How emerging technologies can enhance situational awareness during emergency responses.
Key Terms
Wireless Sensor Web/Network (WSN/WSW):
A network of nodes that collect and transmit data throughout their network (Pine, 2018).
Drone:
Any remotely piloted vehicle, including UAV’s.
Unmanned Aerial Vehicle (UAV):
A type of drone (remotely piloted vehicle), that takes aerial flight.
Evolutionary Algorithm (EA):
A type of artificial intelligence that specializes in optimization problems.
EMERGENCY OPERATIONS CENTRES
An Emergency Operations Centre (EOC) is a physical location which functions to support incident operations as they occur (Donohue, 2016). In an Incident Command (IC) Model, decisions are made by a leadership group comprised of a safety officer, public information officer, intelligence officer, liaison officer, operations leader, planning leader, logistics leader, and a finance and administration leader (Donohue, 2016). This leadership group is supported by teams working in the EOC who coordinate activities and gather information related to several emergency support functions. These functions include transportation, communication, public works and engineering, firefighting, emergency management, mass care, logistics and resource support, public health and medical services, search and rescue, oil and hazardous materials response, agriculture and natural resources, energy, public safety and security, long-term community recovery, and external affairs (Donohue, 2016). These complex factors impact each other, and the decisions made to facilitate these functions can have implications on the level of damage to people, property, the environment, and the economy.
Another major function of an EOC is to develop an Incident Action Plan (Bryan, 2011). Other functions include developing response plans, managing resources, and responding to requests from the public and media (Shouldis, 2010). This plan can be supported by up-to-date quality information about the state of the incident. A higher level of situational awareness can support these functions.
SITUATIONAL AWARENESS IN EMERGENCY MANAGEMENT
Situational Awareness is critical in supporting the functions in an EOC because it enhances the level at which personnel understand the current state of an incident and the contextual information about the environment to forecast how the incident will progress (ESRI, 2008). Enhancing situational awareness can support the decision-making functions of an EOC. Seppänen, Mäkelä, Luokkala, and Virrantaus (2013), state that response operations are coordinated quickly and efficiently when a shared sense of situational awareness is possessed by all groups involved. They go on to identify information gaps as a negative influence on achieving enhanced situational awareness. An information-driven decision support system includes inputs like data, processes, and procedures (Pine, 2018). It then takes this data and transforms it through an analysis or sorting function to provide an output that will help an EOC make decisions about their incident action plan (Pine, 2018).
Information is a critical tool in enhancing situational awareness, so gathering more quality data is necessary. Incident data that is relevant to EOC personnel includes incident date, time, type, location, response agencies assigned, injury numbers, fatality numbers, and economic damage (Bryan, 2011). Additionally, information about the status of shelters, damage assessments, utilities, infrastructure and transportation, schools, medical facilities, and weather forecasts can support the decision-making functions of an EOC to develop an Incident Action Plan (Bryan, 2011). Johnson, Zagorecki, Gelman, and Comfort (2011) highlight situational awareness as a crucial component of quality decision-making. Johnson et al. (2011) built their situational awareness model (SAM) around three levels of situational awareness. These levels are perception, comprehension, and projection. These levels can be compared to the process of data collection, analysis, and visualization/modelling.
According to cognitive load theory, working memory in the human brain handles new information but cannot hold it for long periods, since this is the function of long-term memory (Reedy, 2015). In his paper on simulation design in clinical nursing, Reedy (2015), states that the ease at which incoming information is processed, the frequency of interaction with the information, and familiarity with the source of information, all enhance the ability of working memory to process and store information in long term memory. Given the complex and rapid nature of information flow in an EOC, the cognitive load demands are typically high. Using technology that can visualize, analyze, and model data to reduce cognitive the load of EOC personnel can improve their situational awareness through improved comprehension and projection abilities.
In their comprehensive review of big data in disaster management, Yu, Yang, and Li (2018) identified several sources of data collection including satellites, drones, the wireless sensor web, vector-based spatial data, crowdsourcing, social media, mobile GPS, simulations, and call data records (CDR). Huang and Xiao (2015) comment on social media, specifically Twitter, being a critical data source to enhance situational awareness through understanding how a disaster is progressing. Erdelj, Król, and Natalizio (2017), introduce the added benefit of combining data sources like drones and a wireless sensor network (WSN) to reap compounding benefits. Dusse, Júnior, Alves, Novais, Vieira, and Mendonça (2016) discuss how data visualization can enhance situational awareness. The data sources of the visualizations they studied were local, external, XML-based, 3D-Hybrid, remote sensing images, social media, and user-inputted. User inputs can be facilitated by EOC personnel who are gathering information from people, community leaders, and organizations.
TECHNOLOGY AND SITUATIONAL AWARENESS
How can more accurate, live data be provided to EOC personnel?
The accuracy and relevance of data directly impacts the level or situational awareness the data can provide. Thus, it is imperative to provide more accurate, live data to EOC personnel. To accomplish this task, systems must be in place to sift through the complex data and filter it by variables that matter to EOC personnel like timing and relevance.
Huang and Xiao (2015) developed a tweet classifier is trained to filter out tweets and report relevant tweets to emergency management personnel, categorized by the phase of emergency management to which the tweet correlates. The purpose of their classifier was to provide a tool to gauge live conditions of an affected area so that EOC personnel know when the event shifts from response to recovery in order to allocate resources as required.
Erdelj et al. (2017) proposed a combination of unmanned aerial vehicles (UAV) and a wireless sensor network (WSN) to aid in enhancing situation awareness, damage assessment, search and rescue missions, communications systems, logistics, disaster information fusion and sharing, monitoring, forecasting, and early warning systems. They foresee improvements to situational awareness by leveraging the mobility of drones and the data collection and integration capacities of a wireless sensor network, which includes the internet of things (IoT).
Netten and van Someren (2011) applied the concept of classification to all forms of information in their proposed Task Adaptive Information Distribution (TAID) system. This system classifies information and distributes it to emergency response groups for which the information is relevant. The purpose of this system was to distribute information to groups like fire services, police, and medical services that were pertinent to them. The TAID system architecture is comprised of components responsible for data acquisition, segmentation, feature construction, classification, and distribution (Netten and van Someren, 2011).
Building system architectures to filter and classify information as accomplished by TAID and Huang and Xiao’s (2015) tweet classifier is critical to handling the large amounts of data required to enhance situational awareness. These systems are a primary analysis tool with which an EOC can manage the flow of information.
Expanding the sources of data and implementing system architectures to filter, organize, and distribute relevant information can accomplish the goal of providing more accurate live data to EOC personnel.
How impacts to people and communities be tracked by synergizing several sources of information?
Poslad, Middleton, Chaves, Tao, Necmioglu, and Bügel (2015) introduce a semantic-type framework for managing data to enhance the operability of the features of early warning systems which use the data inputs to trigger warnings and to run simulations. Integrating a similar system in an EOC can make for easier tracking and combining of live data.
How can impacts to people and communities be predicted and planned for by analyzing existing data?
Analyzing and modelling big-data can provide insights to facilitate decision-support and enhance situational awareness. These techniques aid in the comprehension and projection levels of situational awareness. Analysis can help EOC personnel comprehend the large sets of complex data rapidly flowing their way. Modelling and simulations can allow an EOC to project the impacts of a hazard.
Chae, Thom, Jang, Kim, Ertl, and Ebert (2014) demonstrate how analyzing Twitter data can feed a spatiotemporal visualization that is used to map how people moved during Hurricane Sandy using a heat map. Although their application was not used live, implementing a similar system in an EOC can add an important set of live data to EOC personnel off of which they can make resource allocation and transportation decisions. Chae et al. (2014) noted how they used their tool to understand infrastructure data like the locations of supermarkets frequented near the onset of Hurricane Sandy. Information that EOC personnel can infer from visualizations after the emergency or disaster can provide critical information for the planning phase of the next emergency.
In their review of big data in supply chain management, Zhong, Newman, Huang, and Lan (2016) reference how the United Parcel Service of North America (UPS) uses big data analysis to optimize fuel use and company costs. They highlight the prevalence of big data in the manufacturing sector as a tool to monitor assets and to increase the efficiency of operations and gain live insights to guide strategy. A notable example includes the pharmaceutical company, Merk, leveraging big data analytics to refine their manufacturing decisions (Zhong et al. 2016). Implementing the use of data analytics in an EOC setting can provide similar benefits to guide strategic decisions in response to a disaster or planning for a possible hazard. However, as Zhong et al. (2016) notes, when big data visualization offers expansions to how decision-makers can interact with data, it offers the ability to adjust parameters and visualize how those adjustments affect outcomes in real-time.
Simulations can project outcomes based on given inputs. Krejci (2015), proposed a simulation model that can evaluate different behaviour and logistical coordination strategies for humanitarian relief. Krejci’s model found that increases in coordination from different groups leads to reductions in shipping costs. The model also found that increases in coordinated groups did not have a significant effect on fulfillment time. By adopting a simulation model to evaluate different strategies in an EOC, decision-makers can gain insights about the implications of their potential actions. Krejci (2015) notes that future research into simulation models can benefit humanitarian relief operations by identifying positive strategies and behaviors and encouraging them.
Mihăiţă, Dupont, and Camargo (2018) proposed a 3D traffic simulation by using evolutionary algorithms (EA) that could simultaneously optimize multiple objectives with randomly-determined operators. They note the prevalence of EA in optimizing traffic. Mihăiţă et al. (2018) successfully determined a plan that offered higher inflows of traffic through intersections during peak traffic hours using their simulation model. Integrating a simulation based on EA into an EOC can support decisions regarding evacuations. Data models are used in the aerospace industry to manage the risk of collisions.
Alam, Lokan, Aldis, Barry, Butcher, and Abbass (2013) use an evolutionary multiple objective model to minimize flight risks. They used their model to assess the risk of different flight scenarios. Their findings include many insights that can inform decision making for air traffic controllers. Some insights are that lower density airspace does not correlate to a decrease in collision risk and changes in flight plans have a major impact on increasing flight risks (Alam et al. 2013). Their proposed model is of use for air traffic controllers to evaluate the risks associated with specific maneuvers and traffic flow strategies as well. For an EOC, such simulations can similarly support disaster risk reduction mandates and evaluate the impacts of potential decisions or strategies.
By using existing big data sources to facilitate data analysis, visualization, simulations, and models, EOC’s can gain insights to predict and plan for the impacts of a hazard to people and communities.
How can physical access to technological interfaces improve situational awareness for Field and EOC personnel?
Integrating technology into EOC’s can enhance situational awareness by increasing the amount of data flowing in, the ability to comprehend the data, and forecast implications of new information and potential decisions.
The system of drones integrated into a wireless sensor network proposed by Erdelj et al. (2017) can work to provide damage assessments and aid in search and rescue missions. The integration with a wireless sensor network allows the mobility of drones to work with the analytic capacities of a wireless sensor network to enhance communication, logistical planning, resource allocation, and forecasting efforts - as well as expand the capacities of an early warning system (Erdelj et al., 2017).
In their analysis of information visualization tools in emergency management, Dusse et al. (2016) discusses information visualization as a method to bring order to abstract information and enhance people’s cognition with respect to interfacing with the data. They note that information visualization can enable quicker decision-making in EM. Their analysis also found that the visualization types that garnered the most publications were geospatial-2D, iconographic, and geospatial-3D. The visual attributes which ranked with the highest publication numbers were spatial position, colour, and shape. Movement garnered the lowest publication numbers of all attributes listed. An analysis of the phases of EM which these visualization tools supported identified response as the most populous with 78.1% of publications, mitigation and preparedness garnered 47.4% and 44.9% respectively, and recovery had 25% of publications. Dusse etl al. (2016) also note that 9.7% of the published works involved all four phases and emphasize how mobile crowdsourcing data and can enhance situation awareness by combining these large sources of live-data with contextual information. Finally, they noted the need for these large data sources to contain intelligent modules that can organize the information prior to its final output.
According to Schmalstieg and Höllerer (2016) augmented reality (AR) presents information in a physical environment while virtual reality (VR) occurs wholly in a computerized environment. Augmented reality presents the opportunity for EOC personnel to view and interact with data visualizations and models. The AR company, Fracture, demonstrated how the technology can be used in an airport command centre by creating a dynamic 3D environment with which one can interact and draw insights from (Fracture, 2017). Projecting dynamic maps in an interactive 3D setting can further enhance the situational awareness that data visualization provides and EOC by adding another dimension.
In their 2014 report on the use of social media for enhanced situational awareness and decision support, the United States Department of Homeland Security noted that the analysis of social media data within a sensor network can benefit early warning systems, situational awareness, and response functions (USDHS, 2014). The Namibian Flood SensorWeb emergency Response Pilot project of 2009 made use of geospatial tools by the to integrate multiple sources of data related to flooding, flood risks, vegetation, disease risk, and historical data to build a tool that can forecast outcomes of flooding in a given area (USDHS, 2014).
Geospatial Information Systems (GIS) are essential to situational awareness in emergency management as they can provide information about the geographical location of critical infrastructure, assets, and people (ESRI, 2008). GIS infrastructure can also allow for the analysis and modelling of hazards (ESRI, 2008). An EOC could benefit from access to a GIS system that can provide live geographic data that can be analyzed in real time.
Combining sources of data like drones, sensors, and GIS into a wireless sensor network and visualizing this data in an interactive output can enhance situational awareness for field and EOC personnel and reduce their cognitive load while attempting to process new information. Adding AR functionality to data visualization applications adds another dimension that can represent current conditions in a more intuitive and realistic manner.