Leveraging Collective Intelligence Methods: Participatory Mapping and Citizen-Generated Data to Address Environmental Degradation and Displacement — A Burundi Case Study

Since June 2022, the International Organization for Migration (IOM) Displacement Tracking Matrix (DTM) and the Joint IDP Profiling Service (JIPS) have been collaborating on the ECHO-funded project titled “Environmental Degradation and Displacement: Leveraging Citizen-Centered Data to Enhance Humanitarian Programming.” This initiative aims to address the growing challenge of displacement driven by environmental factors. To address this, IOM and JIPS are spearheading a Collective Intelligence (CI) initiative focused on Disaster Displacement and Anticipatory Action, particularly in Humanitarian Implementation Plan (HIP) priority areas in the East and Horn of Africa. The study’s findings will contribute to strengthening early warning systems already operational in Burundi.

Collective Intelligence to Address Environmental Degradation and Displacement 

Collective Intelligence can be understood as the enhanced capability that emerges when groups of people work together, pooling their knowledge, skills, and insights to solve problems and make decisions more effectively than individuals working alone (UNDP, 2021). Using analytical tools to process and synthesise this information, CI can reveal patterns and insights that might not be evident otherwise and ensures that solutions and decisions reflect the collective input of the group, creating effective and inclusive outcomes.

Collective Intelligence Project 

The project, as described below, included Community Consultations in nine collines with 115 Focus Group Discussions (FGDs) participants (51% male, 49% female). Data collection using the CI tool covered 65 collines in six communes across four provinces in Burundi, three of which border Lake Tanaganyika, which included 3,373 survey respondents (59% male, 42% female). Throughout the project 2,520 incidences of damage or impacts of weather-related disasters as well as 2,311 prevention and mitigation measures were mapped. The project closed with Community Validation Sessions held in nine collines with 144 FGD participants (52% male, 48% female). This case study can be used to replicate this CI approach in other regions facing similar challenges. As such, this approach will contribute to global efforts in disaster risk reduction (DRR) and enhance community resilience, demonstrating how leveraging the collective knowledge of affected populations can lead to sustainable and impactful solutions.

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