## 3 Data and Methods  
### Data Source  
In order to estimate the interregional dependency, we constructed a panel data model based on the data from five Korean metropolitan cities (Seoul, Busan, Deagu, Dejon, and Gwangju) observed between 2001 and 2017. The data released pertain to KTX GIS data ends in 2017 and the rapid development of transportation construction in the 2000s; therefore, we chose this period. The dependent variable used in the study is interregional dependency (\(RD_{ijt}\)), which measures to what degree city \(i\) depends on city \(j\) in terms of social and economic activities in year \(t\). Given previous studies, we calculated it based on the interregional trips data from the website of the Korea Transport Database (KTDB). This database includes the operation of transport infrastructure, origin-destination  
Figure 1: Study area. Notes: Data source comes from Korea Transport Database ([[https://www.ktdb.go.kr/eng/index.do](https://www.ktdb.go.kr/eng/index.do)]([https://www.ktdb.go.kr/eng/index.do](https://www.ktdb.go.kr/eng/index.do))). (O/D) travel, traffic attributes, and transport networks, which is an official and reliable data source on the topic of Korean transportation. The specific equation can be written as follows:  
\[RD_{ijt}=\Big{(}T_{ijt}/T_{i\star t}\Big{)}/\Big{(}T_{jt}/T_{\star t}\Big{)} \tag{1}\]  
where \(T_{ijt}\) is the traffic volume from city \(i\) to city \(j\) in year \(t\), \(T_{i\star t}\) indicates the total outflow volume of city \(i\) in year \(t\), \(T_{\star jt}\) represents the total inflow volume of city \(j\), and \(T_{\star t}\) represents the total interregional trips across the country in year \(t\). Table 1 reports a summary of interregional travel inflow and outflow with the example of Busan. Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted. In order to examine the impact before and after the opening of KTX, we used the dummy value (\(ktx1\)), which shows the situation of interregional KTX connection (1, connected; 0, unconnected) as our key explanatory variable. We also collected the city characteristic variables, including the proportion of economically active population (_ecopopr_), the number of enterprises per capita (_enterm_), the ratio of university students (_univer_), green area (_gland_), number of higher education teachers per 10,000 population (_teaclrp_), price index with the base year 2005 (_cpi_), number of beds in health care institutions (_bed_), and the number of travel enterprises (_travel_)--some of them are all logarithmically transformed in order to avoid non-normality and heteroscedasticity. These city characteristic variables are all obtained from the website of the Korea Statistical Office (KOSIS). The specific descriptive statistics of involving variables in this study can be found in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Obs.** & **Mean** & **Std. Dev.** & **Min** & **Max** \\ \hline _RD_ & 340 & 1.2269 & 0.6482 & 0.1724 & 2.5499 \\ _ktx1_ & 340 & 0.7647 & 0.4248 & 0 & 1 \\ _lnecoppr_ & 340 & 1.7972 & 2.2399 & 0.2442 & 9.0465 \\ _enterm_ & 340 & 3.8919 & 0.0501 & 3.7861 & 4.0026 \\ _lmuniver_ & 340 & 0.0656 & 0.0135 & 0.0354 & 0.0835 \\ _lnpland_ & 340 & 1.5473 & 0.3411 & 0.8311 & 2.0307 \\ _lnteaclrp_ & 340 & 6.0266 & 0.3336 & 5.4570 & 6.6495 \\ _lncpi_ & 340 & 2.5331 & 0.4217 & 1.6240 & 3.2625 \\ _lnbed_ & 340 & 4.4688 & 0.1274 & 4.2237 & 4.6367 \\ _lndravel_ & 340 & 10.2548 & 0.6301 & 9.1152 & 11.4231 \\ \hline \hline \end{tabular}  
* Notes: Data source come from Korea Transport Database ([[https://www.ktdb.go.kr/eng/index.do](https://www.ktdb.go.kr/eng/index.do)]([https://www.ktdb.go.kr/eng/index.do](https://www.ktdb.go.kr/eng/index.do))).  
\end{table} 
Table 2: Descriptive statistics of main variables.