RIS ID

110583

Publication Details

Safadi, M., Ma, J., Wickramasuriya, R., Daly, D., Perez, P. & Kokogiannakis, G. (2017). Mapping for the future: Business intelligence tool to map regional housing stock. Procedia Engineering, 180 1684-1694.

Abstract

The amount of data available and the lack of data integration represent an increasing challenge to effective planning for government agencies. Integration of data from multiple sources has the potential to enable a user to draw valuable insights, which can be used to enhance service targeting and delivery, and to improve program evaluation. In recognition of the need to improve data integration the University of Wollongong and the NSW Office of Environment and Heritage (OEH) partnered to create an integrated housing stock database for the Illawarra region. The database serves as the backbone for an online and interactive Housing Stock Mapping Dashboard (HSMD). It assembled multilevel granular information (including at the Statistical Area Level 1 (SA1) and Local Government Area (LGA) level) collected from multiple historical programs by multiple agencies. This centralised, integrated data repository can help agencies understand the existing housing stock, and improve access to information to support evidence-based policy. This paper presents a model of how data can be integrated from multiple agencies to provide an online collaboration platform. The platform, HSMD, was designed to demonstrate to government, industry, and the research community the opportunity of data integration and advanced analytics. Potential applications of the HSMD include characterisation of the existing housing stock according to a range of building attributes, for instance the presence of ceiling insulation or rainwater tanks. Comparison of these attributes with energy consumption data can indicate the influence of the attribute, or the impact of a specific intervention. This can help policy makers understand uptake and penetration of previous rebate schemes.

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.proeng.2017.04.331