must balance and resolve the contradiction between ecological protection and regional construction, particularly in the modernization construction stage, where natural environmental factors such as forest coverage and terrain slope have become increasingly prominent.

Figure 7

Results of interaction analysis of impact factors from 1990 to 2020: (a) interaction analysis of impact factors, 1990; (b) interaction analysis of impact factors, 2000; (c) interaction analysis of impact factors, 2010; and (d) interaction analysis of impact factors, 2020.

5.1 The complexity of rural construction land evolution and its global comparison

The Nanxi River Basin’s rural construction area exhibits notable intricacy, which confirms previous studies’ conclusions on the complexity of rural settlements in the basin [55]. At the same time, this study continues to conduct in-depth research on the variability and adaptability of the basin over multiple time spans, extending the breadth of this research field. This study shows that the “multi-aggregation, multi-scale, and multi-expansion” features of rural construction land in the Nanxi River Basin are similar to and different from the general rules of rural transformation around the world. For example, European villages also face the contradiction between the decline of traditional agriculture and the protection of cultural heritage, but their spatial evolution tends to be more “dispersed and contracted,” while the study area shows “expansion aggregation” [56]. This might be because of the two-way flow of urban and rural factors that happens when developing countries’ cities grow quickly: hollowing out from people leaving and local agglomeration caused by the tourism industry. Similar phenomena are also reflected in other regions of China, India, Indonesia, Nepal, and other countries, but their driving mechanism is dominated by a single agricultural economy [57]. Through the geographic detector model, this study shows how culture, economy, and nature all work together in a way that is not linear. This makes up for the problems with traditional one-dimensional analysis.

The spatiotemporal evolution of rural construction land in the Nanxi River Basin presents significant complexity, as shown in the following aspects: (1) in terms of dynamic evolution, the total area of rural construction in the Nanxi River Basin from 1990 to 2020 is increasing year by year, demonstrating the movement characteristics of “multiple agglomerations, multi-scales, and multi-expansions.” (2) Many things, like nature, society, and policy, act on their own and also interact in ways that are not linear, changing the pattern of space. (3) Patches that grow and shrink are mostly points and blocks, centered around towns and scenic areas. This finding is different from what scientists have found about the Ganges River Basin in India [58] and the Andes Mountains in South America [59]. The Ganges River Basin shows a pattern of linear extension because of religious culture, while the Andes Mountains are more limited by geography. This study shows a new way to look at how rural areas are being rebuilt in developing countries where cities are growing quickly. It does this by looking at how cultural and economic factors are linked. It’s interesting to note that this study found that the spatial correlation of educational resources ( q mean 0.559) has a much stronger guiding effect on construction land than similar studies in developing countries. This may have something to do with the southern Zhejiang culture of “farming and reading to pass on the family.”

5.2 The river estuary influences the number of patches in rural construction areas

The estuary is often an important geographical and economic factor that determines the spatial and temporal distribution and development of rural settlements in a river basin [60]. After looking at things from an economic point of view in earlier research [61], this study looks at how the construction areas of rural settlements change over time and space, connecting the different levels of construction areas with the river basin’s own spatial development. This study found that the Nanxi River estuary has an important influence on the number of rural construction area patches due to its special geographical and economic location. The majority of rural construction areas are located in the Nanxi River Basin. The Nanxi River Basin, as the final tributary of the Ou River estuary, boasts a significant number of dynamic construction areas, particularly at the intersection of the southern part of its lower reaches and the Ou River estuary. Generally, the Nanxi River Basin experiences a decrease in the total number of rural construction area patches from the southern (downstream) Ou River estuary to the northern (upstream) mountainous terrain, with the majority of these patches located in townships near the main urban areas. Huangtian Street, for example, is the township area with the most densely populated rural construction areas in the Nanxi River Basin (Figure 8). Economic development, topography, landforms, and other conditions accompany the main three-stage structure of upper, middle, and lower reaches, reflecting regional differences. The downstream distribution in the basin is the most dense, forming a spatial pattern of urban–rural integration; the ancient villages and residential buildings in the middle reaches are well protected, and the associated distribution along the basin generally presents a “large gathering, small dispersion” structure of scattered villages-collected villages. Meanwhile, the upstream construction area is scattered and irregular along the basin. Overall, the rural construction area at the estuary of the Nanxi River Basin has a significant impact on the number and distribution pattern of rural construction patches due to its unique geographical location, superior economic conditions, and convenient transportation network.

Figure 8

The landscape features encompass the upper, middle, and lower reaches of the Nanxi River, as well as its estuary.

5.3 Theoretical innovation of space–time evolution mechanism

This study suggests a model of cultural, natural, and economic factors interacting with each other. It is different from the Red River Basin of Southeast Asia’s “water network-oriented” evolution mechanism [62]. The second model focuses on how the hydrological network affects the location of villages in a straight line. This model, on the contrary, uses geographic detectors to show how cultural genes (like the remains of the Yongjia School) affect the distribution of villages in space. This finding forms an interesting dialogue with the study of the Rhine River Basin in Europe [63] – although both attach importance to the protection of cultural heritage, the case of southern Zhejiang shows that the guiding intensity of cultural factors on economic activities is significantly higher than that of the former, which may be due to the institutional support of China’s rural revitalization policy for the transformation of cultural capital. In addition, the nonlinear enhancement effect of terrain slope and visual range ( q = 0.59) corrects the cognitive limitations of the “single factor determinism of slope” [64] in traditional mountain settlement research and provides a more refined decision-making basis for rural planning in complex terrain areas.

This study summarizes the dynamic mechanism of factors affecting the spatiotemporal evolution of rural construction areas and believes that the formation of rural construction areas in the Nanxi River Basin is affected by a combination of cultural, economic, and natural factors (Figure 9).

Figure 9

Mechanism framework of factors affecting spatiotemporal evolution.

First, cultural factors affect the spiritual foundation of regional construction (Yongjia School, Dong’ou heritage, ancient village protection, and school tradition). Cultural factors include rich cultural heritage, traditional architecture, and the pastoral culture of “farming and reading to pass on to the family.” Rural tourism not only protects these cultural resources but also develops them, thereby enhancing the cultural identity of local residents. The Nanxi River Basin contains a large amount of Dong’ou regional intangible cultural heritage, architectural space heritage, and the profound literary research and education background of the Yongjia School. From the 1990s to the present, the Nanxi River Scenic Tourist Area’s development has entailed significant financial investments by the local government to preserve the academy and school culture, safeguard traditional southern Zhejiang residential buildings, and revive the ancestral farming and reading traditions, thereby transforming the rural construction area in the Nanxi River Basin into a unique cultural landscape.

Second, economic factors determine the direction and trend of the construction area, which includes activities such as tourism revitalization, rural farming, transportation construction, and city gathering. In terms of economic factors, the development of rural construction areas depends on agricultural production, tourism, and industrial manufacturing. The improvement of transportation infrastructure has promoted the connection between rural and urban areas, as well as the diversification and agglomeration effects of economic activities. The urban and rural planning of the Nanxi River Basin emphasizes the implementation of the pastoral landscape as a local landscape expression. The Nanxi River Basin provides a large number of natural resources for agricultural production and the development of rural construction areas. Therefore, numerous construction areas enhance the convenience and advantages of living along the river. With the development of transportation, communication between the construction area and the outside world has become more frequent. The convenience of the transportation network has gradually become a communication channel between the construction area and the external urban space. The development and changes in transportation have also expanded the construction area beyond the traditional farming and handicraft industries. The concentration of rural construction areas in the Nanxi River Basin is mainly the village-style industrialized space formed by the ancient village protection area in the middle reaches, the Nanxi River scenic tourist area, and the industrial manufacturing park in the downstream. With the continuous expansion of urban areas from 1990 to 2020, economic factors have driven the construction areas to shift to urban spatial development patterns and, to a certain extent, intensified the concentration of population mobility.

Third, natural factors govern the framework of regional construction, which includes alluvial plains, gentle slopes, landscape patterns, and clustered living areas. The upper reaches of the Nanxi River Basin have dense elevation fluctuations and slope changes; the middle reaches are river valley impact plains (flat river valley terrain) with gentle elevation and slope; and the lower reaches are plain terrain. Therefore, elevation and slope determine the spatial distribution and expansion changes of the construction area to a certain extent.

Overall, this study reveals the complex interactions of cultural, natural, and economic factors in different temporal and spatial dimensions by analyzing the rural construction areas in the Nanxi River Basin. The results show that these factors do not act in isolation but play a complementary and counterbalancing role in different stages and regions of rural construction. Early development stage (1990–2000): In this stage, natural factors (such as topography and elevation) played a key role in the spatial distribution of rural construction. Villages often spread along river valleys or plains, near agricultural resources, as a result of the underdeveloped transportation infrastructure. During this period, the agricultural economy primarily drives the slow and indirect influence of cultural factors on economic development. In the mid-term development stage (2000–2010), the value of cultural heritage has gradually gained prominence due to the development of tourism and infrastructure, and economic factors have started to play a more significant role. The cultural resources of traditional villages have become an important driving force for economic growth, and tourism and handicrafts have begun to rise. Cultural and economic factors are currently fostering the growth of rural construction areas, transforming cultural resources from passive objects of protection into actively utilized economic assets. Late development stage (2010–2020): This stage accelerates the process of urban–rural integration, diversifies the economic development model, and further strengthens the synergy between cultural and economic factors. Cultural heritage not only promotes the development of tourism but also becomes an important consideration for local governments to attract investment and expand urbanization. While natural factors continue to play a significant role at this stage, economic factors are gradually taking the lead.

In addition, the spatial differences between upstream and downstream also cause an imbalance in the development of influencing factors. Due to the limitations imposed by the terrain, the economic development of the upstream area is relatively slower, and the influence of cultural factors is more pronounced. Modernization has less impact on traditional villages, allowing them to maintain a relatively complete cultural landscape. However, due to the proximity to the city, convenient transportation, and rapid economic development, the rural construction area in the downstream area shows a trend of large-scale expansion. The cultural heritage of such areas is often protected and developed through tourism and crafts.

5.4 Regional research paradigm and methodological path of rural construction in river basin areas

The most important new thing about this study is that it builds a three-factor model that takes into account cultural genes, geographical environment, and economic potential. This model solves two major problems with previous research: (1) In terms of method, this paper uses the multi-scale analysis methods of standard deviation ellipse, kernel density, and geographic detector to do the first spatial quantitative analysis of cultural landscape genes. This is different from the single landscape index analysis that was used to study the spatiotemporal pattern of settlement areas during the same time period [65]. (2) In theory, the proposed “farming and reading landscape” collaborative development model is different from the static protection logic of the Western European “ecological museum” paradigm. It gives a Chinese way for cultural villages around the world to grow in a way that is sustainable by showing how the revitalization and use of cultural heritage and industrial clusters are linked in space and time.

This study is not only a specific case analysis of the Nanxi River Basin but also constructs a set of multi-factor action mechanism models through the interactive analysis of culture, nature, and economy. This model can explain the spatiotemporal evolution of similar regions in rural construction. Therefore, it provides a theoretical framework for other rural areas with rich cultural heritage and complex natural conditions. Other regions with rich cultural and natural resources around the world can also apply this framework. Furthermore, this study proposes the “culture and natural resource interaction model” for cultural heritage protection areas worldwide, particularly those grappling with economic modernization challenges in the globalization process. Researchers applied the geographic detector model to interactively analyze cultural, natural, and economic factors, an innovative method not fully utilized in previous studies. This method can provide new tools for the development and planning of rural areas around the world to reveal complex multi-factor interactions. While some studies have examined the spatiotemporal evolution of rural construction, they typically focus solely on natural factors, neglecting the extensive influence of cultural factors. Compared with these studies, this study is more comprehensive in its analysis of cultural factors and shows how it works together with economic and natural factors to promote the evolution of rural construction. Some studies have begun to explore the role of cultural heritage in rural construction, but they focus more on qualitative analysis and do not make full use of spatial analysis tools. Through quantitative geospatial analysis, this study not only reveals the role of cultural factors but also shows their dynamic interaction with economic development. This provides a more empirical basis for the protection of cultural heritage and the formulation of rural revitalization policies.

5.5 Optimization suggestions for rural construction in the Nanxi River Basin

To guarantee the sustainable growth of rural construction in the Nanxi River Basin, this study proposes the following optimization strategies: First, scientific planning and reasonable layout. To coordinate rural construction with environmental protection, the planning process should fully consider natural environmental conditions and ecological carrying capacity. Second, prioritize protecting cultural heritage and promote coordinated development. The government should formulate policies to encourage the promotion of economic modernization through the protection of cultural heritage. The Nanxi River Basin can combine traditional buildings and scenic spots with modern economic activities to promote the sustainable development of rural areas. Third, improve the level of infrastructure and public services. Improve the infrastructure, such as transportation, communications, and electricity, in the middle and upper reaches of the Nanxi River, and improve the level of public services to meet the basic living needs and development needs of rural residents. Fourth, promote the integration of industries in the upper, middle, and lower reaches. In conjunction with the abundant natural resources in the upper and middle regions and the distinctive features of the light industrial sector in the lower regions, cultivate distinctive agriculture, rural tourism, and traditional handicrafts. This approach fosters the diverse growth of the rural economy, thereby boosting the economic vitality and competitiveness of rural regions. Finally, the government should enhance policy guidance, provide financial support and technical guidance, encourage social capital to participate in rural construction, and establish a favorable environment for multi-party cooperation. Formulate scientific and reasonable land use policies, protect farmers’ land rights and interests, and promote land transfer and intensive management.

6.1 Research discovery

The conclusions are as follows: (1) The changes in the number and area of rural construction areas in the Nanxi River Basin showed the development characteristics of “fast growth, multiple scales, and multiple expansions” and experienced the changing trend of “rapid fluctuation period-point-by-point divergence period-stable development period.” (2) The annual rate of migration of the center of gravity of the rural construction area showed a linear development of “slowing down and increasing speed,” gradually migrating from “southwest” to “northeast.” (3) The areas with a high nuclear density of rural construction areas are mainly distributed in Huangtian, Sanjiang, and Nancheng Streets (urban areas or estuaries) in the southern part of the lower reaches and Shatou and Yantou Town (scenic tourist areas) in the middle reaches. (4) The complexity of the spatiotemporal evolution of rural construction areas is the result of the combined effects of natural, economic, and cultural factors rather than the result of a single factor. At different stages of development in the Nanxi River Basin, natural factors dominated early rural construction, while cultural and economic factors played a greater role in the later process of urban–rural integration, especially in the economically developed downstream areas.

Cultural heritage not only promotes economic development, especially in the application of tourism and handicraft industries, but also shapes the spatiotemporal evolution of rural construction together with natural and economic factors. The multidimensional interaction model of culture, nature, and economy proposed in this study reveals the dynamic relationship between these factors in different times and regions. This comprehensive analytical framework makes up for the shortcomings of single-dimensional analysis in previous studies and provides a new tool for understanding the complexity of rural construction. The practical contribution of the study is that it provides a basis for rural revitalization policies, especially in the formulation of strategies combining cultural heritage protection with economic development. Future research should further verify the universality of the model and explore the dynamic interaction between these factors in different regions to provide broader theoretical support for global rural development.

6.2 Limitations and future research

This study sheds light on the spatiotemporal evolution characteristics and influencing factors of the rural construction area in the Nanxi River Basin; yet, it requires further enhancements in data acquisition, factor considerations, and research perspectives to bolster its comprehensiveness and scientificity.

First, this study focuses on the spatiotemporal evolution process of rural construction in the past 30 years, with a short time span, and fails to fully reflect the trend of change on a longer time scale. Future research should consider data analysis with a longer time span to reveal the long-term evolution law of rural construction. For example, by extending the research period to a century-long scale, combining historical maps and oral history, we can capture the long-term evolution of rural construction. Second, this study may not be comprehensive enough in the selection of economic and cultural indicators and lacks the intervention of factors affecting policies and regional governance. Future research can introduce policy intervention indicators (such as cultural heritage protection intensity and land transfer policies) to analyze the impact of institutional factors on spatial reconstruction. Finally, although this study focuses on the evolution process of the spatiotemporal pattern of rural construction and its driving factors, it does not deeply explore the reconstruction and optimization of the construction regional pattern, which is also a hot issue in current research. At the same time, future research should further explore the obstacles that this trend may encounter, such as physical limitations of production space and social space, climate impacts, and land use conflicts. In addition, future research should look into how to predict dynamic changes in rural construction areas. It should take information from various time periods and use cellular automata and GWR analysis to model the spatial expansion threshold of rural construction areas in a number of different situations. It should also look at how different factors affect the future growth trend of rural construction areas. Also, more comparative research needs to be done to see if the multidimensional interaction model works in different places and cultures. For example, it could be used to compare cultural heritage villages in Europe with villages in Southeast Asia that are based on agriculture. This will help plan the development of rural areas in southern Zhejiang in a way that is based on science, and it will also help the region grow sustainably and make living in rural areas better.