## 4 Color-Area Ratio Factor Analysis
### Data and Analysis
#### 4.2.2 SD Factor Analysis  
The total variance explanation was obtained by the factor analysis for dimensionality reduction using SPSS 22.0 software, showing that just one factor can be chosen as the common factor, and the common factor can evaluate 76.9% of the internal relationship of color-area ratio quantification (Table 8). Therefore, when the number of landscape factors is small, and the independence of a few factors is high, these factors can also reflect the validity of quantification of the original data of color-area ratio factors in ethnic minority areas.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**X\({}_{1}\)**} & \multicolumn{2}{c}{**X\({}_{2}\)**} & \multicolumn{2}{c}{**X\({}_{3}\)**} & \multicolumn{2}{c}{**X\({}_{4}\)**} & \multicolumn{2}{c}{**X\({}_{5}\)**} & \multicolumn{2}{c}{**X\({}_{6}\)**} \\ \cline{2-9}  & \multicolumn{1}{c}{**Spatial**} & \multicolumn{1}{c}{**Spens of**} & \multicolumn{1}{c}{**Color**} & \multicolumn{1}{c}{**Richness**} & \multicolumn{1}{c}{**Attraction**} & \multicolumn{1}{c}{**Color Brightness**} & \multicolumn{1}{c}{**Aesthetic Degree**} & \multicolumn{1}{c}{**Value**} \\ \hline \multirow{5}{*}{**Sample Statistics**} & S\({}_{6}\) & 0.66 & 0.75 & 1.32 & 0.92 & 0.98 & 0.94 & 0.985 \\  & S\({}_{7}\) & 0.83 & 0.49 & 0.74 & 0.72 & 0.87 & 0.78 & 0.761 \\  & S\({}_{8}\) & 0.70 & 0.45 & 0.23 & 0.35 & 0.76 & 0.33 & 0.474 \\  & S\({}_{9}\) & 1.27 & 0.76 & 0.66 & 0.73 & 1.02 & 0.76 & 0.881 \\  & S\({}_{10}\) & 1.29 & 0.42 & 0.32 & 0.57 & 0.54 & 0.65 & 0.626 \\  & S\({}_{11}\) & 0.94 & 0.64 & 0.57 & 0.52 & 0.78 & 0.58 & 0.689 \\  & S\({}_{12}\) & 0.78 & 0.62 & 0.72 & 0.67 & 1.00 & 0.75 & 0.776 \\  & S\({}_{13}\) & 0.55 & 0.37 & 0.25 & 0.29 & 0.65 & 0.44 & 0.425 \\  & S\({}_{14}\) & 0.09 & 1.01 & 1.43 & 1.10 & 1.17 & 1.13 & 1.005 \\ \hline \multicolumn{2}{c}{Average} & 0.86 & 0.65 & 0.68 & 0.64 & 0.88 & 0.71 & 0.715 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**KMO Sampling Suitability Quantity**} & \multicolumn{1}{c}{**0.781**} \\ \hline \hline \multirow{3}{*}{**Bartlett Sphericity Test**} & Approximate chi-square & 228.289 \\ \cline{2-3}  & Degree of freedom (DF) & 15 \\ \cline{2-3}  & Significance (sig) & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: KMO and [PERSON]’s test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Initial Eigenvalue**} & \multicolumn{3}{c}{**Extract the Sum of Squares of the Load**} \\ \cline{2-5}  & **Total** & **Variance\%** & **Total \%** & **Total \%** \\ \hline
**1** & 4.616 & 76.934 & 76.934 & 4.616 & 76.934 & 76.934 \\ \hline
**2** & 0.847 & 14.122 & 91.056 & & \\ \hline
**3** & 0.318 & 5.304 & 96.359 & & \\ \hline
**4** & 0.170 & 2.830 & 99.189 & & \\ \hline
**5** & 0.031 & 0.521 & 99.710 & & \\ \hline
**6** & 0.017 & 0.290 & 100.000 & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Total variance explanation.  
The correlation matrix of color-area ratio evaluation factors (Table 9) can intuitively reflect the correlation between various color-area ratio factors and show that human vision and landscape interaction will change psychological perception. Therefore, colors are related to a person's psychological feelings. While color gives people a sense of beauty, it also affects people's psychological activities in many ways. Color psychological feelings can be generated from direct visual stimuli or obtained through simple associations and symbols. In short, it unconsciously influences people's emotions, thoughts, and behaviors [6]. The X1 in the SD factor is low, with a low load. Therefore, X1 has a relatively weak relationship with X2, X3, X4, X5, and X6, showing that the effect of spatial openness on the evaluation of color visual perception and psychological perception is weak.