In crowdsourced cartographic projects, mappers coordinate their efforts through online tools to produce digital geospatial artefacts, such as maps and gazetteers, which were once the exclusive territory of professional surveyors and cartographers. In order to produce meaningful and coherent data, contributors need to negotiate a shared conceptualisation that defines the domain concepts, such as road, building, train station, forest, and lake, enabling the communication of geographic knowledge. Considering the OpenStreetMap Wiki website as a case study, this article investigates the nature of this negotiation, driven by a small group of mappers in a context of high contribution inequality. Despite the apparent consensus on the conceptualisation, the negotiation keeps unfolding in a tension between alternative representations, which are often incommensurable, i.e., hard to integrate and reconcile. In this study, we identify six complementary dimensions of incommensurability that recur in the negotiation: (i) ontology, (ii) cartography, (iii) culture and language, (iv) lexical definitions, (v) granularity, and (vi) semantic overload and duplication.

Daring predictions of the proximate future can establish shared discursive frameworks, mobilize capital, and steer complex processes. Among the prophetic visions that encouraged and accompanied the development of new communication technologies was the “Digital Earth,” described in a 1998 speech by Al Gore as a high-resolution representation of the planet to share and analyze detailed information about its state. This article traces a genealogy of the Digital Earth as a techno-scientific myth, locating it in a constellation of media futures, arguing that a common subtext of these envisionments consists of a dream of wholeness, an afflatus to overcome perceived fragmentation among humans, and between humans and the Earth.

The computational representation of place is one of the key research areas for the advancement of geographic information science (GIScience), bridging the gap between place-based human cognition and experience, and space-centered information systems. While many conceptual schemas, vocabularies and ontologies contain some notion of place, the concept is either left implicit or articulated in widely divergent ways. Because of its ubiquity, an ontological clarification of place seems overdue. Adopting the perspective of ontology engineering, and not that of philosophical Ontology, this article paves the way towards the formalization of a place ontology in two steps. First, it provides a critical survey of how this concept is currently represented from lightweight vocabularies to formal ontologies. Second, it presents a set of prolegomena for a place ontology which would overcome the limitations of current approaches. Acknowledging the cultural dependency of place, I argue that such an ontology should be seen as a module positioned between foundational and domain ontologies. This place ontology would provide (i) a conceptual tool to support the modeling of place in any domain, and (ii) a widely applicable ontology, whose deployment would increase the interoperability of datasets, particularly in the context of Linked Data.

Maps constitute a ubiquitous medium through which we understand, construct, and navigate our natural and built surroundings. At the intersection of the explosion of geographic information online, data-mining techniques, and the increasing popularity of Web maps, a novel possibility has emerged: Instead of generating one map for large numbers of users, user pro ling and implicit feedback analysis can support creation of a different map for each person. The automated personalization of the map-making process is still in its infancy but has the potential to provide more relevant maps to millions of users worldwide.

The assessment of the quality of volunteered geographic information (VGI) is cornerstone to understand the fitness for purpose of datasets in many application domains. While most analyses focus on geometric and positional quality, only sporadic attention has been devoted to the interpretation of the data, i.e., the communication process through which consumers try to reconstruct the meaning of information intended by its producers. Interpretability is a notoriously ephemeral, culturally rooted, and context-dependent property of the data that concerns the conceptual quality of the vocabularies, schemas, ontologies, and documentation used to describe and annotate the geographic features of interest. To operationalize conceptual quality in VGI, we propose a multi-faceted framework that includes accuracy, granularity, completeness, consistency, compliance, and richness, proposing proxy measures for each dimension. The application of the framework is illustrated in a case study on a European sample of OpenStreetMap, focused specifically on conceptual compliance.

We present the design rationale underlying a language for spatial computing and sketch a prototypical implementation in Python. The goal of this work is to provide a high-level language for spatial computing that is executable on existing commercial and open source spatial computing platforms, particularly Geographic Information Systems (GIS). The key idea of the approach is to target an abstraction level higher than that of GIS commands and data formats, yet meaningful within and across application domains. The paper describes the underlying theory of spatial information and shows its evolving formal specification. An embedding in Python exemplifies access to commonly available implementations of spatial computations.

This video was recorded live at the annual Spatial Lightning Talks on February 25, 2014 at the Mosher Alumni House, UC Santa Barbara. Each speaker was allotted three minutes to present a topic related to space —geographic or otherwise.

Graphs have become ubiquitous structures to encode geographic knowledge online. The Semantic Web’s linked open data, folksonomies, wiki websites and open gazetteers can be seen as geo-knowledge graphs, that is labeled graphs whose vertices represent geographic concepts and whose edges encode the relations between concepts. To compute the semantic similarity of concepts in such structures, this article defines the network-lexical similarity measure (NLS). This measure estimates similarity by combining two complementary sources of information: the network similarity of vertices and the semantic similarity of the lexical definitions. NLS is evaluated on the OpenStreetMap Semantic Network, a crowdsourced geo-knowledge graph that describes geographic concepts. The hybrid approach outperforms both network and lexical measures, obtaining very strong correlation with the similarity judgments of human subjects.

Spatial user-generated content (UGC) is increasingly being used to study a variety of geographical phenomena, including urban change in social and economic dimensions. Wikipedia content evolves over time and includes articles about geographical areas, points of interest, and geo-located events. In this article, we explore the spatio-temporal variability of geo-located Wikipedia pages, considering their complete editing history. Selecting Greater London as a case study, we study the association between Wikipedia activity and the socio-demographic characteristics of the spatial context. Editing activity grows rapidly at first, and is then followed by a slowdown, reaching a stable rate, with occasional spikes. The initial growth is distributed throughout the study area, but activity becomes gradually more concentrated in central areas. The socio-demographic variability is strongly related to the presence of Wikipedia pages, but only partially to the editing. This approach may support the detection and characterisation of socio-economic change at the urban scale.