When Dara Dotz, an industrial designer, travelled to Haiti after the devastating earthquake in 2010, she saw firsthand the supply chain challenges people were facing that had life threatening consequences – most vividly, a nurse having to use her medical gloves to tie off the umbilical cords of newborn babies, because she didn’t have access to an umbilical clamp. Deploying a 3D printer, Dara was able to design a locally manufactured, inexpensive plastic clamp that could be used in the local hospitals for newborns.
From there, Dara co-founded Field Ready, an NGO that is part of the “maker movement,” which pilots new technologies to rapidly manufacture components of essential supplies in the field. Using 3D printing and a range of software, Field Ready works with volunteers to make lifesaving medical components like IV bag hooks, oxygen splitters, and umbilical cord clamps, an approach that has often proven to be both quicker and cheaper than waiting for shipments to arrive.
This is one example of local innovation and design in disaster situations. With trends of rising population growth, increased urbanization, and climate projections of more frequent and intense weather, more people and assets are at risk from natural hazards. Communities and governments need to think creatively and find new ways to build resilience, and some of the latest developments in science and technology can provide promising solutions.
Over the past few decades, there has been an exponential increase in the amount of information and data that is open and available – whether from satellites and drones collecting data from above, or from crowdsourced information and social media from citizens on the ground. When analyzed holistically, this data can provide valuable insight for understanding the risks and establishing a common operating picture.
Cloud to Street is an example of a platform adopting this approach by combining the daily imagery from microsatellites with information from citizens reporting in the field to create maps that show the extent and duration of flooding events. These maps can be used both by first responders and by decision-makers to understand where flood risks are located, and how situations progress over time.
Advances in machine learning and artificial intelligence can also provide benefits to disaster risk management.
For example, algorithms have been developed to analyze scientific data on earthquakes (shaking parameters, soil and seismic hazard characteristics, building characteristics) with real-time responses (digital media, tweets, and on-the-ground reports) to predict how new structures will respond to ground tremors and quakes.
One Concern, a startup out of Palo Alto, has developed a web platform that will alert users when an earthquake occurs, and provide a map of likely structural damage based on machine learning modelling on a block-by-block basis. The algorithm also details what structures could be impacted before, during, and immediately after an earthquake.
These methods to use innovation and new technologies in disaster risk management were recently featured at the 2018 Understanding Risk Forum, a five-day conference that showcases the latest developments in disaster risk assessment. To learn more, watch these short videos featuring the founders of Field Ready, Cloud-to-Street, and One Concern.
Interested in innovative approaches for understanding disaster risk? On September 17-19, 2018, hundreds of disaster risk management practitioners will gather in Belgrade, Serbia to exchange ideas and best practice in gathering, assessing and communicating disaster risk at Understanding Risk Balkans! Stay tuned to Understanding Risk, @WBG_Cities, and @GFDRR for the latest insights and updates from #URBalkans.
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