On the Dominant Role of Returners’ Human Mobility Networks on Urban Energy Consumption

As a result of population growth and urbanization, the interdependencies between infrastructure, services, and individuals in urban areas continue to increase. Urban areas already consume up to 80% of the world’s energy, and the expected population increase of nearly 70% by 2050 will drive a further rise in energy consumption. It is, therefore, vital for us to develop a better understanding of variabilities in human-related effects on buildings’ energy consumption within the urban spatial context in which they exist. Intra-city trips of urban population are undertaken as a result of individuals engaging in activities across various locations. However, people exhibit variations in their daily activities and the number of locations they visit over time. Here, we investigate the spatial interdependencies between human mobility networks of two distinct populations (i.e., returners and explorers) as an indicator of their daily activity patterns, as well as gas consumption to explore how variations in human mobility networks can be used to explain spatial fluctuations in energy use. We compare 2,015,339 positional records from an online social networking platform, Twitter, with energy consumption (gas) across 983 areas in Greater London over the course of a single month (May 2014). Our findings indicate a stronger statistically significant spatial dependency between human mobility networks of the returners and gas consumption, indicating domination of this population in urban energy use. This suggests that spatial fluctuations in urban energy consumption are governed by the structure of human mobility networks, among other factors. These results provide a clear picture of demand-side diversity and its drivers, establishing a foundation for human mobility-based predictive models for urban energy consumption. The relationship between energy consumption and human mobility is key to creating effective policies for urban areas, leading to more reliable predictions and effective management decisions about future patterns of energy use. Our findings will be of value to urban planners, researchers and policy-makers.