Please use this identifier to cite or link to this item:

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

File Size Format  
paper0213.pdf 3.58 MB Adobe PDF View/Open

Item Summary

Title:A Multi-Scale Correlative Approach for Crowd-Sourced Multi-Variate Spatiotemporal Data
Authors:Gorko, Thomas
Yau, Calvin
Malik, Abish
Harris, Matt
Tee, Jun Xiang
show 5 moreMaciejewski, Ross
Qian, Cheryl
Afzal, Shehzad
Pijanowski, Bryan
Ebert, David
show less
Keywords:Collective Intelligence and Crowds
Geospatial Aggregation, Multivariate Categorical Data, Soundscape Ecology, Visual Analytics, Visual Correlation.
Date Issued:03 Jan 2018
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.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Collective Intelligence and Crowds

Please email if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons