Core Concepts of Spatial Information

Geographic Information Systems (GIS) support spatial problem solving by large repositories of procedures, which are mainly operating on map layers. These procedures and their parameters are often not easy to understand and use, especially not for domain experts without extensive GIS training. This hinders a wider adoption of mapping and spatial analysis across disciplines. Building on the idea of core concepts of spatial information, and further developing the language for spatial computing based on them, we introduce an alternative approach to spatial analysis, based on the idea that users should be able to ask questions about the environment, rather than finding and executing procedures on map layers. We define such questions in terms of the core concepts of spatial information, and use data abstraction instead of procedural abstraction to structure command spaces for application programmers (and ultimately for end users). We sketch an implementation in Python that enables application programmers to dispatch computations to existing GIS capabilities. The gains in usability and conceptual clarity are illustrated through a case study from economics, comparing a traditional procedural solution with our declarative approach. The case study shows a reduction of computational steps by around 45 %, as well as smaller and better organized command spaces.

We explore the idea of spatial lenses as pieces of software interpreting data sets in a particular spatial view of an environment. The lenses serve to prepare the data sets for subsequent analysis in that view. Examples include a network lens to view places in a literary text, or a field lens to interpret pharmacy sales in terms of seasonal allergy risks. The theory underlying these lenses is that of core concepts of spatial information, but here we exploit how these concepts enhance the usability of data rather than that of systems. Spatial lenses also supply transformations between multiple views of an environment, for example, between field and object views. They lift these transformations from the level of data format conversions to that of understanding an environment in multiple ways. In software engineering terms, spatial lenses are defined by constructors, generating instances of core concept representations from spatial data sets. Deployed as web services or libraries, spatial lenses would make larger varieties of data sets amenable to mapping and spatial analysis, compared to today’s situation, where file formats determine and limit what one can do. To illustrate and evaluate the idea of spatial lenses, we present a set of experimental lenses, implemented in a variety of languages, and test them with a variety of data sets, some of them non-spatial.

How we categorize certain objects depends on the processes they afford: something is a vehicle because it affords transportation, a house because it offers shelter or a watercourse because water can flow in it. The hypothesis explored here is that image schemas (such as LINK, CONTAINER, SUPPORT, and PATH) capture abstractions that are essential to model affordances and, by implication, categories. To test the idea, I develop an algebraic theory formalizing image schemas and accounting for the role of affordances in categorizing spatial entities.

The chapter shows how minimal assumptions on difficult philosophical questions suffice for an engineering approach to the semantics of geospatial information. The key idea is to adopt a conceptual view of information system ontologies with a minimal but firm grounding in reality. The resulting constraint view of ontologies suggests mechanisms for grounding, for dealing with uncertainty, and for integrating folksonomies. Some implications and research needs beyond engineering practice are discussed.

An ontology of observation and measurement is proposed, which models the relevant information processes independently of sensor technology. It is kept at a sufficiently general level to be widely applicable as well as compatible with a broad range of existing and evolving sensor and measurement standards. Its primary purpose is to serve as an extensible backbone for standards in the emerging semantic sensor web. It also provides a foundation for semantic reference systems by grounding the semantics of observations, as generators of data. In its current state, it does not yet deal with resolution and uncertainty, nor does it specify the notion of a semantic datum formally, but it establishes the ontological basis for these as well as other extensions.

U n c o r r e c t e d p r o o f s - J o h n B e n j a m i n s P u b l i s h i n g C o m p a n y I make the case for ontology of landscape in language, addressing a series of concerns that are hindering a broader take-up of ontology as a tool for intra-and cross-linguistic research. The bottom line of my argument is that ontologies, as formal specifications of vocabularies, address a core need of language studies and that the complications arising from different philosophi-cal views on ontology are largely irrelevant for the practical task of studying landscape in language. I propose a view of ontologies as systems of constraints on interpretations of vocabularies, allowing language researchers to describe conceptualizations partially, but down to an arbitrarily fine level of detail. Foundational ontologies help to structure such specifications and to link them across languages and domains.

The Semantic Web emphasizes encoding over modeling. It is built on the premise that ontology engineers can say something useful about the semantics of vocabularies by expressing themselves in an encoding language for automated reasoning. This assumption has never been systematically tested and the shortage of documented successful applications of Semantic Web ontologies suggests it is wrong. Rather than blaming OWL and its expressiveness (in whatever flavor) for this state of affairs, we should improve the modeling techniques with which OWL code is produced. I propose, therefore, to separate the concern of modeling from that of encoding, as it is customary for database or user interface design. Modeling semantics is a design task, encoding it is an implementation. Ontology research, for applications in the Semantic Web or elsewhere, should produce languages for both. Ontology modeling languages primarily support ontological distinctions and secondarily (where possible and necessary) translation to encoding languages.

Geographic information science is emerging from its niche ‘behind the systems’, getting ready to contribute to transdisciplinary research. To succeed, a conceptual consensus across multiple disciplines on what spatial information is and how it can be used is needed. This article proposes a set of 10 core concepts of spatial information, intended to be meaningful to scientists who are not specialists of spatial information: location, neighbourhood, field, object, network, event, granularity, accuracy, meaning, and value. Each proposed concept is briefly characterized, demonstrating the need to map between their different disciplinary uses.

The work reported here explores the idea of identifying a small set of core concepts of spatial information. These concepts are chosen such that they are communicable to, and applicable by, scientists who are not specialists of spatial information. They help pose and answer questions about spatio-temporal patterns in domains that are not primarily spatial, such as biology, economics, or linguistics. This paper proposes a first selection of such concepts, with the purpose of initiating a discussion of their choice and characterization, rather than presenting a definitive catalog or novel insights on the concepts.

One of the main reasons why software projects fail is the lack of communication between the business users, who actually know the problem domain, and the developers who design and implement the software model. " (Ghosh 2011). Abstract 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.