Geo-Scape, a Granularity Depended Spatialization Tool for Visualizing Multidimensional Data Sets

Sofia, K; Margarita, K; Marinos, K

dc.contributor.author	Sofia, K	en
dc.contributor.author	Margarita, K	en
dc.contributor.author	Marinos, K	en
dc.date.accessioned	2014-03-01T01:33:34Z
dc.date.available	2014-03-01T01:33:34Z
dc.date.issued	2010	en
dc.identifier.issn	10095020	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20466
dc.subject	graphical interface	en
dc.subject	kernel density estimation	en
dc.subject	multidimensional data	en
dc.subject	spatial metaphors	en
dc.subject	spatialization	en
dc.subject.other	data processing	en
dc.subject.other	data set	en
dc.subject.other	estimation method	en
dc.subject.other	spatial data	en
dc.subject.other	visualization	en
dc.title	Geo-Scape, a Granularity Depended Spatialization Tool for Visualizing Multidimensional Data Sets	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s11806-010-0385-8	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s11806-010-0385-8	en
heal.publicationDate	2010	en
heal.abstract	Recently, the expertise accumulated in the field of geovisualization has found application in the visualization of abstract multidimensional data, on the basis of methods called spatialization methods. Spatialization methods aim at visualizing multidimensional data into low-dimensional representational spaces by making use of spatial metaphors and applying dimension reduction techniques. Spatial metaphors are able to provide a metaphoric framework for the visualization of information at different levels of granularity. The present paper makes an investigation on how the issue of granularity is handled in the context of representative examples of spatialization methods. Furthermore, this paper introduces the prototyping tool Geo-Scape, which provides an interactive spatialization environment for representing and exploring multidimensional data at different levels of granularity, by making use of a kernel density estimation technique and on the landscape ""smoothness"" metaphor. A demonstration scenario is presented next to show how Geo-Scape helps to discover knowledge into a large set of data, by grouping them into meaningful clusters on the basis of a similarity measure and organizing them at different levels of granularity. © 2010 Wuhan University and Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Geo-Spatial Information Science	en
dc.identifier.doi	10.1007/s11806-010-0385-8	en
dc.identifier.volume	13	en
dc.identifier.issue	4	en
dc.identifier.spage	275	en
dc.identifier.epage	284	en