ABSTRACT 

Communities are increasingly vulnerable to climatic risks which are estimated to cost $1.8 trillion and lead to 2 million deaths annually by the end of the century. To minimize this vulnerability in the face of the increasing climatic risks, resilience is used as an organizing principal by all scale of governments, decision makers, and international organizations to address climatic risks. Resilience is conceptualized across many fields and is broadly meant to represent the ability of a system to maintain critical functionality, adapt, and `bounce back' after a shock or disruption. Moving from theoretical conceptualizations of resilience to operational decisions which aim to foster adaptive capacity in communities, requires consideration of the dynamics of engineered, social, ecological, economic, and political systems among others. This dissertation develops analytical techniques to leverage `big data' to understand the multifaceted aspects of how communities and engineered systems are impacted by and recover from major disruptions in an effort to bridge the gap between resilience in theory and resilience in practice. The methodologies and algorithms developed in this dissertation can improve the ability of stakeholders and decision makers to understand and analyze how communities adapt and respond to major crisis events, allowing for data-driven decisions to be made to bolster the resilience of communities in response to climate change.