A Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal Data

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dc.contributor.authorGorko, Thomas
dc.contributor.authorYau, Calvin
dc.contributor.authorMalik, Abish
dc.contributor.authorHarris, Matt
dc.contributor.authorTee, Jun Xiang
dc.contributor.authorMaciejewski, Ross
dc.contributor.authorQian, Cheryl
dc.contributor.authorAfzal, Shehzad
dc.contributor.authorPijanowski, Bryan
dc.contributor.authorEbert, David
dc.date.accessioned2017-12-28T00:52:20Z
dc.date.available2017-12-28T00:52:20Z
dc.date.issued2018-01-03
dc.description.abstractWith the increase in community-contributed data availability, citizens and analysts are interested in identifying patterns, trends and correlation within these datasets. Various levels of aggregation are often applied to interpret such large data schemes. Identifying the proper scales of aggregation is a non-trivial task in this exploratory data analysis process. In this paper, we present an integrated visual analytics environment that facilitates the exploration of multivariate categorical spatiotemporal data at multiple spatial scales of aggregation, focusing on citizen-contributed data. We propose a compact visual correlation representation by embedding various statistical measures across different spatial regions to enable users to explore correlations between multiple data categories across different spatial scales. The system provides several scale-sensitive spatial partitioning strategies to examine the sensitivity of correlations at varying spatial extents. To demonstrate the capabilities of our system, we provide several usage scenarios from various domains including citizen-contributed social media (soundscape ecology) data.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.213
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50100
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st 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.subjectCollective Intelligence and Crowds
dc.subjectGeospatial Aggregation, Multivariate Categorical Data, Soundscape Ecology, Visual Analytics, Visual Correlation.
dc.titleA Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal Data
dc.typeConference Paper
dc.type.dcmiText

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