The Convergence of Technology and Emergency Management

I recently received a behind-the-scenes tour of a national laboratory, during which lab employees demonstrated a virtual reality (VR) interface of a three-dimensional (3D) representation of data. As a volunteer interacted with a model concerning wind impacts, I leaned over to ask the host if this same technology could be used to interact with hazard impact modeling data or even post-impact disaster assessments. He responded that if the data exists in the right format, they could find a way to interact with it in a 3D VR environment.

I was curious if this technology could be combined with Light Detection and Ranging (LIDAR) or other visual analysis data that is captured before and after a hurricane or flood to determine the potential or actual damages and to inform debris estimates. The host pointed out a poster of a small town that existed solely in VR. Users were able to walk down the streets of the virtual town, interact with the environment, and see how various decisions, such as utility placement, would impact the environment. While the group physically continued to the next mind-blowing research lab, I have to admit that my mind stayed behind in the VR lab as I pondered all of its uses for emergency management.

For some time, I have been aware of systems that are attached to vehicles, boats or aircrafts to collect high quality LIDAR and provide accurate point in space data. I have seen systems for drones that are used to collect high definition imagery. Also, I have read about software programs that utilize Artificial Intelligence (AI) to detect differences between before-and-after images and/or data sets. Imagine yourself, as I did, being able to walk down a street in VR, turning on and off impact data layers from Digital Flood Insurance Rate Maps (DFIRMs), Sea, Lake and Overland Surges from Hurricanes (SLOSH) models, Hazus models, and even post-incident damage assessment data. The use cases for emergency managers poured out of my brain: verifying freeboard elevations for floodplain management; visualizing storm surge grid layers; acquiring more precise initial damage assessments; and obtaining a clearer understanding of the debris piles and access limitations post incident. These are just a few possibilities.

During a recent conversation with a group of Geographic Information System (GIS) specialists regarding a planning project, the conversation shifted to deliberate how to address a number of questions from various users based on a handful of specific parameters, without having to print dozens of static maps, and then constantly update them all. We discussed developing a product in an interactive geospatial web portal, such as GeoPlatform, that would allow for real time updates and distribution to the end users. The concept for such a solution that gives a non-GIS savvy user, like myself, the ability to determine what information is relevant at any given time using geographic limiters to extract information from the field data was incredible.

The conversation continued, and my probing questions about what data is available to emergency managers, as well as what possibilities and limits may exist, reminded me of other important data sets I had recently been told about.

For example, there are some agencies that are working on building dynamic data about the nation’s infrastructure and interdependencies, and another that is developing tools to assess and analyze crowdsourced information collected from social media. I wondered if all these data sets could be integrated with other data, such as the National Risk Index, or population density and public service information. As it turns out, users of a geospatial portal can do many amazing things; including linking raw data to the Global Positioning System (GPS) coordinates, images and information from their phones, or even integrating drone imagery.  Of course, there are limitations, primarily if the data sets are not shareable due to limiting permissions and agreements or if they are not in the proper format.

Powerful Tools Are Available to Support Emergency Management

These conversations, as well as many others over the last few years, concerning the use of big data, artificial intelligence, and the internet of things, have shown me that there are powerful tools available to support emergency management. There are obvious mitigating factors, such as cost, scalability, and technical learning curves. However, it is evident that we are on the cusp of an incredible leap in technical capability for the profession. History shows that the convergence of technology is where we see the major advances. It is clear to me how important it is for emergency managers to look more closely at the various technologies available and to discuss how they could be combined to build even more powerful and useful tools.

The Counter-Balance Is the Human Element

The counter-balance to all of these great technological tools is the human element. Software is not able to truly model or predict human behaviors or decision-making. Also, disasters are dynamic, and they rarely match a model exactly. Emergency managers must utilize the tools to support decision-making, without abdicating the decision itself to an automated process. Technology can assist us in making faster and more informed decisions, but in the end, an experienced emergency manager needs to make the decisions.

Future technology is fascinating and entertaining to talk about, but we have advanced technologies that are already available right here, right now. Technologies can develop ecosystems of information, models and analysis to support our responsibilities as emergency managers. These tools are able to improve our everyday efforts, from hazard analysis or damage assessments to incident management and prioritizing mitigation projects, all of which enable us to more effectively save lives and create more resilient communities.

 

This article was originally published in the IAEM Bulletin, Vol. 36, No. 6 June 2019.

Author: Benjamin C. Korson, CEM, CO-CEM, FPEM, Senior Planner, IEM