Project overview

Integrable digital tool that highlights potential ‘access disruption’ hotspots related to critical infrastructure or services in potentially conflict-affected areas.

The problem

Violent events not only cause immediate suffering but have secondary impacts by preventing access to critical infrastructure and services. 

Disruption could be due to: 

  • Physical barriers: destruction of critical infrastructure and services centres. 
  • Operational barriers: blockage of infrastructure and shutdown of essential services centres. 

This can lead to access disruption - interruption or restriction of people's ability to reach and use critical infrastructure and services.

The solution

This innovation helps decision-makers visualize conflict events by integrating multiple conflict datasets with socio-economic, population density, critical infrastructure and services data at the subnational level. Using a methodology that combines machine learning (panel forecasting) and natural language processing, Conflict Forecast predicts armed conflict and disruptive events like protests and riots. It correlates conflict predictions with other data layers to understand how risks of conflict, protests and riots relate to access disruptions. The web platform will present this data in multi-layered maps for resource planning and allocation.

Conflict Forecase: Subnational view

 

Hannes Mueller, Principal Investigator, Fundació d'Economia Analítica (FEA)
“We’re really excited by this opportunity to turn our research efforts into a tangible product for organizations working in conflict affected areas.”

The Conflict Forecast team produces sub-national forecasts of conflict intensity at a 55x55 km resolution. It uses news text from ~6 million articles as a predicting feature and its conflict prediction model performs well. Globally it achieves an accuracy score of 0.97 (1 is perfect accuracy and 0.5 - a random guess).

Meet the team

Hannes Mueller
Principal Investigator, FEA / IAE-CSIC
Margherita Philipp
Research Coordinator (Data Scientist), FEA
Tatiana Bakwenya
Research Assistant (Data Scientist), FEA
Zach Mbasu
Director, INNODEMS - implementation partner
Anastasia Mbithe
Data Analyst, INNODEMS - implementation partner
Ateamate Mukabana
Data Analyst, INNODEMS - implementation partner
Last updated: 16/09/2024