The authors propose a topological analysis of large urban street networks based on a computational and functional graph representation. This representation gives a functional view in which vertices represent named streets and edges represent street intersections. A range of graph measures, including street connectivity, average path length, and clustering coefficient, are computed for structural analysis. In order to characterise different clustering degrees of streets in a street network they generalise the clustering coefficient to a k-clustering coefficient that takes into account k neighbours. Based on validations applied to three cities, the authors show that large urban street networks form small-world networks but exhibit no scale-free property.

Although space syntax has been successfully applied to many urban GIS studies, there is still a need to develop robust algorithms that support the automated derivation of graph representations. These graph structures are needed to apply the computational principles of space syntax and derive the morphological view of an urban structure. So far the application of space syntax principles to the study of urban structures has been a partially empirical and non-deterministic task, mainly due to the fact that an urban structure is modeled as a set of axial lines whose derivation is a non-computable process. This paper proposes an alternative model of space for the application of space syntax principles, based on the concepts of characteristic points defined as the nodes of an urban structure schematised as a graph. This method has several advantages over the axial line representation: it is computable and cognitively meaningful. Our proposal is illustrated by a case study applied to the city of GaÈ vle in Sweden. We will also show that this method has several nice properties that surpass the axial line technique.

This paper surveys indoor spatial models developed for research fields ranging from mobile robot mapping, to indoor location-based services (LBS), and most recently to context-aware navigation services applied to indoor environments. Over the past few years, several studies have evaluated the potential of spatial models for robot navigation and ubiquitous computing. In this paper we take a slightly different perspective, considering not only the underlying properties of those spatial models, but also to which degree the notion of context can be taken into account when delivering services in indoor environments. Some preliminary recommendations for the development of indoor spatial models are introduced from a context-aware perspective. A taxonomy of models is then presented and assessed with the aim of providing a flexible spatial data model for navigation purposes, and by taking into account the context dimensions

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