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a b s t r a c tIn this article we analyse the growth of OpenStreetMap (OSM) representations for three street networks in Ireland. In each case we demonstrate the growth to be governed by two elementary spatial processes of densification and exploration which are responsible for increasing the local density of the network and expanding the network into new areas respectively. We also examine summary statistics describing each network topology and show these to be a consequence of the same processes. This represents the discovery of a novel link between different aspects of the growth.

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Several application domains require handling spatio-temporal data. However, traditional Geographic Information Systems (GIS) and database models do not adequately support temporal aspects of spatial data. A crucial issue relates to the choice of the appropriate granularity. Unfortunately, while a formalisation of the concept of temporal granularity has been proposed and widely adopted, no consensus exists on the notion of spatial granularity. In this paper, we address these open problems, by proposing a formal definition of spatial granularity and by designing a spatio-temporal framework for the management of spatial and temporal information at different granularities. We present a spatio-temporal extension of the ODMG type system with specific types for defining multigranular spatio-temporal properties. Granularity conversion functions are introduced to obtain attributes values at different spatial and temporal granularities.

Volunteered geographic information (VGI) is generated by heterogenous 'information communities' that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these 'volunteered lexical definitions' has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geosemantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer's semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity.