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

When Small Is Not Beautiful: The Unexpected Impacts of Trees and Parcel Size on Metered Water-Use in a Semi-Arid City

“…Social-demographic data, as well as house density, were provided by the US Census Bureau's American Community 5-Year Survey program [13] for 2016. Based on previous studies [11,12,[14][15][16], we chose predictor variables that describe features such as ethnicity, tenure, household size, income and educational attainment ( Table 2). These data were provided at a block group scale, which consists of several census blocks within the same census tract.…”

“…Many studies have further investigated the role of social characteristics on water consumption, and often they find similar trends. One common trend is that households of higher socio-economic status use more water to maintain lawns, gardens, swimming pools, and other water features [11,12,21]. Additionally, homeowners tend to use more water than renters [21].…”

Resource-conscious urban planning and design : Exploring the potential of urban metabolism assessments

“…Our results confirm earlier research showing that explanatory variables for resource consumption are sensitive to the spatial units used. So far this was shown only in studies comparing two different levels of spatial resolution, in the case of water for census area versus household or census block level (Chang et al, 2017;Ghavidelfar et al, 2017) and in the case of energy for city versus census area level (Bettignies et al, 2019;Kennedy et al, 2015). By considering four levels of spatial aggregation, we show that it matters at which level of detail within the city spatial variation of urban and resource characteristics is studied.…”

“…Another explanation for the lower fit of the water models could be that the aggregation of data in yearly consumption levels for the spatial units used, is less suitable for water than for energy consumption. In the case of water, the resolutions used may still be too coarse and thus mask variations of key variables, considering that previous research found that indoor and outdoor use have different explanatory variables (Chang et al, 2017;Ghavidelfar et al, 2017;House-Peters et al, 2010;Rasifaghihi et al, 2020). A higher temporal and spatial resolution may thus be required for better performance of the water models.…”

“…Pincetl et al, 2014) and those assessing correlations at different levels of spatial resolution too (e.g. Chang et al, 2017;Ghavidelfar, Shamseldin, & Melville, 2017;Kennedy et al, 2015). Studies that look into spatial variability of consumption using more detailed data aggregation levels than city level and that rely upon metered data are still uncommon (Bettignies et al, 2019;Bich-Ngoc & Teller, 2018;Voskamp et al, 2020).…”