1. Introduction
Since 1978, rural China has experienced rapid urbanization and industrialization, accompanied with rural–urban migration [
1,
2,
3]. Owing to this tremendous transition, in China a unique rural settlement morphology has emerged; that is, hollowed villages [
4]. The “hollowed villages” is a phenomenon of depopulation leading to abandonment of buildings and land in rural communities, due to the dual-track structure of rural–urban development (i.e., urban land is state owned, whereas farmland is collectively owned) and the restriction of hukou (i.e., household registration system) [
4,
5]. Such phenomenon has caused several negative consequences, such as the weakening function of critical rural organizations, the fragility of structures and networks, the chaotic flow of rural development elements, and the lack of economies of scale and output efficiency [
5,
6]. Hence, the current layout of China’s rural settlements, which can be described as “scattered, massy, small, and hollowed,” demands emergency measures. For example, the consolidation of the hollowed villages primarily aims to promote the spatial-territorial reorganization, which is accompanied with administrative reorganization in rural restructuring [
1,
4]. This type of rural restructuring encourages concentrating the rural population in communities or central settlements and merging settlements [
1,
4]. Nevertheless, the majority of regional studies are concerned with urban studies or theoretical research on rural settlements (e.g., policy analysis, strategic decision and planning) [
1,
3,
5,
6,
7]. Existing studies on rural settlement restructuring remain insufficient in terms of two aspects: consideration of inter-settlement connections and absence of dynamic and practical decision-making techniques. To address these research gaps, we propose a dynamic spatial-territorial reorganization model (SRM) of rural settlements based on graph theory and genetic optimization.
The traditional village system consists of relatively independent villages or rural settlements [
7]. Recently, inter-settlement interactive scope and content are expanding with population as well as social, economic, and traffic flows [
7,
8,
9]. The spatial-territorial reorganization or restructuring of rural settlements (involving settlement removal and incorporation) should consider inter-settlement connections [
10]. Moreover, this consideration should entail two aspects. First, consolidated settlements should exert the least effect on the entire village system to maximally maintain the functionality of system and the stability of the villagers’ life when they are removed. Second, consolidated settlements should be relocated to adjacent high-related central settlements to reduce the separation between villagers after consolidation. In response to these requirements, the robustness of network and local connectivity are introduced in this paper. Similar to other complex systems, inter-settlement connection system can be modeled as a network, in which settlements are nodes and interactions and activities among settlements are edges [
9,
11,
12]. The robustness of inter-settlement network refers to its ability to maintain the functionality under attacks or failures [
11]. Accordingly, settlement importance can be evaluated based on how much the removal of the node “disrupts” the graph structure [
11,
13,
14]. Specifically, if removing a settlement exerts no noticeable effect on the network structure, the settlement is a good option to serve as a settlement to be absorbed or relocated. Local connectivity represents the frequency of all types of connections and activities (working, visiting, shopping, and entertainment) from consolidated settlements to central settlements. Local connectivity acts as an important objective in the SRM to search for high-related relocated settlements in the following sections. This paper provides a system perspective to realize the reorganization of rural settlements rather than a simple individual analysis [
6,
10].
Existing studies on rural settlement restructuring mainly concern theoretical research, policy analysis, and macro and static planning [
1,
3,
5,
6,
7]. Practical and dynamic decision-making techniques are in demand to scientifically realize reorganization and optimization. Several decision-making techniques have been proposed for land-use planning [
15,
16,
17]. In particular, most of them make use of linear programming when a single clear objective or even multi-objective problems can be identified [
17]. Although the linear programming models can quickly lead to optimal solutions [
16,
17], they cannot cope with large combinatorial optimization problems within reasonable time [
15,
18,
19], incommensurable and/or conflicting objectives [
19], and spatial optimization [
16,
20]. To overcome these concerns, various heuristic algorithms have been developed, such as simulated annealing algorithm [
15,
21], particle swarm algorithm [
22], and genetic algorithm (GA). GA, as introduced by Holland [
23] and described in detail by Goldberg [
24], optimizes by mimicking the genetic procedures of natural selection and reproduction observed in populations for adaptation and survival. As one of the most robust heuristics [
25], GA has been applied to provide optimization solutions for different spatial optimization problems, such as land-use planning [
16,
18,
25,
26], optimal location search [
15], forestry management [
19], urban planning [
27], and water allocation planning [
28], and confirmed effective. To our knowledge, such an approach has rarely been used in the research on the reorganization and optimization of rural settlements. This paper therefore presents a GA to dynamically realize the reorganization of rural settlements.
Combining the above two points, this paper provides a spatial-territorial modeling technology of rural settlements based on graph theory and GA. The SRM is expected to solve the existing land use problems of rural settlements (e.g., scattered, massy, small, and hollowed). This approach may serve as a valuable reference for planners in devising plans and making decisions.
Section 2 describes the spatial-territorial reorganization in detail.
Section 3 provides the details of the SRM.
Section 4 introduces the study area and relevant data.
Section 5 describes and analyzes the results, and the final part gives conclusions.
6. Conclusions and Future Work
This paper proposes a SRM of rural settlements to overcome the existing rural problems (e.g., scattered, massy, small, and hollowed). The proposed model is constructed based on graph theory and GA, and it involves two parts. In Part 1, we generate a series of experiments to investigate the network performance under numerous successive removals of settlement. Through this approach, we expect to find satisfied consolidated settlements with the least effect on the entire village system. In Part 2, GA model is repeatedly executed to optimize the objectives of suitability (S), compactness (C), and local connectivity (L) under the control of the constraints. The primary goal of this part is to scientifically resettle consolidated settlements into cities, nearby townships, or central settlements.
To verify the validity of the SRM, the proposed method has been applied in Chengui Town, Hubei Province. Two major findings are summarized as follows. First, removing settlements in order of node degree is the least efficient way to destroy the entire village system. Second, the proposed model can produce satisfactory solutions for spatial reorganization of rural settlements. The SRM helps to improve local connectivity and spatial pattern of rural settlements at the expense of a small reduction in suitability and compactness. The model also shows great potential in recognizing administrative boundary and high-quality public services.
The case study of Chengui is only a straightforward application of the SRM. It does not imply that the actual planning can be replaced. The optimization solution is merely a planning scenario based on different preferences. Moreover, the spatial-territorial reorganization of rural settlements is a complex systematic problem rather than a simple technological process. The reorganization may involve many aspects, such as the protection of basic farmland, industrial reorganization (off-farm employment), administrative reorganization, and land legal and managerial system [
4]. The SRM may also be extended by including more objectives and/or constraints. Future research may analyze these perspectives in detail.