Improved Housing Services for Indigenous Communities through Data Integration, Analysis and Mapping Tools

Authors: Jane Hunter, Carroll Go-Sam


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Conference paper

Summary

The “Midja” project (Midja, 2016) is a multi-disciplinary collaboration between data scientists, social scientists and community housing experts from the University of Queensland School of ITEE (eResearch Lab), School of Architecture (Aboriginal Environments Research Centre) and Institute of Social Science Research (ISSR). The aim of the project is to improve researchers’ understanding of regional factors that impact on homelessness and overcrowding in Indigenous communities, in order to deliver more targeted and effective Indigenous housing programs and services. It adopts a data-driven approach that involves providing researchers with: (i) access to relevant quantitative and qualitative datasets (e.g., Australian Bureau of Statistics, AHURI reports, Government agency data) via a common Web Portal, and (ii) innovative mapping and statistical analysis services that enable the integration, spatial overlay, interpretation and correlation of datasets and indicators, to inform policies and programs. The project is helping government agencies responsible for housing assistance programs in Aboriginal and Torres Strait Islander communities, to identify regions with the greatest need and to understand regional patterns, in order to target services tailored for urban, regional and remote communities.

Objectives

The first goal of this project is to provide researchers (and in the longer term planners and policy makers) with a Web Portal to a more comprehensive integrated knowledge-base that aggregates data from multiple organizations into a single data model (or ontology) and online searchable repository. Sources of data include:

  • Quantitative data: ABS data on Aboriginal health and welfare, population and housing (including Community Housing and Infrastructure Needs Survey (CHINS)); Affordability and other Socio-economic indicators; Data from the State/Territory Housing Departments, ICHOs and Community Councils.
  • Qualitative data: LSIC (Longitudinal Study of Indigenous Children); 2002 and 2008 National Aboriginal and Torres Strait Islander Survey (NATSISS); Household, Income and Labour Dynamics in Australia (HILDA) Survey;
  • Publications: AHURI and FaHCSIA reports; AIATSIS Abstracts.
  • Map sources: ATSIC boundaries data (wards and regions); Indigenous Coordination Centres and Regional Operations Centres; AIATSIS Aboriginal Australia Map (ILOCS, IARES, IREGS and Local Government Area (LGA boundaries).

Given this integrated knowledge-base, the second aim is to develop innovative search, retrieval, visualization and analysis services that enable the domain experts to:

  • Overlay different datasets and boundaries spatially to develop hypotheses regarding relationships between regional factors, housing needs and Quality of Life (QoL)/Health and Well-being/socio-economic indicators;
  • Generate scatter plots and trend graphs and apply R (RStudio, 2015) statistical analysis tools to identify correlations between housing attributes and QoL/socio-economic indicators and factors with most impact on a housing program’s success;
  • Spatially overlay existing Indigenous Housing Program data onto areas of greatest need, to identify gaps in service provision and to inform future program delivery.

Figure 1 illustrates the Midja Portal, designed to enable researchers and program planners, to quickly and easily analyse, overlay and visualize integrated datasets.

Figure 1: The Midja User Interface: Dataset selection, overlay, mapping and analysis

Acknowledgements

This research has been funded through an Australia Research Council (ARC) Indigenous Discovery Project IN140100033.

References

Midja, 2016. The Midja Integrated Knowledge Base. Available at: http://midja.org/ [Last accessed 30 May, 2016]

RStudio, 2015. Shiny – a web application framework for R. Available at: http://shiny.rstudio.com/ [Last accessed 30 May, 2016]

DKAN, 2016. DKAN The open data platform. Available at: http://nucivic.com/dkan/ [Last accessed 30 May, 2016]

CartoDB, 2016. CartoDB Open source Geospatial database for the cloud. Available at: https://github.com/CartoDB/cartodb [Last accessed 30 May, 2016]

PostGreSQL, 2016. PostGreSQL-PostGIS. Available at: http://www.postgresql.org/ [Last accessed 30 May, 2016]