Current Search:  Disaster Management (x)

View All Items

  • CSV Spreadsheet
(1 - 6 of 6)
Examining multi-level and inter-organizational collaborative response to disasters: The case of Pakistan Floods in 2010
Title
Examining multi-level and inter-organizational collaborative response to disasters: The case of Pakistan Floods in 2010.
Creator
Khosa, Sana, Kapucu, Naim, Wan, Thomas, Knox, Claire, Sadri, Houman, University of Central Florida
Abstract / Description
Catastrophic disasters are different than routine disasters and managing them requires the mobilization of inter-organizational, inter-governmental, cross-sectoral and international humanitarian support. The role of the international community through International Non-governmental Organizations (INGOs), and multi-lateral organizations such as the United Nations (UN) becomes imperative when the scale of the disaster is unprecedented and difficult for a country to manage on its own. The...
Show moreCatastrophic disasters are different than routine disasters and managing them requires the mobilization of inter-organizational, inter-governmental, cross-sectoral and international humanitarian support. The role of the international community through International Non-governmental Organizations (INGOs), and multi-lateral organizations such as the United Nations (UN) becomes imperative when the scale of the disaster is unprecedented and difficult for a country to manage on its own. The initial response and relief phase of managing disasters is one in which many agencies with different expertise, capacities, working mandates, resources, skills, working cultures and norms come together to coordinate and collaborate to provide timely response and relief services. Thus, the terrain of managing catastrophic disasters is complex and requires a deeper study to understand and delineate the factors shaping and facilitating collaborative response and relief efforts.This study examines the multi-level and multi-layered collaborative response networks present at the national-international level, provincial and district/local level of disaster response and interactions. In this research the nature and effectiveness of collaboration is being studied through a relevant case study of a catastrophic natural disaster, the 2010 Pakistan Floods. The phase of immediate response is explored primarily through Network Theory perspectives including supportive theoretical perspectives such as Social Capital, Resource Dependency, and Institutional Collective Action Theory perspectives that help to explain collaborative interactions in disaster response networks. This study explores and describes factors that influence (either facilitate or hinder) collaboration is disaster response networks.The key research questions for this study are: What factors facilitate and impede collaborative response to catastrophic disasters at the local, provincial, national and international levels? What are the differences and similarities in response systems at different levels? Additional questions address how leadership support (attributed to government and political leaders and organizations), institutional support (in the form of plans, international appeals of response, and development of relief funds to manage aid), network capacity of different organizations (programmatic and relational), nature of resource dependencies between responding agencies, and structural configurations of response systems impact the collaborative response in disasters.A case study method has been applied in this research. The 2010 Pakistan Floods response network/system is identified through content analysis of various newspapers, situation reports and after-action reports using the Social Network Analysis (SNA) method via UCINET Software 6.1. The actual response network is analyzed and compared with existing national disaster response plans to examine the effectiveness of collaborative response through centrality measures, clique analysis and visual display. This approach is supplemented with semi-structured interviews of key institutional representatives that responded to the 2010 Floods. These organizations and institutions were primarily identified through the networks formulated via SNA.Findings and results from the analysis reflect that the response networks at each level of analysis differ both in structural aspects and also in functional aspects. The nature of the international-national response system is focused on mobilizing donor support and receiving and managing aid, both in-kind and cash. Also a major role at the international and national level is to mobilize the UN cluster approach and focus on broader aims of response such as providing shelter and food to affected areas. Some of the factors identified as facilitating collaborative response were leadership of both national and international leaders, and availability of donor support and funds.At the provincial level of analysis, the Chief Minister of Punjab is playing a central and influential role and is partnering closely with the Armed Forces and local district administration. Interviews conducted of provincial level officials help to support the hypotheses concerning leadership support's influence on collaborative response and also the role of institutional support in the form of creation of plans, and policies that help to mobilize quick funds and resources for relief. At the local level of response, networks are highly influenced by local conditions and local capacities of the district administration. Thus, there are diverse factors impacting each level of collaborative disaster response. All in all, leadership support, institutional support and network structural aspects are important variables that impact the effectiveness of collaborative response.Today policy makers are trying to figure out ways to collaborate successfully across sector boundaries for better and effective service delivery, both in the mundane operational tasks and in uncertain and complex situations such as disasters and catastrophic events. Thus, this research helps in expanding the literature on collaborative public management, collaborative emergency management, and network management. Also the frequency of natural disasters throughout the world demonstrate the need to study and examine factors that contribute to or hinder the effectiveness of inter-organizational response in disasters
Show less
Date Issued
2013
Identifier
CFE0005361, ucf:50496
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005361
An Index to Measure Efficiency of Hospital Networks for Mass Casualty Disasters
Title
An Index to Measure Efficiency of Hospital Networks for Mass Casualty Disasters.
Creator
Bull Torres, Maria, Sepulveda, Jose, Sala-Diakanda, Serge, Geiger, Christopher, Kapucu, Naim, University of Central Florida
Abstract / Description
Disaster events have emphasized the importance of healthcare response activities due to the large number of victims. For instance, Hurricane Katrina in New Orleans, in 2005, and the terrorist attacks in New York City and Washington, D.C., on September 11, 2001, left thousands of wounded people. In those disasters, although hospitals had disaster plans established for more than a decade, their plans were not efficient enough to handle the chaos produced by the hurricane and terrorist attacks....
Show moreDisaster events have emphasized the importance of healthcare response activities due to the large number of victims. For instance, Hurricane Katrina in New Orleans, in 2005, and the terrorist attacks in New York City and Washington, D.C., on September 11, 2001, left thousands of wounded people. In those disasters, although hospitals had disaster plans established for more than a decade, their plans were not efficient enough to handle the chaos produced by the hurricane and terrorist attacks. Thus, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) suggested collaborative planning among hospitals that provide services to a contiguous geographic area during mass casualty disasters. However, the JCAHO does not specify a methodology to determine which hospitals should be included into these cooperative plans. As a result, the problem of selecting the right hospitals to include in exercises and drills at the county level is a common topic in the current preparedness stages. This study proposes an efficiency index to determine the efficient response of cooperative-networks among hospitals before an occurrence of mass casualty disaster. The index built in this research combines operations research techniques, and the prediction of this index used statistical analysis. The consecutive application of three different techniques: network optimization, data envelopment analysis (DEA), and regression analysis allowed to obtain a regression equation to predict efficiency in predefined hospital networks for mass casualty disasters. In order to apply the proposed methodology for creating an efficiency index, we selected the Orlando area, and we defined three disaster sizes. Then, we designed networks considering two perspectives, hub-hospital and hub-disaster networks. In both optimization network models the objective function pursued to: reduce the travel distance and the emergency department (ED) waiting time in hospitals, increase the number of services offered by hospitals in the network, and offer specialized assistance to children. The hospital network optimization generated information for 75 hospital networks in Orlando. The DEA analyzed these 75 hospital networks, or decision making units (DMU's), to estimate their comparative efficiency. Two DEAs were performed in this study. As an output variable for each DMU, the DEA-1 considered the number of survivors allocated in less than a 40 miles range. As the input variables, the DEA-1 included: (i) The number of beds available in the network; (ii) The number of hospitals available in the network; and (iii) The number of services offered by hospitals in the network. This DEA-1 allowed the assignment of an efficiency value to each of the 75 hospital networks. As output variables for each DMU, the DEA-2 considered the number of survivors allocated in less than a 40 miles range and an index for ED waiting time in the network. The input variables included in DEA-2 are (i) The number of beds available in the network; (ii) The number of hospitals available in the network; and (iii) The number of services offered by hospitals in the network. These DEA allowed the assignment of an efficiency value to each of the 75 hospital networks. This efficiency index should allow emergency planners and hospital managers to assess which hospitals should be associated in a cooperative network in order to transfer survivors. Furthermore, JCAHO could use this index to evaluate the cooperating emergency hospitals' plans.However, DEA is a complex methodology that requires significant data gathering and handling. Thus, we studied whether a simpler regression analysis would substantially yield the same results. DEA-1 can be predicted using two regression analyses, which concluded that the average distances between hospitals and the disaster locations, and the size of the disaster explain the efficiency of the hospital network. DEA-2 can be predicted using three regressions, which included size of the disaster, number of hospitals, average distance, and average ED waiting time, as predictors of hospital network efficiency. The models generated for DEA-1 and DEA-2 had a mean absolute percent error (MAPE) around 10%. Thus, the indexes developed through the regression analysis make easier the estimation of the efficiency in predefined hospital networks, generating suitable predictors of the efficiency as determined by the DEA analysis. In conclusion, network optimization, DEA, and regressions analyses can be combined to create an index of efficiency to measure the performance of predefined-hospital networks in a mass casualty disaster, validating the hypothesis of this research.Although the methodology can be applied to any county or city, the regressions proposed for predicting the efficiency of hospital network estimated by DEA can be applied only if the city studied has the same characteristics of the Orlando area. These conditions include the following: (i) networks must have a rate of services lager than 0.76; (ii) the number of survivors must be less than 47% of the bed capacity EDs of the area studied; (iii) all hospitals in the network must have ED and they must be located in less than 48 miles range from the disaster sites, and (iv) EDs should not have more than 60 minutes of waiting time.The proposed methodology, in special the efficiency index, support the operational objectives of the 2012 ESF#8 for Florida State to handle risk and response capabilities conducting and participating in training and exercises to test and improve plans and procedures in the health response.
Show less
Date Issued
2012
Identifier
CFE0004524, ucf:49290
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004524
Influence of Personal and State Level Variables on Perception of State Emergency Management Network Resilience In 47 States
Title
Influence of Personal and State Level Variables on Perception of State Emergency Management Network Resilience In 47 States.
Creator
Jennison, Victoria, Wan, Thomas, Zhang, Ning, Ramirez, Bernardo, Kapucu, Naim, University of Central Florida
Abstract / Description
Emergency management coordination in the United States has fallen victim to over a century of strategies to organize, reorganize, consolidate, or decentralize disaster preparedness, planning and response. Regardless of the agency in charge at the federal level, individual citizens have been responsible for their own well-being immediately after any disaster or emergency event for more than 100 years because it takes time to mobilize and deliver aid. The system most often charged with managing...
Show moreEmergency management coordination in the United States has fallen victim to over a century of strategies to organize, reorganize, consolidate, or decentralize disaster preparedness, planning and response. Regardless of the agency in charge at the federal level, individual citizens have been responsible for their own well-being immediately after any disaster or emergency event for more than 100 years because it takes time to mobilize and deliver aid. The system most often charged with managing that mobilization during an emergency event that exceeds the response capacity of local public safety agencies is the state emergency management network. Many entities in a state emergency management network have different responsibilities during disaster states vs. non-disaster states. Regardless of their role and function, entities need to be able to exchange resources and information with each other, often under time, economic, or other constraints during disasters. This resource exchange generates trust, an essential element of a resilient network. Resilient networks suffer fewer negative impacts from disaster related loss and are more likely to retain collective capacity to respond and help communities recover.The purpose of this study is to explore the ability of individual and state level attributes to explain variability in perception of network resilience. One-hundred fifty one state emergency management agency employees were surveyed regarding their perception of 5 constructs of network resilience (rapidity, redundancy, relationships, resourcefulness, and robustness) and individual level attributes. State level indicators from FEMA, NEMA, American Human Development Index, and Social Vulnerability Index were also analyzed. Overall, it was found that the individual attribute of perception of network integrity had the most influence on perception of network resilience, followed by perception of community resilience and state level attributes including disaster experience, state well-being, and number of full time state emergency management agency employees. These findings can improve network resilience by informing state emergency management network development activity. Networks that increase member opportunities to develop relationships of resource and information exchange will increase their resilience. That increased network resilience impacts community resilience because, as Winston Churchill's wise words during World War II reconstruction advise, (")We shape our communities and then they shape us(").?
Show less
Date Issued
2015
Identifier
CFE0005812, ucf:50040
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005812
FACTORS INFLUENCING EFFECTIVENESS OF INTERORGANIZATIONAL NETWORKS AMONG CRISIS MANAGEMENT ORGANIZATIONS: A COMPARATIVE PERSPECTIVE
Title
FACTORS INFLUENCING EFFECTIVENESS OF INTERORGANIZATIONAL NETWORKS AMONG CRISIS MANAGEMENT ORGANIZATIONS: A COMPARATIVE PERSPECTIVE.
Creator
Sahin, Bahadir, Wan, Thomas, University of Central Florida
Abstract / Description
Crisis management has become one of the most important public policy areas in recent decades with greater numbers of manmade and natural disasters. History showed that well-implemented crisis management policies can save lives and reduce costs in a disaster. Literature offered various suggestions for more effective crisis management policies with different techniques utilizing different theoretical frameworks. Informal relationships among crisis management employees were suggested to have a...
Show moreCrisis management has become one of the most important public policy areas in recent decades with greater numbers of manmade and natural disasters. History showed that well-implemented crisis management policies can save lives and reduce costs in a disaster. Literature offered various suggestions for more effective crisis management policies with different techniques utilizing different theoretical frameworks. Informal relationships among crisis management employees were suggested to have a positive impact on crisis management effectiveness. Yet it was not demonstrated with advanced statistical tools if there is such a relationship. This study considers crisis management effort as a network effort and employs complex adaptive systems theory in order to understand factors influencing effectiveness of crisis management networks. Complex adaptive systems theory presents that more open communication lines in a given network or an organization would increase effectiveness of it since inner processes of the network or organization would obtain more information from the chaotic environment. Quality of informal relationships (casual relationships, social capital etc.) was hypothesized as a tool to open more communication lines within an agency which would eventually increase effectiveness of the network constructed by the organization. Based on the theoretical framework, adaptiveness capacity of the agencies was also tested in order to understand a correlation between adaptation and effectiveness of crisis management networks. Multiple case-study method was employed to identify incidents that can represent crisis management in full perspective. Terrorist attacks carried upon by the same terrorist network hit New York in 2001, Istanbul in 2003, Madrid in 2004, and London in 2005 were selected. First response phase of crisis management and policy changes after and before the attacks were discussed. Public administration processes and other social-economical conditions of countries were examined in terms of crisis management structure. Names of key agencies of selected crisis management systems were suggested by a social network analysis tool-UCINET. Six key agencies per incident were targeted for surveys. Surveys included a nine-item-quality of informal relationships, four-item-adaptiveness capability, and ten-item-perceived effectiveness of crisis management networks-scales. Respondents were asked to fill in online surveys where they could refer to their colleagues in the same incidents. 230 respondents were aimed and 246 survey responses were obtained as a result. Surveys formed a structural equation model representing 23 observed factors and 2 latent constructs. Confirmatory factor analysis was utilized to validate hypothesis-driven conceptual models. Quality of informal relationships was found to have a significant positive impact on perceived crisis management network effectiveness (Standardized regression coefficient = .39). Two of the adaptiveness variables, openness to change and intra-organizational training were also positively correlated with the dependent variable of the study (Standardized regression coefficient = .40 and .26 respectively). Turkish and American groups' differences suggested a social-economical difference in societies. Majority of the respondents were some type of managers which made it possible to generalize the results for all phases of crisis management. Discussions suggested improved informal relationships among crisis management employees to provide a better crisis management during an extreme event. Collaborative social events were offered to improve crisis management effectiveness. An agency's openness to change proposed that a crisis management organization should be flexible in rules and structureto gain more efficacy. The other adaptiveness variable, intra-organizational training efforts were proposed to have certain influence on effectiveness of crisis management network. Factors built latent construct of perceived crisis management effectiveness were also found out to be important on crisis management, which of some are ability to carry out generic crisis management functions, mobilize personnel and resources efficiently, process information adequately, blend emergent and established entities, provide appropriate reports for news media etc. Study contributed to the complex adaptive system theory since the fundamentals of the theory were tested with an advanced quantitative method. Non-linear relationships within a system were tested in order to reveal a correlation as the theory suggested, where the results were convincingly positive. Crisis management networks' effectiveness was demonstrated to be validated by a ten-item-scale successfully. Future research might utilize more disaster cases both natural and manmade, search for impact of different communication tools within a system, and look at the relationships among members of crisis management networks instead looking within an organization.
Show less
Date Issued
2009
Identifier
CFE0002709, ucf:48173
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002709
A Human-Centric Approach to Data Fusion in Post-Disaster Managment: The Development of a Fuzzy Set Theory Based Model
Title
A Human-Centric Approach to Data Fusion in Post-Disaster Managment: The Development of a Fuzzy Set Theory Based Model.
Creator
Banisakher, Mubarak, McCauley, Pamular, Geiger, Christopher, Lee, Gene, Shi, Fuqian, Zou, Changchun, University of Central Florida
Abstract / Description
It is critical to provide an efficient and accurate information system in the post-disaster phase for individuals' in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as,...
Show moreIt is critical to provide an efficient and accurate information system in the post-disaster phase for individuals' in order to access and obtain the necessary resources in a timely manner; but current map based post-disaster management systems provide all emergency resource lists without filtering them which usually leads to high levels of energy consumed in calculation. Also an effective post-disaster management system (PDMS) will result in distribution of all emergency resources such as, hospital, storage and transportation much more reasonably and be more beneficial to the individuals in the post disaster period. In this Dissertation, firstly, semi-supervised learning (SSL) based graph systems was constructed for PDMS. A Graph-based PDMS' resource map was converted to a directed graph that presented by adjacent matrix and then the decision information will be conducted from the PDMS by two ways, one is clustering operation, and another is graph-based semi-supervised optimization process. In this study, PDMS was applied for emergency resource distribution in post-disaster (responses phase), a path optimization algorithm based ant colony optimization (ACO) was used for minimizing the cost in post-disaster, simulation results show the effectiveness of the proposed methodology. This analysis was done by comparing it with clustering based algorithms under improvement ACO of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS) and the results also show that the SSL based graph will be more effective for calculating the optimization path in PDMS. This research improved the map by combining the disaster map with the initial GIS based map which located the target area considering the influence of disaster. First, all initial map and disaster map will be under Gaussian transformation while we acquired the histogram of all map pictures. And then all pictures will be under discrete wavelet transform (DWT), a Gaussian fusion algorithm was applied in the DWT pictures. Second, inverse DWT (iDWT) was applied to generate a new map for a post-disaster management system. Finally, simulation works were proposed and the results showed the effectiveness of the proposed method by comparing it to other fusion algorithms, such as mean-mean fusion and max-UD fusion through the evaluation indices including entropy, spatial frequency (SF) and image quality index (IQI). Fuzzy set model were proposed to improve the presentation capacity of nodes in this GIS based PDMS.
Show less
Date Issued
2014
Identifier
CFE0005128, ucf:50702
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005128
Understanding Crisis Communication and Mobility Resilience during Disasters from Social Media
Title
Understanding Crisis Communication and Mobility Resilience during Disasters from Social Media.
Creator
Roy, Kamol, Hasan, Samiul, Eluru, Naveen, Wu, Yina, University of Central Florida
Abstract / Description
Rapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media...
Show moreRapid communication during extreme events is one of the critical aspects of successful disaster management strategies. Due to their ubiquitous nature, social media platforms offer a unique opportunity for crisis communication. Moreover, social media usage on GPS enabled devices such as smartphones allow us to collect human movement data which can help understanding mobility during a disaster. This study leverages social media (Twitter) data to understand the effectiveness of social media-based communication and the resilience of human mobility during a disaster. This thesis has two major contributions. First, about 52.5 million tweets related to hurricane Sandy are analyzed to assess the effectiveness of social media communication during disasters and identify the contributing factors leading to effective crisis communication strategies. Effectiveness of a social media user is defined as the ratio of attention gained over the number of tweets posted. A model is developed to explain more effective users based on several relevant features. Results indicate that during a disaster event, only few social media users become highly effective in gaining attention. In addition, effectiveness does not depend on the frequency of tweeting activity only; instead it depends on the number of followers and friends, user category, bot score (controlled by a human or a machine), and activity patterns (predictability of activity frequency). Second, to quantify the impacts of an extreme event to human movements, we introduce the concept of mobility resilience which is defined as the ability of a mobility infrastructure system to manage shocks and return to a steady state in response to an extreme event. We present a method to detect extreme events from geo-located movement data and to measure mobility resilience and loss of resilience due to those events. Applying this method, we measure resilience metrics from geo-located social media data for multiple types of disasters occurred all over the world. Quantifying mobility resilience may help us to assess the higher-order socio-economic impacts of extreme events and guide policies towards developing resilient infrastructures as well as a nation's overall disaster resilience.
Show less
Date Issued
2018
Identifier
CFE0007362, ucf:52090
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007362