![]() Phys Chem Earth 28(20–27):805–815įotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. ![]() J Water Resour Plan Manage 124:113–117ĭube E, van der Zaag P (2003) Analysing water use patterns for demand management: the case of the city of Masvingo, Zimbabwe. Journal of Applied Meteorology and Climatology (in press)īillings RB, Agthe DE (1998) State-space versus multiple regression for forecasting urban water demand. Appl Geogr 11(2):157–165Īrizona Department of Economic Security (2005) īalling RC Jr, Gober P (2006) Climate Variability and Residential Water Use in Phoenix, Arizona. Model parameters can be used to investigate the effects of policies designed to regulate lot size, pool construction, and landscaping practices on water consumption and to forecast water demand in areas of new construction.Īitken C, Duncan H, McMahon TA (1991) A cross-sectional regression-analysis of residential water demand in Melbourne, Australia. This means that census tracts exhibit water consumption behavior similar to neighboring tracts for these two variables. Improvement of the GWR over the OLS model suggests that there are spatial effects above and beyond the effects for household size and pools – two of the four determinants of water demand. Results confirm the statistical significance of household size, the presence of a pool, landscaping practices, and lot size. Determinants of residential water demand reflect both indoor and outdoor use and include household size, the presence of swimming pools, lot size, and the prevalence of landscaping that requires a moist environment. We compared the results from the OLS model to those of a geographically weighted regression (GWR) model to determine whether there are spatial effects above and beyond the effects of the OLS variables. To better understand the demand side of this important issue, we identified the determinants of water consumption for detached single-family residential units using ordinary least squares regression (OLS). Rapid population growth in the face of an uncertain climate future challenges the desert city of Phoenix, Arizona to consume water more prudently. ![]()
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