Pixel based Visual Mining of Geo-Spatial Data


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KEIM, Daniel A., Christian PANSE, Mike SIPS, Stephen C. NORTH, 2004. Pixel based Visual Mining of Geo-Spatial Data. In: Computers and graphics. 28(3), pp. 327-344

@article{Keim2004Pixel-5441, title={Pixel based Visual Mining of Geo-Spatial Data}, year={2004}, doi={10.1016/j.cag.2004.03.022}, number={3}, volume={28}, journal={Computers and graphics}, pages={327--344}, author={Keim, Daniel A. and Panse, Christian and Sips, Mike and North, Stephen C.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/5441"> <dc:language>eng</dc:language> <dc:contributor>Panse, Christian</dc:contributor> <dc:rights>deposit-license</dc:rights> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:rights rdf:resource="https://creativecommons.org/licenses/by-nc-nd/2.0/legalcode"/> <dc:contributor>Sips, Mike</dc:contributor> <dcterms:title>Pixel based Visual Mining of Geo-Spatial Data</dcterms:title> <dc:creator>Panse, Christian</dc:creator> <dc:contributor>North, Stephen C.</dc:contributor> <dc:format>application/pdf</dc:format> <dc:creator>North, Stephen C.</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:28Z</dc:date> <dcterms:bibliographicCitation>First publ. in: Computers and graphics 28 (2004), 3, pp. 327-344</dcterms:bibliographicCitation> <dc:creator>Sips, Mike</dc:creator> <dc:contributor>Keim, Daniel A.</dc:contributor> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5441"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T15:55:28Z</dcterms:available> <dcterms:abstract xml:lang="eng">In many application domains, data is collected and referenced by geo-spatial location. Spatial data mining, or the discovery of interesting patterns in such databases, is an important capability in the development of database systems. A noteworthy trend is the increasing size of data sets in common use, such as records of business transactions, environmental data and census demographics. These data sets often contain millions of records, or even far more. This situation creates new challenges in coping with scale. For data mining of large data sets to be effective, it is also important to include humans in the data exploration process and combine their flexibility, creativity, and general knowledge with the enormous storage capacity and computational power of today s computers. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially for analyzing geo-spatial data. We provide examples for effective visualizations of geo-spatial data in important application areas such as consumer analysis and census demographics.</dcterms:abstract> <dcterms:issued>2004</dcterms:issued> </rdf:Description> </rdf:RDF>

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