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Local situational awareness for hurricane response

Fall 2017 // UX Design Studio 1

This project was published in the Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. View the full paper here: https://dl.acm.org/citation.cfm?id=3180650

Rizwan Zaki, Oscar Kwong, Kyler Emig, Jonathan Tucker, Mara Healy, Dalana Woodward, Haley Mueller, Franklin Wallace, Leigh Grier
Advised by Professor Eunki Chung
My role
Production Manager + UX Designer
Delivered: Primary research, secondary research, concept development, user journeys, low-fi/hi-fi wireframes + prototypes (EMA control interface), system navigation, team management, case study content strategy
Problem space
Over a billion people in the world live in low-lying coastal regions. Hurricanes in particular present a large threat to people in these regions.

The current process for hurricane preparation, evacuation, and recovery is difficult to handle because of the vast abundance of weather-related information coming from different sources. The facts are not always presented in a manner that effectively and efficiently informs citizens of what will happen to them on an individual level.
Project description
Dawn is a local weather information tool concept that improves the process of communication and recovery during hurricanes for citizens and local governments.

Dawn is comprised of two key systems that interact closely with each other: hyperlocal weather forecasts given to citizens based on their geographic address, and improved situational awareness for local emergency management agencies (EMAs) through drone swarm surveillance. Information from Dawn would be relayed to citizens and EMAs before, during, and after the storm for a significantly more local understanding of the situation.
EMA-facing: Augmented situational awareness
Utilizing drone swarm surveillance operations to efficiently assess damages on a large scale to be sent to Federal Emergency Management Agency (FEMA) for accelerated relief distribution to the city.
Drone swarms are released by local EMA operators before and after the hurricane to assess risk pre-storm and evaluate damages and hazards post-storm on individual properties throughout the city. This information is stored in an internal database that the hyperlocal forecasts are sourcing from to provide citizens with personalized information.

The time saved by utilizing drone swarm surveillance for large-scale assessment will expedite the process of receiving relief from the federal level (FEMA) to the city greatly.
Citizen-facing: Hyperlocal forecasts and predictions
Providing citizens with hyperlocal weather information presented in an easily understandable manner through CUI-style alerts that streamline weather forecasts.
This gives citizens personalized information before, during, and after the hurricane regarding aspects like evacuation likelihood and risk assessment.
This project was created over the course of 9 weeks in my UX Design Studio 1 class. The nature of the work environment in this class was extremely malleable, and we consistently moved between groups and teams in order to best tackle the job at hand. At around week 5, we diverged into three teams to target our two key problem spaces. While we all continued to work closely together, at this point my team focused on the drone swarm surveillance controls to be used by local government EMAs.
Problem space synthesis
User testing and interviews with CEMA