datapartnership/morocco-earthquake-impact

This project uses alternative data to assess the socioeconomic impacts of the 2023 Morocco earthquake. It analyzes indicators such as population impacts, connectivity, economic activity, drought conditions, and conflict trends using geospatial, mobility, survey, and remote sensing data to support damage assessment and displacement monitoring. # Support for Morocco Earthquake Impact Analysis > Using Alternative Data to Understand Economic Impacts of the 2023 Morocco earthquake. ## Challenge In September 2023, a powerful 6.8 magnitude earthquake and a series of strong tremors and aftershocks wrought substantial damages in central Morocco. As of time of writing, the death toll has passed 2,800 and the earthquake’s aftermath is substantially impacting the people, infrastructure, and local economy of the two countries. The World Bank issued a statement shortly after the quake, indicating its full support in the wake of the catastrophe. Effective World Bank and donor interventions will require a deep, data-driven understanding of these impacts. The Morocco Country Economist and Poverty Team have requested advisory on data and analytical resources that may support measurement and monitoring of socio-economic impacts, including population displacement and business impacts. ## Approach The WB Data Lab is exploring use of alternative data to better understand immediate socio-economic impacts of the earthquake and resiliency of the affected communities. To this end, the team prepared a Strategic Brief, which presents available datasets and analytics to support answers to these questions. The team, comprised of colleagues from the Global Operations Support Team (GOST), the Development Impact Monitoring and Evaluation team (DIME), the Development Data Partnership, and the WB Data Lab, are working with the country team to explore use of alternative open and proprietary data sources to generate new data products that can be sustainably updated. ### Data Goods Datasets and methods used to generate insights for this project are being prepared as **Data Goods**. Data Goods are comprised of data, reproducible methods (code), documentation, and sample insights. Unlike a traditional data analysis, which results in a single-use report or visualization, Data Goods are designed to be re-used for future updates and proj …