## 5 Results
### Result of The Spatial Durbin Model for Hypothesis 1
#### 5.2.2 LM Test  
In this study, LM statistics and Robust LM statistics were tested on the corresponding spatial econometric models using R software. According to the Table 5, both LM-ERR and LM-LAG tests passed the significance test at the 10% level, indicating that the lag term and residual sequence of dependent variables have spatial autocorrelation. The results showed that the Robust LM-ERR passed the significance test at 10% level, while the Robust LM-LAG failed the significance test at 10% level. Based on the 2011-2019 data of urban agglomerations in Yangtze River Delta, both LM-LAG and Robust LM-LAG passed the significance test at 10% level, while both LM-ERR and Robust LM-ERR failed the significance test at 10% level. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rank** & **City** & \
\begin{tabular}{c} **Transportation** \\ **Structure Index** \\ \end{tabular} & **Rank** & **City** &
\begin{tabular}{c} **Transportation Function Index** \\ \end{tabular} & **Rank** & **City** & **Transportation Integration Index** \\ \hline \(1\) & Shanghai & 1.0000 & \(1\) & Shanghai & 0.8379 & \(1\) & Shanghai & 0.9189 \\ \(2\) & Naming & 1.0000 & \(2\) & SuzhouJiangju & 0.7383 & \(2\) & Hangzhou & 0.8631 \\ \(3\) & Wuai & 1.0000 & \(3\) & Hangzhou & 0.7227 & \(3\) & Hefei & 0.8351 \\ \(4\) & Xuzhou & 0.9298 & \(4\) & Hefei & 0.6980 & \(4\) & SuzhouJangsu & 0.8179 \\ \(5\) & Changzhou & 0.9298 & \(5\) & Ningbo & 0.6417 & \(5\) & Nanjing & 0.8177 \\ \(6\) & SuzhouJangsu & 0.8956 & \(6\) & Nanjing & 0.6355 & \(6\) & Xuzhou & 0.7801 \\ \(7\) & Namong & 0.8517 & \(7\) & Xuzhou & 0.8585 & \(7\) & Ningbo & 0.7329 \\ \(8\) & Lianyung & 0.8318 & \(8\) & Wuai & 0.5549 & \(8\) & Wuai & 0.7262 \\ \(9\) & Huaiai & 0.8248 & \(9\) & Fuyang & 0.5089 & \(9\) & Jiaxing & 0.7036 \\ \(10\) & Yanheng & 0.7133 & \(10\) & Benghu & 0.4767 & \(10\) & Fayang & 0.6902 \\ \(11\) & Yanuzhou & 0.7133 & \(11\) & Jiaxing & 0.4329 & \(11\) & Benghu & 0.6854 \\ \(12\) & Zhenjiang & 0.7133 & \(12\) & Changzhou & 0.4303 & \(12\) & Changzhou & 0.6639 \\ \(13\) & TiazhouJangsu & 0.6615 & \(13\) & Shaoxing & 0.4133 & \(13\) & Zhenjiang & 0.6367 \\ \(14\) & Sugian & 0.6615 & \(14\) & Chunhou & 0.4045 & \(14\) & Wuhu & 0.6257 \\ \(15\) & Hangzhou & 0.6457 & \(15\) & Huzhou & 0.3957 & \(15\) & Jinghua & 0.6142 \\ \(16\) & Ningbo & 0.6352 & \(16\) & Liuan & 0.3830 & \(16\) & Huaiun & 0.6118 \\ \(17\) & Wenhuoh & 0.6045 & \(17\) & Jinghua & 0.3809 & \(17\) & Wenhuoh & 0.6117 \\ \(18\) & Jiaxing & 0.5386 & \(18\) & Suzhoua & 0.3698 & \(18\) & Huzhou & 0.5998 \\ \(19\) & Huzhou & 0.5718 & \(19\) & Zhenjiang & 0.3473 & \(19\) & Chuxhou & 0.5808 \\ \(20\) & Shaoxing & 0.5545 & \(20\) & Huaiun & 0.3295 & \(20\) & Xuancheng & 0.5748 \\ \(21\) & Jinghua & 0.5449 & \(21\) & Xuxheng & 0.3266 & \(21\) & Suzhoua & 0.5629 \\ \(22\) & Qazhou & 0.5279 & \(22\) & Naming & 0.3230 & \(22\) & Yanzhou & 0.5614 \\ \(23\) & Zhoushan & 0.5114 & \(23\) & Tiazhou & 0.3158 & \(23\) & Lian & 0.5612 \\ \(24\) & Taizhou & 0.4835 & \(24\) & Masshan & 0.3067 & \(24\) & Shaoxing & 0.5506 \\ \(25\) & Lihui & 0.4746 & \(25\) & Wuhu & 0.3050 & \(25\) & Tiazhou & 0.5136 \\ \(26\) & Hefei & 0.4628 & \(26\) & Yanzhou & 0.2923 & \(26\) & Chihouu & 0.5023 \\ \(27\) & Huaiwei & 0.492 & \(27\) & Benhou & 0.2908 & \(27\) & Huaiun & 0.5019 \\ \(28\) & Bochou & 0.4491 & \(28\) & Quazhou & 0.2864 & \(28\) & Nantong & 0.4872 \\ \(29\) & Suzhoua & 0.4436 & \(29\) & Yancheng & 0.2864 & \(29\) & Anqing & 0.4840 \\ \(30\) & Bergbu & 0.4423 & \(30\) & Wenhuow & 0.2752 & \(30\) & Lanyungang & 0.4783 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Transportation integration Index of cities in Yangtze River Delta urban agglomeration in 2020.