Yue ‘Gurt’ Ge
Bio: Yue ‘Gurt’ Ge is an academic researcher from University of Central Florida. The author has contributed to research in topic(s): Shadow (psychology) & Hurricane evacuation. The author has an hindex of 2, co-authored 3 publication(s) receiving 9 citation(s).
01 Jul 2021-Risk Analysis
TL;DR: Using hurricanes as the response context, this article examines three teaming mechanisms for interdisciplinary disaster response research, including ad hoc and/or grant proposal driven teams, research center or institute based teams, and teams oriented by matching expertise toward long-term collaborations.
Abstract: Building an interdisciplinary team is critical to disaster response research as it often deals with acute onset events, short decision horizons, constrained resources, and uncertainties related to rapidly unfolding response environments. This article examines three teaming mechanisms for interdisciplinary disaster response research, including ad hoc and/or grant proposal driven teams, research center or institute based teams, and teams oriented by matching expertise toward long-term collaborations. Using hurricanes as the response context, it further examines several types of critical data that require interdisciplinary collaboration on collection, integration, and analysis. Last, suggesting a data-driven approach to engaging multiple disciplines, the article advocates building interdisciplinary teams for disaster response research with a long-term goal and an integrated research protocol.
TL;DR: Based on a post-Hurricane Matthew household survey, the authors aims to understand the combined effects of shadow evacuation and non-compliance during hurricane events, and find that shadow evacuation is among undesirable behaviors during hurricanes.
Abstract: Shadow evacuation and non-compliance are among undesirable behaviors during hurricane events. Based on a post-Hurricane Matthew household survey, this study aims to understand the combined effects ...
TL;DR: Survey data collected from households in Jacksonville, Florida affected by 2016's Hurricane Matthew identifies perceived consistency of information as a key predictor of uncertainty regarding hurricane impact and evacuation logistics and provides practical implications regarding the need of information coordination for improved evacuation decision‐making.
Abstract: Understanding how information use contributes to uncertainties surrounding evacuation decisions is crucial during disasters. While literature increasingly establishes that people consult m...
01 Jan 2016
TL;DR: Dillman and Smyth as mentioned in this paper described the Tailored design method as a "tailored design methodology" and used it in their book "The Tailored Design Method: A Manual for Personalization".
Abstract: Resena de la obra de Don A. Dillman, Jolene D. Smyth y Leah Melani Christian: Internet, Phone, Mail and Mixed-Mode Surveys. The Tailored Design Method. New Jersey: John Wiley and Sons
TL;DR: Convergence principles and the Science of Team Science undergird the work of CONVERGE, which brings together networks of researchers from geotechnical engineering, the social sciences, structural engineering, nearshore systems, operations and systems engineering, sustainable material management, and interdisciplinary science and engineering.
Abstract: The goal of this article is twofold: to clarify the tenets of convergence research and to motivate such research in the hazards and disaster field. Here, convergence research is defined as an approach to knowledge production and action that involves diverse teams working together in novel ways—transcending disciplinary and organizational boundaries—to address vexing social, economic, environmental, and technical challenges in an effort to reduce disaster losses and promote collective well-being. The increasing frequency and intensity of disasters coupled with the growth of the field suggests an urgent need for a more coherent approach to help guide what we study, who we study, how we conduct studies, and who is involved in the research process itself. This article is written through the lens of the activities of the National Science Foundation-supported CONVERGE facility, which was established in 2018 as the first social science-led component of the Natural Hazards Engineering Research Infrastructure (NHERI). Convergence principles and the Science of Team Science undergird the work of CONVERGE, which brings together networks of researchers from geotechnical engineering, the social sciences, structural engineering, nearshore systems, operations and systems engineering, sustainable material management, and interdisciplinary science and engineering. CONVERGE supports and advances research that is conceptually integrative, and this article describes a convergence framework that includes the following elements: (1) identifying researchers; (2) educating and training researchers; (3) setting a convergence research agenda that is problem-focused and solutions-based; (4) connecting researchers and coordinating functionally and demographically diverse research teams; and (5) supporting and funding convergence research, data collection, data sharing, and solutions implementation.
01 Jul 2021-Risk Analysis
TL;DR: The need for alignment of decision-making agents, time, and space for interdisciplinary research on hurricanes, particularly evacuation and the immediate aftermath is discussed and illustrated.
Abstract: In hazard and disaster contexts, human-centered approaches are promising for interdisciplinary research since humans and communities feature prominently in many definitions of disaster and the built environment is designed and constructed by humans to serve their needs. With a human-centered approach, the decision-making agent becomes a critical consideration. This article discusses and illustrates the need for alignment of decision-making agents, time, and space for interdisciplinary research on hurricanes, particularly evacuation and the immediate aftermath. We specifically consider the fields of sociobehavioral science, transportation engineering, power systems engineering, and decision support systems in this context. These disciplines have historically adopted different decision-making agents, ranging from individuals to households to utilities and government agencies. The fields largely converged to the local level for studies' spatial scales, with some extensions based on the physical construction and operation of some systems. Greater discrepancy across the fields is found in the frequency of data collection, which ranges from one time (e.g., surveys) to continuous monitoring systems (e.g., sensors). Resolving these differences is important for the success of interdisciplinary teams in protective-action-related disaster research.
TL;DR: This article explored the facilitators of and barriers to such collaboration through a case study of a multidisciplinary research initiative at an urban East Coast university in the United States using an applied qualitative approach.
Abstract: Cross-disciplinary (multidisciplinary, interdisciplinary, or transdisciplinary) teams are increasingly recognized as crucial alliances required for solving complex problems, particularly those attributable to natural disasters. Despite this recognition, there has been limited research examining the factors necessary for successful cross-disciplinary collaboration at the outset of a research initiative to address natural disaster preparedness and response. The purpose of this study was to explore the facilitators of and barriers to such collaboration through a case study of a multidisciplinary research initiative at an urban East Coast university in the United States. Using an applied qualitative approach, we conducted individual semi-structured interviews with initiative members to explore facilitators and barriers to natural disaster preparedness and response, and initiative members' attitudes, perceptions, and expectations regarding multidisciplinary collaboration. We identified several common themes across natural disaster preparedness and response efforts and multidisciplinary research collaborations, such as greater human and financial resources. Additionally, we identified several themes unique to perceptions of multidisciplinary research collaboration, such as the importance of clearly defined structures, and the needs of all parties being met. Findings may serve as a framework for multidisciplinary academic collaborations preparing for and responding to natural disasters, as well as multidisciplinary collaboration in general.
TL;DR: A machine learning approach using Long-Short Term Memory Neural Networks (LSTM-NN), trained over real-world traffic data during hurricane evacuation (hurricanes Irma and Matthew) using different combinations of input features and forecast horizons is presented.
Abstract: In recent times, hurricanes Matthew, Harvey, and Irma have disrupted the lives of millions of people across multiple states in the United States. Under hurricane evacuation, efficient traffic operations can maximize the use of transportation infrastructure, reducing evacuation time and stress due to massive congestion. Evacuation traffic prediction is critical to plan for effective traffic management strategies. However, due to the complex and dynamic nature of evacuation participation, predicting evacuation traffic demand long ahead of the actual evacuation is a very challenging task. Real-time information from various sources can significantly help us reliably predict evacuation demand. In this study, we use traffic sensor and Twitter data during hurricanes Matthew and Irma to predict traffic demand during evacuation for a longer forecasting horizon (greater than 1 h). We present a machine learning approach using Long-Short Term Memory Neural Networks (LSTM-NN), trained over real-world traffic data during hurricane evacuation (hurricanes Irma and Matthew) using different combinations of input features and forecast horizons. We compare our prediction results against a baseline prediction and existing machine learning models. Results show that the proposed model can predict traffic demand during evacuation well up to 24 h ahead. The proposed LSTM-NN model can significantly benefit future evacuation traffic management.