This solution starter was created by Team One at the United Nations Human Rights Office in Geneva, Switzerland on June 3-4, 2019. It features contributions by technologists from Persistent Systems, Capgemini, WPP, IBM, and Red Hat.
Building back better, stronger, and faster after a natural disaster is one of the best ways to reduce the impact on a community's livelihood and end the cycle of poverty. Research from the World Bank shows when countries implement a resilient and inclusive rebuilding process, global average losses drop from $555 billion to $382 billion per year.
With an effective and efficient system of managing and assessing information, communities can take action in anticipation of events, strengthen key infrastructure (buildings, roads, and hospitals), and ensure capacities are in place for effective response and recovery at all levels in the future.
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The team tackled the challenges that come in the process of rebuilding after the impact of a disaster. Research has shown that rapid yet well-informed and well-orchestrated rebuilding measures can help to massively reduce the negative impact of disasters on the life, well-being, and health of individuals. An effective and efficient system of accessing information and contributing feedback in a way that can improve the foundation of future decisions is key to this cycle.
In order to leverage the benefits of such a system it must be designed to allow for simple ingestion of information, data, images along with a user-friendly way of drawing insights from it. The team created a platform-based solution that aggregates and analyzes historical and current data related to infrastructure, agriculture, weather, utility and more. It can then be used to derive key insights for future response and reconstruction plans.
The goal of this solution is to provide a better feedback loop that empowers local municipalities, small business owners, and members of the community, especially the most vulnerable. The team approached this problem by looking how a solution could benefit three potential end users who need access to information about the current situation, resources available to them, and how to better prepare next time:
- A disaster victim, who seeks information on how to improve her community by best matching her skills to volunteer opportunities.
- A small business owner who needs to open her shop as soon as possible to restart cash flow.
- A local elected government official, who needs to generate damage assessments and rebuilding recommendations quickly.
By combining cloud-native infrastructure with event-driven data processing and intelligent modeling, this solution could help predict when and where a disaster may strike next and extrapolate the impact. Furthermore, it could allow stakeholders to derive the most promising course of action with the greatest improvement to plans and processes possible.
This solution starter idea combines machine learning models with real-time information to get users the information they need to take action quickly.
- By managing a collection of models about how better to restore infrastructure, the system could store historical data, use that to predict trends, and therefore provide recommendations in the form of an assessment.
- These models could then be referenced by various applications to collect information about the current situation and provide end users with the assessments.
- By rating the success of the recommendation, users can provide information that will help others in turn during future situations to build back better.
- Malawi Spatial Data Platform (MASDAP)
- Land Usage from MASDAP
- Malawi Disaster & Risk Profile
- Disparities in Cellphone Ownership Pose Challenges in Africa
- Generate insights from multiple data sources
- Transform and load big data CSV files into a database
- 2018 Finalist PD3R
- Words into Action guidelines: Build back better in recovery, rehabilitation and reconstruction
- Sendai Framework Priority 4: Build Back Better
- Building Back Better: How to Cut Natural Disaster Losses by a Third
This solution starter is made available under the Apache 2 License.