# Quality Analysis on Spatial Planning Pattern of Rural Area in Southern Shaanxi, China

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Method

#### 2.2.1. Field Research

#### 2.2.2. AHP

#### 2.2.3. GIS

## 3. Results

#### 3.1. The Overall Review of Rural Settlements

#### 3.1.1. Spatial Structure

^{2}. The internal structure of rural settlements is a feature of the spatial layout of the various components within the settlement. This involves both the spatial form and the spatial mode.

- Spatial Form

- 2.
- Spatial Mode

#### 3.1.2. Spatial Distribution

#### 3.2. The Components and Factors of Space Structure

#### 3.2.1. The Components of Spatial Structure

**Land use scale.**The indicators of rural settlement land, such as length, area, etc., are important factors that determine the external and other correlations of the settlement space. The scale and location of the productive and residential land areas of the settlements have changed rapidly due to population levels, the development of the industry, and the relocation of immigrants to alleviate poverty.

**Population.**Population growth will lead to increased housing demand and construction, which will inevitably result in a change of village spatial structure, shape, and area.

**Adjacent distance.**The spatial distribution of rural settlements can be revealed by analyzing the distances between settlements.

**Settlement core**. In the development of villages and towns, traditional settlements along the river are originally single-core, centered around the water. Due to population growth, highway construction, and the reduced importance of rivers, some residential houses began to migrate to both sides of the highway, gradually forming double-core settlements.

**Building group.**The behavior and purpose between groups are the reason for the formation of the settlement’s spatial structure. The relationship between the building groups has gradually changed from the consanguinity building mode, based on private land, to independent dwellings.

**Courtyard unit.**The size of courtyards and their length-to-width ratio influence the spatial form and structure of settlements. The courtyard is the smallest unit of settlements, and the changes in the structure of the settlement are ultimately reflected in the changes in the organization of the courtyard.

**Street scale.**The width and density of the streets affect the aggregation degree of the settlement’s spatial structure.

**Production space.**The agglomeration brought by industrialization, the elimination of the closeness of space, and the various choices of villagers regarding the life of the settlement guide the changes of settlement morphologies into demonstrating the structural characteristics of linear networks.

**Development boundary.**The development boundary of the settlement determines the size and shape of the settlement. It can effectively direct the gathering of population, land, and industry within the settlement.

#### 3.2.2. The Factors of Spatial Structure

- Natural Factors

**Terrain conditions**. The terrain has a great influence on the land scale, adjacent distance, building group, and street space of rural settlements. The terrain in Southern Shaanxi greatly limits the agglomeration and expansion of building groups, leading to a scattered distribution and small scale of building groups.

**Water source.**Due to the farming radius and production needs, the settlement production space is generally located 1–2 km away from the water source in this region.

**Altitude and slope**. Southern Shaanxi is located in the range of elevation from 1000 to 1500 m and slopes of 5–15 degrees; the degree of agglomeration and the size of the production space show a significant negative correlation with elevation and slope.

**Climate and soil.**The climate of the valleys in southern Shaanxi is humid and mild. The climate of hilly and mountainous areas changes most obviously according to elevation.

- 2.
- Humanities and Social Factors

#### 3.2.3. The Relationship between the Components and Factors

#### 3.3. The Influence of Components and Factors

#### 3.3.1. Population (Main Internal Components)

#### 3.3.2. Terrain (Main External Factors)

#### 3.4. The Optimization of Spatial Structure

#### 3.4.1. Strengthening the Connections

#### 3.4.2. Controlling the Scale

## 4. Discussion

## 5. Conclusions

- (1)
- China’s mountainous rural areas have a fragile ecological environment, simple economic activities, and poor infrastructure and public service facilities. Furthermore, mountainous rural settlements have disorderly development and a scattered layout. The most important problem facing the spatial planning of mountainous rural areas is to coordinate ecological protection and village development.
- (2)
- The spatial structure of mountainous settlements can be summarized into three types, those of the agglomeration type, belt type, and dispersion type. The team conducted long-term visits and investigations in southern Shaanxi, and the results showed that 67.3% of the rural settlements in this area are located in the valleys; their spatial layout can be divided into the aggregation type, accounting for 13.6%, the belt type, accounting for 47.2%, and dispersion type, accounting for 39.2%. The modes of the spatial structure are represented as the river valley mode and the mountainous ladder-shaped scattered mode.
- (3)
- The factors influencing rural settlement development are ranked as geomorphological conditions > population quantity > land use in southern Shaanxi. When the value of spatial autocorrelation of the population gets closer and closer to 1, the rural settlement space can form a state of aggregation. The smaller the population, the more obvious the influence of topography on the settlement space, and vice versa, where the settlement space is clustered together.
- (4)
- The greatest resistance to the development of rural settlements and the optimization of their spatial structure in China lies in the lack of strong industrial support. Therefore, rural industrialization is the key factor in the development of rural settlements and the optimization of their spatial structure. The development of rural settlements in southern Shaanxi should strengthen the connection between the villages and control the construction scope of each settlement. The proximity index of the settlement within the scope is an important spatial measurement index, based on which the ideal radius (R) and maximum radius (R + d) of settlement construction can be inferred. Based on the analysis results of the GIS above, a radius of 284.12 m can be used as a measure of the development scope of the village. As a result, the focus of rural planning is to reconstruct the rural space and to guide the moderate concentration of rural population through the construction of central villages.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Li, J.; Liu, Y.; Yang, Y.; Jiang, N. County-rural revitalization spatial differences and model optimization in Miyun District of Beijing-Tianjin-Hebei region. J. Rural Stud.
**2021**, 86, 724–734. [Google Scholar] [CrossRef] - Zhao, D.; Xiao, M.; Huang, C.; Liang, Y.; Yang, Z. Land Use Scenario Simulation and Ecosystem Service Management for Different Regional Development Models of the Beibu Gulf Area, China. Remote Sens.
**2021**, 13, 3161. [Google Scholar] [CrossRef] - Wang, Z.; Wang, T. Subject Cognition and Regional Expression of Rural Settlements. J. Hum. Settl. West China
**2014**, 3, 45–49. [Google Scholar] - Li, G.; Jiang, G.; Jiang, C.; Bai, J. Differentiation of spatial morphology of rural settlements from an ethnic cultural perspective on the Northeast Tibetan Plateau, China. Habitat Int.
**2018**, 79, 1–9. [Google Scholar] [CrossRef] - Wang, Y.; Yuan, Q. Morphological characteristics of rural settlements from morphogenesis perspective: A case study of rural settlements in Heilongjiang Province, China. Energy Procedia
**2019**, 157, 1266–1277. [Google Scholar] [CrossRef] - Bibby, P.; Beddington, J. Land use change in Britain. Land Use Policy
**2009**, 26, S2–S13. [Google Scholar] [CrossRef] - Burnett, R.H. Some rural settlement forms in Japan. Geogr. Rev.
**1931**, 21, 93–123. [Google Scholar] - Conrad, C.; Rudloff, M.; Abdullaev, I.; Thiel, M.; Loew, F.; Lamers, J.P.A. Measuring rural settlement expansion in Uzbekistan using remote sensing to support spatial planning. Appl. Geogr.
**2015**, 62, 29–43. [Google Scholar] [CrossRef] - Feng, Y.; Tong, X. Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules. GIScience Remote Sens.
**2018**, 55, 678–698. [Google Scholar] [CrossRef] - Berdegue, J.A.; Escobal, J.; Bebbington, A. Explaining spatial diversity in Latin American Rural Development: Structures, institutions, and coalitions. World Dev.
**2015**, 73, 129–137. [Google Scholar] [CrossRef] - Cao, Y.; Bai, Z.; Sun, Q.; Zhou, W. Rural settlement changes in compound land use areas: Characteristics and reasons of changes in a mixed mining-rural-settlement area in Shanxi Province, China. Habitat Int.
**2017**, 61, 9–21. [Google Scholar] [CrossRef] - Li, H.; Yuan, Y.; Zhang, X.; Li, Z.; Wang, Y.; Hu, X. Evolution and transformation mechanism of the spatial structure of rural settlements from the perspective of long-term economic and social change: A case study of the Sunan region, China. J. Rural Stud.
**2021**, 3, 5. [Google Scholar] [CrossRef] - Qu, Y.; Jiang, G.-h.; Li, Z.; Tian, Y.; Wei, S. Understanding rural land use transition and regional consolidation implications in China. Land Use Policy
**2019**, 82, 742–753. [Google Scholar] [CrossRef] - Li, G.; Hu, W. A network-based approach for landscape integration of traditional settlements: A case study in the Wuling Mountain area, southwestern China. Land Use Policy
**2019**, 83, 105–112. [Google Scholar] [CrossRef] - Li, G.; Jiang, C.; Du, J.; Jia, Y.; Bai, J. Spatial differentiation characteristics of internal ecological land structure in rural settlements and its response to natural and socio-economic conditions in the Central Plains, China. Sci. Total Environ.
**2020**, 709, 135932. [Google Scholar] [CrossRef] - Lu, M.; Wei, L.; Ge, D. Spatial optimization of rural settlements based on the perspective of appropriateness–domination: A case of Xinyi City. Habitat Int.
**2020**, 98, 102148. [Google Scholar] [CrossRef] - Yang, R.; Xu, Q.; Long, H. Spatial distribution characteristics and optimized reconstruction analysis of China’s rural set-tlements during the process of rapid urbanization. J. Rural. Stud.
**2016**, 47, 413–424. [Google Scholar] [CrossRef] - Jia, K.; Qiao, W.; Chai, Y.; Feng, T.; Wang, Y.; Ge, D. Spatial distribution characteristics of rural settlements under diversified rural production functions: A case of Taizhou, China. Habitat Int.
**2020**, 102, 102201. [Google Scholar] [CrossRef] - Mu, D.; Luo, P.; Lyu, J.; Zhou, M.; Huo, A.; Duan, W.; Nover, D.; He, B.; Zhao, X. Impact of temporal rainfall patterns on flash floods in Hue City, Vietnam. J. Flood Risk Manag.
**2020**, 14, 12668. [Google Scholar] [CrossRef] - Qu, L.; Li, Y.; Feng, W. Spatial-temporal differentiation of ecologically-sustainable land across selected settlements in China: An urban-rural perspective. Ecol. Indic.
**2020**, 112, 105783. [Google Scholar] [CrossRef] - Luo, P.; Xu, C.; Kang, S.; Huo, A.; Lyu, J.; Zhou, M.; Nover, D. Heavy metals in water and surface sediments of the Fenghe River Basin, China: Assessment and source analysis. Water Sci. Technol.
**2021**, 335. [Google Scholar] [CrossRef] - Xue, B.; Liu, B.; Yang, Q.; Sun, X.; Wang, W.; Li, L. Formalizing an evaluation-prediction based roadmap towards urban sustainability: A case study of Chenzhou, China. Habitat Int.
**2021**, 112, 102376. [Google Scholar] [CrossRef] - Yang, Y.; Bao, W.; Wang, Y.; Liu, Y. Measurement of urban-rural integration level and its spatial differentiation in China in the new century. Habitat Int.
**2021**, 117, 102420. [Google Scholar] [CrossRef] - Li, H.; Song, W. Pattern of spatial evolution of rural settlements in the Jizhou District of China during 1962–2030. Appl. Geogr.
**2020**, 122, 102247. [Google Scholar] [CrossRef] - Wei, X.; Wang, N.; Luo, P.; Yang, J.; Zhang, J.; Lin, K. Spatiotemporal Assessment of Land Marketization and Its Driving Forces for Sustainable Urban–Rural Development in Shaanxi Province in China. Sustainability
**2021**, 13, 7755. [Google Scholar] [CrossRef] - Duan, W.; Maskey, S.; Chaffe, P.L.; Luo, P.; He, B.; Wu, Y.; Hou, J. Recent Advancement in Remote Sensing Technology for Hydrology Analysis and Water Resources Management. Remote Sens.
**2021**, 13, 1097. [Google Scholar] [CrossRef] - Liu, Y.; Eckert, C.M.; Earl, C. A review of fuzzy AHP methods for decision-making with subjective judgements. Expert Syst. Appl.
**2020**, 161, 113738. [Google Scholar] [CrossRef] - Abastante, F.; Corrente, S.; Greco, S.; Ishizaka, A.; Lami, I.M. A new parsimonious AHP methodology: Assigning priorities to many objects by comparing pairwise few reference objects. Expert Syst. Appl.
**2019**, 127, 109–120. [Google Scholar] [CrossRef] [Green Version] - Zhang, Y.; Luo, P.; Zhao, S.; Kang, S.; Wang, P.; Zhou, M.; Lyu, J. Control and Remediation Methods for Eutrophic Lakes in Recent 30 years. Water Sci. Technol.
**2020**, 81, 1099–1113. [Google Scholar] [CrossRef] - Drumm, S.; Bradley, C.; Moriarty, F. ‘More of an art than a science’? The development, design and mechanics of the Delphi Technique. Res. Soc. Adm. Pharm.
**2021**, 27, 135–141. [Google Scholar] [CrossRef] - Gong, G.; Mattevada, S.; O’Bryant, S.E. Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environ. Res.
**2014**, 130, 59–69. [Google Scholar] [CrossRef] [PubMed] - Li, Y.; Liu, C.; Hong, Z.; Xin, G. Evaluation on the human settlements environment suitability in the Three Gorges Reservoir Area of Chongqing based on RS and GIS. J. Geogr. Sci.
**2011**, 21, 346. [Google Scholar] [CrossRef] - Chen, N. Rural community unbound: Trans-locality, rural-to-rural connections and the formation of inter-regional surname associations in China. J. Rural Stud.
**2020**, 78, 378–386. [Google Scholar] [CrossRef] - Yanbo, Q.; Guanghui, J.; Wenqiu, M.; Zitong, L. How does the rural settlement transition contribute to shaping sustainable rural development? Evidence from Shandong, China. J. Rural Stud.
**2021**, 82, 279–293. [Google Scholar] [CrossRef] - Nikuze, A.; Sliuzas, R.; Flacke, J.; van Maarseveen, M. Livelihood impacts of displacement and resettlement on informal households—A case study from Kigali, Rwanda. Habitat Int.
**2019**, 86, 38–47. [Google Scholar] [CrossRef] - Zhang, W.; Yu, C.; Dong, Z.; Zhuo, H. Ripple effect of the housing purchase restriction policy and the role of investors attention. Habitat Int.
**2021**, 114, 102398. [Google Scholar] [CrossRef] - Zha, X.; Luo, P.; Zhu, W.; Wang, S.; Lyu, J.; Zhou, M.; Huo, A.; Wang, Z. A Bibliometric Analysis of the Research on Sponge City: Current Situation and Future Development Direction. Ecohydrology
**2021**, 23, 2328. [Google Scholar] [CrossRef] - Xie, D.; Duan, L.; Si, G.; Liu, W.; Zhang, T.; Mulder, J. Long-term 15N balance after single-dose input of 15Nlabeled NH4
^{+}and NO3^{−}in a subtropical forest under reducing N deposition. Glob. Biogeochem. Cycles**2021**, 35, e2021GB006959. [Google Scholar] [CrossRef] - Zhu, Y.; Luo, P.; Su, F.; Zhang, S.; Sun, B. Spatiotemporal Analysis of Hydrological Variations and Their Impacts on Vegetation in Semiarid Areas from Multiple Satellite Data. Remote Sens.
**2020**, 12, 4177. [Google Scholar] [CrossRef] - Duan, W.; Zou, S.; Chen, Y.; Nover, D.; Fang, G.; Wang, Y. Sustainable water management for cross-border resources: The Balkhash Lake Basin of Central Asia, 1931–2015. J. Clean. Prod.
**2020**, 263, 121614. [Google Scholar] [CrossRef] - Huang, W.; Duan, W.; Chen, Y. Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes. Sci. Total Environ.
**2021**, 784, 147193. [Google Scholar] [CrossRef]

**Figure 4.**Agglomeration-type settlement spatial structures. (

**a**) Photographed example of a settlement form. (

**b**) Schematic representation of the settlement form.

**Figure 5.**Belt-type settlement spatial structures. (

**a**) Photographic example of belt-type settlement form. (

**b**) Schematic representation of the belt-type settlement form.

**Figure 6.**Dispersion-type settlement—spatial form. (

**a**) Photographic example of a dispersion-type settlement form. (

**b**) Schematic representation of the dispersed settlement form.

Intensity of Importance | Definition | Content |
---|---|---|

1 | Equally important | Both elements are of equal importance. |

3 | Moderately more important | One element is slightly more important than the other. |

5 | Strongly more important | Judging by experience, there is a strong preference for a certain element. |

7 | Very strongly more important | The responder is very inclined toward a certain element. |

9 | Extremely more important | There is evidence that an element is very important when comparing two elements. One element is significantly stronger than the other element can control. |

2, 4, 6, 8 | Intermediate values | Used for compromises between the above criteria. |

A_{1} | A_{2} | … | A_{i} | … | A_{j} | … | A_{n} | |

A_{1} | 1 | a_{12} | … | a_{1i} | … | a_{1i} | … | a_{1n} |

A_{2} | a_{21} | 1 | … | a_{2i} | … | a_{2j} | … | a_{2n} |

… | … | … | … | … | … | … | ||

A_{i} | a_{i}_{1} | a_{i}_{2} | … | 1 | … | a_{ij} | … | a_{in} |

… | … | … | … | … | … | … | ||

A_{j} | a_{j}_{1} | a_{j}_{2} | … | a_{ji} | … | 1 | … | a_{jn} |

… | … | … | … | … | … | … | ||

A_{n} | a_{n}_{1} | a_{1}_{2} | … | a_{ni} | … | a_{nj} | … | 1 |

n | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R.I. | 0.58 | 0.89 | 1.12 | 1.24 | 1.32 | 1.41 | 1.46 | 1.49 | 1.52 | 1.54 | 1.56 | 1.58 | 1.59 |

Calculate Indicators | Spatial Autocorrelation | Spatial Interpolation | Spatial Metric | |||
---|---|---|---|---|---|---|

By considering the location of points and the attributes of their changes, spatial autocorrelation measures the correlation between the values of various variables according to the spatial distribution status. Moran’s Index is used to measure the spatial correlations of each residential area. | Spatial interpolation is often used to convert measured data at discrete points into continuous data surfaces, for comparison with the distribution patterns of spatial phenomena, to ensure the accuracy of the imaging of geographic data. The inverse distance weight (IDW) value is used in the calculation process. | As a crucial parameter of space, distance is an important research variable for optimizing the regional layout. The position of the centroid point is set as being in the population distribution center rather than the geometric center of the space. | ||||

Equation | $I=\frac{{{\displaystyle \sum}}_{i=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{W}_{ij}\left({x}_{i}-\overline{x}\right)\left({x}_{j}-\overline{x}\right)}{{S}^{2}{{\displaystyle \sum}}_{i=1}^{n}{{\displaystyle \sum}}_{j=1}^{m}{W}_{ij}}$ | (3) | $\widehat{Z}\left({x}_{0}\right)={\displaystyle \sum _{i=1}^{n}}{\lambda}_{i}\cdot z\left({x}_{i}\right)$ $\sum _{i=1}^{n}}{\lambda}_{i}=1$ | (5) | ${X}_{G}=\frac{{{\displaystyle \sum}}_{i}{W}_{i}{X}_{i}}{{{\displaystyle \sum}}_{i}{W}_{i}}$ ${Y}_{G}=\frac{{{\displaystyle \sum}}_{i}{W}_{i}{Y}_{i}}{{{\displaystyle \sum}}_{i}{W}_{i}}$ | (8) |

$E\left(I\right)=\frac{-1}{n-1}$ | (4) | $\widehat{Z}\left({x}_{j}\right)={\displaystyle \sum _{i=1}^{n}}{\lambda}_{i}\cdot z\left({x}_{i}\right)\cdot \frac{{d}_{ij}^{-r}}{{{\displaystyle \sum}}_{i=1}^{n}{d}_{ij}^{-r}}$ | (6) | $d=\sqrt{{\left({X}_{i}-{X}_{j}\right)}^{2}+{\left({Y}_{i}-{Y}_{j}\right)}^{2}}$ | (9) | |

$\widehat{Z}\left({x}_{0}\right)=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}}z\left({x}_{i}\right)$ | (7) | |||||

Explanation of the formula | In Equation (3), C is the value at point i, ${x}_{j}$ is the value of point j adjacent to point i. ${W}_{ij}$ is the coefficient, n is the number of points, and S^{2} is the variance of the value of x and its average value $\overline{x}$. The coefficient ${W}_{ij}$ is the weight used to measure the spatial autocorrelation and is defined as the reciprocal $\left(\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{${d}_{ij}$}\right.\right)$ of the distance $\left(d\right)$ between point i and point j in this study.In Equation (4), when the number of points is large, the value of E(I) is close to 0. | In Equation (5), the weight coefficient is calculated by the function $\varnothing \left(d\left(x,{x}_{i}\right)\right)$, and it is required that when $d\to 0$ is $\varnothing \left(d\right)$, we generally take the reciprocal or negative exponents ${d}^{-r}$, ${e}^{-d}$ and ${e}^{-{d}^{2}}$. In Equations (6) and (7), ${x}_{j}$ is an unknown elevation point, and ${x}_{i}$ is a known elevation data point. The calculation result often shows a malformed distribution pattern, in which the outlier data is significantly higher than the surrounding data points. | In Equation (8), ${W}_{i}$ is the weight of the
i-th discrete target, and X_{i}, Y_{i} is the coordinate of the i-th discrete target.Equation (9) is the Euclidean distance by which we can calculate the distance between adjacent villages. |

Spatial Structure Type | Industry Type | Classification Basis | ||
---|---|---|---|---|

Dominant Reason | Characteristics of Distribution | Morphological Characteristics | ||

Agglomeration type | Industrial cluster | Industry factor | Found along the edge of the tourist area, in the industrial area | Group |

Belt type | Traffic service | Traffic factor | Found with the road traffic | Linear |

Dispersion type | Traditional agriculture | Natural factor | Widely distributed in hilly mountains | Point shape |

Mode | Distribution Area | Spatial Layout Characteristics |
---|---|---|

River valley mode | River valley | The residential buildings are arranged in a checkerboard mode with streets and lanes acting as the skeleton. The structure density is high and the settlement boundary is obvious. The main roadway of the settlement is not only the main passage of the internal traffic of the settlement but also plays a controlling role in the formation and development of the settlement’s spatial mode. |

The mountainous ladder-shaped scattered settlement mode | Hilly mountains | The residential buildings are distributed in narrow platforms at different heights, presenting a ladder-shaped scattered mode. The number of dwellings in each platform is determined by the width of the area. The looseness of the spatial structure of the settlement is affected by topography and socioeconomic characteristics. |

**Table 7.**Relationship between the constituent components of structural characteristics and the influencing factors.

Component Influencing Factor | Land Use Scale | Population | Adjacent Distance | Settlement Core | Building Group | Courtyard Unit | Street Scale | Production Space | Development Boundary | Degree of Influence |
---|---|---|---|---|---|---|---|---|---|---|

Terrain conditions | 4.6 | 3.8 | 3.7 | 3.3 | 3.7 | 3.3 | 3.6 | 3.2 | 4.2 | 33.4 |

Water source factor | 3.5 | 3.7 | 2.6 | 3.2 | 2.6 | 2.1 | 2.0 | 3.2 | 2.8 | 25.7 |

Altitude and slope | 3.2 | 3.1 | 3.3 | 3.1 | 2.7 | 2.4 | 3.0 | 3.1 | 3.1 | 27.0 |

Climate and soil | 3.1 | 3.3 | 2.5 | 2.7 | 2.6 | 2.3 | 2.4 | 2.7 | 2.5 | 24.1 |

Economic factor | 3.8 | 4.1 | 3.1 | 3.6 | 2.7 | 2.9 | 2.3 | 3.6 | 3.2 | 29.3 |

Cultural factor | 2.2 | 2.6 | 2.5 | 3.5 | 3.2 | 3.0 | 2.9 | 2.2 | 2.2 | 24.3 |

Traffic factor | 3.9 | 3.9 | 3.9 | 3.8 | 3.3 | 2.6 | 3.0 | 3.4 | 3.6 | 31.4 |

Policy factor | 3.3 | 3.2 | 2.7 | 2.8 | 2.9 | 2.0 | 2.6 | 2.7 | 3.3 | 25.5 |

Degree of influence of each component | 27.6 | 27.7 | 24.3 | 26.0 | 23.7 | 20.6 | 21.8 | 24.1 | 24.9 |

Classification | Restriction Requirement |
---|---|

Primary school | Service radius: 420–1000 m Maximum travel distance: 2 km Population threshold: 1700 |

kindergarten | Service radius: less than 800 m Population threshold: 1500 |

Tillage radius | Service radius: 1.5–2.5 km Agricultural machinery transportation radius: 7 km |

Medical Facility | Service radius: less than 800 m Population threshold: 1600 |

Commercial facility | Service radius: 400–800 m Population threshold: 1300 |

Group | Distance between Adjacent Villages | Difference | Mean Radius | Ideal Value |
---|---|---|---|---|

1 | D_{01} = r_{0} + r_{1} = 906.19 m | d_{1} = 254.43 m | R_{1} = 389.67 m | R → 284.38 m |

D_{12} = r_{1} + r_{2} = 652.47 m | ||||

2 | D_{34} = r_{3} + r_{4} = 446.32 m | d_{2} = 110.31 md _{3} = 62.54 m | R_{2} = 196.86 m | |

D_{45} = r_{4} + r_{5} = 336.01 m | ||||

D_{56} = r_{5} + r_{6} = 398.64 m | ||||

3 | D_{78} = r_{7} + r_{8} = 741.30 m | d_{4} = 194.66 m | R_{3} = 321.99 m | |

D_{811} = r_{8} + r_{11} = 546.64 m | ||||

4 | D_{910} = r_{9} + r_{10} = 457.98 m | R_{4} = 228.99 m |

_{ab}represents the linear distance between the population centers of any two villages; r

_{a}represents the radiation radius of the construction scope of village a; d represents the numerical difference between adjacent indices.

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**MDPI and ACS Style**

Xu, J.; Yang, M.; Lu, Z.; Liu, D.; Wu, Y.
Quality Analysis on Spatial Planning Pattern of Rural Area in Southern Shaanxi, China. *Sustainability* **2021**, *13*, 12668.
https://doi.org/10.3390/su132212668

**AMA Style**

Xu J, Yang M, Lu Z, Liu D, Wu Y.
Quality Analysis on Spatial Planning Pattern of Rural Area in Southern Shaanxi, China. *Sustainability*. 2021; 13(22):12668.
https://doi.org/10.3390/su132212668

**Chicago/Turabian Style**

Xu, Juan, Mengsheng Yang, Ziliang Lu, Dan Liu, and Yan Wu.
2021. "Quality Analysis on Spatial Planning Pattern of Rural Area in Southern Shaanxi, China" *Sustainability* 13, no. 22: 12668.
https://doi.org/10.3390/su132212668