# Promotion Effects of High-Speed Rail on Urban Development: Evidence from Three Lines in China
## 2 Materials and Methods
### Methods
For economic data, this study chooses GDP data. For construction land data, this study uses the widely-used LUCC data [58; 59]. It should be noted that in determining the statistical spatial scope, county-level cities and counties are defined by their administrative divisions, while prefecture-level cities only include the area of their municipal districts. This is because the number of county-level units administered by different prefecture-level cities varies, resulting in significant differences in city area sizes that cannot accurately reflect the true scale of the cities. Additionally, in some cases, both municipal districts and subordinate county-level cities or counties have HSR stations. By only including municipal district data, spatial overlap in statistical scope can be avoided. Furthermore, considering that the boundaries of some municipal districts in prefecture-level cities have changed due to the merging of surrounding counties and cities, the 2020 boundaries are uniformly used as the basis for statistics. This precise consideration of the statistical scope for prefecture-level cities is also a key feature of this study. Furthermore, it is necessary to design appropriate methods to compare the development performance differences between the two groups of cities across different dimensions. Using the difference-in-differences (DID) method and its derived methods to conduct comparative studies between experimental and control groups is a common practice in the existing literature [33; 53; 60]. This study, based on the basic idea of the DID method, proposes the advantage index \(D_{a}\) to measure the impact of HSR opening on cities. The calculation method is shown in the following formula. \[D_{a}=\frac{Y_{t1}}{Y_{t0}}-\frac{Y_{c1}}{Y_{c0}}\]  
where \(D_{a}\) is the advantage index for a specific dimension; \(Y_{t1}\) is the indicator value for the experimental group city in 2020; \(Y_{t0}\) is the indicator value for the experimental group city in 2010; \(Y_{c1}\) is the indicator value for the control group city in 2020; \(Y_{c0}\) is the indicator value for the control group city in 2010. This study involves comparisons of four indicators, which fall into three dimensions. The advantage index for the resident population is denoted as \(D_{aR}\); for the employment population, it is denoted as \(D_{aE}\); for the total economic output, it is denoted as \(D_{aG}\); and for construction land, it is denoted as \(D_{aL}\). According to the calculation principle of the formula, if the value of \(D_{a}\) is greater than 0, it indicates that the opening and operation of HSR have a positive promoting effect on the development of the experimental group city; if the value of \(D_{a}\) is less than 0, it indicates a negative inhibiting effect on the development of the experimental group city. Based on the results of the advantage index calculations, further analysis will be conducted from three perspectives to examine the impact of HSR on urban development from multiple angles. First, a general comparison of the performance of the two groups of cities will be conducted to roughly assess the overall impact of HSR on different dimensions of urban development. Second, considering differences in city location, administrative level, urban scale, and economic development level, relevant influencing factors will be included, and further analysis will be conducted using regression analysis and grouped statistics to more precisely assess the impact of HSR. Third, systematic cluster analysis will be employed to summarize several typical patterns of how HSR affects urban development based on city performance in various dimensions.