Abstract and discrete models for uncertain spatiotemporal data

Tøssebro, Erlend; Nygård, Mads

doi:10.1109/ssdm.2002.1029727

Cited by 6 publications

(5 citation statements)

References 3 publications

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“…Another model for representing uncertainty in spatial database is introduced by Tossebro et al in [22][23][24][25][26]. In [23] the authors propose a representation of spatial data through uncertain points, uncertain lines and uncertain regions.…”

Section: Related Workmentioning

confidence: 99%

A framework for integrating multi-accuracy spatial data in geographical applications

Belussi

Migliorini

2011

Geoinformatica

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In recent years the integration of spatial data coming from different sources has become a crucial issue for many geographical applications, especially in the process of building and maintaining a Spatial Data Infrastructure (SDI). In such context new methodologies are necessary in order to acquire and update spatial datasets by collecting new measurements from different sources. The traditional approach implemented in GIS systems for updating spatial data does not usually consider the accuracy of these data, but just replaces the old geometries with the new ones. The application of such approach in the case of an SDI, where continuous and incremental updates occur, will lead very soon to an inconsistent spatial dataset with respect to spatial relations and relative distances among objects. This paper addresses such problem and proposes a framework for representing multi-accuracy spatial databases, based on a statistical representation of the objects geometry, together with a method for the incremental and consistent update of the objects, that applies a customized version of the Kalman filter. Moreover, the framework considers also the spatial relations among objects, since they represent a particular kind of observation that could be derived from geometries or be observed independently in the real world. Spatial relations among objects need also to be compared in spatial data integration and we show that they are necessary in order to obtain a correct result in merging objects geometries.

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Section: Related Workmentioning

confidence: 99%

A framework for integrating multi-accuracy spatial data in geographical applications

Belussi

Migliorini

2011

Geoinformatica

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“…This paper will not consider temporal uncertainty in the snapshots. [18] describes some ways of eliminating that uncertainty from the data model by converting it into spatial uncertainty, and this paper assumes that such a method has been used prior to the tetrahedralization.…”

Section: Triangulating Spatiotemporal Objectsmentioning

confidence: 99%

“…In [17] and [18], a method for storing uncertainty in spatiotemporal data was presented. The method is based around storing a region in which the object might be, called the support (a term from fuzzy set theory [20]).…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents an algorithm for quickly interpolating probabilities or fuzzy set values that works for spatiotemporal objects as well. It is based on the uncertain time slice model for uncertain spatiotemporal objects as presented in [18] and described in section II.B and II.C. A time slice of a spatiotemporal object is a special case of a 3D object, and creating something like a Delaunay triangulation in three dimensions is significantly more complex than for the two-dimensional case in [19].…”

Section: Introductionmentioning

confidence: 99%

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Interpolating Probability Values or Fuzzy Set Values for Uncertain Spatiotemporal Objects

Tøssebro

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems

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This paper looks at ways of quickly interpolating probability values or fuzzy set values for uncertain spatiotemporal objects that may change continuously over time. The paper starts with presenting a way to compute a tetrahedralization of an uncertain spatiotemporal object and using that to compute consistent interpolations. This approach also turns out to be able to create fairly good interpolations of the shape of the spatiotemporal object without needing an extra algorithm for this purpose. However, a naïve use of any tetrahedralization turns out to create interpolation artifacts in those objects that become significantly more or less uncertain with time. The paper then presents a way to overcome this issue at the cost of more processing.

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“…Several models have been presented for storing spatial data with uncertain or vague positions, including the models from Nygård 2002a and2002b) that this paper builds on. However, these models do not handle topological relationships well.…”

Section: Introductionmentioning

confidence: 99%

A Discrete Model for Topological Relationships between Uncertain Spatial Objects

Tøssebro

Nygård

Developments in Spatial Data Handling

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Abstract and discrete models for uncertain spatiotemporal data

Cited by 6 publications

References 3 publications

A framework for integrating multi-accuracy spatial data in geographical applications

A framework for integrating multi-accuracy spatial data in geographical applications

Interpolating Probability Values or Fuzzy Set Values for Uncertain Spatiotemporal Objects

A Discrete Model for Topological Relationships between Uncertain Spatial Objects

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