# Using Mixed Reality (MR) to Improve On-Site Design Experience in Community Planning
## 3 Results and Analysis
### On-Site Design Accuracy Using MR  
From the interviews, we found that the mean and standard deviation of the score for on-site design accuracy using MR were 3.65 and 0.56, respectively, chiefly involving three concrete factor evaluations in the questionnaire (Figure 11).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mean Ranks of Scores for the MR Usage Experience Groups** & \(p\)**-Value** \\ \hline  & With MR usage experience & Without MR usage experience & (Kruskal–Wallis) \\ \hline On-site design intuitiveness & 11.00 & 7.36 & 0.112 \\ using MR & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Kruskal–Wallis test for different MR usage experience groups in on-site design intuitiveness using MR.  
Figure 10: The mean and standard deviation of the score for on-site design intuitiveness using MR, the realness of the physical environments, the verisimilitude of the virtual design objects, and the immersion of the MR environments.  
In terms of design material selection, the mean and standard deviation of the score were 3.69 and 0.60, respectively (Figure 11). Most participants stated that MR could render vivid textures and colors for various materials, which contributed to the relatively accurate material selection in specific existing community environments. A minority of participants, however, indicated that the rendering of the material was based on prepared 2D texture maps that could not represent the 3D details. Concerning design position judgment, the mean and standard deviation of the score were 3.88 and 0.81, respectively, as shown in Figure 11. The participants universally stated that MR displayed a totally real spatial relationship; thus, they could easily make position judgments about virtual design objects in the real community scene. With the measurement function of this MR-DSS, they were even able to acquire accurate spatial distances and coordinates. However, a small number of participants with rich design experience did not agree. They noted that they could still make accurate position judgments on site by deducing the planning and design layout in their minds, even without MR. Regarding design size control, the mean and standard deviation of the score were 3.38 and 0.72, respectively (Figure 11). Similar to the results for design position judgment, most of the participants reported that MR technology aided in effectively controlling the design size in this experiment, while those with rich design experience indicated that the effectiveness of MR was only mediocre. Furthermore, some participants responded that the unfamiliar interaction mode challenged them in accurately controlling the size of the virtual design objects in the experiment.  
From the Kruskal-Wallis test (Table 3), we also saw significant differences (\(p=0.049<0.05\)) in the scores for on-site design accuracy using MR between different MR usage experience groups. More precisely, the average rating (4.07) from the group with MR usage experience was significantly higher than that (3.45) from the group without MR usage experience, as shown in Figure 12. The Kruskal-Wallis test (Table 4) additionally revealed significant differences (\(p=0.045<0.05\)) in the ratings of on-site design accuracy using MR among different design experience groups. Further, from Figure 13, we can see that the average scores for on-site design accuracy were 3.89, 3.53, and 2.83 in the 1-10 year, 11-20 years, and 21-30 years design experience groups, respectively, showing a clear downward trend with the increase in design experience.  
Figure 11: The mean and standard deviation of the score for on-site design accuracy using MR, design material selection, design position judgment, and design size control.