## 2 Data and Methods
### Methods
#### 2.3.2 Driving Factors Analysis Method for the Landscape Aggregation Degree of Cultivated Land
(1) Correlation analysis  
Pearson correlation analysis was conducted using Statistical Product and Service Solution (SPSS) 26 software (International Business Machines Corporation, Armonk City, New York, USA, 2019), and the correlation coefficients of each driving factor and the landscape aggregation degree of cultivated land were obtained. Pearson correlation analysis results show that 10 indicators are correlated (\(p\) < 0.05), among which 8 indicators are strongly correlated (\(p\) < 0.01) (Tables 5 and 6). Therefore, this paper selects these 10 driving factors and the landscape aggregation degree of cultivated land for regression analysis. (2) Principal component analysis (PCA)  
We selected 10 driving factors from four aspects to explore the response relationship between cultivated land landscape aggregation degree and these driving factors. In order \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Correlation Coefficient** & \(p\)**-Value** \\ \hline X11 & \(-\)0.934 ** & 0.002 \\ X12 & \(-\)0.849 * & 0.016 \\ X13 & \(-\)0.918 ** & 0.000 \\ X21 & \(-\)0.934 ** & 0.002 \\ X22 & \(-\)0.894 ** & 0.007 \\ X31 & 0.651 ** & 0.001 \\ X32 & \(-\)0.951 ** & 0.001 \\ X33 & \(-\)0.882 ** & 0.009 \\ X34 & \(-\)0.970 ** & 0.000 \\ X41 & 0.790 * & 0.035 \\ \hline \hline \end{tabular} Note. * \(p\) < 0.05, ** \(p\) < 0.01. According to the significance test method, \(p\) < 0.05 is generally as significant, and \(p\) < 0.01 is considered very significant. If the correlation coefficient is positive, it is called positive correlation, indicating that the two variables change in the same direction. If the correlation coefficient is negative, it is called negative correlation, indicating that the two variables change in opposite directions. Pearson correlation coefficient is between 1 and \(-\)1, and the greater the absolute value of the coefficient, the greater the correlation degree.  
\end{table} 
Table 6: Pearson correlation analysis results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Grade I Indexes** & **Grade II Indexes** & **Unit** \\ \hline Economic development (X1) & Per capita gross domestic product (X11) & Yuan \\ \hline  & Rural disposable income per capita (X12) & Yuan \\ \hline  & Newly increased fixed assets (X13) & 100 million yuan \\ \hline Industrial restructuring (X2) & Gross output value of industry (X21) & 100 million yuan \\ \hline  & Proportion of tertiary industry (X22) & 100 million yuan \\ \hline  & Increase in urban resident population (X31) & 10,000 people \\ \cline{2-3} Urbanization (X3) & Proportion of construction land area (X32) & \% \\ \cline{2-3}  & Investment in real estate development (X33) & 100 million yuan \\ \cline{2-3}  & Length of road (X34) & km \\ \hline Agriculture science & Total power of agricultural machinery (X41) & million kilowatts \\ and technology (X4) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Index system for driving force analysis of the landscape aggregation degree of cultivated land. to eliminate the multicollinearity of driving factors data, PCA method was used to reduce the dimensionality of data and extract the main information of driving factors data before multivariate linear regression analysis was carried out [70]. The driving factors data of the landscape aggregation degree of cultivated land is multi-dimensional. In order to eliminate the influence of different dimensions, the original data were standardized. Then, Kaiser-Meyer-Olkin (KMO) and Bartlett spherical tests were conducted to test whether the index data were suitable for PCA. As seen in Table 7, the observed Bartlett test statistic was 280.459; the degree of freedom was 45; the probability was close to 0, less than the significance level of 0.05; and the KMO value was 0.732 \(>\) 0.6, indicating that it was suitable for PCA. The PCA results showed that cumulative contribution rate of the first and second principal components reached 95.24%, and the value of the characteristic root of the two principal components is greater than 1 (Table 8).