A Normative Enterprise Architecture for Guiding End-to-End Emergency Response Decision Support

This article examines the underlying architecture guiding the development and use of enterprise decision support systems that maintain the delivery of time critical public services. A normative architecture, developed from comparative cases involving San Mateo County and Mayo Clinic Emergency Medical Services systems, provides a collection of characteristics meant to guide an emergency response system toward a high level of performance and enable optimal decision-making. At a national symposium, academics and practitioners involved in promoting effective emergency response information systems provided validation for the architecture and next steps for enhancing emergency response information systems. Normative architecture characteristics and expert perspectives from the symposium are integrated into a framework that offers an enterprise approach for delivering time-critical emergency response services. This article provides recommendations for navigating toward a more incremental approach in developing enterprise-oriented emergency information services and examines future trends involving the application of normative architectural concepts to real-world emergency medical settings.

Responses to emergencies are typically based on contingency plans. However, unexpected events can occur during the operation that affect safety and/or effectiveness of the activated response plan. Latest advances in communications and information technology can collect and transfer a large amount of data to the on‐scene commander in real‐time. The commander can then assess the potential impact of such events and decide if and how to revise the planned course of action to maintain safety and efficiency of the operation.This paper proposes a new paradigm for real‐time decision support for emergency response ‐ operational risk management. Emergency response is modelled as a large‐scale operational system, including a human‐machine real‐time controller. The decision model is based on a topological graph structure, where the nodes are decisions and the arcs the activities. The attributes of the activities are expressed as ordinal preference values. The optimal course of action is the sequence of activities with the highest preference for resolving the emergency situation. The implementation of the decision model into a prototype decision support system is discussed.

Harvested social media data has the potential to enhance emergency response decision support if its reliability can be assessed. We are working on an EU-FP7 project called Slandail (Project No. 607691). Slandail aims to develop a prototype system which automates social media data analysis for emergency response. This paper considers some of the ethical concerns that may arise with such alternate use of data. It offers technical (hardware and software) and operational measures intended to improve the security of data and information flow to mitigate those risks.

Preparedness is the main criteria for immediate response for any emergency response provider (ERP) and decision support system. Emergency Response System can be either for fire emergency response system, or police station emergency response system and healthcare emergency response system. Emergency decision support system is imperative for the reduction of disaster losses and the efficiency improvement of emergency resources allocation. The objective of this paper is to establish a Geographic Information System (GIS) based healthcare emergency response system for services that can identify the optimal route from the location of incident to any healthcare service providers, and the optimal route was modeled based on the distance (the shortest path) to the closest healthcare service providers. Therefore, the main outcome from this research is to provide immediate response to any request for incidents or accidents. Basic idea behind developing this web application is that a case (patient) or incident occurs at a particular location (latitude and longitude). Knowing those locations and mapping them using (GIS) would greatly help in analyzing and visualizing the various factors associated with the incident and thus would help in providing better decision making and quicker response for any incident or accident using ArcGIS Server and web application. The rest of the paper is organized as follows: Section 2 describes the aims and objective of the research, Section 3 discusses the methodology which includes the description of the study area, data inputs and healthcare emergency response model parameters. In section 4, analyses including shortest path, spatial queries and also service area for healthcare providers are displayed. Results are presented and interpretation is illustrated in Section 4. Conclusions and discussions of future research works are outlined in Section 5

GIScience Research at the Thirty‐second Annual Esri International User Conference

This paper provides a detailed outline of the plan for the development and deployment of a Red River Basin Decision Support System (REDES). The system is envisaged as serving the needs of decision-makers and stakeholders in the Red River Basin. Making flood management decisions during the planning, flood-fighting and post-flood recovery stages requires comprehensive support in order to properly account for all flood impacts. The objective of REDES is to enhance preparedness planning, response, and recovery with emphasis on flood prediction and monitoring, emergency response and public involvement.

Crowdsourced GPS probe data, such as travel time on changeable-message signs and incident detection, have been gaining popularity in recent years as a source for real-time traffic information to driver operations and transportation systems management and operations. Efforts have been made to evaluate the quality of such data from different perspectives. Although such crowdsourced data are already in widespread use in many states, particularly the high traffic areas on the Eastern seaboard, concerns about latency—the time between traffic being perturbed as a result of an incident and reflection of the disturbance in the outsourced data feed—have escalated in importance. Latency is critical for the accuracy of real-time operations, emergency response, and traveler information systems. This paper offers a methodology for measuring probe data latency regarding a selected reference source. Although Bluetooth reidentification data are used as the reference source, the methodology can be applied to any other ground truth data source of choice. The core of the methodology is an algorithm for maximum pattern matching that works with three fitness objectives. To test the methodology, sample field reference data were collected on multiple freeway segments for a 2-week period by using portable Bluetooth sensors as ground truth. Equivalent GPS probe data were obtained from a private vendor, and their latency was evaluated. Latency at different times of the day, impact of road segmentation scheme on latency, and sensitivity of the latency to both speed-slowdown and recovery-from-slowdown episodes are also discussed.

An environment for development of decision support systems

This paper describes the development of a decision support system that could be used by forensic scientists to discover the profile of a bullet that caused fracture to a glass panel. Analysis of the glass fractures was done using Leica Application Suite to identify the line length, width, diameter and radius. The fractal dimension was analyzed by Harmonic and Fractal Image Analyzer. Image Comparer was used to compare between new image and the image in the database. Micro topographic data and images produced from the analysis were stored in MySQL database and used as the repository of web-based decision support system. This system would be helpful to expedite the identification process and reduce the use of resources in terms of man power and investigation time.

Key Technologies and Their Applications in Digital Flood Control and Emergency Response Decision Support

Review for "Chemical process safety domain knowledge graph-enhanced LLM for efficient emergency response decision support"

Fire departments are one of the most versatile and widespread emergency management organizations. To support their digitalization, innovative firefighter information technologies such as emergency response information systems (ERIS) are suggested. Despite theoretical potentials like raised situation awareness, however, these technologies seem to disseminate only slowly in practice. As existing acceptance models cannot sufficiently explain the acceptance of novel technologies in this special domain, literature calls for increased contextualization. To get insights into the domain-specific acceptance factors, we conducted an ethnography and observed a voluntary fire department's multi-year project to introduce an ERIS. We identified seven factors, like revealing events and error management culture, which acted as triggers for different adoption stages. From overarching characteristics of a voluntary fire department, we derived six additional acceptance factors, like situational adaptability and self-determination. The identified factors can help firefighters in conducting successful digitalization projects, and policy makers in supporting voluntary emergency management organizations. Moreover, the results provide numerous avenues for future research, can be linked to various research fields and contextualize existing acceptance models. Beyond fire departments, the results may be transferred to similar high-reliability organizations.

This article reports on a field investigation into the ways that transportation agencies use quantitative and qualitative information for making strategic decisions regarding airport ground access. The study analyzes the value of this information for planning airport ground access improvements at seven major international airport sites.The major finding of the research is that quantitative modeling for strategic decision support is very difficult, costly and time consuming. Modelers are confident that the models are accurate and reliable but executives generally lack confidence in the results. Transportation officials believe that the information supplied is flawed by a number of defects that minimize its value for strategic decision makers. The information defects described in this article provide an analysis of the structural difficulty of using quantitative modeling for transportation problems of strategic importance. To date, qualitative information is not frequently used, but some transportation agencies are considering its application to designing transportation services. Although this study is limited to airport ground access, the authors feel that this evidence, in conjunction with the evidence from other studies in the transportation area, dictates a need for wariness in the development of decision support systems for transportation planners. Developers of decision support systems for transportation planners must be aware of modeling costs and defects and consider how to improve the timeliness, relevance and credibility of information quantitative models provide transportation executives. Fundamentally it is important to recognize that decision makers tend, either singularly or in concert with other individuals or groups, to be the champions of a long-term vision for the community. When modeling produces inconsistent or wide ranging results that contradict their position, decision makers may not only discard modeling activities, but lose confidence in the models altogether. As a consequence, transportation planners are faced with the challenge of how to improve quantitative modeling. The most reliable and effective means for improvement is incorporation of qualitative techniques which provide greater understanding of customer perceptions and human behavior.

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

The current paper deals with the design and development of a web-based group decision support system for the evaluation and selection of research projects. The paper involves the analysis of the system’s requirements, the definition of its functional structure, the development of an algorithm for the evaluation and ranking of research projects, the development of a related database and of a user interface along with the writing of the business logic of the system.