Interactive geolocational and coral compositional visualisation of Great Barrier Reef heat stress data

Hieu T Nim, Falk Schreiber, Terence J Done, Sarah Elizabeth Boyd

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

The Great Barrier Reef is at critical risk of rapid decline, with mass coral bleaching following heatwave conditions constituting the most recent impact on the ecosystem. Here we describe an information-rich integrative visualisation suite of tools, tailored to ecologist users, for exploring a rich data set collected from the 2002 Great Barrier Reef heat stress event. Integration of data from multiple resources demonstrates how this visualisation is easily adaptable to any equivalent data, including data integration and visualisation from coral reef monitoring programs. The toolkit also makes such data accessible to any user for education, exploration and decisionmaking purposes.
Original languageEnglish
Title of host publication2015 Big Data Visual Analytics (BDVA)
EditorsUlrich Engelke, Julian Heinrich, Tomasz Bednarz, Karsten Klein, Quang Vinh Nguyen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
Volume1
DOIs
Publication statusPublished - 2015
EventInternational Symposium on Big Data Visual Analytics (BDVA 2015) - Hobart, Australia
Duration: 22 Sep 201525 Sep 2015
http://www.bdva.net/2015/

Conference

ConferenceInternational Symposium on Big Data Visual Analytics (BDVA 2015)
Abbreviated titleBDVA 2015
CountryAustralia
CityHobart
Period22/09/1525/09/15
Internet address

Keywords

  • big data analytics
  • human-readable visualisation
  • geolocational data
  • species composition
  • ecological traits
  • coral bleaching

Cite this

Nim, H. T., Schreiber, F., Done, T. J., & Boyd, S. E. (2015). Interactive geolocational and coral compositional visualisation of Great Barrier Reef heat stress data. In U. Engelke, J. Heinrich, T. Bednarz, K. Klein, & Q. V. Nguyen (Eds.), 2015 Big Data Visual Analytics (BDVA) (Vol. 1, pp. 1-7). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BDVA.2015.7314297