Research on the Spatiotemporal Distribution Characteristics and Accessibility of Traditional Villages Based on Geographic Information Systems—A Case Study of Shandong Province, China

Abstract

The traditional settlements are of paramount significance as indispensable elements of China’s cultural heritage, simultaneously functioning as prime assets for the enhancement of rural economic and social dynamics. Nestled within the comprehensive framework of China’s rural revitalization endeavor and Shandong Province’s proactive initiatives toward the amalgamation of cultural and tourism sectors, a meticulous exploration of the spatiotemporal evolution and connectivity of traditional villages in Shandong Province is indispensable for their preservation and forward-thinking evolution. For this study, 557 traditional villages across Shandong Province are identified as pivotal points, with the application of geographic information system (GIS) techniques to scrutinize their spatiotemporal transformation patterns and spatial characteristics. Additionally, a suite of analytical instruments, encompassing metrics for accessibility assessment, ordinary least squares (OLS) linear regression, and geographically weighted regression (GWR) models, are deployed to evaluate the accessibility levels and influential factors shaping traditional villages within the region. The analytical outcomes reveal the following: (1) Chronologically, approximately 80% of the traditional villages in the province of Shandong were established during the Ming and Qing epochs, and they demonstrate a migratory pattern that is spatially and temporally oriented from “southwest to northeast”; geographically, these traditional villages are characterized by pronounced clustering, predominantly situated at the confluence of Jinan and Zibo Cities, the Shantou District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a coherent “four-core” spatial distribution configuration. (2) Considering the criteria for village location, traditional villages in Shandong are predominantly found in areas with a predominantly flat landscape and a certain proximity to water bodies. (3) On the whole, the accessibility of traditional villages in Shandong is relatively high, with the average accessibility assessed at 199.92 min, a range spanning from 175 min, and approximately 57.99% of the villages falling within the 100 to 200 min accessibility bracket, indicating a systematic decline in accessibility from the central areas to the periphery. (4) The pivotal factors influencing the accessibility of traditional villages in Shandong are primarily altitude, slope, and road network density, with altitude and slope showing a negative correlation with accessibility, whereas road network density exhibits a positive correlation, and the proximity to water bodies has a relatively minor impact on accessibility.

1. Introduction

Traditional villages not only represent tangible cultural heritage but also serve as valuable repositories of intangible cultural heritage. They embody significant academic, social, cultural, humanistic, and economic value [1]. These villages chronicle the developmental trajectory of civilization, living conditions, natural environments, architectural techniques, and spatial layouts of ancient Chinese society, playing an indispensable role in promoting rural development and revitalization [2].

From 2012 to 2019, the Chinese government announced five editions of traditional village lists, totaling 6819 villages, underscoring the importance attributed to the preservation and rational utilization of these villages. However, policies such as “village consolidation” and “imbalanced urban-rural development” have presented challenges of diminishment and destruction to traditional villages. This study seeks to investigate the distribution of traditional villages in diverse geographical environments and their relationship with urban areas, with the goal of providing a scientific basis and policy recommendations for the protection and sustainable development of these villages.

Early international research on traditional villages primarily centered on the analysis of settlement geography, focusing on their spatial arrangement, morphology, and structure [3]. With the rise of sustainable development concepts, the research gradually shifted to exploring the harmonious coexistence between traditional villages and the environment and how to achieve sustainable development based on preservation [4,5,6]. For example, Stowe conducted in-depth research on the sustainability of rural landscapes, examining the adaptability and changes of traditional villages in the process of modernization [4]. Vos and Meekes offered a fresh perspective on the safeguarding of traditional villages based on the development trends of European cultural landscapes [5]. Researchers began to utilize geographic information systems (GIS) and other spatial analysis techniques to thoroughly study the spatial characteristics and distribution patterns of traditional villages. For instance, Sesotyaningtyas and Manaf employed GIS technology to examine the sustainable tourism development of Kutoharjo village in Indonesia, significantly enhancing the accuracy and depth of the research [7]. In terms of the protection and utilization of traditional villages, researchers explored how to promote economic development through tourism while safeguarding these villages. For example, Ghadéri and Henderson assessed the potential of sustainable tourism from the perspective of Hawraman village in Iran [6].

Accessibility, as a crucial indicator of the interaction between transportation nodes within a region, has always played a pivotal role in research [8]. The research in this field has a rich history, encompassing various aspects such as transportation accessibility, commuting range, and spatial location. For instance, Dupuy Gabriel and other scholars deeply delved into the highway accessibility of European cities [9]. Research content primarily includes the changes in regional accessibility patterns, such as the analysis of influencing factors on changing regional accessibility patterns by Gutierrez Javier and others [10]; the disparities in accessibility to public services for different social groups, such as Bowen’s evaluation of international aviation accessibility in Southeast Asian aviation hub countries [11]; and the impact of accessibility on regional economy, such as Linneker’s study on the impact of changes in accessibility due to the London Orbital Road on the regional economy [12]. These studies offer a multi-dimensional perspective on the significance of accessibility.

In the 20th century, Chinese scholars initiated research using methods such as spatial syntax [13], GIS spatial analysis [14,15], Moran’s I index [16,17], standard deviation ellipses [18], nearest-neighbor index [19], and GWR model [20,21], combined with qualitative descriptions and quantitative models, to conduct in-depth analysis of the spatial distribution of traditional villages [22,23,24], the knowledge map of village landscapes [25], the relationship between villages and regional culture [26], development and utilization [27], and the impact of tourism development [28], as well as the issues and countermeasures during the process of urbanization [29]. The research scales range from national to provincial regions [30,31,32] all the way down to individual villages, covering the spatial evolution of traditional villages. In recent years, the accessibility of traditional villages has received attention as a key factor in rural revitalization and protection and development [33]. Domestic scholars have analyzed the factors influencing the accessibility of traditional villages through studies at different scales [34] and designed a model for evaluating the potential of tourism development [35], contributing to the protective and structured tourism development of traditional villages [36].

In the context of China’s culturally profound regions, Shandong Province stands out for its extensive collection of ancient rural communities that embody a tapestry of historical significance and cultural heterogeneity. Amidst the forces of urbanization and the augmentation of transportation infrastructures, these historically significant rural territories face a dual challenge of preservation and development. This scholarly investigation focuses on 557 such rural communities within Shandong Province, utilizing comprehensive historical documentation and geographic information system (GIS) methodologies to probe the multifaceted aspects of their spatial, historical, and cultural dynamics. By scrutinizing elements such as environmental factors, economic progression, and cultural legacies, this study endeavors to illuminate the broader historical and cultural implications inherent in these villages, thereby laying a solid scientific groundwork for the formulation of culturally sensitive preservation strategies. Additionally, the research also evaluates the accessibility of these rural areas and their influencing factors, with the aim of optimizing the planning of tourist routes and infrastructure, thereby elevating the overall tourism experience. This initiative is in alignment with the strategic objectives outlined in the “Shandong Province Cultural Tourism Integration Development Plan (2020–2025)” and contributes to the growth of the province’s tourism industry, as well as the balanced expansion of its regional economy.

2. Research Methods and Data Processing

2.1. Research Methods

2.1.1. Moran’s I Index

Moran’s I is a statistical method for measuring the global spatial autocorrelation of data. It was proposed by geographer Patrick Alan P. Moran in 1950 to detect whether the patterns in spatial datasets are randomly distributed or if there are trends of spatial clustering or dispersion [37]. The formula is expressed as follows:

$$I=\frac{n\times {\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j\ne 1}^n{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}}}{\left({\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{W}_{ij}}}\right)\times {\displaystyle \sum _{i=1}^n{\left(x_i-\overline{x}\right)}^2}}$$

(1)

where I represents the value of Moran’s index; n is the number of county-level administrative divisions in the study area; x_i and x_j represent the observed values of the traditional villages in spatial units i and j, respectively; x is the mean value of the number of traditional villages; and W_ij represents the value of the spatial weight matrix. The value of the global Moran’s index (I) ranges from −1 to 1. If I is greater than 0, it indicates a positive correlation between the traditional villages and their spatial distribution. The traditional villages exhibit obvious clustering at the provincial spatial scale, and the closer the Moran’s index value is to 1, the smaller the spatial differences. Conversely, if I is less than 0, it indicates a negative correlation between the traditional villages and their spatial distribution. The observed points are dispersed in space, and the closer the I value is to −1, the larger the spatial differences. If the I value is 0, it indicates that the traditional villages are randomly distributed in the overall space without spatial correlation. Moran’s I provides a global perspective on the spatial distribution of traditional villages at the provincial scale.

2.1.2. Getis–Ord Gi* index

While Moran’s I index is a useful tool for detecting global spatial autocorrelation in a dataset, it does not provide information about the presence of spatial clustering patterns in local areas. To identify the concentration of high or low values in spatial data at a local level, it is necessary to use the local spatial autocorrelation index Getis-Ord Gi*. By combining these two indices, it is possible to gain a comprehensive understanding of the spatial distribution characteristics and trends of traditional villages at both macro and micro levels. In doing so, we can gain a deeper insight into the underlying spatial processes that govern the distribution of traditional villages in a given region [38]. According to the conceptualization of the local Getis-Ord Gi* statistic, a positive Z-score indicates an aggregation of high values, while a negative Z-score underscores an accumulation of low values. The procedure for calculating the local Getis-Ord Gi* statistic unfolds as follows:

$$G_i^{*}\left(d\right)=\frac{{\displaystyle \sum _j^n{W}_{ij}\left(d\right)X_j}}{{\displaystyle \sum _j^n{X}_j}}$$

(2)

To facilitate the analysis, G_i^*(d) has been standardized. The formula for standardization is as follows:

$$Z\left(G_i^{*}\right)=\frac{G_i^{*}-E\left(G_i^{*}\right)}{\sqrt{Var\left(G_i^{*}\right)}}$$

(3)

In this equation, E(G_i^*) and Var(G_i^*) represent the expected value and the variance of the traditional village G_i^* value, respectively, and W_ij(d) represents the spatial weights.

2.1.3. Standard Deviation Ellipse

The standard deviation ellipse serves as a quantitatively robust evaluation method for elucidating the spatial configuration’s mean, variability, and directional inclination of geographical attributes. It achieves this through a sophisticated spatial analysis that encompasses the geographical expanse and spatial structure of the focal study area. This statistical tool illuminates the spatial distribution and spatiotemporal evolution of geographical features [39]. At the heart of the analysis are the ellipse’s center of gravity and its principal axes, which are the foundational parameters of this analytical approach. This scholarly investigation employed the standard deviation ellipse analysis to probe the developmental trajectories and migration trends of traditional villages in Shandong Province across their various founding periods, thereby visually charting their evolution.

2.1.4. Kernel Density Analysis

Kernel density analysis is a non-parametric spatial statistical method used to estimate the probability density function of spatial data. It is widely applied in geographic information systems (GIS) and spatial analysis, especially when studying spatial distribution patterns and spatial clustering [18]. The formula for kernel density estimation is typically expressed as follows:

$$\widehat{f}(x)=\frac{1}{n}{\displaystyle \sum _{i-1}^n{{\displaystyle K}}_h}(x-{{\displaystyle X}}_i)$$

(4)

In this formula, $\widehat{f}$(x) represents the estimated density at location x; n is the total number of data points; X_i is the position of the i-th data point; K is the kernel function, which is typically a symmetric function; and h is the bandwidth, which controls the width of the kernel function and affects the degree of smoothing. Kernel density estimation (KDE) can assist in identifying the spatial distribution patterns of traditional villages, such as clustering, dispersion, or random distribution. By analyzing the density estimates, hot spots of traditional villages can be identified, that is, areas with a higher density of traditional villages. When combined with time series data, KDE can be used to study the spatial distribution changes of traditional villages over different time periods.

2.1.5. Method for Calculating Accessibility

The assessment of accessibility for a varied array of traditional hamlets and villages situated within the geographical domain of Shandong Province was systematically carried out employing the ensuing scholarly approach [40]:

$${{\displaystyle K}}_{\mathrm{j}}=\frac{{\displaystyle \sum _{i=1}^n{{\displaystyle \mathrm{E}}}_{\mathrm{ij}}}}{\mathrm{n}}$$

(5)

$$\begin{array}{l}K=\frac{\begin{array}{c}{{\displaystyle \max K}}_{\mathrm{j}}\\ 1\le \mathrm{j}\le \mathrm{m}\end{array}\begin{array}{c}{{\displaystyle -K}}_{\mathrm{j}}\\ \end{array}}{\begin{array}{c}{{\displaystyle \max K}}_{\mathrm{j}}\\ 1\le \mathrm{j}\le \mathrm{m}\end{array}\begin{array}{c}{{\displaystyle -\min K}}_{\mathrm{j}}\\ 1\le \mathrm{j}\le \mathrm{m}\end{array}}\\ \end{array}$$

(6)

In this formula, K_j represents the accessibility of traditional village j, n refers to the number of prefecture-level cities, E_ij represents the shortest distance from traditional village j to city I, and K represents the standardized value of traditional village accessibility.

2.1.6. The GWR Model

The GWR model, also known as the geographically weighted regression model, is an improvement upon the general ordinary least squares (OLS) linear regression. It highlights the spatial heterogeneity and variability of regression coefficients, breaking away from the global concept of general linear regression. It focuses on measuring the lagged and overflow effects of factors in local space [41]. The basic model is as follows:

$$y_i={\beta }_0(u_i,v_i)+{\displaystyle \sum _m{\beta }_m(u_i,v_i)}{x}_{im}+{\epsilon }_i$$

(7)

In this context, the dependent variable is y_i, the independent variable x_m has a parameter value of x_im at the spatial position i, and the coordinates of the spatial position i are represented as (u_i, v_i). The intercept of the regression equation is denoted by β₀ (u_i, v_i), m represents the number of independent variables in the model, and ε_i is the error term.

Compared to the traditional ordinary least squares (OLS) classical linear regression model, the core advantage of the GWR model lies in its ability to consider the influence of the spatial location [41]. In other words, the model parameters vary with the spatial location, which not only reduces the sum of the squared residuals and improves the model’s goodness of fit but also demonstrates strong performance in parameter estimation and hypothesis testing.

2.2. Data Sources and Preprocessing

2.2.1. Study Area

Shandong Province, situated within the eastern coastal expanse of China (Figure 1), delineates the lower reaches of the Yellow River, a zone distinguished by a rich, sedimentary plain. This geographical sector enjoys advantageous conditions, intricately formed by the Yellow River’s siltation. Between the reign of Emperor Yu in the Xia Dynasty and the Qing Dynasty, Shandong Province has played a critical historical role across multiple eras. It is the cradle of the Qi and Lu cultures, bestowing this province with an extensive and profound historical and cultural legacy. The integration of diverse cultural influences has engendered singular cultural terrains and patterns, culminating in the presence of traditional villages with unique cultural characteristics spanning different historical epochs. These ancient villages, inscribed through the passage of time, function as tangible monuments to past historical transformations and harbor significant scientific and historical research value (Figure 2).

2.2.2. Data Collection and Preprocessing

Information on 557 provincial- and national-level traditional villages in Shandong Province was obtained from the Provincial Traditional Village Directory of Shandong Province and the National Traditional Village Directory of China. Data on the establishment years of these villages were sourced from local chronicles, such as those from various counties and cities in Shandong Province. Field research was conducted in more than ten traditional villages in cities like Jinan and Zibo, as well as in two concentrated and contiguous protected and utilized areas for traditional villages. This research aimed to verify the establishment years of the traditional villages and assess their development in terms of protective tourism. These efforts were undertaken to ensure the accuracy of the historical information about the traditional villages. The coordinates of the traditional villages were determined using Google Maps. A DEM (Digital Elevation Model) dataset with a resolution of 30 m was obtained from the Geographic Spatial Data Cloud, while hydrological data came from a basic 1:1,000,000 dataset of Shandong Province. Information on ancient post roads was referenced from the Chinese Historical Atlas, edited by Tan Qixiang, and was digitized using ArcGIS 10.8.1 [42].

3. Spatiotemporal Distribution of Traditional Villages in Shandong Province

3.1. Spatial Distribution Characteristics of Traditional Villages in Shandong Province

Spatial Distribution Types of Traditional Villages in Shandong Province

The purpose of this inquiry is to demystify the spatial configuration characteristics of traditional villages situated within the jurisdiction of Shandong Province. For this endeavor, a spatial autocorrelation evaluation was carried out at the administrative county level, leveraging the global Moran’s I metric. The resultant analysis disclosed a global Moran’s I index of 0.3617, accompanied by a p-value indicative of statistical significance at a level of 0.000 and a Z-score that attains a notably high figure of 7.214. This evidence unequivocally conveys the existence of pronounced clustering in the geographical disposition of traditional villages across Shandong Province. These outcomes collectively bolster the conclusion that traditional villages within Shandong Province are characterized by a markedly aggregated distribution pattern.

In pursuit of elucidating the subtleties governing the spatial distribution patterns of traditional villages within Shandong Province, the present investigation harnessed the local Getis-Ord Gi* index for a comprehensive analysis. The outcome revealed an unequivocal manifestation of high-value clustering in the traditional village landscape, with no discernible areas reflecting low-value concentrations (Figure 3). Precisely, the zones exhibiting the highest values are predominantly located in the northeastern part of Jinan City, the central region of Zibo City, the Shantou District of Zaozhuang City, and the cities of Zhaoyuan and Rongcheng in Yantai and Weihai, respectively. These contiguous areas amalgamate to form an indistinct yet characteristic “quadruple core” spatial arrangement. This metric underscores the markedly greater density of traditional villages in these four territories in comparison to other regions. Specifically, these territories encapsulate the Qi culture, Dawenkou culture, Yimeng culture, and Longshan culture, respectively, from the annals of ancient Chinese history. This not only illuminates the rich cultural heritage but also underlines the historical depth inherent to these regions, thereby bearing testament to the profound legacy they possess.

3.2. Time Distribution Characteristics of Traditional Villages in Shandong Province

The scholarly endeavor herein compiles and scrutinizes the datums delineating the chronological establishment of traditional villages across the province of Shandong, encompassing diverse historical eras, including the prelude to the Tang Dynasty (prior to 907 AD), the epoch of the Five Dynasties and Ten Kingdoms (907–979 AD), the Song Dynasty (960–1279 AD), the Yuan Dynasty (1271–1368 AD), the Ming Dynasty (1368–1644 AD), and the Qing Dynasty (1644–1912 AD). This comprehensive analysis follows the temporal and spatial trajectories of these traditional settlements. Given the inherent constraints posed by the antiquity of the historical records, which occasionally fall short of delivering a complete account, this investigation focuses on a meticulous selection of 505 traditional villages within Shandong. These were chosen based on their foundation dates, serving as a quintessential exemplar to explore the historical transformation and evolution of traditional villages within this specific geographic expanse.

3.2.1. Evolution of Traditional Villages in Shandong Province

The current scholarly exploration aims to conduct an in-depth analysis of the spatiotemporal characteristics and migratory dynamics pertaining to the epicenters of traditional villages in Shandong Province across a spectrum of historical epochs. By employing the statistical technique of standard deviation ellipses, a comprehensive and rigorous assessment was conducted on the spatial distribution of traditional villages in Shandong Province. The results of this analysis reveal a significant relocation of the spatial focal point for such villages. Upon scrutinizing Figure 4, it becomes apparent that the central position of these vernacular settlements has transitioned from coordinates (117.39° E, 36.04° N) to (118.56° E, 36.50° N), indicating a longitudinal shift of 1.17° from west to east, and a latitudinal shift of 0.46° from south to north. This migratory course is marked by a clear, discernible trajectory emanating from the southwestern region, progressing toward the northeastern area.

In alignment with the characterization provided by the standard deviation ellipse, the centripetal force exerted by spatial distribution is quantified through the ellipse’s eccentricity. As delineated in

Table 1. Standard deviation ellipse values of different dynasties.

Dynasty	Area/km2	Deflection Angle (°)	Flattening Ratio
Tang Dynasty and pre-Tang period	59,306	48.81	1.8
Five Dynasties and Ten Kingdoms period	56,898	52.20	1.9
Song Dynasty	58,233	52.43	2.3
Yuan Dynasty	59,672	58.29	2.7
Ming Dynasty	58,128	58.86	3.1
Qing Dynasty	60,495	58.11	2.9

, there is a discernible progression in this metric. From a value of 1.8 during the era of the Tang Dynasty and preceding periods, the eccentricity escalated to 3.1 during the Ming Dynasty, underscoring a significant augmentation in the spatial aggregation forces inherent to traditional villages. Conversely, during the Qing Dynasty, the eccentricity diminished to 2.9, indicating a reduction in these forces. Over the temporal domain, it is apparent that the spatial aggregation forces pertaining to traditional villages in Shandong Province have undergone a trajectory of progressive intensification. Furthermore, this temporal trend manifests a compression from a “north-south” axis toward the central geographical region, implying an ascending inclination toward the consolidation of these villages.

3.2.2. Temporal Evolution of Traditional Villages in Shandong Province

The genesis of traditional villages in Shandong Province can be traced back to the Warring States period, marking a protracted phase of development. Analysis of Figure 5 and Figure 6 reveals conspicuous disparities in the quantity and clustering of villages across different historical epochs. Prior to and during the Tang Dynasty, the major village clusters were concentrated at the junction of Zibo City and Jinan City, as well as in Zaozhuang City. Despite political upheaval and warfare during the Five Dynasties and Ten Kingdoms period, resulting in an 18-village decrease from the Tang Dynasty, the concentration of villages remained relatively stable. The Song Dynasty witnessed a surge in the number of villages, particularly in the northwestern region of Qingdao City, where novel village clusters emerged. Subsequently, during the Yuan Dynasty, the proliferation of villages extended, primarily concentrated in the Zhoucun District of Zibo City, attributed to its historical status as the former capital of the Qi State and a significant cradle of ancient Chinese Qi culture.

The advent of the Ming Dynasty witnessed a demographic shift and agricultural expansion in the Shandong region following the cessation of conflicts between the Song and Yuan dynasties, resulting in a substantial expansion in the range and number of traditional villages. The Ming government encouraged land reclamation and population migration from the Guanzhong area and introduced new crops, fostering rapid agricultural advancement. Many extant traditional villages owe their origins to the immigration and pioneering ventures of this period. During this era, the concentration of villages was further heightened, with concentration centers beginning to diffuse to other areas, particularly in the northwestern environs of Yantai, where a fresh cluster emerged, establishing a spatial distribution pattern with a principal concentration and multiple auxiliary concentrations. By the Qing Dynasty, the multiplication and intensification of traditional villages persisted, notably in the Zaozhuang City locale, where concentration notably escalated, engendering the formation of numerous regional centers of concentration.

In summary, the historical progression in Shandong Province culminated in the gradual establishment of a “two primary and three secondary” five-core trait in the spatial distribution of traditional villages, which significantly aligns with the spatial distribution findings derived from the Getis-Ord Gi* index analysis. The heartland of central Shandong, encompassing Jinan City and Zibo City, along with the Jiaodong region housing Yantai City, constitute the two principal cores of traditional villages’ spatial distribution in Shandong Province. The southern Shandong region harboring Zaozhuang City and Linyi City, the Jiaodong region housing Weihai City, and the western Shandong region encompassing Tai’an City collectively comprise the three secondary cores representing the east, south, and west, collectively articulating the primary framework of traditional villages’ spatial distribution in Shandong Province. The distribution of villages in other regions is relatively dispersed.

4. Factors Influencing the Distribution of Traditional Villages in Shandong Province

4.1. Elevation Factor

Altitude is a primary factor influencing the site selection for constructing traditional villages, and it is also the primary index considered by scholars when conducting quantitative GIS research on traditional villages. Altitude differences and topographical undulations have an impact on the distribution of water resources and solar radiation heat, the distribution of natural flora and fauna, and the formation of soil types to some extent [14]. To reflect the spatial distribution of traditional villages on different terrains, this study classified the areas where traditional villages are located based on the national terrain classification standards of China, as shown in Figure 7. Traditional villages in Shandong Province are primarily located in plain areas with small terrain undulations near mountainous and hilly areas. As shown in

Table 2. The numbers of traditional villages in different elevation ranges.

Elevation Range	Type	Number of Traditional Villages/Each	Specific Gravity %
<200 m	Plain	360	64.6
200–500 m	Hilly	176	31.6
>500 m	Mountain	21	3.8

, most traditional villages in Shandong Province are distributed in plain areas, with 360 such villages accounting for 64.6% of the total number of traditional villages (577). A total of 176 traditional villages (31.6% of the total) are in hilly areas, and only 3.8% of traditional villages are situated in mountainous areas. This distribution is related to the terrain and landform of Shandong Province, which is characterized by a central mountainous backbone with plains and basins interspersed. The plains ease agricultural production, resident travel, and house construction.

Upon investigating traditional village siting strategies spanning different historical periods, as depicted in Figure 8, it is evident that villages were predominantly sited on plains throughout various dynasties. However, from the Five Dynasties and Ten Kingdoms era, the number of villages located in hilly areas saw a significant increase. By the Qing Dynasty, the total number of traditional villages in the region had reached 135, signifying a migration trend from plains to hilly areas. During ancient times, when productivity levels were relatively primitive, the development and convenience offered by plains made them the preferred location for establishing villages. However, as time passed, particularly during the Song and Yuan dynasties’ turmoil, people migrated toward higher ground in hilly areas. This move not only helped them evade wars and complex external environments, but it also offered better conditions for the continuous development and protection of the villages. Furthermore, the sloping land in hilly areas was ideally suited for terraced fields and allowed residents to take advantage of spring water resources from the mountains. Moreover, the abundant animal resources present in the region provided diverse opportunities for livelihoods for the inhabitants.

4.2. Slope Factor

The slope is a cardinal indicator for assessing the degree of a terrain’s incline or flatness, exerting notable sway over the selection of traditional village sites [34]. As delineated in

Table 3. Numbers of traditional villages in different slope ranges.

Slope Level	Slope Range (°)	Number of Traditional Villages/Each	Specific Gravity %
1	0–10	491	88.15
2	10–15	36	6.46
3	15–20	16	2.87
4	20–25	8	1.44
5	25–30	2	0.36
6	>30	4	0.72

and illustrated in Figure 9, the majority of traditional villages in the jurisdiction of Shandong Province are nestled in areas where the slope is no greater than 10 degrees, with a specific tally of 491. This corresponds to 88.15% of the total surveyed villages across 557. This fact suggests that when determining locations, traditional villages are inclined toward choosing relatively flat terrains, which are not only favorable for the habitation and agricultural pursuits of human societies but also advantageous in promoting trade interactions with external stakeholders. Consequently, this fosters economic growth and income enhancement. Given the significance of slope characteristics, it is apparent that traditional villages in Shandong Province predominantly settle in gently sloping areas, a discovery that underscores the strategic deliberations underlying site selection. The inclination toward such terrains ensures the feasibility of human settlement and agricultural activities while also facilitating meaningful connectivity within the wider economic landscape, thereby promoting the flourishing of these communities. The congruence between slope attributes and the establishment of traditional villages exemplifies the synergy between natural topographic features and human decision-making processes within the realm of rural habitation and development.

4.3. Water System Factor

Shandong Province has a well-developed water system that includes major rivers such as the Yellow River, Yi River, Huai River, Xiaoqing River, Tu Hai River, Shu River, and Dawen River. Rivers and their basins were the birthplaces of ancient agricultural civilizations. The abundant water resources not only provide convenience for agricultural production and daily life but also facilitate the development of water transportation and promote the emergence and growth of commerce. Moreover, rivers also serve as natural barriers that provide safety for human settlements.

In this study, river data were categorized into five levels according to the Chinese national standards that were used. The centerlines of the rivers were extracted and overlaid with the traditional villages for analysis. The distances between the traditional villages and the centerlines of the nearest rivers were measured. This study revealed (Figure 10) that there were only 135 traditional villages within 3 km of a river centerline, accounting for 24.23% of the 557 traditional villages. There were 228 traditional villages within 5 km, accounting for 40.93% of the total.

The observed pattern indicates that traditional villages become more numerous as their proximity to water bodies decreases. This trend is primarily influenced by the topographical and meteorological conditions of Shandong Province, which experiences significant rainfall during spring and summer. The province’s flat terrain makes it susceptible to increased river levels and flooding during these seasons. The Yellow River, which cuts through the Shandong region, is notable for its tendency to breach its banks during the rainy season, causing widespread flooding. As a result, ancient settlers were inclined to select sites farther away from the main rivers when deciding on village locations in Shandong Province in an effort to minimize the risks associated with flood events.

4.4. Road Network Factor

Traffic conditions are a critical factor in determining the location of traditional villages. The layout of the transportation network in Shandong Province reflects, to some extent, the spatial layout of ancient post roads. After overlay analysis of traditional villages and the road network density map in Shandong Province (Figure 11), we found a connection between the distribution of villages and the density of the road network. The transportation network’s density in Shandong Province varies significantly, with around 74.3% of traditional villages situated in areas with a relatively sparse transportation network. This imperfect transportation network restricts the socio-economic development in these areas and reduces modern civilization’s influence on these traditional villages. This, in turn, helps maintain their original appearance and cultural characteristics, creating the current spatial distribution pattern of traditional villages. There is a correlation between the density of the transportation network and the distribution of traditional villages, but not a simple linear positive or negative one. A moderate density transportation network may be more conducive to protective tourism development of traditional villages. Therefore, when formulating tourism development strategies, the transportation network density’s impact should be fully considered in planning transportation infrastructure reasonably, achieving sustainable development of the tourism industry, and effective protection of cultural heritage.

5. Accessibility Analysis of Traditional Villages in Shandong Province

5.1. Accessibility Level of Traditional Villages in Shandong Province

Accessibility is a critical metric for gauging the ease with which various territories can be reached, significantly impacting the conservation and strategic expansion of traditional villages. Shandong Province, distinguished by its broad expanses of flat terrain and a well-established network of highways, provides an ideal setting for this exploration. This study utilizes road transportation as its primary tool for measuring accessibility [41], which aligns closely with the everyday transportation needs of the local population (Figure 12). It takes into consideration a myriad of dynamic aspects during vehicular journeys, including real-time road conditions, the quality of the road infrastructure, the length of time spent at traffic signals, and the current state of traffic congestion. By harnessing the real-time measurement capabilities of Amap, the research assembles the shortest drive times from 557 traditional villages in Shandong Province to their corresponding municipal administrative centers. This method results in more accurate and contemporary data, allowing for a precise assessment of each village’s accessibility level. This insight is invaluable for shaping conservation strategies and tourism development plans for these traditional villages.

The quantitative data compiled in

Table 4. Accessibility distribution frequency of traditional villages in Shandong Province.

Time Accessibility/min	Quantity	Frequency/%	Average/min	Range/min
100~200	323	57.99	199.92	175
200~300	209	37.52
300~400	25	4.49

reveal that the average time required for access to traditional villages in Shandong Province is 199.92 min, showcasing significant variability, as evidenced by the maximum variance of 175 min. Upon a thorough analysis of the accessibility distribution among the traditional villages in Shandong Province, it is found that more than half, precisely 57.99%, or the major portion of the 557 villages, have an accessibility duration within the 100 to 200 min interval. A small segment of villages, accounting for roughly 4.49%, exceed the 300 min limit for accessibility. The visual representation in Figure 11 further illustrates that the spatial accessibility gradient for traditional villages in Shandong Province displays a decreasing sequence from the central areas to the outer regions, featuring a clear pattern of concentric circles and a step-like decrement in accessibility levels.

Zibo and Jinan, as the two major economic centers of Shandong Province, are, respectively, ranked first and third in the scale of cities in the province. These two cities have not only given birth to a profound Qi culture but also, due to their location in the plain area, have a prosperous economy and a well-developed transportation network, which endows them with high accessibility. In contrast, other areas of Shandong Province may face lower accessibility due to differences in terrain, economic level, and regional culture. Among the 557 traditional villages, 110 have an accessibility level of 0.9 and above, accounting for 19.75% of the total. In particular, Qingshi Guan Village in Zhuang Town, Laiwu District of Jinan City, and Dong Huayuan Village and Chengbei Village in Xincheng Town, Huantai County of Zibo City, are listed in the top three for their significant accessibility. On the other hand, Wawushi Village, Yan Dunjiao Community, and Donggu Village in Lidao Town, Rongcheng City of Weihai, have the lowest accessibility. In light of this, traditional villages with high accessibility should be given priority in terms of the potential and value of protective tourism development.

5.2. Analysis of Factors Affecting the Accessibility of Traditional Villages in Shandong Province

5.2.1. Analysis of the Impact Factors of Traditional Village Accessibility Based on OLS Linear Regression

In this study, the researchers have applied the ordinary least squares (OLS) technique to quantitatively assess the impact of potential influencing factors on the accessibility of traditional villages found within the Shandong Province. This approach also allows for the evaluation of the significance level and the intrinsic characteristics linked to these factors. To further explore the variability and spatial differences in regression coefficients, a geographically weighted regression (GWR) method was implemented.

For the OLS linear regression analysis, the accessibility of traditional villages in the Shandong Province is considered the dependent variable, whilst the shortest distances to water sources, elevation, slope, and proximity to road networks are identified as independent variables. The detailed results of this analysis are compiled in

Table 5. OLS regression results.

Model Parameters	Coefficient	T	p	Standard Deviation	VIF
Intercept	0.511115	24.502133	0.000000 *	0.020352	……
The shortest distance near a body of water	−0.000007	−1.190428	0.180932	0.000006	1.010481
Elevation value	0.000912	16.311051	0.000000 *	0.000059	1.095921
The slope value	0.000052	24.778488	0.000000 *	0.000020	1.007325
Network density	0.014331	3.923822	0.000107 *	0.003829	1.100781
${\mathrm{R}}^2$			0.312984
$\mathrm{Adjusted} {\mathrm{R}}^2$			0.308005
Jarque–Bera Test			72.011633
AICC			−223.550286

Note: * indicates significance at the 10% level.

. The findings within the OLS regression model reveal that the Variance Inflation Factor (VIF) values for all independent variables are notably low, below 7.5, which indicates there are no signs of multicollinearity among the selected variables, thus supporting the validity of the model. Furthermore, the Jarque–Bera test for normality confirms that the regression equation satisfies the assumption of normal distribution, ensuring the reliability and effectiveness of the parameter estimation. Upon analyzing the T-value and P-value tests, it is concluded that elevation, slope, and road network density have a significant influence on the accessibility of the traditional villages. Conversely, the shortest distance from the village to a water body does not show a significant impact on accessibility.

Traditional villages in Shandong Province are often located near mountains and water, displaying similar hydrological characteristics and natural environments. The proximity to water bodies does not significantly limit the accessibility of these villages. Although Shandong Province is dominated by plains and hills, the site selection for villages not only considers the flatness of the terrain but also other factors. In ancient China, agriculture was the core of the family economy, so people tended to build their homes near the farmland to keep an eye on the crops at all times, ensuring the stability of the family economy. This habit led to the scattered layout of villages and the imperfect construction of roads, making it take more time to enter some villages. The influence of elevation, slope, and road network density on the accessibility of traditional villages demonstrates significance when subjected to the 10% test yet falls short of the 5% test.

5.2.2. Analysis of Impact Factors of Traditional Village Accessibility Based on GWR Model

This investigation employed a geographically weighted regression (GWR) model to conduct a profound examination of the various factors impacting the accessibility of traditional villages [43]. The outcomes of the regression analysis (

Table 6. Regression parameters of GWR model.

Model Parameters	Parameter Value
Bandwidth	0.577834
AICC	−1640.82998
Sigma	0.053704
${\mathrm{R}}^2$	0.953921
$\mathrm{Adjusted} {\mathrm{R}}^2$	0.948424

) revealed that the corrected Akaike Information Criterion (AICC) for the GWR model was −1640.82998, a figure that surpasses the equivalent value for the ordinary least squares (OLS) linear regression model. This finding underscores the superiority of the GWR model in the context of this study. Furthermore, by computing the global spatial autocorrelation and evaluating the standardized residuals of each traditional village, we derived a Moran’s I index of 0.182398, accompanied by a Z-score of 11.435649 when the significance level p-value is 0.000. The amalgamation of these figures confirms that the predictive outputs of the GWR model are spatially dispersed randomly, meaning the model adeptly simulates the fluctuation patterns in the accessibility of traditional villages without manifesting any discernible systematic bias or spatial aggregation. This comprehensive evaluation vindicates the model’s overall simulation efficacy, making it well-suited for further research and forecasting of the changes in accessibility of traditional villages.

Through a comprehensive examination of the geographically weighted regression (GWR) methodology, we have delineated and quantified three pivotal factors influencing the accessibility of traditional villages. The precise statistical information for these critical variables is meticulously compiled and displayed in

Table 7. Quintile observation table of GWR model in traditional villages.

Impact Factors	Minimum	Upper Quartile	Median	Lower Quartile	Maximum	Average	Inspection
Elevation value	−0.007460	−0.000180	0.000158	0.000561	0.001703	0.000246	0.000000
The slope value	−0.013595	−0.003096	−0.000665	0.000035	0.052443	−0.001311	0.000000
Network density	−0.007438	0.001272	0.003739	0.005286	0.021539	0.003567	0.000000

, elucidating the significance of each factor with a precision down to six decimal places. The analysis reveals that, while the regression coefficient values vary notably across geographic space, their median and mean values are closely aligned, indicating that the essence of the regression estimations exhibits spatial stability and uniformity across a broad spectrum of spatial scales.

In order to deepen our comprehension of these regression coefficients, a geographical spatial visualization was carried out, which not only illustrates the spatial distribution of the coefficients across various regions with clarity, revealing a discernible lag effect where changes in some areas impact neighboring regions; it also emphasizes the presence of geographical spillover effects. These effects demonstrate that alterations at a specific location do not only directly influence that area but also extend their influence to other regions in a broader context. The spatial dynamics analysis through visualization offers us a novel perspective on understanding the intricate nature of traditional village accessibility levels and the underlying spatial interaction dynamics.

Figure 13 visualization results demonstrate a positive correlation between elevation values and the accessibility level of traditional villages. The central part of Zibo City and the Jinan City and Zibo City junction in the central part of Shandong Province exhibit high negative regression coefficients that show an increasing trend toward the surrounding areas. The terrain at higher elevations reduces the accessibility of traditional villages. The areas with high negative values are primarily distributed in the mountainous regions of central Shandong Province and on the Jiaodong Peninsula, which coincides with the topographical distribution pattern of Shandong Province. Some areas exhibit positive, high values, including western Jining City and Tai’an City in Shandong Province. These areas with positive, high values have elevation values below 200 m, indicating that traditional villages’ accessibility is linked to various factors, including geographic location, infrastructure quality, population density, and human social activities, aside from geographical elevation values.

Based on the slope values shown in Figure 14 and their impact on the accessibility level of traditional villages, we can intuitively observe a significant negative correlation between the two. This indicates that in the western, central, and eastern regions, the slope values are relatively high, while they are comparatively lower in the southern region, affecting the accessibility of the villages. Regression analysis reveals that the high positive regression coefficients are mainly concentrated in the Jinan, Zibo, and Weihai Cities of Shandong Province, as well as in the western part of Jinan City and the southwestern part of Liaocheng City. These areas, characterized by mountainous terrain and slopes, result in a higher time cost for residents’ travel, thereby affecting the accessibility of the villages.

Conversely, the high negative regression coefficients are more commonly seen in the northeastern part of Zaozhuang City and the western part of Linyi City. These areas, with hilly terrain and significant undulations, suggest that the accessibility of traditional villages is influenced not only by slope but may also be constrained by other factors. For instance, the distance of these villages from the administrative centers of prefecture-level cities may be another important factor affecting their accessibility.

The visual analysis of Figure 15 reveals a significant positive correlation between road network density and the accessibility of traditional villages. In areas with lower road network density, such as the first and second levels, including the southwest of Jining City and the northwest of Linyi City, the high regression coefficients indicate relatively poor traffic convenience in these regions. In contrast, in the central part of Shandong Province, an economically developed area, the road network density is relatively high, usually in the third to fifth levels, and the regression coefficients are relatively low, reflecting a higher level of accessibility. Nevertheless, due to the already well-developed transportation infrastructure in this area, the marginal benefits of further increasing road network density to improve the accessibility of traditional villages are diminishing, and its sensitivity is also decreasing. To further enhance the accessibility of these areas, it may be necessary to explore other methods to achieve this.

6. Discussion

Shandong Province, as one of the important birthplaces of Chinese civilization, has nurtured the cultures of Qi and Lu, which are reflected in the traditional villages of the province, showing a profound cultural connotation and unique regional characteristics. An in-depth analysis of the spatiotemporal evolution of these traditional villages and exploration of the driving factors behind them is of great significance for revealing the spatial distribution patterns of villages and optimizing urban and rural planning. This not only helps to protect and pass on these precious cultural heritages in the context of rapid urbanization, avoiding their destruction but also provides important scientific support for promoting the deep integration of cultural tourism in Shandong Province and promoting the high-quality development of the tourism industry [44]. In addition, by measuring the accessibility of traditional villages and analyzing their main influencing factors, a theoretical basis can be provided for improving the transportation conditions and public service level of the villages. This will help traditional villages to better integrate into modern society while maintaining their unique cultural characteristics and historical value.

Based on the comprehensive research and analysis results, several suggestions are proposed for the protective tourism development of traditional villages in Shandong Province: First, improve the infrastructure construction: It is recommended that Shandong Province further enhance its transportation network, especially the road system from townships to villages. By constructing key transportation nodes, the accessibility of these villages with historical value is improved. Second, the strategy for protective tourism development: On the basis of respecting and maintaining the original historical features of the villages, it is recommended that villages with high accessibility and economic returns implement protective tourism development. For those villages with lower accessibility but higher economic potential, it is suggested that they prioritize improving their transportation conditions to promote their protection and optimization of the industry. Third, industry linkage and tourism product development: When developing villages with high accessibility, an industry linkage development strategy should be adopted to promote the integration of tourism, culture, and agriculture. Utilizing the rich historical and cultural resources of Shandong Province, combined with the uniqueness of each village, develop tourism products with local characteristics, such as characteristic accommodations, agritourism experiences, and handicrafts. Fourth, diversification of tourism services: Provide a variety of tourism services, including professional guide explanations and cultural experience activities, to meet the personalized needs of tourists, achieve in-depth integration of culture and tourism, and the integrated development of culture and agriculture [45].

Finally, it is essential to rely on the “Yellow River Basin Ecological Protection and High-quality Development Strategy” formulated by the State Council of China and the “Construction and Protection Plan of the Yellow River National Cultural Park (Shandong Section)” developed by the Shandong Provincial People’s Government. By leveraging the characteristics of the cultural heritage of traditional villages in Shandong Province and seizing the opportunity to build the Yellow River National Cultural Park, a chain of cultural pearls should be created, connecting and integrating various elements. This can be achieved through the construction of the Yellow River National Cultural Park, the Yellow River National Museum, and ancient cities, along with the development of the ecological corridor and scenic tourist areas along the Yellow River. Efforts should be made to create experiences related to ancestral heritage, patriotism, revolutionary history, and entrepreneurship. Additionally, the self-sustainability of traditional village tourism in the Yellow River Basin should be enhanced, shaping it as a destination for authentic Shandong experiences and making traditional villages a top priority in tourism development along the Yellow River. This will ensure that the towns and cities along the Yellow River contribute to the development of the traditional villages along the river [46].

Shandong Province is widely acclaimed for its distinctive geographic and societal features. This study focuses on analyzing the impact of natural conditions, such as terrain elevation and slope, on the distribution patterns and accessibility of historically significant villages in specific areas across time and space. However, this research has certain limitations, particularly in the in-depth analysis of humanistic elements such as regional cultural characteristics and economic conditions, which require strengthening. Moreover, this study has not yet delved into the micro-level of villages, such as their traditional customs, architectural styles, and living patterns.

Looking ahead, the research can further scrutinize the historical development of these traditional settlements in more detail from the perspective of village expansion and contraction. Concurrently, based on the inherent characteristics of these villages and the supporting factors that have sustained them thus far, they can be systematically classified to promote a more profound comprehension of these traditional settlements and elevate their contribution to academic discourse.

7. Conclusions

This study utilizes ArcGIS10.8.1 software as an analytical tool to comprehensively explore the spatiotemporal distribution characteristics of traditional villages in Shandong Province. By employing an accessibility calculation formula, this study quantifies the accessibility level of traditional villages within the area. Moreover, the research utilizes the ordinary least squares (OLS) linear regression analysis and the geographically weighted regression (GWR) model to investigate various factors influencing the accessibility of traditional villages in Shandong Province. By employing these methods, this study effectively addresses spatial heterogeneity and local effects, providing a more accurate analytical perspective for the preservation and development of traditional villages. The main conclusions are as follows:

(1)

The traditional villages in Shandong Province have a wide range of formation times, with the majority (80%) originating during the Ming and Qing dynasties. This suggests a spatial–temporal migration trend from the “southwest to northeast” over time. Spatially, traditional villages in Shandong Province demonstrate a noticeable pattern of clustering, primarily concentrated in the border areas between Jinan City and Zibo City, Shanting District of Zaozhuang City, Zhaoyuan City of Yantai City, and Rongcheng City of Weihai City, forming a “quad-core” distribution pattern.

(2)

It seems that the influencing factors for the location of traditional villages in Shandong Province are primarily related to the natural environment. These factors include being located in flat areas below an elevation of 200 m, having a slope of less than 10 degrees, and being at a considerable distance from rivers. These factors suggest that the natural topography and water features play a significant role in determining the location of traditional villages in Shandong Province.

(3)

The traditional villages in Shandong Province generally demonstrate a high level of accessibility, with minor variations among different villages. In terms of temporal distribution, the accessibility of these villages exhibits a distinct “step-like” pattern. Spatially, traditional villages in Shandong Province are concentrated around the central region, displaying a layered arrangement where accessibility gradually declines as one moves farther from the center. Notably, regions with more advanced economies and dense transportation networks, such as the central part of Shandong Province and the Jiaodong Peninsula, feature particularly prominent accessibility in their traditional villages.

(4)

It is clear that in Shandong Province, the accessibility of traditional villages is influenced by factors such as altitude, slope, and road network density. There is a negative correlation between altitude and slope with the accessibility of villages, indicating that as the altitude increases and the slope becomes steeper, the accessibility of the villages decreases. Conversely, there is a positive correlation between the density of the road network and the accessibility of villages, implying that a denser road network leads to higher accessibility of the villages.

(5)

However, the shortest distance to water bodies does not significantly affect the accessibility of the villages. In general, reaching traditional villages at higher altitudes takes longer. It is worth noting that traditional villages in Shandong Province are typically located in areas with slopes between 0° and 10°, which aligns with the density of the road network and the level of accessibility in the region.

(6)

From the perspective of policy implementation, Shandong Province has introduced a series of policy measures in recent years to promote the protection and tourism development of traditional villages. These policies have not only provided solid support for the sustainable development of traditional villages but also offered more specific and practical guidelines for conservation work.