Dataset

Spatio-Temporal patterns of Barmah Forest Virus Disease: spatial autocorrelation analysis for BFV disease in Queensland, 1993-2008

Queensland University of Technology
Adjunct Professor Shilu Tong (Aggregated by) Distinguished Professor Kerrie Mengersen (Aggregated by) Dr Sue Naish (Aggregated by) Professor Wenbiao Hu (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://figshare.com/articles/_Spatial_autocorrelation_analysis_for_BFV_disease_in_Queensland_1993_8211_2008_/395355&rft.title=Spatio-Temporal patterns of Barmah Forest Virus Disease: spatial autocorrelation analysis for BFV disease in Queensland, 1993-2008&rft.identifier=10378.3/8085/1018.15769&rft.publisher=Queensland University of Technology&rft.description=The dataset comes from a study investigating the spatio-temporal patterns of Barmah Forest Virus (BFV) disease in Queensland and provides the spatial autocorrelation analysis for BFV disease across the area.The global Moran's I test statistic was used to assess the presence of significant spatial autocorrelation of BFV disease incidence rates in four different periods of 1993–1996, 1997–2000, 2001–2004 and 2005–2008. Moran's I ranges from −1 to 1: a value close to 0 indicates spatial randomness while a positive value indicates positive spatial autocorrelation. Statistical significance was tested using randomisation based on 999 permutations. The weight distance matrix, essential for the computation of spatial autocorrelation statistics, was based on Queen contiguity and Euclidean distance. &rft.creator=Adjunct Professor Shilu Tong&rft.creator=Dr Sue Naish&rft.creator=Distinguished Professor Kerrie Mengersen&rft.creator=Professor Wenbiao Hu&rft.date=2015&rft.relation=http://eprints.qut.edu.au/52474/&rft.coverage=153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730&rft_rights=Copyright: © 2011 Naish et al.&rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/au/&rft_subject=Disease Surveillance&rft_subject=Information and Computing Sciences&rft_subject=Infectious Diseases&rft_subject=Infectious Disease Control&rft_subject=Microbiology &rft_subject=Biotechnology&rft_subject=Spatial Autocorrelation&rft_subject=Geographic Information &rft_subject=Interpolation&rft_subject=Mosquitoes &rft.type=dataset&rft.language=English Access the data

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Open Licence view details
CC-BY

Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/3.0/au/

Copyright: © 2011 Naish et al.

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Contact Information

Postal Address:
Distinguished Professor Kerrie Mengersen
Ph: +61 7 3138 2063
Fax: +61 7 3138 2310

k.mengersen@qut.edu.au

Full description

The dataset comes from a study investigating the spatio-temporal patterns of Barmah Forest Virus (BFV) disease in Queensland and provides the spatial autocorrelation analysis for BFV disease across the area.

The global Moran's I test statistic was used to assess the presence of significant spatial autocorrelation of BFV disease incidence rates in four different periods of 1993–1996, 1997–2000, 2001–2004 and 2005–2008. Moran's I ranges from −1 to 1: a value close to 0 indicates spatial randomness while a positive value indicates positive spatial autocorrelation.

Statistical significance was tested using randomisation based on 999 permutations. The weight distance matrix, essential for the computation of spatial autocorrelation statistics, was based on Queen contiguity and Euclidean distance.

Data time period: 1993 to 2008

Click to explore relationships graph

153.552920,-9.929730 137.994575,-9.929730 137.994575,-29.178588 153.552920,-29.178588 153.552920,-9.929730

145.7737475,-19.554159

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Identifiers
  • Local : 10378.3/8085/1018.15769