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

and industrial areas (e.g. Brindisi, Barletta, Termoli, Giulianova, Pesaro); and canals (e.g. Chioggia Sottomarina, Lido, Venice).

The argument is developed in four steps: First, urban shapes are analyzed with three measures that gauge compactness and fragmentation of shape (Shpuza 2001); Second the linear maps in the sample are analyzed with the conventional space syntax measures; Third, statistical analysis is used to discover the existence of three types of urban grid based on patterns connectivity; urban grids show generic commonalities with office layouts, thus illustrating fundamental characteristics of the built environment; Fourth, according to the interaction between shapes and grids, it is argued that the effect of shape upon the integration of urban grid is exerted according to different urban grid types.

Analysis of urban shapes

The representation and analysis of built environment with modular units (Batty 2001, Dalton and Dalton 2001, Turner et al 2001) has a threefold advantage: first, the association of descriptions and measures with continuous qualities of space; second, gauging these qualities in a configurational manner; third, these methods take into account dimensional aspects of shape by weighing the configurational analysis with metrics of size and distance. Space syntax studies using modular analysis have addressed differences between parts of buildings and cities without characterizing the systems globally. Here, in contrast, the focus is on describing the entire shape with a few indices, where the aggregate measures of each and every modular unit in the shape are summed up and relativized for size.

U r b a n s h a p e s a r e r e p r e s e n t e d w i t h m o d u l a r s q u a r e u n i t s a n d a n a l y z e d w i t h Q e l i z e i i a c c o r d i n g t o t h r e e m e a s u r e s o f R e l a t i v e G r i d D i s t a n c e D r e l a t i v e i i i ( f i g u r e s 5 a a n d 5 b ) , C o n v e x F r a g m e n t a t i o n F c o n v e x (figures 5d and 5e) (Shpuza 2001, Shpuza and Peponis 2005), and the proposed measure of Visual Fragmentation Fvisual which is i v

calculated by the formula:

i =n,j =n

Fvisual

=

f ) i j ( v i s u a l i =1,j =1

n2

(1)

where fvisual(ij) is the visual depth between two unit tiles i and j, whereas n is the total number of unit tiles in the shape. The visual depth between two tiles is similar to the visual accessibility (Hillier and Hanson 1984) and visual shortest path length (Turner et al 2001). The first group of tiles visible from a given tile has a visual depth of zero (figures 5g and 5h); the next group of tiles visible from the first group of tiles has a depth of one and so on. Similar to Convex Fragmentation, for a sufficient large number of representation units, the measure of Visual Fragmentation is not affected by the number of representation units. The value 0 indicates a convex shape, whereas larger values show the fragmentation in the shape due to indents, wings or holes.

The three shape measures are based on aggregating distances between shape locations: grid metric distances in the case of Drelative; distances across overlapping convex areas for Fconvex; and distances across isovists for Fvisual. Former research has used the distribution of metric distances in shapes to characterize geographical shapes (Luu- Mau-Thanh 1962, Bunge 1966, Maceachren 1985) and the skewness of a few geometrical measures to describe isovists (Batty 2001). Taylor (1971) has shown that the skewness of distribution of metric distances within shapes is directly affected by changes in shapes: skewness increases markedly by increasing elongality; it decreases

Proceedings, 6th International Space Syntax Symposium, stanbul, 2007

009-03

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