# Geodetector-Based Livability Analysis of Potential Resettlement Locations for Villages in Coal Mining Areas on the Loess Plateau of China

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## Abstract

**:**

## 1. Introduction

^{2}, endangering the safety of mining construction facilities and the daily life of the residents, and leading to the deterioration of the regional ecological environment [1,2,3,4,5]. At present, when mining coal under a township region with a high settlement density in China, coal mining techniques such as infill mining and strip mining are often used to alleviate surface deformation and ensure building safety. Using the above coal mining techniques has the drawbacks of low operating efficiency, high production costs, and low coal mining efficiency. Furthermore, traditional villages on the Loess Plateau are scattered and the main buildings are made of brick and tile with a low deformation resistance. Therefore, the most common solution to prevent the negative consequences of coal mining in village areas is village resettlement [6]. That is, the villages are concentratedly resettled to a more livable area outside the mining area before mining commences.

## 2. Study Area and Data

^{2}. It covers the area between 107°47′–108°0′ E and 35°0′–35°5′ N. Regarding the local policy in China, the village resettlement locations cannot cross the scope of county-level administrative regions. Hence, 37 traditional villages in a 10 km radius around the Dafosi coal mine subsidence area on the Loess Plateau were selected, as shown in Figure 1.

^{2}. According to the basic principle of mining subsidence, the surface collapse area caused by underground face mining is located directly above the mining area, but its scope is significantly larger than the area of the mining location [37]. To successfully resettle all the villages from the collapse area to the newly selected area in advance, the authors of this paper were commissioned by the Dafosi Mining Company to identify areas suitable for resettlement. The company participated in a research project on the livability of the village resettlement location selection area in this mining area. The village number in the study area and the range of the coal mine collapse region where the villages resettlement needs to be implemented are shown in Figure 2 below. All villages overlapping with the red area have to be resettled within the black areas outside of the red area.

^{2}.

## 3. Methods

#### 3.1. Data Sources and Processing

#### 3.2. Roadmap

#### 3.3. Entropy Weight Method

#### 3.4. Geodetector

## 4. Results

#### 4.1. Evaluation of the Livability of Each Village and Its Results

#### 4.2. Results of Quantitative Attribution Detection of Village Livability

#### 4.3. Resettlement Location Selection Result

^{2}), and the results are shown in Figure 7.

^{2}was used as the searching window to search for the optimum resettlement location according to the following principles:

- located outside the boundary of the Dafosi coal mine;
- located in the high livability zone (Zone I in Figure 6) and no less than 500 m from the low livability zone;
- located in the area where the kernel density value of houses is less than 100 (blue area in Figure 7), preferably an open space;
- the distance from the original village location in the collapse area does not exceed the working radius of the villagers to go to the fields for farming—since the villagers farming land in the resettlement location is still at the original location, the maximum labor radius for the villagers to go to the field for daily farming is set at 10 km.

#### 4.4. Validation

## 5. Discussion

#### 5.1. Analysis of Human Settlement Factor for the Livability of Villages in Coal Mining Area

#### 5.2. Geographic Factor Analysis of the Potential Resettlement Location Selection Area

#### 5.3. Analysis of the Optimum Resettlement Location Based on the Livability Grading Map

## 6. Conclusions

- A comprehensive evaluation model for livability based on the weighted combination of a series of human settlement factors. Based on the analysis of the human settlement environment characteristics of the Loess Plateau and the current situation of China’s rural development, the complex and macro concept of “livability” is decomposed into human settlement factors, and livability scores of the existing villages, and the entropy weight method was used to obtain these livability scores.
- A Geodetector-based livability driving force and quantitative attribution model. The connotation is to treat the livability of existing villages as a geographic phenomenon, using the Geodetector for attribution analysis of the geographic factors which affect livability in order to obtain the correlation coefficients between livability and geographic factor, and to establish a quantitative relationship between livability and objective as well as deterministic geographic factors.
- Optimum resettlement location selection evaluation model based on the overlay of geographic factors. The weighted overlay of the main geographic factor generates the livability grading map. The livability grading map is also validated with the existing settlement, and high accuracy is obtained. The kernel density map of existing village houses is overlaid and analyzed, and the optimum resettlement location for village relocation and resettlement is obtained by setting reasonable location selection constraints.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Distribution of villages and coal mining subsidence areas around the Dafosi mine, numbers in the figure represent the village numbers.

**Figure 5.**Distribution map of each geographic factor. (

**a**) ${Z}_{1}$: terrain slope, (

**b**) ${Z}_{2}$: slope orientation, (

**c**) ${Z}_{3}$: surface water system, (

**d**) ${Z}_{4}$: groundwater level, (

**e**) ${Z}_{5}$: transportation accessibility, (

**f**) ${Z}_{6}$: education resource accessibility, (

**g**) ${Z}_{7}$: medical resource accessibility, (

**h**) ${Z}_{8}$: procurement radius, (

**i**) ${Z}_{9}$: tourism resource accessibility.

**Figure 6.**Livability grading in the study area (the points represent the settlements, the red line includes the coal mining subsidence area).

**Figure 7.**Kernel density distribution map of houses in each village, the red line includes the coal mining subsidence area.

**Figure 8.**GIS-based search for optimum resettlement location selection. (

**a**) Resettlement site, (

**b**) satellite map.

Criteria Layer | Factor Layer |
---|---|

Dwelling environment (${X}_{1}$, ${X}_{2}$) | Living area per capita (${X}_{1}$) |

Population density (${X}_{2}$) | |

Ecological health (${X}_{3}$, ${X}_{4}$) | Vegetation cover per capita (${X}_{3}$) |

Livestock and poultry breeding area per capita (${X}_{4}$) | |

Infrastructure (${X}_{5}$, ${X}_{6}$) | Road area per capita (${X}_{5}$) |

Village road hardening rate (${X}_{6}$) | |

Public facilities (${X}_{7}$, ${X}_{8}$) | Number of nursing home beds per capita (${X}_{7}$) |

Number of village health room beds per capita (${X}_{8}$) | |

Economic development (${X}_{9}$, ${X}_{10}$) | Arable land per capita (${X}_{9}$) |

The proportion of villagers engaged in the coal industry (${X}_{10}$) |

Factor (Z) | Explanation and Computational Method |
---|---|

${Z}_{1}$: terrain slope (${}^{\circ}$) | Extraction slope value data from DEM |

${Z}_{2}$: slope orientation (${}^{\circ}$) | Extraction slope orientation data from DEM |

${Z}_{3}$: surface water system (m) | Shortest distance to a river |

${Z}_{4}$: groundwater level (m) | Distance from groundwater level |

${Z}_{5}$: transportation accessibility (m) | Shortest distance to a road above the county road level |

${Z}_{6}$: education resource accessibility (m) | Shortest distance to a school |

${Z}_{7}$: medical resource accessibility (m) | Shortest distance to hospitals and clinics |

${Z}_{8}$: procurement radius (m) | Shortest distance to malls and markets |

${Z}_{9}$: tourism resource accessibility (m) | Shortest distance to tourist attractions |

Criterion | Interaction |
---|---|

$q\left({X}_{1}\cap {X}_{2}\right)<min\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)$ | Non-linear attenuation |

$min\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)<q\left({X}_{1}\cap {X}_{2}\right)<max\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)$ | Single factor non-linear attenuation |

$q\left({X}_{1}\cap {X}_{2}\right)>max\left(q\left({X}_{1}\right),q\left({X}_{2}\right)\right)$ | Two-factor enhancement |

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

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

Factor | ${\mathit{X}}_{1}$ | ${\mathit{X}}_{2}$ | ${\mathit{X}}_{3}$ | ${\mathit{X}}_{4}$ | ${\mathit{X}}_{5}$ | ${\mathit{X}}_{6}$ | ${\mathit{X}}_{7}$ | ${\mathit{X}}_{8}$ | ${\mathit{X}}_{9}$ | ${\mathit{X}}_{10}$ |
---|---|---|---|---|---|---|---|---|---|---|

Information entropy ${E}_{j}$ | 0.930 | 0.872 | 0.896 | 0.936 | 0.940 | 0.893 | 0.879 | 0.962 | 0.960 | 0.933 |

Factor | ${\mathit{W}}_{1}$ | ${\mathit{W}}_{2}$ | ${\mathit{W}}_{3}$ | ${\mathit{W}}_{4}$ | ${\mathit{W}}_{5}$ | ${\mathit{W}}_{6}$ | ${\mathit{W}}_{7}$ | ${\mathit{W}}_{8}$ | ${\mathit{W}}_{9}$ | ${\mathit{W}}_{10}$ |
---|---|---|---|---|---|---|---|---|---|---|

Weight | 0.088 | 0.160 | 0.131 | 0.080 | 0.075 | 0.133 | 0.151 | 0.047 | 0.050 | 0.084 |

Village Number | Livability | Village Number | Livability | Village Number | Livability |
---|---|---|---|---|---|

1 | 84.96 | 14 | 86.67 | 27 | 88.71 |

2 | 84.87 | 15 | 86.17 | 28 | 89.34 |

3 | 85.54 | 16 | 85.84 | 29 | 87.58 |

4 | 87.60 | 17 | 85.58 | 30 | 88.51 |

5 | 85.96 | 18 | 85.46 | 31 | 86.75 |

6 | 84.72 | 19 | 84.59 | 32 | 87.85 |

7 | 82.28 | 20 | 87.54 | 33 | 86.47 |

8 | 86.80 | 21 | 86.32 | 34 | 85.39 |

9 | 85.08 | 22 | 85.51 | 35 | 84.97 |

10 | 87.61 | 23 | 86.79 | 36 | 85.48 |

11 | 87.71 | 24 | 85.12 | 37 | 84.18 |

12 | 86.61 | 25 | 87.42 | ||

13 | 85.96 | 26 | 88.58 |

Geographic Factor | ${\mathit{Z}}_{1}$ | ${\mathit{Z}}_{2}$ | ${\mathit{Z}}_{3}$ | ${\mathit{Z}}_{4}$ | ${\mathit{Z}}_{5}$ | ${\mathit{Z}}_{6}$ | ${\mathit{Z}}_{7}$ | ${\mathit{Z}}_{8}$ | ${\mathit{Z}}_{9}$ |
---|---|---|---|---|---|---|---|---|---|

Correlation coefficient q | 0.058 | 0.017 | 0.032 | 0.045 | 0.074 | 0.039 | 0.109 | 0.080 | 0.129 |

Geographic Factor | ${\mathit{Z}}_{1}$ | ${\mathit{Z}}_{2}$ | ${\mathit{Z}}_{3}$ | ${\mathit{Z}}_{4}$ | ${\mathit{Z}}_{5}$ | ${\mathit{Z}}_{6}$ | ${\mathit{Z}}_{7}$ | ${\mathit{Z}}_{8}$ | ${\mathit{Z}}_{9}$ |
---|---|---|---|---|---|---|---|---|---|

${Z}_{1}$ | 0.058 | ||||||||

${Z}_{2}$ | 0.287 | 0.017 | |||||||

${Z}_{3}$ | 0.084 | 0.163 | 0.032 | ||||||

${Z}_{4}$ | 0.182 | 0.194 | 0.108 | 0.045 | |||||

${Z}_{5}$ | 0.134 | 0.253 | 0.122 | 0.241 | 0.074 | ||||

${Z}_{6}$ | 0.133 | 0.243 | 0.092 | 0.120 | 0.109 | 0.039 | |||

${Z}_{7}$ | 0.296 | 0.289 | 0.195 | 0.247 | 0.225 | 0.240 | 0.109 | ||

${Z}_{8}$ | 0.180 | 0.175 | 0.127 | 0.242 | 0.330 | 0.179 | 0.176 * | 0.080 | |

${Z}_{9}$ | 0.298 | 0.206 | 0.157 * | 0.264 | 0.208 | 0.221 | 0.197 * | 0.167 * | 0.129 |

**Table 9.**Results of correlation detection between geographic factors and spatial distribution of livability.

${\mathit{Z}}_{1}$ | ${\mathit{Z}}_{2}$ | ${\mathit{Z}}_{3}$ | ${\mathit{Z}}_{4}$ | ${\mathit{Z}}_{5}$ | ${\mathit{Z}}_{6}$ | ${\mathit{Z}}_{7}$ | ${\mathit{Z}}_{8}$ | |
---|---|---|---|---|---|---|---|---|

${Z}_{2}$ | Y | |||||||

${Z}_{3}$ | Y | Y | ||||||

${Z}_{4}$ | Y | N | Y | |||||

${Z}_{5}$ | Y | Y | Y | N | ||||

${Z}_{6}$ | Y | N | Y | Y | Y | |||

${Z}_{7}$ | Y | N | Y | N | Y | Y | ||

${Z}_{8}$ | Y | Y | Y | N | Y | Y | Y | |

${Z}_{9}$ | Y | Y | Y | Y | Y | Y | Y | Y |

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**MDPI and ACS Style**

Tang, J.; Sui, L.
Geodetector-Based Livability Analysis of Potential Resettlement Locations for Villages in Coal Mining Areas on the Loess Plateau of China. *Sustainability* **2022**, *14*, 8365.
https://doi.org/10.3390/su14148365

**AMA Style**

Tang J, Sui L.
Geodetector-Based Livability Analysis of Potential Resettlement Locations for Villages in Coal Mining Areas on the Loess Plateau of China. *Sustainability*. 2022; 14(14):8365.
https://doi.org/10.3390/su14148365

**Chicago/Turabian Style**

Tang, Jingya, and Lichun Sui.
2022. "Geodetector-Based Livability Analysis of Potential Resettlement Locations for Villages in Coal Mining Areas on the Loess Plateau of China" *Sustainability* 14, no. 14: 8365.
https://doi.org/10.3390/su14148365