Urban sprawl is a global concern, however, developing countries are failing to effectively overcome this problem. For instance, in Pakistan, urban development policies are tenuous and urban areas... Show More

Urban sprawl is a global concern, however, developing countries are failing to effectively overcome this problem. For instance, in Pakistan, urban development policies are tenuous and urban areas remained without certified boundaries. This failure is mainly causing an unfettered sprawl in major cities of the country. An alarming fact is that most of the urban sprawl is consistently taking place on fertile agricultural land in the peripheries of cities. The researcher has verified the problem through literature and by supervised classification of the satellite imageries to specifically verify growth of Peshawar city. This research contributed to unveil the fundamental causes of the unrestricted urban sprawl and its aftereffects in the context of Pakistan. Finally certain reforms and techniques are suggested to curb leapfrogged and low-density haphazard growth of cities.

Previous studies examining the electricity consumption behavior using traditional research methods, before the smart-meter era, mostly worked on fewer variables, and the practical implications of... Show More

Previous studies examining the electricity consumption behavior using traditional research methods, before the smart-meter era, mostly worked on fewer variables, and the practical implications of the findings were predominantly tailored towards suppliers and businesses rather than residents. This study first provides an overview of prior research findings on electric energy use patterns and their predictors in the pre and post smart-meter era, honing in on machine learning techniques for the latter. It then addresses identified gaps in the literature by: 1) analyzing a highly detailed dataset containing a variety of variables on the physical, demographic, and socioeconomic characteristics of households using unsupervised machine learning algorithms, including feature selection and cluster analysis; and 2) examining the environmental attitude of high consumption and low consumption clusters to generate practical implications for residents.