Catastrophes like Katrina destroy a community's critical infrastructure-a situation that instigates several dilemmas. Immediately, the community experiences information disruption within the community, as well as between the community and the outside world. The inability to communicate because of physical or virtual barriers to information instigates instant isolation. Prolonged, this scarcity of information becomes an information poverty spell, placing hardship on a community accustomed to easily accessible and applicable information. Physical devastation causes the scarcity of what Abraham Maslow calls basic survival needs-physiological, security, and social-a needs regression from the need to self-actualize, to meet intellectual and aesthetic needs. Because needs regress, the type of information required to meet the needs, also changes-regresses to information regarding survival needs. Regressed information needs requires altered information behaviors-altered methods and means to meet the information needs of the post-disaster situation. Situational information behavior follows new mores-altered norms-norms constructed for the post-disaster situation. To justify the unconventional, situational social norms, residents must adjust their beliefs about appropriate behavior. Situational beliefs support situational social norms-and situational information behaviors prevail. Residents find they must trust strangers, create makeshift messaging systems, and in some cases, disregard the law to meet their post-disaster survival needs.

The Master's Thesis deals with the usage of various software tools in emergency management and in environmental protection. Nowadays, on every step we make, we can face emergency situations which can cause great harm to the environment as well as to human life and property. The theoretical part provides an overview of software tools. Different software tools, their basic parameters, development and usage are described. There are also explained terms like geographic information system, etc. The practical part is focused on the instruments used in selected countries, including Czech Republic, Slovak Republic, Hungary, USA, Canada and the United Kingdom. In the conclusion is the comparison of the effectiveness of software tools in emergency management and in environmental protection.

The Master's Thesis deals with the usage of various software tools in emergency management and in environmental protection. Nowadays, on every step we make, we can face emergency situations which can cause great harm to the environment as well as to human life and property. The theoretical part provides an overview of software tools. Different software tools, their basic parameters, development and usage are described. There are also explained terms like geographic information system, etc. The practical part is focused on the instruments used in selected countries, including Czech Republic, Slovak Republic, Hungary, USA, Canada and the United Kingdom. In the conclusion is the comparison of the effectiveness of software tools in emergency management and in environmental protection.

Indiana University-Purdue University Indianapolis (IUPUI) / During a 2011 summer dominated by headlines about an earthquake and a hurricane along the East Coast, extreme heat that silently killed scores of Americans largely went unnoticed by the media and public. However, despite a violent spasm of tornadic activity that claimed over 500 lives during the spring of the same year, heat-related mortality annually ranks as the top cause of death incident to weather. Two major data groups used in researching vulnerability to extreme heat events (EHE) include socioeconomic indicators of risk and factors incident to urban living environments. Socioeconomic determinants such as household income levels, age, race, and others can be analyzed in a geographic information system (GIS) when formatted as vector data, while environmental factors such as land surface temperature are often measured via raster data retrieved from satellite sensors. The current research sought to combine the insights of both types of data in a comprehensive examination of heat susceptibility using knowledge-based classification. The use of knowledge classifiers is a non-parametric approach to research involving the creation of decision trees that seek to classify units of analysis by whether they meet specific rules defining the phenomenon being studied. In this extreme heat vulnerability study, data relevant to the deadly July 1995 heat wave in Chicago’s Cook County was incorporated into decision trees for 13 different experimental conditions. Populations vulnerable to heat were identified in five of the 13 conditions, with predominantly low-income African-American communities being particularly at-risk. Implications for the results of this study are given, along with direction for future research in the area of extreme heat event vulnerability.

The purpose of this study was to analyze critical elements for crisis planning at seven private four-year residential colleges and universities in Northern California. The researcher reviewed each campus's written crisis plans and interviewed campus officials in charge of leading their respective institution's crisis planning efforts. The data revealed that the threat of natural disasters was a common impetus for formal crisis planning. Institutions borrowed information from other campuses and public and private organizations to develop institutional crisis plans. Outside agencies both contributed to and gained from collaboration with these institutions, although all institutions sought a degree of self-sufficiency for food and water supplies. Emergency Operations Centers were designed to focus staffing and resources in a single, in some cases moveable, location in the event of a crisis. Campuses in this study invested considerable resources in systems of communication with students, faculty, and staff, including sirens, digital displays, and Connect-ED, but individual subscription remained a barrier to the smooth functioning of Connect-ED. Multi-layered communication systems enhance a campus's ability to communicate with all stakeholders. Philosophies varied on specificity versus flexibility as the framework for crisis planning. Campuses used threat assessment teams as proactive intervention to identify students who pose a threat to themselves or others. The State of California Standardized Emergency Management System (SEMS), which became a template for the Federal National Incident Management System (NIMS), has become the unifying factor for crisis planning among these institutions. SEMS/NIMS, while not a mandate, emerged as a driving force for planning, because compliance with SEMS/NIMS is a requirement for receiving federal disaster emergency reimbursement for property damage. Practicing the plan, through tabletop and functional simulation exercises, allowed campus officials and civic safety agencies to develop a shared vocabulary and procedures. Crisis planning is a means to help a campus prepare for and respond to an incident in an effective manner, thus reducing harm to people and property damage. Although crisis planning cannot completely prevent incidents from occurring, appropriate and advanced planning and preparation can allow campus leaders to contain both the duration of and the damage caused by major crises.

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The current study projects the third-person perception phenomenon into the area of emergency management, specifically regarding risk communication in the context of religious gatherings. This study utilized the Umrah religious gathering in Makkah, Saudi Arabia, during summer 2019 as a case study (N = 257). This study aimed to investigate whether pilgrims perceive there was a greater effect of health information on others than on themselves. Survey results were translated and then coded and analyzed statistically using SPSS software. The findings indicated that third-person perception existed among pilgrims. Specifically, the perception of pilgrims that the influence of news about MERS-CoV, believed to be undesirable in its effect on themselves, was greater on others than on themselves was found statistically significant. Further, the findings indicated that the more pilgrims watched, listened to, or read news about MERS-CoV, the larger the effect of the news they perceived on themselves and others was. Thus, exposure to MERS-CoV news did not increase, but rather decreased the perception of difference between self and others. Also, the empirical findings indicated that pilgrims who were knowledgeable about MERS-CoV could relate to the coverage. Moreover, if pilgrims believed they were affected by MERS-CoV news, they believed that the MERS-CoV news had a similar or greater effect on other pilgrims. The findings indicated socio-demographics had a partial effect on third-person perception, Finally, the stronger the perceived effect of MERS-CoV news on oneself, the more likely these pilgrims were to take protective actions against the MERS-CoV epidemic. However, the third-person perception anticipated in the use of impersonal communication (pamphlets, television, radio, newspapers, Internet, social media, text message, health clinics, mosques messages, public events, and billboards) and of interpersonal communication (friends, family member, or others you know) was not found significant. Moreover, the perceived effects of MERS-CoV news on others did not show third-person perception regarding behavior intention or consequences. These findings have implications for risk communication and its governance during religious gatherings as well as for the prepared individuals to promote preparation for risk and actions toward risk mitigation.

In recent years, social media platforms such as Twitter and Facebook have emerged as effective tools for broadcasting messages worldwide during disaster events. With millions of messages posted through these services during such events, it has become imperative to identify valuable information that can help the emergency responders to develop effective relief efforts and aid victims. Many studies implied that the role of social media during disasters is invaluable and can be incorporated into emergency decision-making process. However, due to the "big data" nature of social media, it is very labor-intensive to employ human resources to sift through social media posts and categorize/classify them as useful information. Hence, there is a growing need for machine intelligence to automate the process of extracting useful information from the social media data during disaster events. This dissertation addresses the following questions: In a social media stream of messages, what is the useful information to be extracted that can help emergency response organizations to become more situationally aware during and following a disaster? What are the features (or patterns) that can contribute to automatically identifying messages that are useful during disasters? We explored a wide variety of features in conjunction with supervised learning algorithms to automatically identify messages that are useful during disaster events. The feature design includes sentiment features to extract the geo-mapped sentiment expressed in tweets, as well as tweet-content and user detail features to predict the likelihood of the information contained in a tweet to be quickly spread in the network. Further experimentation is carried out to see how these features help in identifying the informative tweets and filter out those tweets that are conversational in nature.

Optimization of relief networks in humanitarian logistics often exemplifies the need for solutions that are feasible given a hard constraint on time. For instance, the distribution of medical countermeasures immediately following a biological disaster event must be completed within a short time-frame. When these supplies are not distributed within the maximum time allowed, the severity of the disaster is quickly exacerbated. Therefore emergency response plans that fail to facilitate the transportation of these supplies in the time allowed are simply not acceptable. As a result, all optimization solutions that fail to satisfy this criterion would be deemed infeasible. This creates a conflict with the priority optimization objective in most variants of the generic vehicle routing problem (VRP). Instead of efficiently maximizing usage of vehicle resources available to construct a feasible solution, these variants ordinarily prioritize the construction of a minimum cost set of vehicle routes. Research presented in this dissertation focuses on the design and analysis of efficient computational methods for optimizing high-consequence variants of the VRP for relief networks. The conflict between prioritizing the minimization of the number of vehicles required or the minimization of total travel time is demonstrated. The optimization of the time and capacity constraints in the context of minimizing the required vehicles are independently examined. An efficient meta-heuristic algorithm based on a continuous spatial partitioning scheme is presented for constructing a minimized set of vehicle routes in practical instances of the VRP that include critically high-cost penalties. Multiple optimization priority strategies that extend this algorithm are examined and compared in a large-scale bio-emergency case study. The algorithms designed from this research are implemented and integrated into an existing computational framework that is currently used by public health officials. These computational tools enhance an emergency response planner's ability to derive a set of vehicle routes specifically optimized for the delivery of resources to dispensing facilities in the event of a bio-emergency.