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

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Item Summary Gorko, Thomas Yau, Calvin Malik, Abish Harris, Matt Tee, Jun Xiang Maciejewski, Ross Qian, Cheryl Afzal, Shehzad Pijanowski, Bryan Ebert, David 2017-12-28T00:52:20Z 2017-12-28T00:52:20Z 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.description.abstract With 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.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Collective Intelligence and Crowds
dc.subject Geospatial Aggregation, Multivariate Categorical Data, Soundscape Ecology, Visual Analytics, Visual Correlation.
dc.title A Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal Data
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.213
Appears in Collections: Collective Intelligence and Crowds

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