VIEWS-PIN is an academic research project set to provide early warnings of the need for humanitarian assistance for all months in a rolling three-year forecasting window, for all Low and Middle Income Countries (LMICs).
Forecasts of humanitarian need will be made publicly available for decision-makers and stakeholders through our interactive dashboard and website, contributing to allocating resources where most needed, and improving the relevance, timeliness, and cost-efficiency of forward-looking policies aimed at minimising human sufferings. The project will also produce policy reports on the expected trends and patterns of humanitarian need, academic publications shedding light on the drivers of need, analytics and visual tools to enhance the understanding of the results, and new evaluation metrics to comprehensively assess the predictive performance of the forecasting models.
VIEWS-PIN is funded by the   Complex Risk Analytics Fund (CRAF’d).
  • Host institution: PRIO
  • Project duration: June 2023 – June 2025
  • Project members: Håvard Hegre, Paola Vesco, Chandler Williams. 
CRAF'd logo
Background UN OCHA estimates that over 200 million people are in need of humanitarian assistance worldwide, mostly driven by the adverse consequences of climate-related extremes and violence (OCHA, 2021). Timely, targeted anticipatory action and cost-effective humanitarian assistance are needed to prevent crises and minimise human sufferings. The success of these policies, in turn, depend on empirically driven, accurate and timely foresight of the magnitude of humanitarian crises and the expected need of assistance.
Climate hazards and armed conflicts often compound each other, precipitating affected societies in a spiral of violence, vulnerability, and harmful climate-related impacts. Deepening our understanding of how societies respond to compound shocks is critical to provide international support in fragile settings, and to minimise the risk that crises turn into humanitarian disasters.
VIEWS-PIN will address these gaps by providing an early warning system that predicts the need for humanitarian assistance for all months up to 3 years into the future, for all Low and Middle Income Countries (LMICs, as defined by the World Bank).
Main objectives Building on and expanding the infrastructure provided by VIEWS, the main objectives are as follows:
  1. Enhanced understanding of drivers of need: improved knowledge of how climate hazards, armed conflict, and compound shocks affect need
  2. Systematic review of the literature on the drivers of need
  3. Retrieval, download, storage, and curation of weather data at country and sub-national level from Copernicus ERA5
  4. Retrieval, download, storage, and curation of data on indicators of need at country and sub-national level
  5. Mapping and quantification of people exposed to armed conflict and climate hazards
  6. Statistical and machine-learning models to estimate the effect of armed conflict, climate hazards, and the combination thereof on need

  7. Expanded coverage of armed conflict forecasts and estimates of uncertainty in conflict data to increase conflict forecasting accuracy
  8. Expansion of the ViEWS pilot system to provide forecasts of armed conflict globally at the PRIO-GRID-month level
  9. Systematic assessment of armed conflict data to estimate measurement error, minimise bias, and improve forecasting performance

  10. Monthly forecasts of need: early warning of humanitarian need and its key components, incl. policy reports presenting the forecasts of people in need, and visual tools and analytics of emergency situations, disaggregated by group and sector
  11. Training, calibration, testing of machine-learning models to forecast need in LMICs at country and sub-national level for every month up to 3 years ahead
  12. Validation of the forecasting models
  13. Interpretative tools, visualisations and analytics to illustrate the forecasts
  14. Co-production of policy reports to highlight hotspots of humanitarian need in the future
  15. Outreach and dissemination of project results