Dawn is a local weather information tool concept that improves the process of information retrieval and city-wide recovery from hurricanes for citizens and localgovernments. Dawn targets some of the key problems found in primary and secondary research regarding hurricane preparation and relief. The design provides localgovernment Emergency Management Agencies (EMAs) with a drone swarm weather monitoring system to assess infrastructure damage costs on a large scale, andcitizens with hyperlocal weather information based on their specific locations.
Over one billion people in the world live in low-lying coastal regions. Hurricanes in particular present a huge threat to the world's population as they form over warm ocean waters near the equator. We wanted to create a project that would address the topic of how local governments can aid and engage communities through disaster-related situational awareness.
After conducting both primary and secondary research on the problem space, we identified the key problems regarding the hurricane recovery process that continuously emerged. We condensed our problem spaces into two key problems. The first is about the noise and ambiguity of hurricane information available to citizens. Untrustworthy information and ambiguous weather forecasts leads people to make poor decisions. Second, the Chatham Emergency Management Agency (CEMA) states that damage collection is the key to faster relief however, it can take weeks and sometimes even months for the Federal Emergency Management Agency (FEMA) to process city damage assessments and distribute money to repair affected infrastructure.
At this point we created a diverse array of concepts targeting our key problem spaces found from our initial research. From these concepts we focused on situational awareness from the government and citizen side for optimized preparation and recovery from hurricanes.
Our project is Dawn: a local weather information tool concept that improves situational awareness of hurricanes and expedited city-wide recovery for citizens and local governments.
Days before the projected storm, local Emergency Management Agency (EMA) specialists will release drone swarms to assess risk of individual properties and key buildings. This risk analysis is sourced from the drones, and drone footage is sent to Dawn's internal EMA platform which utilizes computer vision to identify structures and materials that have high risk of damage based on various factors such as water levels, structural stability, electric danger, and elevation.
An individual risk analysis is generated per property and is available for citizens to view on a public web application in order to receive a hyperlocal prediction with details on the types of damages they may expect to encounter, as well as suggestions on whether or not they should evacuate. This condenses large amounts of confusing weather information into digestible pieces given on an individual property level.
After the storm, drone swarms are released once again by EMAs, this time to identify hazards and evaluate damages of the city. The damage assessment footage from the drones is then autonomously translated into repair cost estimates per individual property. As soon as these costs are compiled, the information is sent to FEMA to receive federal relief and begin repairing the city's infrastructure. The key to relief lies in the fastest way to accurately assess property damage on a large scale. Evacuated citizens are able to view the status of their house from drone footage that is uploaded onto the public web application in order to know when they are able to return and what they should prepare.
Overall, Dawn provides a more efficient and effective experience for the hurricane preparation and recovery process because it addresses two of the key problems found in our research: indigestible information presentation and inefficient federal relief distribution. By expediting damage information and cost retrieval, EMAs are able to send information to the federal level and receive money to repair infrastructure much more quickly.