Statistical analysis and visualization services for Spatially Integrated Social Science datasets

Irfan Azeezullah, Friska Pambudi, Tung-Kai Shyy, Imran Azeezullah, Nigel Ward, Jane Hunter, Robert J. Stimson
2012 2012 IEEE 8th International Conference on E-Science  
The field of Spatially Integrated Social Science (SISS) recognizes that much data of interest to social scientists has an associated geographic location. SISS systems use geographic location as the basis for integrating heterogeneous social science data sets and for visualizing and analyzing the integrated results through mapping interfaces. However, sourcing data sets, aggregating data captured at different spatial scales, and implementing statistical analysis techniques over the data are
more » ... y complex and challenging steps, beyond the capabilities of many social scientists. The aim of the UQ SISS eResearch Facility (SISS-eRF) is to remove this burden from social scientists by providing a Web interface that allows researchers to quickly access relevant Australian socio-spatial datasets (e.g. census data, voting data), aggregate them spatially, conduct statistical modeling on the datasets and visualize spatial distribution patterns and statistical results. This paper describes the technical architecture and components of SISS-eRF and discusses the reasons that underpin the technological choices. It describes some case studies that demonstrate how SISS-eRF is being applied to prove hypotheses that relate particular voting patterns with socio-economic parameters (e.g., gender, age, housing, income, education, employment, religion/culture). Finally we outline our future plans for extending and deploying SISS-eRF across the Australian Social Science Community.
doi:10.1109/escience.2012.6404421 dblp:conf/eScience/AzeezullahPSAWHS12 fatcat:eye6rypnwffovhx365dejzv6c4