## 5 Conclusions and Limitations  
### Conclusions and Discussion  
By focusing on understanding the patterns and processes of transport flows and their socio-economic determinants, this study has provided empirical evidence of the inter-dependence between transport and economy at the regional scale (the Jiangsu province in China) by deploying an innovative analytical method of multiscale spatial analysis for flows and big data processed from georeferenced transactions at expressway toll-gates. Methodologically, this paper reports the first empirical study of flow modelling by considering multi scales in spatial dependence and non-stationarity or heterogeneity. Based on the spatial weight of distance-band, the flow-focused global Moran Indice has been successfully used to measure the spatial autocorrelation existing in the flow data, and the results indicate a clustering pattern at the regional scale. The follow-up flow-focused local Moran Indice detected four significant clusters of flow, among which the hot clusters (high volume flows surrounded by high volume flows) help identify two areas with a high concentration of transport flow at different spatial scales. These clusters reveal the impacts of urban and economic agglomerations on regional transport. The global spatial interaction model calibrated by OLS confirms the positive pushing effect of Topp in the origin county and the pulling effect of GDP in the destination county. It is clear that such strong spatial interaction is influenced by growing economic development and efficient transport infrastructure in the well-known Yangtze River Delta. The positive spatial autocorrelation in the model residuals (measured by the above-mentioned flow-focused global Moran I of flows) and the two clusters at varied scales (measured by flow focused local Moran I of flows), justify the development and application of the multiscale flow-focused geographically weighted regression approach. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Diagnostics**} & \multicolumn{2}{c}{**FGWR**} & \multicolumn{3}{c}{**MFGWR**} \\ \cline{2-7}  & **Model** & **Model** & **Intercept** & **Topp** & **GDP** & **Distance** \\ \hline Bandwidth & 69 & - & 53 & 57 & 47 & 3463 \\ ENP & 725.146 & 678.537 & 218.609 & 1.0 & 195.036 & 263.892 \\ Adjusted \(\alpha\) & 0.00028 & 0.00029 & 0.00029 & 0.000256 & 0.000189 & 0.05 \\ Critical t (95\%) & 3.641 & 3.6234 & 3.6888 & 3.6596 & 3.7367 & 1.9606 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of bandwidth, effective number of parameters, and critical \(t\)-values between FGWR and MFGWR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Explanatory Variables**} & **Intercept** & **Topp** & **GDP** & **Distance** \\ \hline \multirow{2}{*}{FGWR} & Critical t (95\%) & 1379 & 2416 & 2995 & 2970 \\  & Adjusted Critical t (95\%) & 329 & 1208 & 2128 & 2202 \\ \hline \multirow{2}{*}{MFGWR} & Critical t (95\%) & 3481 & 3481 & 3481 & 3481 \\  & Adjusted Critical t (95\%) & 3113 & 3481 & 3481 & 3481 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Number of significant t statistic values filtered by the critical values of FGWR and MFGWR. The MFGWR modelling results have many implications. First of all, the spatial autocorrelation in the local modelling residuals was considerably reduced, compared with that from the global model and FGWR mentioned above. This means MFGWR has largely reflected the spatial dependence present in the flow data. Secondly, the statistics of adjusted R\({}^{2}\) and AICc have proved that MFGWR outperforms both the global flow model and FGWR in explaining flow patterns. The spatial distributions of parameter estimations from MFGWR reflect the heterogeneous relationships between transport and socioeconomic forces across the study area. Thereby, it is argued that MFGWR has successfully considered spatial heterogeneity. Classically, the local models are run on the same spatial scale, without considering scale effects between pulling and pushing forces or spatial barriers.