Comparative Analysis of Urban Morphology: Evaluating Space Syntax and Traditional Morphological Methods

This thesis examines the urban morphology of fifty cities using spatial syntax analysis. The analysis compares the urban street networks of European, USA, Islamic and East Asian cities. Street connectivity was the main metric and natural roads were the basis for the analysis. The aim of the study was to analyze determine how sensitive space syntax methods are for uncovering the hierarchical patterns of urban street networks and examining their scale-free and small-world properties. Street data was collected from OpenStreetMap. ArcGIS 10 with the Axwoman extension was used to study the hierarchical levels of street networks. Matlab provided the platform to examine the scale-free property of street data. Pajek software was used to measure the small-world behavior. Based on the hierarchical representation, the fifty sample cities were classified into different groups and their scale-free and small-world properties were studied. From a traditionally morphological perspective, it was found that some cities in Europe have a close-knit cellular and organic urban morphology. Cities in the USA exhibit gridiron patterns on the whole. Some Islamic cities have special urban structure with houses grouped around the cul-de-sac lanes. Several of the East Asian cities studied also have grid forms. According to the space syntax analysis, urban street networks that have a connectivity value greater than the average value were less than 40%. The results showed that for most cities, the street connectivity distribution follows a power-law distribution and exhibits scale-free properties. Urban street networks of all sample cities were found to have a small-world property. Space syntax cannot detect all of the morphological patterns recognized in traditional morphological studies. The method can, however, efficiently quantify the spatial configuration of a large sample. Space syntax’s topological and scaling metrics thus provide a way to compare urban street networks. These metrics can thus help classify cities according to their street patterns but also contribute to an understanding of human behavior within and thus the design of urban spaces. For example, an urban street network with a small-world property could have high efficiency for traffic flows at local and global levels and should be considered in further study