Enabling SDMX-based retrieval and spatio-statistical analysis of national census and related datasets

Hunter, Jane, Azeezullah, Imran, Ward, Nigel, Barker, Ross, Shyy, Tung-Kai, Beer, Chris, Girvan, Stuart, Nairn, Alister, Branson, Merry, Stimson, Robert, Dentrinos, James, Galang, Gerson, Wallace, v and Pettit, Chris (2015). Enabling SDMX-based retrieval and spatio-statistical analysis of national census and related datasets. In: Proceedings - 2015 IEEE 11th International Conference on eScience. IEEE International Conference on eScience, Munich, Germany, (88-97). 31 August - 4 September 2015. doi:10.1109/eScience.2015.9

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Hunter, Jane
Azeezullah, Imran
Ward, Nigel
Barker, Ross
Shyy, Tung-Kai
Beer, Chris
Girvan, Stuart
Nairn, Alister
Branson, Merry
Stimson, Robert
Dentrinos, James
Galang, Gerson
Wallace, v
Pettit, Chris
Title of paper Enabling SDMX-based retrieval and spatio-statistical analysis of national census and related datasets
Conference name IEEE International Conference on eScience
Conference location Munich, Germany
Conference dates 31 August - 4 September 2015
Convener IEEE
Proceedings title Proceedings - 2015 IEEE 11th International Conference on eScience   Check publisher's open access policy
Journal name Proceedings - 11th IEEE International Conference on eScience, eScience 2015   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE (The Institute of Electrical and Electronics Engineers)
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/eScience.2015.9
Open Access Status Not Open Access
ISBN 9781467393256
ISSN 2325-372X
Start page 88
End page 97
Total pages 10
Collection year 2016
Language eng
Formatted Abstract/Summary
This paper describes a collaborative project between the Australian Urban Research Infrastructure Network (AURIN), the Australian Bureau of Statistics (ABS), the University of Queensland eResearch Lab and a number of social science research centres across Australia - that provides programmatic access to ABS Census data sets to enable its reuse within a range of research projects. The project successfully demonstrates machine-to-machine access to 2011 Census data through a federated data hub model that dynamically delivers statistical datasets to the research community. Using an SDMX web services approach, combined with advanced data analytics and visualization services available through the AURIN workbench, social scientists are able to manipulate and visualize national census data overlaid with other related data sets through a sophisticated mapping interface. Integrated statistical analysis services (R-based) also enable social scientists to quantify correlations between different demographic and socio-economic parameters. A number of socio-economic use cases are presented that illustrate how the system enables researchers to understand and quantify changes in industry sectors, labor force needs, employment, population needs and disadvantage, over space and time. The paper also outlines problems and limitations revealed through the demonstrator projects, lessons learnt and areas that will require further effort to deliver optimum access to national census data sets and associated e-social science infrastructure for both the Australian and global social science community.
Keyword e-social science
Spatio-statistical analytics
SDMX
Census data
Socio-economics
Demographics
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Wed, 09 Sep 2015, 16:36:02 EST by Professor Jane Hunter on behalf of School of Information Technol and Elec Engineering