In an increasingly urbanizing world with growing threats of climate change and terrorism, hazards occur more frequently with more severe consequences, bringing significant long-term impacts and requiring years for a community to recover. In order to be better prepared and reduce the impacts of adverse events, communities should conduct effective emergency and mitigation planning. This requires engineers and planners to model pre-event community resilience and consequently come up with strategies to enhance it.
A novel framework will be introduced based on multi-scale community resilience modeling. The framework will emphasize "macroscopic" modeling of communities, "mesoscopic" modeling of interdependent infrastructures providing critical services to communities, and "microscopic" modeling of a single critical infrastructure. At the macroscopic level, I will introduce a dynamic county-based resilience index and a hazard-specific weighting scheme for resilience indicators by using a data-driven approach. At the mesoscopic level, I will show a risk assessment tool for analyzing urban food security of Baltimore City under acute (e.g., earthquake, hurricane, flooding, etc.) and chronic (e.g., climate change) stressors; also, I will discuss how to use our model to engage different stakeholders of urban food systems. At the microscopic level, I will describe two new models of evaluating performance of single distributed networks under hazards: transportation and cyberinfrastructure. A novel Markovian framework is developed to analyze the transportation performance before and after disruptions. Furthermore, I will describe a risk assessment tool for the cyberinfrastructure and Medical Records Services in healthcare systems.
This dissertation explores multi-scale modeling of community resilience by using methods and tools in applied mathematics, statistics, and systems engineering. This work presented, is to our knowledge, the most comprehensive and multidisciplinary effort to analyze community resilience as multi-level systems.
This Supplement provides statistics that complement those in Household Food Security in the United States in 2014 (ERR-194). That research report provides the primary national statistics on household food security, food spending, and use of Federal food and nutrition assistance programs by foodinsecure households. This Supplement provides additional statistics on component items of the household food security measure, the frequency of occurrence of food-insecure conditions, and selected statistics on household food security, food spending, and use of Federal and community food and nutrition assistance programs.
Objective
Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster.
Methods
We developed a system dynamics computational model that predicts community functioning after a disaster. The computational model outputted the time course of community functioning before, during, and after a disaster, which was used to calculate resistance, recovery, and resilience for all US counties.
Results
The conceptual model explicitly separated resilience from community functioning and identified all key components for each, which were translated into a system dynamics computational model with connections and feedbacks. The components were represented by publicly available measures at the county level. Baseline community functioning, resistance, recovery, and resilience evidenced a range of values and geographic clustering, consistent with hypotheses based on the disaster literature.
Conclusions
The work is transparent, motivates ongoing refinements, and identifies areas for improved measurements. After validation, such a model can be used to identify effective investments to enhance community resilience.( Disaster Med Public Health Preparedness . 2017;page 1 of 11)
This paper outlines a process for exploring food system vulnerability and resilience using scenario modelling with the Australian Stocks and Flows Framework (ASFF). The capacity of ASFF to simulate how diverse shocks and stressors affect food system behaviour across multiple sectors—with diverse, interconnected and dynamic variables shaping system response—renders ASFF particularly suited for exploring complex issues of future food supply. We used ASFF to explore the significance of alternative agricultural policies for land use, crop production, livestock production, fisheries, food processing, transport, food waste and ultimately food supply. Policies in different scenarios varied with regard to the timetable for reducing greenhouse gas emissions, the degree of government participation or regulation in the food system and the scale of solutions (varying from centralized and global to decentralized and local). Results from the scenarios suggest that Australia does not have the ability to maintain a domestic surplus of foods required for a nutritious diet. In particular, the health of the current food system is highly vulnerable to constraints in oil supply, and increased food production threatens to precipitate a drastic decline in critical water supplies. We conclude by outlining a proposed method for using ASFF to delve deeper into the dynamics of the food system, probe the consequences of various adaptive responses to food production and supply challenges and devise potential indicators for food system resilience. Shocks and stressors to be added to the next phase of scenario modelling include soil salinity, climate extremes and credit scarcity. The ASFF methodology should be applicable to other parts of the world, although appropriate recalibration and adjustment of model assumptions would be required to reflect regional differences.
California's driest 12-month period on record occurred during 2013/14, and although global warming has very likely increased the probability of certain large-scale atmospheric conditions, implications for extremely low precipitation in California remain uncertain.
Full text available at: http://link.springer.com/article/10.1007/s11252-015-0489-x
This paper is dedicated to the topic of food resilience in the context of urban environments and aims at developing a qualitative tool for measuring it. The emphasis is laid on urban food security with a significant global relevance due to the interconnectedness of our urban and global food systems. We argue that food and agriculture have to be understood as integral components of contemporary urban and peri-urban landscapes as urban agriculture supports in many cases also ecosystems, biodiversity, urban ecology and urban landscape architecture. The topic is introduced through contemporary urban food system models and definitions followed by characteristics of a resilient urban food system, including consumer, producer, food processing, distribution and market resilience. Based on the review of food system models and assessment tools, a new food system model for resilience analysis has been developed. This is then applied to worked examples and further developed on the Christchurch case study, where the tool is applied to existing intra-urban and peri-urban landscape components of Christchurch, New Zealand.
Full text available at: http://link.springer.com/article/10.1007/s11252-015-0489-x
Community resilience is the ability of a community to resist and recover from adversity, such as natural disasters, terror attack, and influenza pandemic. Quantifying community resilience can help communities better understand their strengths and vulnerabilities, prepare for different types of hazards, estimate losses in case of adverse situations, and take effective measures to reduce losses. However, such a task is extremely challenging, because community resilience is essentially a comprehensive and complex concept with entrenched difficulties in defining appropriate criteria for its quantification. The commonly-used approach usually considers multiple domains of a community and selects some indicators to capture features of each domain. Then, indicators are equally weighted across the domain and aggregated together to come up with an index to quantify community resilience. This study chooses a set of commonly used indicators in the engineered system domain and aims to develop a multi-hazard weighting scheme for these indicators. A multi-hazard weighting scheme is meaningful because the importance of each indicator, as a contributing factor to worsen or lessen damages, could vary significantly across different hazards. In this study, we mainly focus on earthquakes and hurricanes, which are the two typical representatives of natural hazards. We choose different response variables from recent earthquakes and hurricanes. The historical data of engineered system indicators and hazard response variables can be collected from publicly available databases. Based on these data, we apply linear regression method to form statistical models and use these models to determine the variable importance for each indicator for different hazards. By comparing the weighting schemes for earthquakes and hurricanes, we discuss the possible reasons accounting for the differences and summarize the pros and cons of our multi-hazard weighting scheme. Moreover, the direction of indicators obtained by the regression models coincides with the direction obtained from expert judgment, which validates our methodology and choice for response variables. This multi-hazard weighting scheme contributes to quantifying community resilience and assessing urban risks under attacks of earthquakes and hurricanes.
Previous work (Spall, 2014) introduced a method for estimating the parameters of a stochastic system with binary subsystems, based on a combination of full system and subsystem experiments. This paper extends the theoretical results from the previous work to a complex transportation network system. We build up the mathematical model for the transportation network, derive the maximum likelihood estimator (MLE) for the system, and give a numerical example to illustrate our approach. In this study, we propose to collect test data of travel time from Google Maps and identify a novel method to process the rounded quantized data. Finally, our work concludes by discussing the pros and cons of the approach and identifying future work based on this study.
The problem of estimating the turning flows at a junction from a knowledge of the approach road flows has attracted some attention in recent years. In this paper the Information Minimising (IM) and Bayesian (B) approaches will be summarized, together with a new Maximum Likelihood (ML) method. A comparison of results given by the three methods will show that they are very similar. The B and ML methods have the considerable advantage that they give not only point estimates, but also the standard errors of these estimates.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.