## 3 Evaluation Data and Related Algorithms
### Data Standardization Processing  
Due to the different dimensions of evaluation indicators, standardization processing is required for each evaluation method. The standardization function includes linear and non-linear methods. The most commonly used methods include min-max standardization, log function transformation, and arctan function transformation. In this study, the widely used min-max standardization method, also known as range standardization, is employed to linearly transform the original data, making the results fall within the [0, 1] range. Assuming that the number of indicators is \(m\), and the number of the sample is \(n\) in the year \(t\), \(X_{ij}\) is the value of the \(j\)th indicator of the \(i\)th sample. \(Z_{ij}\) is the normalized value of \(X_{ij}\). \(X_{j-max}\) and \(X_{j\,min}\) represent the maximum and minimum value of the \(j\)th indicator in the \(n\) sample, respectively. The transformation function is shown as follows:  
\[Z_{ij}=\frac{X_{ij}-X_{j-min}}{X_{i-max}-X_{j-min}} \tag{1}\]  
Generally, standardization functions are designed for indicators where a larger value is considered to be better. However, there are cases where smaller values are considered  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Enterprise Indicators**} & \multicolumn{3}{c}{**Park Indicators**} \\ \hline  & & \multicolumn{3}{c}{**Gross**} & \multicolumn{3}{c}{**New**} \\  & & \multicolumn{3}{c}{**Individual**} & \multicolumn{3}{c}{**Total Fiscal**} & \multicolumn{3}{c}{**Industrial**} & \multicolumn{3}{c}{**Number of**} \\  & & \multicolumn{3}{c}{**Courpourd**} & \multicolumn{3}{c}{**Number of**} & \multicolumn{3}{c}{**Revenue**} & \multicolumn{3}{c}{**Universi-**} & \multicolumn{3}{c}{**Technolololololumnumn{1}{c}{**} \\  & & **R\&D** & **Value** & **Employees** & **(Thousand** & **Tues** & **Hustium** & **Hustium** \\  & & & & **Yuan**)} & & & **tions** & **2017** \\ \hline Max & 22,013 & 182,450.2334 & 1,108,438,915 & 2,620,437 & 75,380,980 & 47 & 136 & 74,368 \\ Min & 23 & 7.0833333 & 3,214,506.7 & 5017 & 0 & 0 & 0 & 17 \\ Mean & 11,018 & 10,500 & 131,833,827.6 & 125,410.18 & 84,344,747.92 & 5.53 & 7.7 & 37,192.5 \\ Standard & 1883.18 & 21,446.31455 & 168,250,294.4 & 238,861.6828 & 131,858,59.58 & 7.98 & 16.46 & 8679.91 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical summary of indicators in China’s national high-tech zones.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Number of National High Tech Zones** & **Statistical Indicators** \\ \hline
2013 & 115 & 246 \\
2014 & 116 & 232 \\
2015 & 147 & 232 \\
2016 & 147 & 244 \\
2017 & 157 & 350 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of Chinese national high-tech zones and evaluation indicators.  
better (in such cases, the monotonicity needs to be changed by taking the reciprocal or negating the value). In this study, almost all indicators can be considered as \"the larger, the better\" type and, hence, can be directly transformed. Taking the year 2017 as an example, the standardized values of various indicators for national high-tech zones are shown in Table 3.