A Multi-Scale Analysis of Single-Unit Housing Water Demand Through Integration of Water Consumption, Land Use and Demographic Data

Saeed Ghavidelfar (), Asaad Y. Shamseldin () and Bruce W. Melville ()
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Saeed Ghavidelfar: The University of Auckland
Asaad Y. Shamseldin: The University of Auckland
Bruce W. Melville: The University of Auckland

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 7, No 8, 2173-2186

Abstract: Abstract Studies evaluating the determinants of water demand typically use household-scale data or aggregated data. The household-scale data basically is preferred since it can reveal the heterogeneity in responses to the demand drivers across different consumer groups. However, the scarcity of household-scale data and its high data collection cost generally have limited the studies to rely on small samples of household data. Thus, they failed to show the spatial variation of water demand. In contrast, the aggregated studies have assessed the spatial variation of water use however they overlooked the variations across households. Using a rich source of GIS-based urban databases in Auckland, New Zealand, this study overcame this challenge by developing a large sample of 31000 single-unit housing through integration of household-level water consumption and property data with micro-scale household demographics information. This large dataset enabled this study to evaluate the water consumption both at the household scale and the census area unit scale. Panel data models were used for the water demand analysis in both scales. The proposed multi-scale analysis approach provided detailed knowledge about water consumption and its major determinants across different consumer groups and urban areas. This information may help water planners to more reliably plan water supply systems and manage consumption in the complex urban environments.

Keywords: Residential water use; Planning; Data integration; GIS; Price elasticity; Panel data model (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11269-017-1635-4

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