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Our relationship with technology is constantly evolving, and how we use technology in disasters has evolved even faster. Understanding how to utilize human interactions with technology and the limitations of those interactions will be a crucial building block to contextualizing crisis data. The impact of geographic scale on behavioral change analyses is an unexplored facet of our ability to identify relative severities of crisis situations, magnitudes of localized crises, and total durations of disaster impacts. Within this paper, we aggregate Twitter and hurricane damage data across a wide range of geographic scales and assess the impact of increasing scale on both the recognition of extreme behaviors and the correlation between activity and damage. The power-law relationships identified between many of these variables indicate a direct, definable scalar dependence of social media aggregation analyses, and these relationships can be used to inform more intelligent, equitable, and actionable social media usage in emergency response. 

When subject to disruptive events, the dynamics of human-infrastructure interactions can absorb, adapt, or, in a more abrupt manner, undergo substantial change. These changes are commonly studied when a disruptive event perturbs the physical infrastructure. Infrastructure breakdown is, thus, an indicator of the tipping point, and possible regime shift, in the human-infrastructure interactions. However, determining the likelihood of a regime shift during a global pandemic, where no infrastructure breakdown occurs, is unclear. In this study, we explore the dynamics of human-infrastructure interactions during the global COVID-19 pandemic for the entire United States and determine the likelihood of regime shifts in human interactions with six different categories of infrastructure. Our results highlight the impact of state-level characteristics, executive decisions, as well as the extent of impact by the pandemic as predictors of either undergoing or surviving regime shifts in human-infrastructure interactions. 

One of the major barriers to closing the energy efficiency gap is the failure to successfully inform the population about measures to conserve energy. This paper introduces the design of a mobile application developed to improve energy conservation of residential buildings by informing occupants of transferrable energy efficient green features in a green-certified, non-residential building. The application was developed to investigate dissemination of transferable energy saving practices to explore spillover effects from non-residential to residential buildings. Our research aims to capitalize on such spillover effects to narrow the energy efficiency gap. 

Recent advances in energy technologies, policies, and practices have accelerated the global rate of improvements in energy efficiency, bringing the energy targets identified in the 2030 United Nations (UN) Sustainable Development Agenda within reach. However, Target 7.3 requires this rate to double by 2030, demanding a more substantial response to energy interventions. At present, energy interventions are failing to reach optimal levels of adoption in buildings, which are the largest urban energy consumers. This is due to a combination of direct and indirect effects generally referred to as the energy efficiency gap. Here, we compare over 18.8 million positional records of individuals against Greater London’s buildings energy consumption records over the course of one year. We demonstrate that indirect (i.e., spillover) effects, arising fromrecurrent mobility, govern the diffusion of urban buildings’ energy efficiency, far outpacing direct effects. This has been understood as a consequence of underlying spatiotemporal dependencies at the intersection of energy use and social interactions. We add to this the critical role of recurrent mobility (i.e., the mobility of those urban populations who repeatedly visit certain locations, such as home and work) as a diffusion conduit. These findings suggest that in order to improve the current levels of adoption, interventions must target times and locations that function as dense hubs of energy consumption and social interactions. Recurrent mobility thus provides a viable complement to existing targeted intervention approaches aimed at improving energy efficiency, supporting efforts to meet the UN’s 2030 energy targets.

 

A growing number of community energy initiatives have enlarged energy-related social networks to the community level. Information provision is deemed as an important role in such programs while energy data disclosure offers a great opportunity to promote energy savings by engaging energy-related actors. However, it is crucial to communicate this data in an effective way. In this research, we develop a virtual reality (VR) integrated eco-feedback system that enables both occupants and facility managers to interact with real-time energy consumption data represented in a community scale 3D immersive environment. This paper presents the detailed front-end and back-end design and development of this novel VR-integrated eco-feedback system using Georgia Tech’s campus as a test case for implementation. The VR-integrated community scale eco-feedback system is capable of visually characterizing differences in energy consumption across a large number of buildings of different types, and will be tested by users in future research. This research, when deployed broadly in cities, may help promote energy-aware behaviors of occupants and timely intervention strategies to achieve energy savings in urban areas.