# Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial Form: A Case Study of Jiangsu Province, China

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## Abstract

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## 1. Introduction

## 2. Methods and Data Sources

^{2}. The latest accessible year of land use data and point of interest (POI) data is 2015, and other statistical data also has a certain lag. In order to match spatial data with social and economic data, we selected 2015 as the study year. In 2015, Jiangsu Province had a total population of 80 million, with a GDP of 7012 billion Chinese yuan (CNY). The area of the built-up area, the proportion of built-up area, and the global Moran’ I of the built-up area of each district and county were used to characterize urban spatial expansion size, development intensity, and distribution aggregation degree. First, spatial autocorrelation analysis was used to analyze the spatial distribution pattern and characteristics of urbanization in Jiangsu Province. Then, the traditional statistical methods and spatial statistical methods were combined to analyze 30 commonly considered potential driving variables related to physical geography, economy, and society [12,36]. Pearson correlation analysis was used to screen out the variables that were significantly related to the spatial pattern of urbanization. Finally, linear regression (ordinary least squares: OLS) and geographically weighted regression (GWR) were used to identify the driving variables that led to the difference in urbanization spatial form.

#### 2.1. Data

#### 2.2. Methods

#### 2.2.1. Pearson Correlation Analysis

#### 2.2.2. Spatial Autocorrelation Analysis

#### 2.2.3. Linear Regression (Ordinary Least Squares Regression: OLS)

#### 2.2.4. Geographically Weighted Regression

^{2}; the adjusted-R

^{2}and the adjusted Akaike information criterion (AICc) [37]. The standard error of the residuals quantifies the variability of the residuals around the fitted regression line. The coefficient of determination quantifies the proportion of the variability in the response variable that is explained by the model and take as a value between 0 and 1 (larger is better). The adjusted-R

^{2}is the R

^{2}adjusted for a number of covariates. Adding covariates to a linear regression model will not reduce the R

^{2}, but does increase the model complexity. Hence, the adjusted-R

^{2}supports evaluating whether adding a covariate is sufficiently useful to justify the increase in model complexity. AICc is commonly used to compare models. It gives a trade-off between goodness-of-fit and model complexity. A lower AICc indicates a better model.

## 3. Results

#### 3.1. Spatial Pattern and Spatial Autocorrelation of Urban Form in Jiangsu Province

^{2}(Figure 2). Figure 2 shows that the districts and counties with larger spatial expansion sizes were mainly distributed in southern Jiangsu. Most districts and counties (52 out of 99) had a built-up area in the range 25–50 km

^{2}. Most (87%) of the districts and counties’ size were less than 75 km

^{2}. As shown in Figure 3, the proportion of the built-up area in the districts and counties in Jiangsu Province varied greatly, ranging from 0.0042 to 0.7286. The counties with urban development intensity >0.05 were mainly concentrated in southern Jiangsu. Most (59%) districts and counties had a development intensity ≤0.05. The urban aggregation degree (Figure 4) of built-up area did not show a distinct pattern. The values ranged from 0.14 to 0.71, with the largest values in the south and the north-east.

#### 3.2. Correlation Analysis of Covariates of Urban Spatial Form in Jiangsu Province

#### 3.3. Driving Force Analysis of Urban Spatial Form in Jiangsu Province

#### 3.3.1. Driving Force Analysis of Urban Spatial Expansion Size

^{2}, and adjusted R

^{2}were similar for OLS and GWR. The AICc was larger for GWR than for OLS, reflecting the greater model complexity. We concluded that there is no benefit to using GWR rather than OLS. Put another way, the variability in the urban spatial expansion size is sufficiently explained by OLS without needing to allow the regression coefficients to vary in space. This allows a simpler interpretation of the model results.

#### 3.3.2. Driving Force Analysis of Urban Development Intensity

^{2}and R

^{2}-adjusted value (Table 7). The results of OLS (Table 8) showed that the four main variables influencing urban development were road density, urban population density, per capita GDP, and distance to Shanghai. The standardized coefficient for the absolute value of road density was the largest, followed by urban population density, per capita GDP, and the distance to Shanghai, while the influence of per capita GDP was negative. Therefore, as road density, urban population density, and distance to Shanghai increased and per capita GDP decreased, the urban development intensity also increased.

#### 3.3.3. Driving Force Analysis of Urban Distribution Aggregation Degree

^{2}, and adjusted-R

^{2}were the same for OLS and GWR. The AICc was actually slightly larger for GWR compared to OLS. These results indicated that there was no benefit to using GWR rather than OLS. Note also that the R

^{2}= 0.32 (2 DP) was low. This model explained 32% of the variability in urban distribution aggregation degree. By comparison, the first two models explained a large proportion of the variability in urban spatial expansion size (R

^{2}= 0.82) and urban development intensity (R

^{2}= 0.95).

## 4. Discussion

#### 4.1. Statistical Methods for Studying Regional Urban Spatial Form

#### 4.2. Unbalanced Development Characteristics of Regional Urban Spatial Form

#### 4.3. Driving Mechanism of Unbalanced Regional Spatial Urban Form

#### 4.4. Optimization Strategies for a Balanced Development of Regional Urban Spatial Form

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Spatial Distribution Map of Influencing Variables and Their Estimated Coefficients

**Figure A1.**Spatial distribution map of estimated coefficient of proportion of the population over 60 years old on urban spatial expansion size in Jiangsu Province.

**Figure A2.**Spatial distribution map of proportion of the population over 60 years old in Jiangsu Province.

**Figure A3.**Spatial distribution map of estimated coefficient of the total number of point of interest (POI) on urban spatial expansion size in Jiangsu Province.

**Figure A5.**Spatial distribution map of estimated coefficient of the output of secondary industry on urban spatial expansion size in Jiangsu Province.

**Figure A6.**Spatial distribution map of proportion of the output of secondary industry in Jiangsu Province.

**Figure A7.**Spatial distribution map of estimated coefficient of total export–import volume on urban spatial expansion size in Jiangsu Province.

**Figure A10.**Spatial distribution map of income of national and provincial development zones in Jiangsu Province.

**Figure A11.**Spatial distribution map of estimated coefficient of road density on urban development intensive in Jiangsu Province.

**Figure A13.**Spatial distribution map of estimated coefficient of urban population density on urban development intensive in Jiangsu Province.

**Figure A15.**Spatial distribution map of estimated coefficient of distance to Shanghai of districts and counties on urban development intensive in Jiangsu Province.

**Figure A16.**Spatial distribution map of distance to Shanghai of districts and counties in Jiangsu Province.

**Figure A17.**Spatial distribution map of estimated coefficient of per capita GDP on urban development intensive in Jiangsu Province.

**Figure A19.**Spatial distribution map of estimated coefficient of terrain undulation on urban distribution aggregation degree in Jiangsu Province.

**Figure A21.**Spatial distribution map of estimated coefficient of proportion of urban population on urban distribution aggregation degree in Jiangsu Province.

#### Appendix A.2. Models Comparison Results of Influencing Variables of Urban Spatial Development Intensity

^{2}and adjusted R

^{2}are the lowest. According to AIC, OLS and GWR model in 3.3.2 was the best performing, although R

^{2}and adjusted R

^{2}were similar to model 2-1. Therefore, it is better to choose the model in 3.3.2 for OLS.

**Table A1.**Summary of OLS and GWR model 2-1 of influencing variables of urban development intensive in Jiangsu Province.

ID | Statistics | OLS Model Parameters | GWR Model Parameters |
---|---|---|---|

1 | σ | 0.0394 | 0.0394 |

2 | AICc | −355.3750 | −352.7313 |

3 | R^{2} | 0.9446 | 0.9446 |

4 | R^{2} Adjusted | 0.9428 | 0.9428 |

**Table A2.**Estimated coefficients of OLS regression model 2-1 for influencing variables of urban development intensive in Jiangsu Province.

Influence Variables | Coefficients | Standard Error | Standardized Coefficients | t | p-Value |
---|---|---|---|---|---|

Constant | −0.0388 | 9.5918 × 10^{−3} | −4.0490 | 0.0001 | |

Road density | 4.7599 × 10^{−5} | 1.1112 × 10^{−7} | 0.8131 | −3.6487 | 0.0004 |

Urban population density | 8.9568 × 10^{−6} | 1.9739 × 10^{−6} | 0.2166 | 4.5376 | 0.0000 |

Per capita GDP | −4.0544 × 10^{−7} | 2.9950 × 10^{−6} | −0.1029 | 15.8927 | 0.0000 |

**Table A3.**Estimated coefficients of GWR regression model 2-1 for influencing variables of urban development intensive in Jiangsu Province.

Statistics | Constant | Road Density | Urban Population Density | Per Capita GDP |
---|---|---|---|---|

Minimum | −3.8846 × 10^{−2} | 4.7591 × 10^{−5} | 8.9546 × 10^{−6} | −4.0557 × 10^{−7} |

Lower quartile | −3.8840 × 10^{−2} | 4.7597 × 10^{−5} | 8.9558 × 10^{−6} | −4.0548 × 10^{−7} |

Median | −3.8837 × 10^{−2} | 4.7599 × 10^{−5} | 8.9564 × 10^{−6} | −4.0542 × 10^{−7} |

Upper quartile | −3.8834 × 10^{−2} | 4.7601 × 10^{−5} | 8.9570 × 10^{−6} | −4.0535 × 10^{−7} |

Maximum | −3.8823 × 10^{−2} | 4.7604 × 10^{−5} | 8.9589 × 10^{−6} | −4.0516 × 10^{−7} |

**Table A4.**Summary of OLS and GWR model 2-2 of influencing variables of urban development intensive in Jiangsu Province.

ID | Statistics | OLS Model Parameters | GWR Model Parameters |
---|---|---|---|

1 | σ | 0.0419 | 0.0386 |

2 | AICc | −344.3910 | −356.1783 |

3 | R^{2} | 0.9368 | 0.9479 |

4 | R^{2} Adjusted | 0.9355 | 0.9451 |

**Table A5.**Estimated coefficients of OLS regression model 2-2 for influencing variables of urban development intensive in Jiangsu Province.

Influence Variables | Coefficients | Standard Error | Standardized Coefficients | t | p-Value |
---|---|---|---|---|---|

Constant | −6.6062 × 10^{−2} | 6.4024 × 10^{−3} | −10.3183 | 0.0000 | |

Road density | 4.2090 × 10^{−5} | 2.7475 × 10^{−6} | 0.7190 | 15.3195 | 0.0000 |

Urban population density | 1.1679 × 10^{−5} | 1.9411 × 10^{−6} | 0.2824 | 6.0170 | 0.0000 |

**Table A6.**Estimated coefficients of GWR regression model 2-2 for influencing variables of urban development intensive in Jiangsu Province.

Statistics | Constant | Road Density | Urban Population Density |
---|---|---|---|

Minimum | −0.0819 | 4.1303 × 10^{−5} | 1.0126 × 10^{−5} |

Lower quartile | −0.0736 | 4.2338 × 10^{−5} | 1.0790 × 10^{−5} |

Median | −0.0673 | 4.2584 × 10^{−5} | 1.1688 × 10^{−5} |

Upper quartile | −0.0594 | 4.2778 × 10^{−5} | 1.2219 × 10^{−5} |

Maximum | −0.0507 | 4.3181 × 10^{−5} | 1.2914 × 10^{−5} |

**Table A7.**Summary of OLS and GWR model 2-3 of influencing variables of urban development intensive in Jiangsu Province.

ID | Statistics | OLS Model Parameters | GWR Model Parameters |
---|---|---|---|

1 | σ | 0.0396 | 0.0402 |

2 | AICc | −354.3870 | −349.3812 |

3 | R^{2} | 0.9440 | 0.9435 |

4 | R^{2} Adjusted | 0.9423 | 0.9406 |

**Table A8.**Estimated coefficients of OLS regression model 2-3 for influencing variables of urban development intensive in Jiangsu Province.

Influence Variables | Coefficients | Standard Error | Standardized Coefficients | t | p-Value |
---|---|---|---|---|---|

Constant | −1.0106 × 10^{−1} | 1.1696 × 10^{−2} | −8.6409 | 0.0000 | |

Road density | 4.5714 × 10^{−5} | 2.7983 × 10^{−6} | 0.2426 | 16.3360 | 0.0000 |

Urban population density | 1.0035 × 10^{−5} | 1.8958 × 10^{−6} | 0.7809 | 5.2935 | 0.0000 |

Distance to Shanghai | 9.5004 × 10^{−5} | 2.7158 × 10^{−5} | 0.0922 | 3.4983 | 0.0007 |

**Table A9.**Estimated coefficients of GWR regression model 2-3 for influencing variables of urban development intensive in Jiangsu Province.

Statistics | Constant | Road Density | Urban Population Density | Distance to Shanghai |
---|---|---|---|---|

Minimum | −0.1210 | 4.3721 × 10^{−5} | 9.3556 × 10^{−6} | 4.5310 × 10^{−5} |

Lower quartile | −0.1111 | 4.5522 × 10^{−5} | 9.7236 × 10^{−6} | 8.9686 × 10^{−5} |

Median | −0.1048 | 4.5832 × 10^{−5} | 1.0202 × 10^{−5} | 1.1073 × 10^{−4} |

Upper quartile | −0.0960 | 4.6080 × 10^{−5} | 1.0530 × 10^{−5} | 1.2704 × 10^{−4} |

Maximum | −0.0750 | 4.6463 × 10^{−5} | 1.0984 × 10^{−5} | 1.5359 × 10^{−4} |

**Figure A24.**Standard error map of GWR model for influencing variables of urban development intensive in Jiangsu Province (model 2-1).

**Figure A25.**Spatial distribution map of estimated coefficient of road density on urban development intensive in Jiangsu Province (model 2-1).

**Figure A26.**Spatial distribution map of estimated coefficient of urban population density on urban development intensive in Jiangsu Province (model 2-1).

**Figure A27.**Spatial distribution map of estimated coefficient of per capita GDP on urban development intensive in Jiangsu Province (model 2-1).

**Figure A28.**Standard error map of GWR model for influencing variables of urban development intensive in Jiangsu Province (model 2-2).

**Figure A29.**Spatial distribution map of estimated coefficient of road density on urban development intensive in Jiangsu Province (model 2-2).

**Figure A30.**Spatial distribution map of estimated coefficient of urban population density on urban development intensive in Jiangsu Province (model 2-2).

**Figure A31.**Standard error map of GWR model for influencing variables of urban development intensive in Jiangsu Province (model 2-3).

**Figure A32.**Spatial distribution map of estimated coefficient of road density on urban development intensive in Jiangsu Province (model 2-3).

**Figure A33.**Spatial distribution map of estimated coefficient of urban population density on urban development intensive in Jiangsu Province (model 2-3).

**Figure A34.**Spatial distribution map of estimated coefficient of distance to Shanghai of districts and counties on urban development intensive in Jiangsu Province (model 2-3).

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**Figure 5.**Local indicators of spatial association (LISA) (Local Moran’s I) cluster map of urban expansion size in Jiangsu Province.

**Figure 6.**Local indicators of spatial association (LISA) (Local Moran’s I) cluster map of urban development intensity in Jiangsu Province.

Types of Variables | Influencing Variables |
---|---|

Physical geography | Area of districts and counties, average elevation, terrain undulation, proportion of urban blue and green space, distance to Shanghai |

Economy | GDP, per capita GDP, output value of primary industry, output of secondary industry, output value of tertiary industry, proportion of primary industrial output-value, proportion of secondary industrial output-value, proportion of tertiary industrial output-value per capita disposable income, total retail sales of consumer goods, total sales of wholesale and retail, total export–import volume, number of national and provincial development zones, income of national and provincial development zones |

Society | Total population, urban population, rural population, proportion of population aged 15–59, proportion of population over 60 years old, proportion of urban population, density of urban population, number of colleges and universities, fixed asset investment, road density, and total number of POI |

Urban Spatial Form Indicators | Moran’ I | Z Value | p-Value |
---|---|---|---|

Spatial expansion size | 0.212 | 3.5868 | 0.003 |

Development intensity | 0.394 | 6.7049 | 0.001 |

Distribution aggregation degree | 0.076 | 1.4222 | 0.081 |

**Table 3.**Correlation coefficients of 30 potential variables of urban spatial form in Jiangsu Province.

Potential Variables | The Urban Spatial Expansion Size | The Urban Development Intensity | The Urban Distribution Aggregation Degree | |||
---|---|---|---|---|---|---|

Pearson Correlation | Significance Test (Two-Sided) | Pearson Correlation | Significance Test (Two-Sided) | Pearson Correlation | Significance Test (Two-Sided) | |

Area of districts and counties | 0.028 | 0.784 | −0.633 ** | 0.000 | −0.394 ** | 0.000 |

Average elevation | 0.003 | 0.976 | 0.069 | 0.500 | −0.127 | 0.212 |

Terrain undulation | 0.239 * | 0.017 | −0.126 | 0.215 | −0.213 * | 0.035 |

Proportion of urban blue and green space | 0.208 * | 0.039 | −0.125 | 0.218 | −0.037 | 0.713 |

Distance to Shanghai | −0.299 ** | 0.003 | −0.182 | 0.071 | −0.113 | 0.265 |

GDP | 0.794 ** | 0.000 | 0.042 | 0.681 | 0.030 | 0.765 |

Per capita GDP | 0.455 ** | 0.000 | 0.233 * | 0.021 | 0.036 | 0.720 |

Output value of primary industry | −0.022 | 0.829 | −0.609 ** | 0.000 | −0.417 ** | 0.000 |

Output of the secondary industry | 0.780 ** | 0.000 | −0.173 | 0.086 | −0.064 | 0.531 |

Output value of tertiary industry | 0.683 ** | 0.000 | 0.359 ** | 0.000 | 0.183 | 0.070 |

Proportion of primary industrial output-value | −0.334 ** | 0.001 | −0.563 ** | 0.000 | −0.341 ** | 0.001 |

Proportion of secondary industrial output-value | 0.281 ** | 0.005 | −0.654 ** | 0.000 | −0.336 ** | 0.001 |

Proportion of tertiary industrial output-value | −0.114 | 0.261 | 0.783 ** | 0.000 | 0.408 ** | 0.000 |

Per capita disposable income | 0.402 ** | 0.000 | 0.668 ** | 0.000 | 0.369 ** | 0.000 |

Total retail sales of consumer goods | 0.514 ** | 0.000 | 0.559 ** | 0.000 | 0.340 ** | 0.001 |

Total sales of wholesale and retail | 0.537 ** | 0.000 | 0.393 ** | 0.000 | 0.287 ** | 0.004 |

Total export-import volume | 0.615 ** | 0.000 | 0.058 | 0.572 | 0.128 | 0.206 |

Number of national and provincial development zones | 0.358 ** | 0.000 | −0.347 ** | 0.000 | −0.030 | 0.766 |

Income of national and provincial development zones | 0.654 ** | 0.000 | −0.198 | 0.050 | −0.015 | 0.886 |

Total population | 0.581 ** | 0.000 | −0.104 | 0.304 | −0.017 | 0.869 |

Urban population | 0.573 ** | 0.000 | 0.376 ** | 0.000 | 0.302 ** | 0.002 |

Rural population | 0.258 ** | 0.010 | −0.622 ** | 0.000 | −0.391 ** | 0.000 |

Proportion of population aged 15–59 | 0.479 ** | 0.000 | 0.473 ** | 0.000 | 0.349 ** | 0.000 |

Proportion of population over 60 years old | −0.400 ** | 0.000 | −0.227 * | 0.024 | −0.262 ** | 0.009 |

Proportion of urban population | 0.082 | 0.418 | 0.784 ** | 0.000 | 0.518 ** | 0.000 |

Density of urban population | −0.052 | 0.606 | 0.884 ** | 0.000 | 0.507 ** | 0.000 |

Number of colleges and universities | 0.410 ** | 0.000 | 0.371 ** | 0.000 | 0.247 * | 0.014 |

Fixed asset investment | 0.655 ** | 0.000 | −0.263 ** | 0.008 | −0.183 | 0.070 |

Road density | 0.036 | 0.727 | 0.955 ** | 0.000 | 0.475 ** | 0.000 |

Total number of POI | 0.756 ** | 0.000 | 0.137 | 0.177 | 0.159 | 0.115 |

**Table 4.**Summary of ordinary least squares (OLS) and geographically weighted regression (GWR) model of influencing variables of urban spatial expansion size in Jiangsu Province.

ID | Statistics | OLS Model Parameters | GWR Model Parameters |
---|---|---|---|

1 | $\widehat{\sigma}$ | 13.0042 | 13.0043 |

2 | AICc | 794.68 | 797.92 |

3 | R^{2} | 0.8157 | 0.8157 |

4 | R^{2}-adjusted | 0.8058 | 0.8058 |

**Table 5.**Estimated coefficients of OLS regression model for influencing variables of urban spatial expansion size in Jiangsu Province.

Influencing Variables | Coefficient | Standard Error | Standardized Coefficient | t | p-Value |
---|---|---|---|---|---|

Constant | 44.0779 | 7.2639 | 6.068 | 0.0000 | |

Proportion of population over 60 years old | −223.3734 | 40.1911 | −0.273 | −5.558 | 0.0000 |

Income of national and provincial development zones | 3.97 × 10^{−7} | 0 | 0.2102 | 3.514 | 0.0007 |

Total number of POI | 0.0015 | 0.0003 | 0.3454 | 5.916 | 0.0000 |

Output value of secondary industry | 0.0527 | 0.0081 | 0.5412 | 6.517 | 0.0000 |

Total export–import volume | −0.0518 | 0.0214 | −0.1857 | −2.425 | 0.0173 |

**Table 6.**Estimated coefficients of GWR regression model for influencing variables of urban spatial expansion size in Jiangsu Province.

Statistics | Constant | Proportion of Population over 60 Years Old | Income of National and Provincial Development Zones | Total Number of POI | Output Value of Secondary Industry | Total Export–Import Volume |
---|---|---|---|---|---|---|

Minimum | 44.0583 | −223.4729 | 3.9565 × 10^{−7} | 1.5014 × 10^{−3} | 5.2697 × 10^{−2} | −5.1828 × 10^{−2} |

Lower quartile | 44.0726 | −223.4334 | 3.9575 × 10^{−7} | 1.5017 × 10^{−3} | 5.2708 × 10^{−2} | −5.1817 × 10^{−2} |

Median | 44.0865 | −223.4089 | 3.9579 × 10^{−7} | 1.5018 × 10^{−3} | 5.2715 × 10^{−2} | −5.1809 × 10^{−2} |

Upper quartile | 44.0919 | −223.3459 | 3.9585 × 10^{−7} | 1.5022 × 10^{−3} | 5.2719 × 10^{−2} | −5.1793 × 10^{−2} |

Maximum | 44.1005 | −223.2773 | 3.9598 × 10^{−7} | 1.5026 × 10^{−3} | 5.2724 × 10^{−2} | −5.1770 × 10^{−2} |

**Table 7.**Summary of OLS and GWR model of influencing variables of urban development intensive in Jiangsu Province.

ID | Statistics | OLS Model Parameters | GWR Model Parameters |
---|---|---|---|

1 | $\widehat{\sigma}$ | 0.0388 | 0.0380 |

2 | AICc | −357.45 | −357.24 |

3 | R^{2} | 0.9468 | 0.9513 |

4 | R^{2}-adjusted | 0.9445 | 0.9469 |

**Table 8.**Estimated coefficients of OLS regression model for influencing variables of urban development intensive in Jiangsu Province.

Influence Variables | Coefficient | Standard Error | Standardized Coefficient | t | p-Value |
---|---|---|---|---|---|

Constant | −0.0697 | 0.0182 | −3.8328 | 0.0002 | |

Road density | 4.82 × 10^{−5} | 0.0000 | 0.8238 | 16.257 | 0.0000 |

Urban population density | 8.74 × 10^{−6} | 0.0000 | 0.2114 | 4.4897 | 0.0000 |

Per capita GDP | −2.80 × 10^{−7} | 0.0000 | −0.0711 | −2.2198 | 0.0288 |

Distance to Shanghai | 6.10 × 10^{−5} | 0.0000 | 0.0592 | 1.9864 | 0.0499 |

**Table 9.**Estimated coefficients of GWR regression model for influencing variables of urban development intensive in Jiangsu Province.

Statistics | Constant | Road Density | Urban Population Density | Per Capita GDP | Distance to Shanghai |
---|---|---|---|---|---|

Minimum | −0.1083 | 4.2908 × 10^{−5} | 8.5359 × 10^{−6} | −2.9358 × 10^{−7} | −3.7536 × 10^{−6} |

Lower quartile | −0.0887 | 4.7198 × 10^{−5} | 8.8612 × 10^{−6} | −2.6391 × 10^{−7} | 5.4025 × 10^{−5} |

Median | −0.0769 | 4.8200 × 10^{−5} | 8.9572 × 10^{−6} | −2.4282 × 10^{−7} | 8.5492 × 10^{−5} |

Upper quartile | −0.0659 | 4.8845 × 10^{−5} | 9.2030 × 10^{−6} | −2.2128 × 10^{−7} | 1.1813 × 10^{−4} |

Maximum | −0.0387 | 4.9247 × 10^{−5} | 1.0515 × 10^{−5} | −1.8915 × 10^{−7} | 1.6948 × 10^{−4} |

**Table 10.**Summary of OLS and GWR model of influencing variables of urban distribution aggregation degree in Jiangsu Province.

ID | Statistics | OLS Model Parameters | GWR Model Parameters |
---|---|---|---|

1 | $\widehat{\sigma}$ | 0.1137 | 0.1137 |

2 | AICc | −146.54 | −144.11 |

3 | R^{2} | 0.3174 | 0.3174 |

4 | R^{2}-adjusted | 0.3032 | 0.3032 |

**Table 11.**Estimated coefficients of OLS regression model for influencing variables of urban distribution aggregation degree in Jiangsu Province.

Influence Variables | Coefficients | Standard Error | Standard Coefficients | t-Test | Significant |
---|---|---|---|---|---|

Constant | 0.2831 | 0.0427 | 6.6229 | 0.0000 | |

Terrain undulation | 0.3546 | 0.0573 | 0.5218 | 6.1872 | 0.0000 |

Proportion of urban population | −3.8107 × 10^{−4} | 0.0001 | −0.2209 | −2.6194 | 0.0102 |

**Table 12.**Estimated coefficients of GWR regression model for influencing variables of urban distribution aggregation degree in Jiangsu Province.

Statistics | Constant | Terrain Undulation | Proportion of Urban Population |
---|---|---|---|

Minimum | 0.2830 | −3.8156 × 10^{−4} | 3.5453 × 10^{−1} |

Lower quartile | 0.2830 | −3.8114 × 10^{−4} | 3.5459 × 10^{−1} |

Median | 0.2830 | −3.8101 × 10^{−4} | 3.5464 × 10^{−1} |

Upper quartile | 0.2831 | −3.8088 × 10^{−4} | 3.5468 × 10^{−1} |

Maximum | 0.2832 | −3.8067 × 10^{−4} | 3.5472 × 10^{−1} |

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## Share and Cite

**MDPI and ACS Style**

Xiong, G.; Cao, X.; Hamm, N.A.S.; Lin, T.; Zhang, G.; Chen, B.
Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial Form: A Case Study of Jiangsu Province, China. *Sustainability* **2021**, *13*, 3121.
https://doi.org/10.3390/su13063121

**AMA Style**

Xiong G, Cao X, Hamm NAS, Lin T, Zhang G, Chen B.
Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial Form: A Case Study of Jiangsu Province, China. *Sustainability*. 2021; 13(6):3121.
https://doi.org/10.3390/su13063121

**Chicago/Turabian Style**

Xiong, Guoping, Xin Cao, Nicholas A. S. Hamm, Tao Lin, Guoqin Zhang, and Binghong Chen.
2021. "Unbalanced Development Characteristics and Driving Mechanisms of Regional Urban Spatial Form: A Case Study of Jiangsu Province, China" *Sustainability* 13, no. 6: 3121.
https://doi.org/10.3390/su13063121