## 4 Experimental Application of the Model
### Evaluation of Experiment Results  
In order to evaluate the results of the performed experiment, we have to determine the differences in the numerical scale of the calculated measures values as the form of the spatial meta-pattern changes. The main results of the conducted spatial meta-pattern identification experiment are as follows: (1) Isovist Compactness measure is the most suitable for SP identification as its values show the features of the form of the spatial object and its proximity to the circle using certain clear ranges for different spatial meta-pattern values (measure value approaching to 0 shows Line, and approaching to 1--Circle meta-pattern value). MAX and MEAN values of Isovist Compactness can be used potentially as the additional values for more precise SP description; (2) Drift Magnitude measure values also show clear ranges for different spatial meta-pattern values and can be used to identify the degree of the compositional axiality; the bigger value of Drift Magnitude measure is found in more prolonged spatial meta-pattern values; (3) the normalized values of the two measures \"Isovist Compactness and Visual Integration\" can be used to compare SP of different size and to have the more convenient scale of SP values for SP identification; \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Instance\_Name: Measure**} \\ \hline
**Name** & **Formula** & **Component** & **Description** \\ \hline \({}^{\prime}\)Isovist Compactness\({}^{\prime}\) & \(\frac{\text{4}\text{T}\text{T}\text{A}^{\prime}}{\text{T}\text{B}^{\prime}}\) & \({}^{\prime}\)A, \(\Pi^{\prime}\) & \({}^{\prime}\)measure of Isovist shape that is invariant to its area\({}^{\prime}\) \\ \hline \({}^{\prime}\)Drift Magnitude\({}^{\prime}\) & \({}^{\prime}\sqrt{\left(c_{x}-g_{x}\right)^{2}+\left(c_{y}-g_{y}\right)^{2}}\), & \({}^{\prime}\)g, \(c^{\prime}\) & \({}^{\prime}\)vector from generating point to center of polygon gravity\({}^{\prime}\) \\ \hline \({}^{\prime}\)Isovist Occlusivity\({}^{\prime}\) & (found in DepthmapX) & - & \({}^{\prime}\)parts of isovist perimeter\({}^{\prime}\) \\ \hline \({}^{\prime}\)Visual Integration\({}^{\prime}\) & \({}^{\prime}\frac{1}{\text{R}\text{R}\text{A}_{D}}\), & \({}^{\prime}\)RRA\({}^{\prime}\) & \({}^{\prime}\)amount transitions needed from graph segment to reach all segments\({}^{\prime}\) \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Instance\_Name: Class\_Name**} \\ \hline
**Instance\_Name:** & \multicolumn{3}{c}{**Instance\_Name:**} \\
**Form** & \multicolumn{3}{c}{**Structure**} & **Form Measure** \\ \hline Name & Type & Variant & Description \\ \hline \({}^{\prime}\)Spatial Grid\({}^{\prime}\) & \({}^{\prime}\)Grid\({}^{\prime}\) & \({}^{\prime}\)Structure\({}^{\prime}\) & NULL \\ \hline \multirow{6}{*}{Basic Meta-pattern\({}^{\prime}\)} & \multirow{6}{*}{\({}^{\prime}\)Visuality Graph\({}^{\prime}\)} & \multirow{6}{*}{\({}^{\prime}\)Structure\({}^{\prime}\)} & \({}^{\prime}\)Point First Moment\({}^{\prime}\), \\  & & & \({}^{\prime}\)Through Vision\({}^{\prime}\), \({}^{\prime}\) Isovist Compactness\({}^{\prime}\), \\ \cline{1-1}  & & & \({}^{\prime}\)Drift Magnitude\({}^{\prime}\), \\ \cline{1-1}  & & & \({}^{\prime}\)Isovist Occlusivity\({}^{\prime}\), \\ \cline{1-1}  & & & \({}^{\prime}\)Visual Integration\({}^{\prime}\), \\ \cline{1-1}  & & & \({}^{\prime}\)Visual Mean Depth\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forms and structures for basic meta-pattern. (4) Visual Mean Depth as the measure based on topological distance can be used not normalized and its higher MAX or MEAN values show more Labyrinth, such as spatial meta-pattern structure; and (5) other measures do not demonstrate clear relations between the spatial meta-pattern form and their values, drawing the conclusions from the differences in the numerical scale of the calculated measures.