Paper Info
Title
A Multi-Scale Correlative Approach For Crowd-Sourced Multi-Variate Spatiotemporal Data
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.
Year
Venue
Field
2018
PROCEEDINGS OF THE 51ST ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS)
Data science,Correlative,Random variate,Computer science,Soundscape ecology,Visual analytics,Management science
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
20
10
Name
Order
Citations
PageRank
Thomas Gorko100.34
Calvin Yau211.69
Abish Malik3848.88
Matt Harris400.34
Jun Xiang Tee500.34
Ross Maciejewski614918.52
Cheryl Z. Qian720.72
Shehzad Afzal8656.94
Bryan C. Pijanowski950.86
David S. Ebert102056232.34