Integrating Risk Science and Urban Planning: Mitigating Hazards and Protecting Our Communities

Abstract

The climate crisis is an unprecedented threat. We urgently need to design our infrastructure, economic, and agricultural systems and our communities to withstand hazards and reduce risk to address this threat. This dissertation contributes by exploring the potential of data-driven urban planning and through increasing our understanding of how risk and data science can be used to build the resilience of our communities. Central to this thesis is the understanding that risk analysis (the assessment, characterization, communication, and management of risk, along with related policy) can enhance urban planning to better mitigate hazards and protect our communities. To improve risk analysis's efficacy for use in urban planning, there are a series of necessary advances to the science of risk (i.e., the knowledge, frameworks, and principles that underlie risk analysis). Each chapter of this dissertation contributes to these advances, including how we focus risk analysis for the betterment of people, how we leverage data science to understand the role of urban form in hazard mitigation, how we incorporate spatiotemporal and behavioral feedbacks into risk analysis, and how we capture resilience within the risk concept. The primary aims of the dissertation were to: 1) Explore the potential for risk science to be used to support urban planning, 2) Advance methods and understanding of spatiotemporal risk analysis, and 3) Propose an operational approach to building the resilience of communities to hazards. The first chapter identifies how urban planning challenges can develop and motivate developments in risk science. I then advance approaches for conducting risk analysis that captures spatiotemporal and behavioral feedbacks using a coupled complex system model in the second chapter. The third chapter uses machine learning and spatial data to explore how urban characteristics are associated with high temperature, that could lead to higher risk. The next section, chapters four and five, focuses risk analysis on people. I propose that the focus of resilience efforts be on the equitable provision of essential services, such as health care, food, and education. Specifically, we can measure how people's access to essential services changes due to a hazard and across demographic groups. The framework I propose can be used by decision makers before, during, and after a hazard to improve the social sustainability and reduce the long-term risk of a community. In the final two chapters I argue that we must explicitly consider the dimension of time in risk analysis and that this means that the pillars of resilience can be addressed within the concept of risk. This understanding, coupled with the other work within this dissertation, means that resilience, and resilience analysis, is well within the purview of modern risk analysis.