Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Abstract

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly in rapidly developing regions with harsh climates. This study examines the seasonal variation of water and electricity consumption in residential buildings in Doha, Qatar, exploring the interconnectedness of land use/land cover (LULC) and socio-demographic characteristics with household water and electricity consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation and Bootstrap analysis) and advanced geostatistical models, including Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), to analyze and monitor the spatial and seasonal variations of water and electricity consumption. The methods involved assessing the relationship between land surface temperature (LST), household water-electricity consumption, and analyzing the impact of demographic variables. Key findings indicate significant spatiotemporal variations in consumption influenced by changes in LULC and demographic characteristics such as household size and structure. The findings highlight the need for integrated urban planning and energy policies that consider the impacts of LULC and demographic changes to enhance energy efficiency and sustainability in urban settings. Furthermore, the results underscore the importance of addressing the complex interplay between urban development and resource consumption in policy-making.