Using hierarchical spatial data structures for hierarchical spatial reasoning

Timpf, Sabine; Frank, Andrew U.

doi:10.1007/3-540-63623-4_43

Cited by 48 publications

(37 citation statements)

References 6 publications

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“…?higher order capabilities (leading to leaner, more elegant descriptions). Encouraged by a series of successful applications to non-trivial software engineering tasks (Car and Frank 1995;Frank and Kuhn 1995;Timpf and Frank 1997), we have used functional languages for ontological research into the structure of application domains (Medak 1997;Frank 1999;Kuhn 2001). The work presented here continues on this path by formalizing hierarchical navigation tasks on highway networks.…”

Section: Formalizationmentioning

confidence: 99%

Granularity Transformations in Wayfinding

Timpf¹,

Kuhn²

Lecture Notes in Computer Science

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Abstract. Wayfinding in road networks is a hierarchical process. It involves a sequence of tasks, starting with route planning, continuing with the extraction of wayfinding i nstructions, and leading to the actual driving. From one task level to the next, the relevant road network becomes more detailed. How does the wayfinding process change? Building on a previous, informal hierarchical highway navigation model and on graph granulation theory, we are working toward a theory of granularity transformations for wayfinding processes. The paper shows the first results: a formal ontology of wayfinding at the planning level and an informal model of granularity mappings.

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Section: Formalizationmentioning

confidence: 99%

Granularity Transformations in Wayfinding

Timpf¹,

Kuhn²

Lecture Notes in Computer Science

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“…More complex tasks, such as planning of routes, computing shortest path problems, and allocation of resources, can be categorised in the broad area of spatial reasoning as well (Yang, 2001). In fact, spatial reasoning can be seen as any deduction of knowledge from a situation having spatial properties (Timpf, Frank, 1997). In most geographical software packages, the absolute positions of spatial entities are represented by sets of coordinates in the Euclidean space, and information is extracted by means of arithmetic and trigonometric computations.…”

Section: Qualitative Representation and Reasoning About Space And Timementioning

confidence: 99%

Geographical information systems

Fischer¹

2016

Image Processing and GIS for Remote Sensing

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“…Hierarchy is fundamental to human cognition [12, p.310], and "our ability to conceptualize the world at different granularities and to switch among these granularities is fundamental to our intelligence and flexibility." [10] In particular, since the spatial environment is infinitely complex, humans typically use hierarchies as the major conceptual tool to structure and reason about the infinite levels of details [30]. This coarse-to-fine approach is usually very efficient since a lot of unrelated information has been discarded, and we need to focus only on a rather restricted domain.…”

Section: The Problem With Current High-level Approachesmentioning

confidence: 99%

“…The importance of hierarchical reasoning has been identified by several authors in the field of Geographical Information Science [30,29,2,32]. Timpf and Frank [30] give a definition of hierarchical spatial reasoning, using hierarchical spatial data structures, which computes increasingly better results in a hierarchical fashion and stops the computation when a 'good enough' result is achieved.…”

Section: Related Workmentioning

confidence: 99%

Qualitative Spatial Representation and Reasoning: A Hierarchical Approach

Nebel

2007

The Computer Journal

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The ability to reason in space is crucial for agents in order to make informed decisions. Current high-level qualitative approaches to spatial reasoning has serious deficiencies in not reflecting the hierarchical nature of spatial data and human spatial cognition. This paper proposes a framework for hierarchical representation and reasoning about topological information, where a continuous model of space is approximated by a collection of discrete sub-models, and spatial information is hierarchically represented in discrete sub-models in a rough set manner. The work is based on the GRCC theory, where continuous and discrete models of space are coped in a unified way. Reasoning issues such as determining the mereological (part-whole) relations between two rough regions are also discussed. Moreover, we consider an important problem that is closely related to map generalization in cartography and Geographical Information Science. Given a spatial configuration at a finer level, we show how to construct a configuration at a coarser level while preserving the mereological relations.

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Using hierarchical spatial data structures for hierarchical spatial reasoning

Cited by 48 publications

References 6 publications

Granularity Transformations in Wayfinding

Granularity Transformations in Wayfinding

Geographical information systems

Qualitative Spatial Representation and Reasoning: A Hierarchical Approach

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