# Effect of Space Configurational Attributes on Social Interactions in Urban Parks
## 3 Results
### Relationship between Space Characteristics and Social Interaction  
A multivariate analysis of variance (MANOVA) was applied to detect the associations between the social interaction activity and space type (pathway or activity zone), activity type (personal or social), time (weekday or weekend), and lateral visibility (open, moderate open or closed). The results (Table 3) indicate that space type and activity type have significant influences on the number of people involved in social interaction activities (Sig. = 0.000). The average number of observed people in activity zones (22.76) is much larger than that on pathways (1.19). Meanwhile, the average size a the social group (7.13) is much larger than that of a personal group (2.16). The partial eta square indicates the effect size of each factor. As shown in Table 3 and Figure 5, the effect of the space type (\(\eta_{p}^{2}\) = 0.226) is much more significant than that of the activity type (\(\eta_{p}^{2}\) = 0.028). Different from walking behavior, 78% of social interactions occur in social groups, which are commonly fixed in the same space rather than being randomly selected by visual information. Meanwhile, these two parks are neighborhood parks, and the visitors are mainly local residents. Therefore, the time factor and lateral visibility are not statistically significant (Sig. > 0.05). In the second stage, regression analysis was used to reveal the relationship between space characteristics and social interaction activities with the space type and activity type as control variables. Because of the significant correlation between space configurational attributes, the stepwise algorithm was applied to minimize the collinearity. A significant joint effect of the space scale factors (length or area) and configurational attributes (depth, NACH, or NAIN) on social interaction activity can be found, as shown in Table 4. A small VIF for all predictors (<1.5) indicates that there is no significant collinearity. All four regression models based on space characteristics are significant (Sig. < 0.001) in explaining the social interaction behaviors in urban parks. Figure 4: Comparison of space configurational attributes between two parks. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **SS** & **df** & **MS** & **F** & **Sig.** & \(\eta_{p}^{2}\) \\ \hline Time & 9.896 & 1 & 9.896 & 0.046 & 0.830 & 0.000 \\ Space Type & 67,216.550 & 1 & 67,216.550 & 311.516 & 0.000 *** & 0.226 \\ Activity Type & 6625.210 & 1 & 6625.210 & 30.705 & 0.000 *** & 0.028 \\ Lateral Visibility & 1009.242 & 2 & 504.621 & 2.345 & 0.096 & 0.004 \\ \hline \hline \end{tabular} Note: SS = Type III sum of squares; df = degrees of freedom; MS = mean square; F = F ratio; Sig. significance; \(\eta_{p}^{2}\) = partial eta square (effect size). ** indicates significance at 0.01 level.  
\end{table} 
Table 3: MANOVA analysis of the number of observed people involved in social interaction activities. For 78% of the observed interactions (interaction occurring in the activity zone), a strong influence of the space characteristics is revealed with the high proportion of explained variances (35.1-49.2%). For the other 13% of the observed interactions (personal groups on pathways), 14.7% of the variance is explained by the combination of spatial scale and configurational attributes. For the other rare interactions (social groups on pathway, 9% of the total observed people), the interaction location contains high level of randomness and strong uncertainty, which led to a small proportion of variance that can be explained by space characteristics (2.8%). It can be seen that the key configurational attributes for different activity groups are different. For the personal groups, NAIN-1K, which indicates walking accessibility, was found to have a significant influence on social interaction activities. Moreover, for the personal groups on pathways, NACH-10K, which indicates the usability in the urban context, was found to have a positive effect on the number of observed people.