Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41318

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

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Item Summary

Title: Space-Time Kernel Density Estimation for Real-Time Interactive Visual Analytics
Authors: Eaglin, Todd
Cho, Isaac
Ribarsky, William
Keywords: Analytics
GPU
Space-Time
Visualization
Issue Date: 04 Jan 2017
Abstract: We 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.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41318
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.165
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Interactive Visual Decision Analytics Minitrack



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