Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2017

Abstract

The datasets released for the VAST Challenge 2017 comprise vehicle movement data captured with RFID sensors, chemical emission data from factories captured by gas sensors, and image attributes of the wildlife plant health obtained from satellites, all pertaining to a fictional wildlife preserve. Using visual analytics, a compelling hypothesis is established to link the spatiotemporal datasets to the phenomenon, where the count of a bird specimen is found to decline over a given year. Anomalies in vehicle traffic patterns are linked to proximal factory emissions, and further associated with satellite imagery that show proof of degradation in plant quality in the preserve. The evidences are supported with visualizations created in Tableau, R, QGIS & SAS-JMP. Raster image analysis is also done to identify other key features in the preserve, such as the existence of a lake. This is achieved by using NDVI and NDMI measures, which also help understand the change in climate over the years.

Keywords

Spatiotemporal analysis, Geo-spatial analytics, Visual analytics, Traffic pattern detection, Raster image processing, NDVI, MITB student

Discipline

Databases and Information Systems | Geographic Information Sciences

Research Areas

Data Science and Engineering

Publication

2017 IEEE Conference on Visual Analytics Science and Technology (VAST): October 3-6, Phoenix, AZ: Proceedings

First Page

185

Last Page

186

ISBN

9781538631638

Identifier

10.1109/VAST.2017.8585493

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/VAST.2017.8585493

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