Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics

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dc.contributor.authorEaglin, Todd
dc.contributor.authorCho, Isaac
dc.contributor.authorRibarsky, William
dc.date.accessioned2016-12-29T00:35:28Z
dc.date.available2016-12-29T00:35:28Z
dc.date.issued2017-01-04
dc.description.abstractWe present a GPU-based implementation of the Space-Time Kernel Density Estimation (STKDE) that provides massive speed up in analyzing spatial- temporal data. In our work we are able to achieve sub- second performance for data sizes transferable over the Internet in realistic time. We have integrated this into web-based visual interactive analytics tools for analyzing spatial-temporal data. The resulting inte- grated visual analytics (VA) system permits new anal- yses of spatial-temporal data from a variety of sources. Novel, interlinked interface elements permit efficient, meaningful analyses.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.165
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41318
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnalytics
dc.subjectGPU
dc.subjectSpace-Time
dc.subjectVisualization
dc.titleSpace-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics
dc.typeConference Paper
dc.type.dcmiText

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