Efficient communication is a major challenge for emergency responders during crisis management. Reports show that missing information and information overload are important factors that determine the success of crisis management. We propose a method as basis for a software system that improves text or voice-based communication. Communication is split into segments and the system determines from the content of the communication, the tasks of actors and their locations for which responders in the crisis the information is relevant. The system is tested on data recorded at a fire fighting disaster management exercise and found to be accurate enough to be useful.
No abstract
Today, our environment and the objects therein are equipped with an increasing number of devices such as cameras, sensors, and actuators, which all together produce a huge amount of data. Furthermore, we observe that citizens generate data via social media applications running on their personal devices. Smart cities and societies are seeking for ways to exploit these vast amounts of data. In this paper, we argue that to take full advantage of these data, it is necessary to set up data governance properly, which includes defining, assigning, and allocating responsibilities. A proper setting up of data governance appears to be a challenging task since the data may be used irresponsibly, thoughtlessly and maliciously, resulting in many (un)wanted side effects such as violation of rules and regulations, human rights, ethical principles as well as privacy and security requirements. We elaborate on the key functionalities that should be included in the governance of a data ecosystem within smart cites, namely provisioning the required data quality and establishing trust, as well as a few organizational aspects that are necessary to support such a data governance. Realizing these data governance functionalities, among others, asks for making trade-offs among contending values. We provide a few solution directions for realizing these data governance functionalities and making trade-offs among them.
Crisis response organizations operate in very dynamic environments, in which it is essential for responders to acquire all information critical to their task execution in time. In reality, the responders are often faced with information overload, incomplete information, or a combination of both. This hampers their decisionmaking process, workflow, situational awareness and, consequently, effective execution of collaborative crisis response. Therefore, getting the right information to the right person at the right time is of crucial importance.The task of processing all data during crisis response situations and determining for whom at a particular moment the information is relevant is not straightforward. When developing an information system to support this task, some important challenges have to be taken into account. These challenges relate to the structure and truthfulness of the used data, the assessment of information relevance, and the dissemination of relevant information in time. While methods and techniques from big data can be used to collect and integrate data, machine learning can be used to build a model for relevance assessments. An example implementation of such a framework of big data is the TAID software system that collects and integrates data communicated between first responders and may send information to crisis responders that were not addressed in the initial communication. As an example of the impact of TAID on crisis response, we show its effect in a simulated crisis response scenario.
An important determinant for the well-functioning of a criminal justice system is elapsed times. The elapsed time of a case is the period that is required to handle a case that pertains to a suspect or convict. Long elapsed times may be interpreted as delays in a criminal justice system, which in turn may lead to "justice delayed, justice denied". Such a development may undermine the public trust in the government. Therefore insight in elapsed time is of crucial importance for policy-makers to define a sound and healthy justice policy. To gain this insight, we propose a model to measure the elapsed time of criminal cases.The task of measuring elapsed times in the justice domain is not straightforward. Some challenges have to be taken into account before elapsed time can be measured. These include the type of case that is being processed, choosing the starting and finishing point of a criminal case, and integrating data pertaining to a criminal case from different sources. We propose a pragmatic approach to measuring elapsed times, which takes these challenges into account. As an example, we show how the elapsed times of criminal cases in the execution phase of the justice system can be calculated. This example also illustrates the effect of two different calculation methods on the measured elapsed times.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.