We started the 'Global Dynamic Exposure' project to address the inherent problem in nowadays state-of-the-art risk assessment: the dynamic nature of exposure. So far, exposure models were limited in either resolution or coverage due to the lack of expert manpower to capture the necessary data and were by nature static. Our approach in harvesting open data and involving communities and the world-wide 'crowd' to capture this data is set to provide the first global and dynamic exposure model covering each building on Earth. Every day, 100 000 buildings are added by volunteers. This paradigm shift in exposure capturing and assessment is perfectly in line with the United Nations 2015 Sendai program. It addresses the important goals defined for risk modellers to involve communities in the process of assessing the exposure and risk but also to communicate risk to communities in a way that makes the community understand their risk. Only under these conditions a fruitful dialog can take place that can help a community understand the resilience measures it needs to take.
We received the support and endorsement of the United Nations Office for Disaster Risk Reduction (UNISDR) and are invited to participate in a joint risk assessment study in Haiti. Simultaneously, we have initiated a collaboration with the National Research Institute for Earth Science and Disaster (NIED) and Earthquake Research Institute of the University of Tokyo in Japan.
The 'Global Dynamic Exposure' project should also be seen in the context of our funded 'Dynamic Risk Quantification' project in which our section, in collaboration with the King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, is developing the first data-driven and testable earthquake hazard and risk model. Both projects are harvesting the increasing amount of new, or so-called big, data. The guiding principle of transparency and openness will be the key to communicating findings and results and to involve all stakeholders from the experts to the affected individuals.x