%0 Journal Article %1 Bailey-KelloggZ04 %A Bailey-Kellogg, Christopher %A Zhao, Feng %D 2004 %J AI Magazine %K imported %N 4 %P 47-60 %T Qualitative Spatial Reasoning: Extracting and Reasoning with Spatial Aggregates %U http://www.cs.dartmouth.edu/~cbk/papers/aimag04.pdf %V 24 %X Reasoning about spatial data is a key task in many applications, including geographic information systems, meteorological and fluid flow analysis, computer-aided design, and protein structure databases. Such applications often require the identification and manipulation of qualitative spatial representations, for example, to detect whether one öbject" will soon occlude another in a digital image, or to efficiently determine relationships between a proposed road and wetland regions in a geographic data set. Qualitative spatial reasoning (QSR) provides representational primitives (a spatial "vocabulary") and inference mechanisms for these tasks. This paper first reviews representative work on QSR for data-poor scenarios, where the goal is to design representations that can answer qualitative queries without much numerical information. It then turns to the data-rich case, where the goal is to derive and manipulate qualitative spatial representations that efficiently and correctly abstract important spatial aspects of the underlying data, for use in subsequent tasks. This paper focuses on how a particular QSR system, Spatial Aggregation (SA), can help answer spatial queries for scientific and engineering data sets. A case study application of weather analysis illustrates the effective representation and reasoning supported by both data-poor and data-rich forms of QSR.

@article{Bailey-KelloggZ04, abstract = {Reasoning about spatial data is a key task in many applications, including geographic information systems, meteorological and fluid flow analysis, computer-aided design, and protein structure databases. Such applications often require the identification and manipulation of qualitative spatial representations, for example, to detect whether one "object" will soon occlude another in a digital image, or to efficiently determine relationships between a proposed road and wetland regions in a geographic data set. Qualitative spatial reasoning (QSR) provides representational primitives (a spatial "vocabulary") and inference mechanisms for these tasks. This paper first reviews representative work on QSR for data-poor scenarios, where the goal is to design representations that can answer qualitative queries without much numerical information. It then turns to the data-rich case, where the goal is to derive and manipulate qualitative spatial representations that efficiently and correctly abstract important spatial aspects of the underlying data, for use in subsequent tasks. This paper focuses on how a particular QSR system, Spatial Aggregation (SA), can help answer spatial queries for scientific and engineering data sets. A case study application of weather analysis illustrates the effective representation and reasoning supported by both data-poor and data-rich forms of QSR.}, added-at = {2009-04-21T21:50:18.000+0200}, author = {Bailey-Kellogg, Christopher and Zhao, Feng}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/20b84eab64e4d40f0a7016ba4e6c5991a/neilernst}, interhash = {12b1bd62e0c37cde1b115bb511f1d883}, intrahash = {0b84eab64e4d40f0a7016ba4e6c5991a}, journal = {AI Magazine}, keywords = {imported}, number = 4, pages = {47-60}, timestamp = {2009-04-21T21:50:18.000+0200}, title = {Qualitative Spatial Reasoning: Extracting and Reasoning with Spatial Aggregates}, url = {http://www.cs.dartmouth.edu/~cbk/papers/aimag04.pdf}, volume = 24, year = 2004 }