An urban environment can be abstracted in form of a street network in order to be further analysed structurally. The urban street network can be represented in various ways by taking different principles and constraints into account. Therefore the aim of this work is to investigate human behaviour and communication in emerged urban phenomena, namely traffic flow and wayfinding, by structural characterization of an appropriate representation of an urban street network and modifying the conventional methods. / In order to characterize the depicted urban street network, centrality measure and specifically betweenness centrality is utilized. This analysis is then implemented to characterize the studied urban phenomena with respect to their structural, temporal and dynamic properties. In case of studying only the structural properties of the phenomena such as route description or self localization the conventional betweenness centrality is performed. But in case of studying the dynamic and temporal properties of a phenomenon such as traffic flow a modified version of betweenness centrality is proposed which considers dynamic and temporal aspects of human travel behaviour. / Experiments are designed to test the implementation of the suggested methods in the studied urban phenomena. The results of experiments demonstrate the efficiency of the proposed model in characterization of the studied urban phenomena in this thesis and then mention some of the problems and potential areas for future works.

Streets provide the framework of a city and they are necessary for human life. Some underlying patterns of street networks cannot be directly recognized by people. In this study, topological analysis of urban street networks was adopted to build up new insight into urban morphology. Space syntax, which has been integrated into GIS, was applied for the analysis of spatial configuration, and fifty Swedish cities were chosen as samples to uncover various urban patterns. Street connectivity was the focus of the analysis and axial lines were the main analytical tools. The aim of this study was to hierarchically represent the cities’ streets and classify the sample cities into different types by urban morphology. Street data for Swedish cities were collected from OpenStreetMap. ArcGIS 10, with the Axwoman extension, provided a platform to carry out the topological analysis. Natural roads, axial lines and space syntax parameters were generated automatically with the functions of Axwoman. Hierarchical levels of streets were visually represented and the underlying pattern of each city was gotten from the hierarchical representation. Based on street hierarchy, the fifty sample cities were classified into nine groups, wherein cities of the same group had uniform hierarchical levels. Using the hierarchical pattern of each group’s axial lines, the nine city groups were further reclassified into three types. It was found that, for the street network of most sample cities represented with axial lines, not more than 40% of their streets have connectivity larger than the average value. The hierarchical representation also revealed that streets with high connectivity, which provide greater accessibility, were only minorities in the sample cities. Moreover, minor streets with high connectivity were almost distributed in city centers. In some of the studied cities, axial lines made better representation of the hierarchical patterns of streets, while in others, it did not provide a suitable way of uncovering urban patterns compared to natural roads. A limitation of axial lines manifested in this study was that it chopped curved roads into several segments, thus, disrupting the continuity of streets. In general, axial lines can provide a way to uncover urban patterns. They have meaningful effect to city residents and these patterns can help people gain better understanding of the urban structure. In addition, the hierarchical patterns of streets can be used to model pedestrian and traffic flows, predict crime occurrences, and make spatial plans. The hierarchical representation of streets can also contribute to people’s wayfinding performance.

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.