# Spatial Pattern Evolution and Driving Mechanism of Rural Settlements in Rapidly Urbanized Areas: A Case Study of Jiangning District in Nanjing City, China

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- What were the evolution characteristics of rural settlements pattern in rapidly urbanized areas? Based on multi-temporal remote sensing images, the characteristics of settlement spatial evolution in rapidly urbanized areas were analyzed from two different dimensions of scale and space distribution, by comprehensive use of landscape pattern index, rank-scale law, and local hotspot detection model.
- What were the evolution-driving mechanisms of rural settlements pattern in rapidly urbanized areas? Natural environment, traffic conditions, and economic social factors were selected; a geographical-detector model was used to quantitatively reveal the leading factors of pattern evolution; and the driving mechanism of the settlement pattern evolution was elaborated.

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Research Methods

#### 2.2.1. Landscape Pattern Index

#### 2.2.2. Rank-Scale Rule

#### 2.2.3. Local Hotspot Detection Model

_{i}

^{*}index is [69,70]

#### 2.2.4. Geographical-Detector Model

#### 2.3. Data Collection

## 3. Results

#### 3.1. Evolution Characteristics of Rural Settlement

#### 3.1.1. Scale Evolution Characteristics of Rural Settlement

^{2}in 2010 to 70,050,296 hm

^{2}in 2020. In 10 years, the area decreased by 48.29%, nearly by half. This indicates that the settlement number has shown a sharp decline trend since 2010. In addition, the average patch area and maximum patch area decreased from 6673 hm

^{2}and 479,309 hm

^{2}in 2010 to 4231 hm

^{2}and 142,750 hm

^{2}in 2020, respectively, indicating that the rural settlement scale showed a decreasing trend.

^{2}of the model was greater than 0.8, and the overall estimation effect was good. In 2010, large-scale rural settlements were slightly lower than the fitted curve, while small and medium-sized rural settlements were mostly on the fitted curve, indicating that there was little difference in the scale of rural settlements of different grades. By 2020, the distance of large-scale settlements below the fitted curve and the small and medium-sized rural settlements above the fitted curve had increased, indicating that the settlement scale of different grades had a certain polarization trend. The Zipf index of 2010 and 2020 was 0.593 and 0.629, respectively, both less than 1, indicating that the settlement scale was generally well developed, and the scale system of rural settlements was evenly distributed.

#### 3.1.2. Spatial Distribution Evolution of Rural Settlements

^{2}.

#### 3.2. Driving Mechanism Analysis

#### 3.2.1. Selection of Influencing Factors

_{1}, slope X

_{2}, and cultivated land resource X

_{3}were selected as the natural environment indicators; distance from river X

_{4}and distance from town X

_{5}were selected as the traffic location indicators; per capita disposable income of farmers X

_{6}, per capita GDP X

_{7}, and agricultural population X

_{8}were selected as socio-economic indicators; the proportion of financial support to agriculture X

_{9}was selected as an indicator of policy and system. Empirically, the scale of rural settlements in 2010 and 2020 was taken as the explained variable, and nine influencing factors were taken from four aspects, namely, physical geography, traffic location, social economy, and policy system, as explanatory variables. The main controlling factors and driving mechanism of the rural settlement pattern evolution in Jiangning District were quantitatively revealed by geographical-detector model.

#### 3.2.2. Factor Detection Analysis

_{8}, per capita GDP X

_{7}, the proportion of financial support to agriculture X

_{9}, and distance from towns X

_{5}had a significant impact on the distribution of rural settlements in Jiangning District. The per capita disposable income of farmers X

_{6}also had important effects on the scale distribution of rural settlements in Jiangning District. However, elevation X

_{1}, slope X

_{2}, and cultivated land resource X

_{3}had no significant influence on the scale distribution of rural settlements in Jiangning District, and showed a certain weakening trend. Therefore, the main influencing factors of the distribution of rural settlements in Jiangning District from 2010 to 2020 were as follows: socio-economic factors > traffic location factors > physical geography factors.

_{8}, per capita GDP X

_{7}, the proportion of financial support to agriculture X

_{9}, and distance from towns X

_{5}were all more than 0.5, which further indicated that X

_{8}, X

_{7}, X

_{9}, and X

_{5}were the dominant factors affecting the evolution of rural settlement pattern in Jiangning District.

#### 3.2.3. Analysis of Driving Mechanism

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## References

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Index | Formula | Connotation |
---|---|---|

NP | $\mathrm{NP}={\mathrm{n}}_{i}$ | The index indicates the aggregation degree of landscape types. |

PD | $\mathrm{PD}=\mathrm{NP}/\mathrm{CA}$ | The index indicates the number of plaques per unit area. |

CA | $\mathrm{CA}={\displaystyle \sum}_{i=1}^{\mathrm{NP}}{\mathrm{S}}_{i}$ | The index indicates the sum of each plaque area. |

MPA | $\mathrm{MPA}=1/{\mathrm{n}}_{\mathrm{i}}{\displaystyle \sum}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{a}}_{\mathrm{ij}}$ | The index indicates the average of plaques and reveals the degree of plaque fragmentation. |

LAP | $\mathrm{LAP}=ma{x}_{i=1}^{NP}{S}_{i}$ | The index indicates the largest patch area in the landscape patch. |

ANN | $\mathrm{ANN}={\displaystyle \sum}_{s=1}^{n}{a}_{ijs}/{h}_{ijs}^{2}$ | The index indicates the proximity degree of spatial distribution between plaques. |

Judgment Basis | Interaction |
---|---|

$q\left({\mathrm{X}}_{1}{\displaystyle \cap}{\mathrm{X}}_{2}\right)<\mathrm{Min}$$(q\left({\mathrm{X}}_{1}\right),q\left({\mathrm{X}}_{2}\right))$ | Nonlinearity reduction |

$\mathrm{Min}$$(q\left({\mathrm{X}}_{1}\right),q\left({\mathrm{X}}_{2}\right))<q\left({\mathrm{X}}_{1}{\displaystyle \cap}{\mathrm{X}}_{2}\right)<\mathrm{Max}$$(q\left({\mathrm{X}}_{1}\right),q\left({\mathrm{X}}_{2}\right))$ | Single factor nonlinearity decreases |

$q\left({\mathrm{X}}_{1}{\displaystyle \cap}{\mathrm{X}}_{2}\right)>\mathrm{Max}$$(q\left({\mathrm{X}}_{1}\right),q\left({\mathrm{X}}_{2}\right))$ | Double factor enhancement |

$q\left({\mathrm{X}}_{1}{\displaystyle \cap}{\mathrm{X}}_{2}\right)=q\left({\mathrm{X}}_{1}\right)+q\left({\mathrm{X}}_{2}\right)$ | Independence |

$q\left({\mathrm{X}}_{1}{\displaystyle \cap}{\mathrm{X}}_{2}\right)>q\left({\mathrm{X}}_{1}\right)+q\left({\mathrm{X}}_{2}\right)$ | Nonlinear enhancement |

Year | NP | CA | MPA | LAP |
---|---|---|---|---|

2010 | 18,800 | 135,464,111 | 6673 | 479,309 |

2020 | 16,553 | 70,050,296 | 4231 | 142,750 |

Year | ANN | Z Value | p Value |
---|---|---|---|

2010 | 0.5851 | −120.7652 | 0.0001 |

2020 | 0.5042 | −123.0836 | 0.0001 |

Indicator | 2010 | 2020 | ||
---|---|---|---|---|

q Value | Rank | q Value | Rank | |

elevation X_{1} | 0.323 | 8 | 0.315 | 8 |

slope X_{2} | 0.295 | 9 | 0.287 | 9 |

cultivated land resource X_{3} | 0.358 | 7 | 0.343 | 7 |

distance from river X_{4} | 0.393 | 6 | 0.385 | 6 |

distance from town X_{5} | 0.487 | 4 | 0.492 | 4 |

per capita disposable income of farmers X_{6} | 0.456 | 5 | 0.473 | 5 |

per capita GDP X_{7} | 0.517 | 2 | 0.526 | 2 |

agricultural population X_{8} | 0.523 | 1 | 0.535 | 1 |

proportion of financial support to agriculture X_{9} | 0.506 | 3 | 0.515 | 3 |

Interaction Factor | $\mathit{q}\left(\mathrm{A}{\displaystyle \cap}\mathrm{B}\right)$ | $\mathit{q}\left(\mathrm{A}\right)$ | $\mathit{q}\left(\mathrm{B}\right)$ | Interaction Result |
---|---|---|---|---|

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{2}$ | 0.062 | 0.018 | 0.027 | NE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{3}$ | 0.083 | 0.026 | 0.031 | NE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{4}$ | 0.085 | 0.032 | 0.028 | NE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{5}$ | 0.503 | 0.038 | 0.032 | DE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{6}$ | 0.238 | 0.021 | 0.028 | NE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{7}$ | 0.516 | 0.038 | 0.037 | DE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.546 | 0.046 | 0.045 | DE |

${X}_{1}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.532 | 0.037 | 0.041 | DE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{3}$ | 0.077 | 0.019 | 0.025 | NE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{4}$ | 0.123 | 0.027 | 0.029 | NE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{5}$ | 0.508 | 0.037 | 0.024 | DE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{6}$ | 0.263 | 0.042 | 0.031 | NE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{7}$ | 0.521 | 0.046 | 0.043 | DE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.555 | 0.052 | 0.048 | DE |

${X}_{2}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.536 | 0.045 | 0.047 | DE |

${X}_{3}{\displaystyle \cap}{\mathrm{X}}_{4}$ | 0.203 | 0.031 | 0.028 | NE |

${X}_{3}{\displaystyle \cap}{\mathrm{X}}_{5}$ | 0.512 | 0.042 | 0.035 | DE |

${X}_{3}{\displaystyle \cap}{\mathrm{X}}_{6}$ | 0.256 | 0.033 | 0.037 | NE |

${X}_{3}{\displaystyle \cap}{\mathrm{X}}_{7}$ | 0.512 | 0.038 | 0.042 | DE |

${X}_{3}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.575 | 0.069 | 0.059 | DE |

${X}_{3}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.561 | 0.062 | 0.052 | DE |

${X}_{4}{\displaystyle \cap}{\mathrm{X}}_{5}$ | 0.511 | 0.053 | 0.038 | DE |

${X}_{4}{\displaystyle \cap}{\mathrm{X}}_{6}$ | 0.324 | 0.032 | 0.041 | NE |

${X}_{4}{\displaystyle \cap}{\mathrm{X}}_{7}$ | 0.521 | 0.042 | 0.044 | DE |

${X}_{4}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.588 | 0.067 | 0.066 | DE |

${X}_{4}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.563 | 0.061 | 0.059 | DE |

${X}_{5}{\displaystyle \cap}{\mathrm{X}}_{6}$ | 0.528 | 0.056 | 0.055 | DE |

${X}_{5}{\displaystyle \cap}{\mathrm{X}}_{7}$ | 0.602 | 0.057 | 0.052 | DE |

${X}_{5}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.616 | 0.068 | 0.062 | DE |

${X}_{5}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.585 | 0.055 | 0.052 | DE |

${X}_{6}{\displaystyle \cap}{\mathrm{X}}_{7}$ | 0.578 | 0.049 | 0.053 | DE |

${X}_{6}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.552 | 0.052 | 0.055 | DE |

${X}_{6}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.571 | 0.061 | 0.059 | DE |

${X}_{7}{\displaystyle \cap}{\mathrm{X}}_{8}$ | 0.598 | 0.065 | 0.064 | DE |

${X}_{7}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.585 | 0.062 | 0.059 | DE |

${X}_{8}{\displaystyle \cap}{\mathrm{X}}_{9}$ | 0.625 | 0.073 | 0.065 | DE |

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## Share and Cite

**MDPI and ACS Style**

Zhang, R.; Zhang, X. Spatial Pattern Evolution and Driving Mechanism of Rural Settlements in Rapidly Urbanized Areas: A Case Study of Jiangning District in Nanjing City, China. *Land* **2023**, *12*, 749.
https://doi.org/10.3390/land12040749

**AMA Style**

Zhang R, Zhang X. Spatial Pattern Evolution and Driving Mechanism of Rural Settlements in Rapidly Urbanized Areas: A Case Study of Jiangning District in Nanjing City, China. *Land*. 2023; 12(4):749.
https://doi.org/10.3390/land12040749

**Chicago/Turabian Style**

Zhang, Rongtian, and Xiaolin Zhang. 2023. "Spatial Pattern Evolution and Driving Mechanism of Rural Settlements in Rapidly Urbanized Areas: A Case Study of Jiangning District in Nanjing City, China" *Land* 12, no. 4: 749.
https://doi.org/10.3390/land12040749