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Shpuza; Urban Shapes and Urban Grids: A Comparative Study of Adriatic and Ionian Coastal Cities

and three types of office layouts are strictly maintained: unbiased- sparse < biased < unbiased-dense (figure 7c and 7e). This suggests the third fundamental similarity between the two kinds of built environment.

The Mean Depth Slope for offices at 0.41 is higher than 0.31 for urban grids, while Mean Depth Magnitude at 0.18 (figure 7f) is much smaller than 9.72 for urban grids. For urban grids the ranking order is unbiased-sparse>biased>unbiased-dense, whereas for offices the ranking order is biased>unbiased-dense>unbiased-dense (figure 7d and 7f). These reversed orders are also explained by the fact that the vector of Mean Depth for offices is oriented in an opposite direction to the vector of Mean Depth for cities.

# Interaction of Urban Shapes and Urban Grids

This time, pairwise correlations among 6 grid measures and 6 shape measures are calculated for the sub-samples of 12 biased, 29 unbiased-sparse and 9 unbiased-dense urban grids (table 5). The discussion is focused at correlations that satisfy the yardstick r>0.500 and p<0.050. With two exceptions, measures of Length, Length Skewness, Connectivity and Connectivity Skewness, which are based on geometrical and local characteristics, show no correlation with measures of shape across three sub-samples. This reinforces the earlier conclusion drawn for the entire sample that urban shapes do not affect and do not constrain urban grids at the local level. When syntactic measures of Mean Depth, Mean Depth Skewness, Integration and Integration Skewness are concerned, the correlations with shape measures for three urban types are more numerous, stronger and more significant in comparison to a few weak correlations for the entire sample (table 3). Figure 8 shows a few correlations displayed according to the three types, unbiased-sparse on top, biased on middle left and unbiased-dense on middle right.

Unbiased-sparse grids do not display strong correlations between Integration and shape measures. However, Mean Depth is affected by fragmentation indices as shown by strong and significant positive correlations with F_{convex }and F_{visual}. Another peculiarity of this type is the strong negative correlation D_{relative }Skewness vs. Integration Skewness (table 5, figure 8a). The correlation is improved for D_{relative }Skewness() vs. Integration Skewness, i.e. when shape hulls are considered (figure 8b). This indicates that the asymmetry of shapes with regard to the distribution of compactness is passed onto the symmetry of integration of urban grids. As internal regions of urban shapes are differentiated to each other according to their metric proximity, so are parts of urban grids resemble each other according to their relativized graph step depth.

F o r b i a s e d u r b a n g r i d s , s t r o n g a n d s i g n i f i c a n t n e g a t i v e c o r r e l a t i o n s between the two measures of shape fragmentation, i.e. F_{convex }and

Table 5:

Correlations between grid measures and shape measures for sub-samples of 12 biased 29 unabiased- sparse and 9 unabiased- dense urban grids (next page)

F_{visual}, and Integration indicate that the effect of urban shape on urban grids is exerted according to the shape fragmentation, where more convex and less fragmented urban shapes coincide with more integrated street networks (table 5, figure 8d). Similar to unbiased- sparse grids, there exist positive correlations between shape fragmentation and grid Mean Depth (table 5). In contrast, the nonexistent correlations between D_{relative }and Integration (figure 8c) as well as to other grid measures point out that compactness of shape does not affect the syntactic properties of biased urban grids.