Parametric Reconstruction of Traditional Village Morphology Based on the Space Gene Perspective—The Case Study of Xiaoxi Village in Western Hunan, China

Abstract

Traditional settlement space contains regional, natural, economic, historical, and cultural characteristics. The spatial texture serves as a material carrier of rural life and production and a vital landscape resource for the traditional villages. Traditional rural settlements have formed relatively unique and stable spatial form genes over time, which contain the “order” and “law” of spatial creation traditionally established in villages. The metropolis erodes traditional village spaces due to fast socioeconomic development and urbanization. In addition, the lack of adequate recognition and continuation of spatial texture in current mainstream village construction planning methods also limits the ability of villages to adapt to environmental changes and promote self-repair and adjustment, which, in turn, causes the gradual disappearance of their distinctive appearance. The reason is the need for more quantitative research and planning on the genes controlling the evolution of spatial texture morphology in traditional villages. They are faced with issues such as blind construction and development, a fracture in the rural characteristic spatial texture inheritance, and a loss of the distinctive vernacular landscape. Adopting an objective and in-depth approach to the cognition of traditional village space texture is an essential demand for the preservation, optimization, and renewal of the spatial appearance of rural settlements. We use the spatial genes of village settlements as its starting point. It then uses the spatial texture of village settlements connected to gene information mining as its specific method. We investigate the autogenous law of traditional village spatial form and determine its application using the CityEngine parametric platform, digitalization, and 3D visualization as the applied technical means. The feasibility and implementation path of the parameterization technique are explored using the traditional village of Xiaoxi in western Hunan Province as an example. We effectively promote the integration of rural spatial landscape resources, feature assessment, optimization guidance, and management control and provide an innovative research perspective and scientific planning path for analyzing the spatial morphological evolution of traditional villages.

1. Introduction

According to research published in the 2017 edition of the Blue Book of Chinese Traditional Villages: Investigation Report on the Protection of Chinese Traditional Villages [1], from 3.63 million in 2000 to 2.71 million in 2010, the number of natural villages in China declined dramatically. Only a few of these gradually deteriorating settlements result from a natural decline caused by geographical factors. Most traditional villages are being tested because of hollowing-out issues, a lack of planning and management, and a lack of self-motivation [2]. Although China has made significant progress in protecting traditional villages in recent years, many issues remain unresolved. According to the recent year’s Xiangxi Statistical Yearbook, the urbanization level of Xiangxi Prefecture was 36.07% in 2011, 41.1% in 2016, and 50.72% in 2021. This indicates that urbanization in Xiangxi Tujia and Miao Autonomous Prefecture is accelerating. There is a growing threat of traditional villages disappearing if they are not given the attention they deserve. To this end, China put forward the “Rural Revitalization Strategy” in 2017 [3]. Simultaneously, the rural areas’ space configuration and space regulation under the national spatial planning system has, to some degree, hastened the rural building process. However, it has significantly influenced the countryside’s traditional space and rural landscape. On the one hand, rural development and construction lack natural environment excavation and absorption, rustic style, cultural qualities, and vernacular construction techniques, resulting in a dichotomy between contemporary rural construction and vernacular landscape inheritance [4]. On the other hand, vernacular planning deviates from the personal demands of rural residents and, to a certain extent, disintegrates rural life customs, production methods, and rural civilization.

At the level of planning practice, urban planning and design methods have been transposed into village planning and construction practice. Due to this, traditional villages’ value of spatial forms has been neglected, and the spatial texture features passed down through generations have become fragmented [5]. Looking at current mainstream planning approaches, the deeper cause for neglecting the spatial morphological value of village settlements is a lack of attention to traditional villages’ spatial subjectivity and regularity. The limitations of current planning techniques in terms of analysis and application are gradually emerging. However, the urgent issues in the conservation and development of traditional villages include how planners should intervene in traditional village construction, how to actualize the organic evolution of rural spatial forms, and how to adapt the constructed technical methods to make them compatible with the local culture and environment. According to the academic research community’s perspective, the existing studies are mainly described and summarized statically, the quantitative analysis tools for the natural evolution of villages are not sufficiently applied, and the technical means especially stay at the stage of subjective cognitive analysis and qualitative research. Based on the complexity of the spatial-temporal and dynamic changes in the spatial structure of villages, traditional planning and design methods are difficult to adapt to their conservation and heritage.

The concept of “spatial genes” [6,7] points out that the key to transmitting historical and cultural veins in urban construction and development is not only the protection and single reproduction of historical forms and symbols themselves. Instead, it is the continuation of the territorial combination pattern of spatial elements and their intrinsic texture. It effectively analyzes the “deep structure” that underlies the general spatial shape of both urban and rural environments [8]. It not only acts as a study tool for evaluating urban and rural spatial landscapes but also helps build a new cognitive system for describing urban and rural spatial development. Traditional villages have formed a reasonably stable spatial combination pattern via natural selection and spatial variation, inheriting the countryside’s distinctive ecological beauty and vernacular culture [9]. As a result, in the village planning and building process, it is critical not to reproduce the spatial texture but to explore the hidden underlying logic of its texture while also paying attention to the randomness and chance in its production. Depending on the preceding, this study takes the spatial genes of traditional settlements as a perspective for recognizing and analyzing their spatial forms using the parametric technology of the CityEngine system. Using the Xiaoxi village in western Hunan Province as a sample, the spatial gene sequence corresponding to its spatial form is analyzed in depth. The extracted parameter values and the combination of parameter values can be interpreted as various spatial gene element fragments, which can be reconstructed into the CE system to generate a spatial morphological inversion model of Xiaoxi Village. The reconstructed rules developed by the model can be interpreted as a sequence of spatial gene element combinations. Through the correlation detection of spatial gene parameter sequences, a three-dimensional visualization technique is used to identify and mine the spatial morphology and spatial landscape information of Xiaoxi Village, which is used as the genetic information basis for the spatial evolution of the village [10]. The traditional settlement’s spatial heritage is optimized by integrating the integrity of the village’s traditional spatial texture, the era of the village’s growth, and the immediate demands of the village residents, and then proposing a strategy for guiding the spatial form and appearance [11]. This research aims to investigate the inner logic of the space morphological law of traditional villages using the spatial gene parameterization technique and to provide practical application strategies and an objective scientific basis for conventional village spatial gene control and inheritance.

2. Literature Review

In terms of research fields, morphology and typology theories at home and abroad are on the same track. However, in the past hundred years, the proportion of research on spatial morphological types has still been dominated by urban planning, urban design, and landscape planning. The study of rural settlement morphology is marginal in morphological research. Existing studies on the evolution of spatial patterns have been carried out by analyzing spatial patterns (village boundaries, street networks, etc.) and interpreting the symbolic profiles of characteristic elements. The street layouts and architectural elements impact the spatial structure of the town, which leads to a proclivity for cluster formation [12]. Saleh explored the correlation of architectural features affecting the spatial evolution of the countryside [13]. The spatial patterns of different types of traditional villages are illustrated and analyzed, and the driving mechanisms of the evolution of traditional settlements are explored [14,15]. The research on spatial morphology mentioned above is based on the two primary levels of spatial morphological characteristics and critical influencing factors. There is still much room for growth and refinement. Based on the “landscape gene theory”, the “cell-chain-shape” structure of traditional settlements was proposed. Liu Peilin et al. pioneered the genetic mapping of the settlement landscape, which provides a deeper interpretation of the settlement landscape’s cultural characteristics and spatial and temporal evolution [16,17]. However, previous studies are still at the stage of static description and are limited to typical urban morphological-typological planning thinking. The effective transmission of traditional village spatial features necessitates an in-depth analysis of spatial morphological patterns, and it is evident that prior planning methodologies have limits in their practical application.

Since the early twentieth century, traditional village spatial research methods have evolved from the early qualitative description of the physical environment at various levels to the quantitative study of the structure of spatial relationships, based primarily on mathematical and rational models, spatial syntax, 3S technology, and non-linear science [18]. Use the spatial syntax method to study ancient and characteristic villages’ spatial structure orders. Indicators such as average area and distance describe the spatial geometric composition of traditional settlements [19]. Pu Xincheng and other scholars have formed a complete set of quantitative research methods for spatial morphology utilizing a comprehensive analysis of traditional settlement boundary definition, settlement shape fitting, and settlement order quantification [20,21]. Using Smart PLS3.0 software, the scale’s reliability and validity were verified, and descriptive analysis, correlation analysis, factor analysis, and a pretty full model of traditional village public space were generated [22]. Using the comparative analysis of space syntax, the paper explains the differences in the external space of three villages with different layout forms from the space syntax diagrams and correlation coefficients [23]. According to the village’s overall layout, the building layout’s distribution degree, and the boundary shape characteristics, a clustering algorithm is applied to identify the number of village subcategories and complete the identification of village types [24]. Use ArcGIS and fractal dimension measurement to conduct quantitative analysis on the nuclear density distribution and boundary dispersion of village buildings and study the overall distribution of village space, the heterogeneity characteristics of settlement boundaries, and public space [25]. The studies mentioned above on the spatial genes of rural settlements mainly focus on the identification and extraction of morphological genes from a single object, which is not representative of the whole element‘s characteristics. There are still areas for improvement in exploring the stability and differential characteristics of spatial genes in the genetic aspects of the spatial landscape. With the in-depth study of the quantitative analysis of various spatial elements, dynamic modeling techniques are gradually being applied to the study of traditional villages. The fractal theory was used to examine the spatial configuration of traditional settlements [26]. Utilizing cellular automata, a model for the self-organizing development of spatial structures was created. The role of dynamic modeling in urban and traditional settlement planning practices is explored [27,28]. He proposed a quantitative gene system model for traditional village spatial genes. A quantitative index system for traditional village morphology is built, revealing the logic of traditional village material form formation [29]. The parametric technique is used to investigate its spatial morphological properties’ inherent laws and propose strategies for their application in planning and design practice [30]. Li Xin studies Dong settlements, using artificial intelligence methods to genetically code them and determine the gene parameters in conjunction with the quantification results to conduct a computer growth simulation of the generation of traditional Dong settlements [31]. Although existing studies have dealt with the modeling of natural environmental factors and socio-cultural factors in traditional settlements, the modeling methods are highly subjective [32]. The analysis of various spatial elements is only carried out for a single element, lacking integrated correlation studies, which is not conducive to the overall consideration of the spatial morphological evolution of villages.

Applying quantitative results to growth patterns for the simulation of settlement evolution is a much-needed supplementary research direction. The “spatial gene” concept for analyzing traditional villages can effectively fill this gap. The CityEngine parametric analysis and reconstruction approach is employed from the viewpoint of spatial gene inheritance and control. The spatial genes and parameter rules are dynamically related, and the spatial morphological features are described in a dynamic evolutionary way by constructing a numerical set of relationships to express the inheritance rules of spatial genes. Instead of always remaining in linguistic and literal textual analysis, the parametric simulation reconstruction technique considers the study of the spatial self-organization of villages in the context of complex systems (

Table 1. A Comparison of Existing Research Methods on Traditional Village Spatial Form Evolution. Source: Author.

Research Methodology *	Qualitative Research Analysis			Quantitative Research Analysis
	Qualitative Text Description Summary	Explanation of Characteristic Element Symbols	Spatial Syntax	Nonlinear Science	Simple Parametric Refactoring
Specific methods	Illustration of spatial patterns	Landscape Genetic Mapping	Axial analysis, convex space analysis, view field analysis	Metacellular automata models (CA)	Simple numerical model
Global evaluation	General	Moderate	Moderate	General	Moderate
Application of the method	Analysis of spatial form components and landscape intention	Analysis of inter-area variability and spatial correlation in settlement landscapes	Analysis of the spatial characteristics of villages about the morphological characteristics of spatial heterogeneity	Adaptation of integrated constraint state integrated city models	Related research is at the initial exploratory stage
Overall rating	Stuck at the level of subjective perception, not adapted to the process of the spatial evolution of the countryside	It has already been implemented at the spatial level but is limited to the spatial attributes of the landscape	Limited to pattern summaries and insufficient explanatory research on their causes	Self-organization avoids the influence of subjective factors and improves the accuracy of simulation results	Vital subjective factors and lack of quantitative analysis of the factors influencing the settlement object for a specific geographical area
Research Overview	Currently, the mainstream planning model of villages is more often applied to urban planning methods. Accordingly, there is relatively little research literature on the simulation and reconstruction of traditional spatial forms of towns. The methods of reconstructing village spatial texture are also sporadic, and there has yet to be a mature methodological system.

* The applicable scenarios of different research methods are analyzed, and the application potential of parametric simulation technology in relevant research fields is demonstrated by comparison.

). It dynamically describes their non-equilibrium and non-linear spatial development characteristics. This paper proposes a method for recognizing and learning to transmit traditional rural culture rooted in rural, regional spatial landscape resources. This is consistent with the basic logic of rural spatial landscape evolution and contributes to the transmission of rural customs, vernacular landscape, and nostalgic feelings during the urban and rural development processes. It exposes the logic behind how traditional village spatial forms are developed, enabling the preservation and transmission of the unique spatial characteristics of the village’s historical past.

3. Materials and Methods

3.1. Village Boundary Demarcation

Xiaoxi Village belongs to Donghe Street, Jishou City, Xiangxi Tujia, and Miao Autonomous Prefecture, Hunan Province. It is located in the combination of the Jishou urban and Jilue rural areas. The landform in the village area is dominated by the Zhongshan landform, with prominent high peaks and heavy mountains, steep slopes and cliffs, and the mountain range in the northeast direction. The geographical location is excellent, and it is the symbolic deep Miao area with the most distinctive Miao landscape in the area and the epitome of Miao villages (Figure 1).

The heritage environment within the village area, which includes historic buildings, structures, and natural environmental elements, all records significant historical information about the Hmong settlement’s local material production, lifestyle, ideology, and customs during the Ming and Qing dynasties. The landscape pattern and comprehensive environmental characteristics of the ancient villages are of outstanding value, reflecting the traditional form of choosing a site and choosing a place to live in ancient Chinese villages, and are essential samples for studying the environment and ecology of ancient villages. In Xiaoxi Village, there are still a number of historical buildings from the Ming and Qing dynasties, such as the Hong Family Courtyard, which dates back about 150 years. The structure has a magnificent aspect with flying eaves and corners, and the door and window designs are of various forms and superb workmanship with high artistry value. The historic building complex preserves architectural history information, such as the building techniques and building technology of the Miao fortress during the Qing Dynasty, reflecting the local architectural culture and architectural art achievements. It has high research value for studying the socioculture, Miao folk customs, and regional architectural culture of Hunan’s western Hunan region.

3.2. Date Collection and Process

3.2.1. Date Collection

The primary data used in this study includes satellite image data, village and town administrative boundary data, road and building data, digital elevation model data, and aerial photography collection images for field exploration. All of the information presented above are vector data.

The satellite imagery data used in this study were from a level 19 LocaSpaceViewer-downloaded image map of the study area and its environs, which assisted in determining building roof patterns and supplementing building profile and courtyard space vector data. The Hunan Provincial Architectural Design Institute’s high-resolution database offered village and town administrative zoning data and road and building data, which assisted in identifying the study region. The Geospatial Data Cloud provided the DEM data, which offered data with a precision of 30 m (Figure 2). Due to the survey data’s inherent complexity and irrelevant information to the study, they may introduce some errors in the parametric analysis and reconstruction. To guarantee the data’s correctness for the study, location information obtained from the Resource and Environmental Sciences and Data Center and all vector data for the study region were precisely calibrated in ArcGIS 10.3.

3.2.2. Date Process

• The current rural spatial texture morphology is objectively identified and extracted using the acquired satellite image data. It relies on the collaborative work of the ArcGIS software platform in the data processing stage and helps analyze the relationship between spatial texture and its influencing elements;
• The spatial texture that comprehensively controls and expresses the rural spatial structure is parametrically transferred with mathematical statistics methods. By extracting the inner rules and influencing factors of village growth and supported by the quantitative statistical analysis of the research subjects, the “genetic parameters” of the spatial texture of traditional village growth are obtained;
• The obtained spatial texture parameters are substituted into the Cityengine software platform, combined with CGA rule language, to build a complex mathematical relationship model. Based on the traditional spatial morphological features, three-dimensional morphological modeling is carried out. It uses spatial analysis methods to study the root causes of different distribution characteristics and analyzes the spatial distribution of spatial texture quantitative data to ensure the accuracy of the study area.

3.3. Parametric Analysis and Reconstruction Technology Path

The parameterization technique established in this study combines the control indicators under the current specification system to guide construction. Simultaneously, the zoning parameters are adjusted according to public demand to guide construction, considering both the indicators’ scientificity and the rationality of the planning scheme. The parametric research process produces results in the form of parameters, rules, graphs, evaluation index systems, etc., and can be grouped into three categories:

• A collection of gene parameters describing the spatial morphology of villages;
• An index system for the dynamic adjustment of spatial gene combination sequences;
• A three-dimensional visualization model of the driving mechanism of spatial gene parameters.

Technically, Figure 3 depicts the parameterized analysis and reconstruction procedure.

4. Data Pre-Processing and Parametric Parsing

4.1. Traditional Settlements Boundary Demarcation

In adapting to the mountainous terrain’s natural growth, the siting of residential buildings in Xiaoxi Village is usually considered to conform to the natural topography while considering the shaping of production and living spaces. While this is a positive development in the integration and symbiosis between artificial and natural systems, it makes defining village borders somewhat murky and complex. Simultaneously, both parametric analysis and reconstruction must be well-defined in terms of spatial extent [33]. Therefore, it is necessary to base it on the current construction status and remote sensing mapping topographic map of Xiaoxi Village and combine geographic data information with field exploration methods. Secondary verification and amendment were carried out to determine the study area boundary of Xiaoxi Village. Pu Xincheng’s proposed method of extracting traditional settlement boundaries is more rigorous theoretically and can guarantee the uniqueness of the extraction results. However, the method only considers the building base plane, excluding the influence of natural bodies (rivers and mountains) and spatial elements such as fences, which can reflect site ownership to some extent [34].

As a result, we are utilizing the method proposed by Xincheng Pu as a blueprint for extracting the current status of traditional settlement boundaries with further optimization. Here are the individual optimization stages: For regions with distinct property boundaries, the village boundary is defined by the property boundaries. In cases with distinct natural borders, the village boundary line is defined by the natural boundary line. If the natural boundary is more than 5 m away from the village building, the village boundary is based on the natural boundary. When a road joins two structures, the distance from the building to the edge of the road defines the village boundary (

Table 2. Traditional settlements boundary optimization rules and diagrams. Source: Author.

Optimization Rules Method	Optimization Rules	Optimization Rules Diagram *
Property boundary optimization	There is an explicit property rights line (usually with the fence as the carrier), and the property rights line is used to decide (part of the need for verification through site exploration).
Natural boundary optimization	If there is a clear natural boundary line and the distance between the buildings in the settlement and the natural boundary line is less than 5 m, the village boundary line will be determined along the property boundary; otherwise, it will be determined along the natural boundary line (part of which needs to be verified through on-site exploration).
Building road optimization	When a road crosses the boundary between two buildings, and the buildings are misaligned, the village boundary should be determined by first considering the interface between the building and road boundaries.

* To define the village’s boundary, the following methods are used for optimization.

).

Combined with the above optimization methods, based on the spatial relationship of “road-plot-buildings”, the detailed research scope is extracted after optimization, as shown in Figure 4.

4.2. Road Space Texture Pre-Processing and Parametric Parsing

4.2.1. Road Space Texture Pre-Treatment

The mountainous terrain and natural water bodies limit the spontaneous growth of traditional streets and alleys [35]. As a result, some of the road sections are overly curvy. The “Douglas-Pook” algorithm [36] is proposed to be used to straighten the curved sections because the existing algorithm does not retain the characteristics of road intersections well (Figure 5). The road is decomposed into multiple sections, and multiple sections of the decomposed road are sampled. The width of each segment is then measured, and the width of the road section is determined using the average sample value discovered from the sampling measurements [37]. Due to the intricate texture of the road space, optimizing the road intersections and shapes is necessary, such as taking the road intersection turning radius optimization, redundant areas for excision, and staggered intersections combined (Figure 6).

The pre-processing principles are as follows: first, to avoid affecting the overall spatial morphology of the reconstructed village; second, to ensure that the road spatial texture is optimized for substitution into the CE system for simulation. Guarantee that the optimized roads and alleys can reflect the spatial texture of traditional streets and alleys while considering modern society’s needs. Figure 7 depicts the results of the optimization of the original village road texture that was extracted.

4.2.2. Road Space Gene Parameterization Parsing

The spatial gene parameters that reflect traditional village road texture characteristics are extracted based on the above optimization path. These parameters should be able to express its spatial form characteristics fully, but they also need to effectively reflect the characteristics of residents’ life communication scenarios and consider the behavioral needs of residents’ life services. The resulting spatial gene parameters are divided into sub-graphic collections by analyzing the traditional street road texture and obtaining the essential elements constituting the road spatial texture. The centrality of the road network, for example, reflects the heterogeneity and centrality of street layout, which can be interpreted as “central development preference” [38]. To some extent, the orientation of the overall street organization and the intersection angle guide the orientation of traditional houses. Therefore, the CE system can realize the expression relationship between “parameter-morphology.” It is possible to investigate further the intrinsic spatial gene mechanisms that affect the growth of road texture.

We integrated the CityEngine system’s ability to manage various parameters with the concrete needs of safeguarding traditional settlements with a history of cultural traditions. Six spatial gene parameters, such as overall road network form, road network irregularity, road intersections, road length, road declination, and the number of roads [39], were selected to describe the distribution characteristics of road texture (

Table 3. Space gene parameter set for the road in Xiaoxi Village. Source: Author.

Category	Definition of Parameters	Parametric Analysis Methods	Road Spatial Gene Extraction Formula or Diagram *
The overall layout of the road system	Road network form	A mixed road network can incorporate three different types of road networks, depending on the properties of the road network.
Road intersections	Capture Distance	The minimum distance between the intersection	$f\left(snappingdistance\right)=Min\left(d_1,d_2,d_3\cdots d_n\right)$
	Ratio of intersections	Number of major road nodes/intersection nodes	-
	Minimum intersection angle	Road intersection statistics minimum angle value	$f\left(minangle\right)=Min\left({\theta }_1,{\theta }_2,{\theta }_3\cdots {\theta }_n\right)$
	Maximum intersection angle	Maximum angle value for road intersection statistics	$f\left(maxangle\right)=Max\left({\theta }_1,{\theta }_2,{\theta }_3\cdots {\theta }_n\right)$
Road length	Longer road length ($l_a$)	l1, l2, l3 … ln is the length of the single road	$\overline{l_a}=Average\left(l_{a1},l_{a2},l_{a3}\cdots l_{an}\right),\overline{l_{an}}>\overline{l}$
	Shorter road length ($l_b$)		$\overline{l_b}=Average\left(l_{b1},l_{b2},l_{b3}\cdots l_{bn}\right),\overline{l_{bn}}\le \overline{l}$
	Longer road flexibility interval (${l^{\prime }}_a$)		$\overline{{l^{\prime }}_a}=\left[\left|max\left(l_{a1},l_{a2},l_{a3}\cdots l_{an}\right)-\overline{l_a}\right|+\left|min\left(l_{a1},l_{a2},l_{a3}\cdots l_{an}\right)-\overline{l_a}\right|\right]/2$
	Shorter road flexibility interval (${l^{\prime }}_b$)		$\overline{{l^{\prime }}_b}=\left[\left|max\left(l_{b1},l_{b2},l_{b3}\cdots l_{bn}\right)-\overline{l_b}\right|+\left|min\left(l_{b1},l_{b2},l_{b3}\cdots l_{bn}\right)-\overline{l_b}\right|\right]/2$
Road width	Main road width ($w_m$)	Determining the road class and whether it is a major road	$\overline{w_m}=Average\left(w_{m1},w_{m2},w_{m3}\cdots w_{mn}\right)$
	Secondary road width (ws)		ws = Average ws1, ws2 … wsn
	Major road flexibility interval (${w^{\prime }}_m$)		$\overline{{w^{\prime }}_m}=\left[\left|max\left(w_{m1},w_{m2},w_{m3}\cdots w_{mn}\right)-\overline{w_m}\right|+\left|min\left(w_{m1},w_{m2},w_{m3}\cdots w_{mn}\right)-\overline{w_m}\right|\right]/2$
	Secondary road flexibility interval (w′s)		$\overline{{w^{\prime }}_s}=\left[\left|max\left(w_{s1},w_{s2},w_{s3}\cdots w_{sn}\right)-\overline{w_s}\right|+\left|min\left(w_{s1},w_{s2},w_{s3}\cdots w_{sn}\right)-\overline{w_s}\right|\right]/2$
Road deflection angle	The junction angles between roadways	Take the largest value as the “maximum road inclination angle” after counting the lesser angles at the intersection of two roads and removing any extreme values.
Amount of roads	The number of road segments	Total number of roads calculated by the Douglas–Peucker algorithm, which decomposes curved roads into straight sections.

* The algorithm formula in the table extracts characteristic parameters reflecting road spatial texture.

).

4.3. Plots Space Texture Pre-Processing and Parametric Parsing

4.3.1. Plots Space Texture Pre-Treatment

Due to the geographical fortress distribution, the influence of traffic obstructions. Most of the aborigines in traditional villages live locally, and the layout of villages has a strong tendency to be centripetal [40]. The sites of ancestral house halls and other public structures are often large in scale and have a relatively uniform shape. There is a high concentration of structures in the middle of traditional villages since homes are often organized around ancestral halls [41,42]. At the same time, the different spatial functions undertaken by different functional plots also have a certain influence on the spatial texture distribution of the plots. As a result, parcel extraction optimization aims to get the extracted parcels as close to the real property parcels as possible while avoiding extreme errors before and after reconstruction.

Here are the detailed procedures for extracting: first, determine the division of dwelling units; second, determine the shared space between each dwelling unit. According to the different extraction contents, different extraction rules are formulated, as shown in

Table 4. Plots texture extraction rules and diagrams. Source: Author.

Extracted Content	Extraction Rules	Extraction Diagram *
Residential units	Where there is a clear fence line division, the dwelling unit is determined according to the extent of the fence line.
	Where there is no fence line division, but the main building and the annex are adjacent to each other in an affiliated relationship.
Public Space	The parametric space enclosing the building is divided into two or more groups, and the resulting public space is then divided into pairs.

* Under the premise of inheriting the property rights of buildings, the following methods are used to optimize a single plot.

.

The extracted traditional village block groups, based on the above path; the extracted plot texture features are close to the actual plot tenure when considering the morphological features (Figure 8).

4.3.2. Plots Space Gene Parameterization Parsing

The parcel spatial gene analysis path is determined by two aspects based on the analysis of the parcel texture characteristics. The parcel group is interpreted from the overall spatial characteristics and its scale characteristics are analyzed first. The characteristics are then analyzed at the individual parcel level. It is divided into two crucial indicators: parcel function and parcel size.

Table 5. Space gene parameter set for the plot in Xiaoxi Village. Source: Author.

Category	Definition of Parameters	Parametric Analysis Methods	Road Spatial Gene Extraction Formula or Diagram *
Plot organization	Skeleton subdivision types	According to the characteristics of the plot cluster type, it is transformed into the plot division method in the spatial gene reconstruction of the plot.
	Recursive subdivision types
	Inward regressive subdivision type
Plot size characteristics	Maximum Plot Size	By eliminating the extreme outliers, the values of the maximum, minimum, and average areas were obtained. The ratio of the number of plots to the area of plots.	$f\left(LotAreaMax\right)=Max\left[area\left(a\_1,a\_2,a\_3\cdots a\_n\right)\right]$
	Minimum plot size		$f\left(LotAreaMin\right)=Min\left[area\left(a_1,a_2,a_3\cdots a_n\right)\right]$
	Average Plot Size		$f\left(LotAreaAverage\right)=Average\left[area\left(a\_1,a\_2,a\_3\cdots a\_n\right)\right]$
	Plot Density Distribution		$f\left(LotPlotDensityAverage\right)=Average\left[density\left(a_1,a_2,a_3\cdots a_n\right)\right]$
Geometrical features	The plot’s shortest side	After the highly abnormal values were proposed, the values of the above factors were counted, and the maximum and minimum values were taken.	$f\left(LotEdgeShortest\right)=Min\left(l_1,l_2,l_3\cdots l_n\right)$
	The plot’s longest side		$f\left(LotEdgeLongest\right)=Max\left(l_1,l_2,l_3\cdots l_n\right)$
	The maximum plot length-to-width ratio		$f\left(MaxLength/widthratio\right)=Max\left(a_1,a_2,a_3\cdots a_n\right)$
	Plot aspect ratios at their lowest		$f\left(MinLength/widthratio\right)=Min\left(a_1,a_2,a_3\cdots a_n\right)$
	The maximum off-set angle of the plot		$f\left(CornerAngleMax\right)=Max\left[area\left(a_1,a_2,a_3\cdots a_n\right)\right]$
	Plot minimum declination		$f\left(CornerAngleMin\right)=Min\left[area\left(a_1,a_2,a_3\cdots a_n\right)\right]$
Plot orientation features	Plot orientation	The angle value of the inland block direction of each block was calculated, and the minimum value was taken after the extremely abnormal value was eliminated.	$f\left(LotDirection\right)=Min\left({\beta }_1,\beta ,{\beta }_3\cdots {\beta }_n\right)$
Plot adaptation characteristics	Mutual adaptation between the plot and the corresponding terrain	There are four values according to Alignment, Uneven, Minimum, Maximum, and Average.

* Extracting characteristic parameters reflecting plot spatial texture by using the algorithm formula in the table.

lists the feature parameters that optimize traditional settlement plots’ spatial texture by fusing traditional village conservation and development requirements with the parameters that can be supported and controlled by the CE system. By parametrically analyzing the spatial genes at both the overall and individual plot levels, it is expected to trace the cultural context related to village construction and revive the total spatial landscape vision of traditional villages by activating their spatial gene [43].

4.4. Architectural Space Texture Pre-Processing and Parametric Parsing

4.4.1. Architectural Space Texture Pre-Treatment

The distinctive architectural style of the western Hunan area is a result of its unique, pristine nature, rich history, and autonomous geographic setting. The architectural texture, which reflects the local area’s social living conditions and historical evolution characteristics of a particular historical period, is the most perceptible visual expression and cultural feature. In contrast to cities, naturally evolved villages have more irregular geometric forms in their building footprints to accommodate topographic conditions [44]. Based on the efficiency of the building space and the cost of building construction, the building’s foundation’s shape is mainly in the shape of “L”, “hui”, and “one”, similar combinations and different combinations (Figure 9).

Moreover, the building materials build the façade texture according to specific rules and combinations, shaping the distinctive regional architectural characteristics and promoting the harmonious coexistence between the village and nature [45].

This paper discusses macroscopic spatial texture and does not discuss window and door construction in depth. Based on the above dimensions, the planar spatial texture and façade spatial texture, which constitute the spatial of the building, are analyzed separately in a subjective, qualitative way, combined with quantitative parameters. In order to adapt to the reconfiguration demand, the building space texture is extracted and optimized, and the specific steps are shown in

Table 6. Architectural texture extraction rules and diagrams. Source: Author.

Extracted Content	Extraction Rules	Extraction Diagram *
Single building unit	The principle of space regularization of a single building applies to buildings without clear wall lines.
Boundary line of building	The spatial optimization of the building boundary line is to facilitate the calculation statistics after reconstruction.

* In order to make the reconstructed architectural texture inherit the traditional architectural features, the following methods are adopted for optimization.

.

4.4.2. Architectural Space Gene Parameterization Parsing

Urban civilization penetrates the villages, the village clan culture and autonomy system gradually disintegrate, and the influence of cultural factors on the layout and orientation of buildings gradually weakens. On the other hand, the cost of new building materials have reduced, and more and more buildings with modern landscapes have appeared in villages, which are less harmonious with the environment and have significantly impacted the architectural texture of villages.

Based on the above analysis of the village’s architecture spatial texture, we combined the current development trend of the village. The three elements of a building’s base shape—building base area, building depth, and building width—are determined to reflect its planar morphological characteristics; the four elements of a building’s façade form—building height, wall skin, and roof form—reflect its façade morphological characteristics. According to the different extracted contents, different extraction rules are formulated, as shown in

Table 7. Space gene parameter set for the architecture in Xiaoxi Village. Source: Author.

Category	Definition of Parameters	Parametric Analysis Methods	Road Spatial Gene Extraction Formula or Diagram *
Building plan texture	Building footprint shapes	“L”, “hui”, “one” shape	A typological approach to inductive refinement
	Building footprint proportions	-	-
	Building width	Max/min/average percentage	$f\left(SubstratePercentage\right)=Frequency\left(a,b,c\right)/Count\left(a,b,c\right)$× 100%
	Depth of building	Max/min/average percentage	$f\left(BuildingWidth\right)=Max/Min/Average\left(w_1,w_2,w_3\cdots w_n\right)$
	Building footprint	Building footprint interval distribution	$f\left(BuildingDepth\right)=Max/Min/Average\left(d_1,d_2,d_3\cdots d_n\right)$
	Street space	-	$f\left(BuildingArea\right)=Max/Min/Average\left(a_1,a_2,a_3\cdots a_n\right)$
Building façade texture	Roof forms	Determining the characteristics of a building’s roof form	Inductive distillation using a typological approach
	Roof shape type proportions	The proportion of the building occupied by each roof shape type	Number of buildings by roof type divided by the total number of buildings
	Roof Texture	The materials and colors used in the roofs of buildings	Constructing a library of corresponding building features by capturing them on site
	Wall Texture	The materials and colors used in the walls of the building	Constructing a library of corresponding building features by capturing them on site
	Building Height	Height of the main body of the building	$f\left(BuildingHeightRatio\right)=\frac{Frequency\left(h_1,h_2,h_3\cdots h_n\right)}{Countif\left(h_1,h_2,h_3\cdots h_n\right)}$× 100%
	Probability distribution of roof texture types	Percentage of buildings with each type of roof texture	Number of buildings of each roof texture type divided by the total number of buildings
	Probability distribution of building wall textures	Percentage of buildings with each type of wall texture	Number of buildings of each wall texture type divided by the total number of buildings

* We are extracting characteristic parameters reflecting architecture spatial texture by using the algorithm formula in the table.

.

5. Traditional Village Spatial Texture Reconstruction by Parametric

5.1. Parametric Reconstruction of Road Space Texture

According to the retrieved parameterized attribute values, the parametric reconstruction of the road space texture aims to calculate and derive the road space texture that is compatible with the original village texture and satisfies current living requirements. A road space texture parametric analysis for Xiaoxi Village was performed, and the values of each parameter in the parameter set were extracted using the method described in this paper. The findings were achieved using the road skeleton as the object, as shown in

Table 8. Parameter set of road spatial texture in Xiaoxi Village. Source: Author.

Category	Type	Definition of Parameters	Hongjia Camp	Dazhai Camp
The overall layout of the road system	Road network form	Central radial pattern	Central radial pattern	Network pattern
		Network pattern
Characteristics of road organization structure	Road intersections	Capture Distance	19.8	21.7
		Intersection ratio	3.6	3.8
		Minimum intersection angle	34.8°	42.6°
	Amount of roads	Amount of road sections	117	164
Road geometric morphological characteristics	Road length	Longer road length	47.4 m	63.3
		Longer road flexibility interval	32.9 m	44.3 m
		Shorter road length	14.6 m	19.7 m
		Shorter road flexibility interval	8.3 m	13.8 m
	Road width	Main road width	5.5 m	6.3 m
		Major road flexibility interval	3.4 m	4.1 m
		Secondary road width	3.2 m	3.5 m
		Secondary road flexibility interval	2.2 m	1.9 m
	Road deflection angle	Smaller angles of intersection between roads	107.6°	119.4°

.

The following are the precise stages involved in reconstruction:

• First, modern villagers’ demands are taken into consideration while optimizing the “parameter values” that were discovered via the investigation of the current scenario;
• Second, the extracted spatial texture is re-associated with the translated parameters;
• Third, substituted into the spatial texture modeling module to generate the road spatial texture model;
• Finally, the spatial texture of the road before and after the reconstruction was evaluated for similarity.

The road’s spatial texture before and after reconstruction is if the similarity meets the threshold value and the extracted parameters and road spatial texture reconstruction rules are reasonable. Manual correction is required if the similarity does not meet the threshold value. The CE system also offers a more suitable environment for manual interaction. When one road is changed, the geometry of the nearby road network is also modified in real-time, making it easier to determine whether manual adjustments to the road network are reasonable.

Since the parameter values obtained from the parsing contain the present road network’s spatial texture characteristics, they are substituted into the CE road generation module to obtain the new road network scheme shown in Figure 10. As can be seen, when compared to the original road spatial texture, the new road network produced by these parameter values essentially inherits the spatial characteristics of the original road network.

5.2. Parametric Reconstruction of Block Space Texture

Carry out parameter resolution analysis on the spatial texture of the Xiaoxi village block and extract corresponding parameters and parameter values. The extraction results of some core parameter values are shown in

Table 9. Parameter set of block spatial texture in Xiaoxi Village. Source: Author.

Category	Type	Definition of Parameters	Hongjia Camp			Dazhai Camp
Characteristics of Organizational structure	Block organization form	Subdivision type of land. The proportion of subdivision types of land	Grid type 54.37%	Retreat type 27.82	Skeleton type 17.81%	Grid type 74.65%	Retreat type 9.08%	Skeleton type 16.27%
	Block structure characteristics	Land leveling mode	Average			Minmum
		Average plot density	0.004			0.003
		Terrain adaptive calibration of the plot	Higher			Lower
		Spatial location of ancestral hall building plot	LotConer			LotConer
	Block size characteristics	Maximum plot Size	1037.1 m2			1590.5 m2
		Minimum plot size	37.2 m2			45.8 m2
		Average plot Size	202.9 m2			220.5 m2
	Total number of blocks	A number of various forms of land	149			288
Geometric morphological features	Block side length	The plot’s longest side	46.2 m			51.7 m
		The plot’s shortest side	3.7 m			4.6 m
	Block length-to-width ratio	The maximum plot length-to-width ratio	3.41			3.74
		The minimum plot length-to-width ratio	1.0			1.07
	Block deflection Angle	The plot’s maximum off-set angle	121.4°			153.1°
		The plot’s minimum off-set angle	28.3°			33.6°

.

The spatial texture reconstruction of the plot is the outcome of the plot’s stepwise subdivision based on the extracted texture characteristics. Following the reconstruction of the road’s spatial texture, the composition of parcels bounded by roads, traditional settlements, and natural boundaries will be generated automatically. It is written and used as a rule language in CGA and uses the “Lot” representation.

The specific steps of the reconstruction are as follows:

• First, determine each plot group according to the road spatial texture generated by the parameters, the settlement boundary, and the natural boundary within the boundary;
• Second, integrate the constituent elements of the spatial plot texture and the constraints between the elements to form a complete plot spatial texture relationship model by writing the CGA rule file;
• Finally, substitute the parameter values obtained in the parametric analysis process into the file. The parameter values obtained in the parametric analysis process are substituted into the CGA rule file, and the CGA file is executed with “Lot” as the object to complete the reconstruction of road spatial texture.

The system’s plot plan generated entirely automatically does not necessarily meet real needs, such as uneven plot division and a too narrow plot shape, which require manual interactive modification and plot grouping adjustment. Unlike the road spatial texture, the reconfiguration process of the spatial plot texture has optimized its traditional features to a greater extent. As a result, the spatial texture analysis and reconstruction were tested numerous times. The new parcel scheme shown in Figure 11 was obtained after verifying the rationality of the parameters controlling the spatial texture generation of the parcels, the parameter extraction planning, and the rules for reconstructing the spatial texture of the parcels.

5.3. Parametric Reconstruction of Architectural Space Texture

Since there are many different types of buildings in Xiaoxi Village, more than mapping to maps is needed to reflect the architectural space’s texture accurately. In order to ensure the transmission of the spatial features of diverse building types, field surveys were conducted throughout the research period. The parametric analysis of the architectural space texture of Xiaoxi Village was carried out after optimizing the processing method based on the above architectural space texture.

Table 10. Parameter set of architecture spatial texture in Xiaoxi Village. Source: Author.

Category	Type	Definition of Parameters	Hongjia Camp				Dazhai Camp
Building plan texture	Building footprint shapes	Building footprint shapes Building footprint proportions	L 2.73%	hui 1.12%	one 93.17%	An unusual combination 2.98%	L 1.87%	hui 0.18%	one 96.37%	An unusual combination 1.58%
	The building faces are widely distributed	Maximum building face width	25.3				27.6
		Minimum building face width	6.9				8.2
		Average building face width	14.2				13.4
	Building depth distribution	Maximum building depth	13.5				17.9
		Minimum building depth	5.3				4.7
		Average building depth	7.4				6.9
	Depth to surface width ratio interval		[0.4–0.7]				[0.2–0.7]
	Floor area of building	Maximum building base area	175.8				214.8
		Minimum value of building base surface	36.4				32.4
		Average building base area	72.6				82.7
Building façade texture	Building height distribution	Height of one floor	8.4%				10.9%
		Height of two floor	79.5%				67.4%
		Height of three floor	12.1%				21.7%
	Roof form
Architecture—street space	The most concessional distance in the street		0.37 m				0.41 m

displays the results of the core parameter value extraction.

The CE system does not have a module dedicated to generating buildings, and all the building space texture has to be realized based on a rule file written in CGA syntax.

The specific steps of the reconstruction are as follows (Figure 12):

• First, based on parameters such as the shape and area of the building footprint, as well as the location of the street space, use copying, dividing, offsetting, and other instructions to identify the placement of the building foundation and street space;
• Second, the primary form of the building is constructed by extending, splitting, and dividing the geometric form of the building foundation based on the criteria of building height and number of stories;
• Finally, the building shape is polished and mapped to provide a nuanced portrayal of the texture of the building space.

Figure 12. Combined with the CGA rule language, the CE system reconstruct the path diagram of the architectural spatial texture. Source: Author.

Figure 12. Combined with the CGA rule language, the CE system reconstruct the path diagram of the architectural spatial texture. Source: Author.

When writing CGA rules, the control variables change the building’s shape, height, and texture. After creating the model, choose the building and change the property in the property panel. The model effect will update at the same time. When subdivision plots are given CGA rules based on building texture characteristics, the computer quickly produces 3D building models, as seen in Figure 13.

5.4. Similarity Evaluation System and Construction Guidelines

5.4.1. Similarity Evaluation System

In this paper, we refer to theoretical methods such as spatial syntax [46], fractal theory [47], and Euclidean geometry [48] to measure the similarity of road texture, plot space, and the similarity of village space façade texture, respectively.

Table 11. The evaluation system of spatial texture characteristic parameters. Source: Author.

Index Category	Evaluating Indicator	Indicator Description	Indicator Source	Evaluation Purpose
Road texture evaluation system	Road density	Classification of building base shape	Euclidean geometry	Verify whether the parameters can effectively reflect the road spatial texture characteristics of the case village
	Intersection density	Ratio of the number of intersections to the area of villages (Nr./km2)
	Fractal dimension	Fractal dimension at four scale levels of 100-50-25-12.5	Fractal theory
	Average integration	The higher the integration between roads, the higher the accessibility, and vice versa	Space Syntax
	Average connection value	The higher the connection value between roads, the higher the spatial permeability, and vice versa
Plot texture evaluation system	Number of parcels	Verify parcel quantity difference	Euclidean geometry	Verify whether the parameters can effectively reflect the spatial texture characteristics of the plot in the case village
	Average plot area	Verify parcel area difference
	Maximum plot length width ratio	Verify parcel area difference
	Minimum plot length width ratio
Architecture texture evaluation system	Number of buildings	Verify building quantity difference	Euclidean geometry	Verify whether the parameters can effectively reflect the architectural space texture characteristics of the case village
	Average area of building base	Verify the difference of building base area	Euclidean Geometry
	Fractal dimension of building base	Fractal dimension at four scale levels of 100-50-25-12.5	Fractal theory
	Facade texture visibility	Verify the difference of parcel geometry	CityEngine
Parameter evaluation criteria	If the comprehensive similarity of various indicators reaches 85%, and the similarity of individual indicators reaches 70%, it means that the characteristics of spatial texture parameters are reasonable.

lists the evaluation indices in detail.

According to the importance of each evaluation index in the spatial muscle characteristics, the Delphi method [49] was used to assign a certain weight to each index, respectively. The individual similarity of each index and the comprehensive similarity of all the indexes were calculated. The comprehensive similarity was the weighting of the similarity of each index. The formula for calculating the single indicator similarity is as follows: S = (1 − |x − x′|/x) × 100%. In the formula, S represents the single indicator similarity, x represents the original indicator value, and ′ represents the indicator value of the newly generated model.

The model constructed from the case villages’ original texture and characteristic parameters was indexed to obtain each index value. The individual and comprehensive similarities were calculated using the above formula, as shown in

Table 12. Evaluation of spatial texture similarity. Source: Author.

Evaluating Indicator *		Index Weight	Hongjia Camp			Dazhai Camp
			Index Value	Reconstruction Value	Similarity	Index Value	Reconstruction Value	Similarity
Road spatial texture	Road network density	0.2	18.7 km/km2	19.2 km/km2	97.3%	25.3 km/km2	23.2 km/km2	91.6%
	Road intersection density	0.2	137.3↑/km2	152.6↑/km2	88.9%	173.5↑/km2	177.8↑/km2	97.6%
	Fractal dimension	0.15	1.17	1.31	88.1%	1.47	1.33	90.5%
	Average integration	0.15	0.64	0.53	82.8%	0.66	0.72	90.9%
	Average connection value	0.15	1.65	1.71	96.4%	2.15	2.04	94.8%
	Comprehensive similarity		90.7%			93.08%
Plot spatial texture	Number of parcels	0.25	149	126	84.6%	288	327	86.5%
	Average plot area	0.25	202.9 m2	225.6 m2	88.8%	220.5 m2	185.3 m2	84.1%
	Maximum parcel side length	0.25	46.2 m	59.7 m	70.8%	51.7 m	65.1 m	74.1%
	Minimum parcel side length	0.25	3.7 m	4.4 m	81.1%	4.6 m	5.5 m	80.4%
	Comprehensivesimilarity		81.3%			81.2%
Architecture spatial texture	Number of buildings	0.3	157	132	84.1%	263	286	91.2%
	Average area of building	0.3	72.6 m2	83.2 m2	85.4%	82.7 m2	102.6 m2	75.9%
	fractal dimension	0.2	1.71	1.74	98.2%	1.55	1.61	96.1%
	Facade texture visibility	0.3	10.3%	9.7%	94.3%	11.5	10.2%	88.7%
	Comprehensive similarity		90.5%			87.9%

* Compare the similarity of spatial texture before and after reconstruction to accurately judge the rationality of model construction.

. The evaluation results show that the similarity of each model index reaches more than 70%, and the comprehensive similarity reaches more than 80%. It can be seen that the optimized parameters of the spatial texture characteristics can reflect the spatial texture characteristics of the case villages more effectively.

5.4.2. Parametric Results Conversion

Parametric studies include everything from parsing to reconstruction to similarity evaluation. The entire process will include parameter rules, simulation schemes, and spatial genetic feature fragment detection and can be divided into three categories.

Translated into Planning and Design Guidelines

A set of parameters and rules can be used to describe the spatial structure of the village. This parameterized set can be used to interpret various types of village spatial textures (see Table 5, Table 6 and Table 7). The core parameters and related rules were incorporated into the optimized parameter values and translated into planning and design guidelines for the regulatory style (Figure 14). For example, the “classified control and guidance of road features” in the guidelines is slightly weak in controlling the overall road shape. In the implementation process, there will be a grid road shape that is inconsistent with the characteristics of villages. When guiding the road elements, attention should be paid to the guidance of the road texture shape, and the guidance should be strengthened by utilizing indicators, diagrams, etc. In addition, the architectural texture guidance should be based on the villagers’ needs and only take the external form guidance of the building as the critical point. In contrast, the interior of the building is reserved as a space for flexible development. It is helpful for planners to better understand the characteristics of villages and provide some references for planning schemes.

Parametric Simulation Design Solutions

The “genes” of traditional settlement growth are obtained by extracting the inner rules and influencing factors of settlement growth and supporting them with quantitative statistical analysis of the research subjects. The parametric reconstruction results have high realistic significance and objectivity when combined with the village spatial texture planning scheme, which is one of the significant achievements of this research (see Figure 13).

Spatial Genetic Signature Detection

The parameterized value of the original spatial texture extracted from Xiaoxi Village is compared with the clustering statistical division results of a large sample size after repeated reconstruction simulations. We analyze the network integration degree [50], building group stability [4], and boundary complexity [51] in the reconstructed model to determine whether there is a spatial gene of variation phenomenon. This way, the evolutionary trend of the village’s overall spatial form, the edge space’s irregularity, and the architectural texture’s differentiation can be studied. We translated the generated genetic sequence of “network integration degree—spatial sub-dimensionality—building group stability degree—boundary complexity” into a rural spatial planning and design vocabulary, namely “street composition—The spatial morphology sequence of “public space form—building texture—boundary space”.

For the characteristics of spatial texture corresponding to gene fragment representation, the spatial texture reconstruction data of Xiaoxi Village were quantified with the original texture data, and the statistical division results were compared. The interaction analysis function synthesized the spatial gene data folded line graph with the statistical box graph [52]. Figure 15 depicts the analysis findings.

The characteristics of gene fragment characterization variation were combined with the villagers’ planning demands. The following planning suggestions were made:

(1)

To follow the “genetic” advice of conserving the original look of genetic entries that do not yet have substantial incompatibilities with rural modernization;

(2)

Adopt the “repair” guidance of spatial adjustment for spatial problems that appear unreasonable or dilapidated in the bottom-up evolution;

(3)

Adopt the “regeneration” guidance of strict control for morphological factors with abnormal values in morphological gene fragment testing, depending on the specific situation.

The ultimate goal of the parametric analysis and reconstruction of village spatial texture is to solve and optimize existing village planning and construction problems. Its main content is to investigate the planning application methods of parametric analysis and reconstruction and to construct a bridge between parametric analysis and reconstruction research results and village planning and construction practice using parametric planning methods. It will realize the path from planning research to the practical application of “transformation of parameter set—planning design guideline—a variation of gene fragment characteristics—construction planning guidance,” increasing the degree of refinement management and optimizing current village planning technology. The path from planning research to practical application is to increase the level of fine management while also supporting and optimizing current village planning technology. It can be used to decompose village spatial morphology inheritance and optimize goals. It also compensates for the village spatial morphology identification application’s one-sidedness. This way, targeted spatial style inheritance and optimization guidance are carried out to achieve the dual purposes of rural spatial inheritance and optimization.

6. Discussion

The spatial development of traditional settlements is primarily a bottom-up development process. Regarding the development of its development process, the village development did not experience artificial planning management and control. As a result, using parameterized evolution rules, it is appropriate to simulate the natural evolution of village space. The spatial model developed in the article combines traditional and modern design elements with some predictive modeling characteristics [52]. The spatial genes of traditional settlements contain information on the scale parameters, creation patterns, and evolutionary laws of rural spaces. The excavation, extraction, analysis, and application of spatial genetic information is a practice of cognition, research, and planning design based on respecting the characteristics of traditional villages [53]. Generally speaking, the “spatial genetic parameter technique” for CityEngine simulation of traditional settlement expansion put out in this research is a new and rigorous quantitative study fusing computer technology, parameterization technology, and urban and rural planning. The simulation of the self-organization mode and organic sequence of rural settlement growth is possible through the combined application of the new technologies mentioned above. The result is a spontaneously growing rural planning design based on rural history, culture, and geography.

This study met the following goals: (1) The study of cultural and spatial characteristics, such as settlement growth, layout, and expansion, used spatial genetic information as the base data. (2) The town’s growth, expansion, and organic renewal were simulated using the CityEngine visualization platform as a three-dimensional visualization system. (3) The simulation method based on genetic parameters can realize the diversity of colony models and quickly adjust according to their needs.

The CityEngine system learns the unique expansion pattern of Hmong settlements based on the central network, simulates the growth and expansion of the settlements, and can simulate the growth and renewal process of buildings one by one. Through the dynamic simulation, it is possible to plan and design the countryside from the perspective of development, ensure the appropriate and sustainable countryside construction, and realize the preservation of the historical texture of the countryside and the organic renewal of the rural settlements [27].

The research involves a lot of mathematical statistics and computer programming. Therefore, the research cannot be exhaustive, and there are more omissions and shortcomings. The research scope and the research objects selected in this paper represent traditional Hmong villages, and in reality, there are many kinds of village spatial textures with significant differences. The research results of this paper cannot be directly applied to all types of village settlements. Many parameters influence and control the generation of traditional village spatial texture. In this study, some parameters with little influence are eliminated in order to extract the core set of parameters. However, they still influence the research results’ accuracy and need further optimization. Non-theoretical issues are yet to be studied in this paper, especially factors such as traditional village culture and local beliefs that are difficult to be simulated by a computer. The research focus in the subsequent stage is on introducing cultural, institutional, economic, and other non-spatial factors with the help of planning and design schemes.

7. Conclusions

The spatial organization logic of the current mainstream planning model of villages is too modern, with distinctive characteristics of urban spatial organization [54,55]. Spatial organization logic, on the other hand, is critical in establishing the spatial texture form of traditional settlements [56]. Therefore, under the previous spatial organization system, the generated village spatial texture is far from the traditional spatial texture, both in geometric form and functional characteristics [57]. The ultimate purpose of parametric analysis and reconstruction of village spatial morphology is to resolve and improve current village planning and construction challenges. In addition, using the town of Xiaoxi village as an example, this study covers the approaches and concepts of problem-solving utilizing parametric planning methodologies. Combined with the fact that villages are full of uncertainty and random characteristics in natural evolution [58,59], the inheritance and continuation of such random characteristics can be realized by introducing elastic control factors in the reconstruction process. The simulation method proposed in this paper creates an “orderly” and “regular” planning and design scheme based on the local space of the countryside. The introduction of the artificial intelligence design method discards the ultimate blueprint planning method. By introducing random factors, internalization of parameter values, and occupation functions, the generated roads, plots, and buildings may produce various spatial forms and shape a rich and diverse spatial texture through the organic combination of spatial gene sequences.

Specifically, we found inheritable features of the parametric planning scheme and the original spatial texture:

(1)

The Honjia Camp’s roadways are naturally dispersed over the landscape. The roads are lengthy, extensive, and unevenly distributed; the Dazhai Camp is generally straight, but the surrounding countryside is rocky, with rich strata and many “synapses.” The organic distribution along Dazhai Road is relatively straight, rich, “synapse”-less, and long and wide;

(2)

The region is irregularly distributed and has more irregular polygons; the central area of Honjia Camp has a high density, and the surrounding terraces have a low density; the terrain is typically adaptable. Dazhai Camp is more consistent in its area distribution, with more roughly rectangular shapes; the population density is low, and the boundaries between groups are vertical;

(3)

The smaller size of the Honjia Camp’s building footprint base is a result of the smaller size of the plot. Due to the spatial oppression caused by the high plot density, the depth and breadth of the buildings are minimized, and the base plans are often square. The unique terrain of the Dazhai Camp dictates that the structures be spacious in all directions, with rectangular footings that maximize natural lighting.

The planning design is intelligently generated from the underlying framework because the simulation method learns the “genetic characteristics” of traditional settlements. From the inside out, it achieves unity and harmony with the local environment and social culture. At the same time, the parametric technique may better sustain the uncertainty features in the natural growing process of village space, as seen by the comparable but not identical spatial texture before and after reconstruction. Under the rapid pace of new rural construction, the traditional planning and design methods often present poor quality and simple and rough design results due to the shortage of time and human resources, which are not conducive to preserving the characteristic space and historical heritage of rural settlements. As a result, the planning and design research findings produced by the “spatial genetic parameter reconstruction method” can sufficiently protect traditional settlements’ history, culture, and natural environment at the design level and prevent constructive damage to traditional settlements. The current new rural construction has a wide range of potential applications for preserving the historical culture and natural environment of traditional village settlements.

Generally speaking, the parametric planning and design method is more creative than the traditional manual planning and design method. It is easier to create a form that contains a rigorous logic of its spatial organization, which is the most essential and core feature of the current traditional village space.