Twitter and Disasters: A Social Resilience Fingerprint

Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that major disasters such as hurricanes and earthquakes have a unique resilience fingerprint which is consistent between different events of the same type. Specifically, hurricanes have a distinct fingerprint which differentiates them from other major events. We analyze the components underlying the similarity among hurricanes and find that ecological, infrastructure and economic components of community resilience are the primary drivers of the difference between the community resilience of hurricanes and other major events

Asymmetrical Response of California Electricity Demand to Summer-Time Temperature Variation

Current projections of the climate-sensitive portion of residential electricity demand are based on estimating the temperature response of the mean of the demand distribution. In this work, we show that there is significant asymmetry in the summer-time temperature response of electricity demand in the state of California, with high-intensity demand demonstrating a greater sensitivity to temperature increases. The greater climate sensitivity of high-intensity demand is found not only in the observed data, but also in the projections in the near future (2021–2040) and far future periods (2081–2099), and across all (three) utility service regions in California. We illustrate that disregarding the asymmetrical climate sensitivity of demand can lead to underestimating high-intensity demand in a given period by 37–43%. Moreover, the discrepancy in the projected increase in the climate-sensitive portion of demand based on the 50th versus 90th role= presentation \u3eth quantile estimates could range from 18 to 40% over the next 20 years

Analytical Methods for Computing the Resilience, Recovery, and Transformation of Communities and Their Constituent Systems in the Age of Big Data

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 [1]. 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 [2]. 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. In the light of the disciplinary variations in conceptualization and operationalization of resilience, the introduction to this dissertation begins by unpacking the myriad of resilience definitions and how they relate to communities and engineered systems; describing analytical techniques which are used to model and quantify communities and engineered systems. Chapters 2-5 summarize the articles included as a component of the dissertation. First (Chapter 2) I analyze the characteristics of large-scale disruptions in network-based infrastructure systems. There is a large body of work which utilizes graph-theoretic representations of engineered systems to model resilience to shocks. However, the way by which shocks or disruptions are simulated in the system are either based on random failures –indicative of component aging– or targeted failures –based on an intentional threat like terrorism– and do not reflect the explicit spatial structure of natural hazards. To address this gap, I propose two methods for generating failures in network based infrastructure models which have a connected, spatial structure similar to that of a large-scale natural disaster such as a hurricane. When evaluating the performance of the system after a disruption using network-based performance metrics, the networks with spatially-distributed outages show statistically different measures of performance compared with similarly sized randomly-distributed outages. Additionally, when simulating the recovery of the system; the spatial characteristics of the outages drastically alter the way in which the network recovers. Of note, systems disrupted with random outages showed antifragile properties, while spatially-distributed outages do not. This work is extended to interdependent infrastructure systems in Chapter 3. In Chapter 4, I contribute to the nascent literature on harnessing social media data for resilience analytics. Specifically, I develop algorithms for analyzing how community members perceive the dynamics of their community during a crisis event, using twitter data during 14 major crises events. Grounded in theories of community resilience and sociological risk appraisal, these algorithms —called the Social Resilience Fingerprint— capture the patterns of discourse in communities related to the attributes of communities which contribute to its resilience, such as infrastructure, economic, and ecological systems. Using this framework, I show how different types of major disruptions (hurricanes, earthquakes, political events etc) have signatures identifiable in social media data and discuss the trends driving these similarities