7 Topics
5 Days
35 Sessions
Programme Overview
Training Description
- Government Disaster Management Officials
- Policy and Strategic Planning Professionals
- Humanitarian Information Management Specialists
- Crisis Management Professionals
- Business Continuity and Resilience Managers
- Infrastructure Risk Professionals
- Environmental Risk Specialists
- Monitoring, Evaluation and Learning Professionals
- Researchers and Academic Professionals
- Professionals responsible for disaster risk analysis, data intelligence, predictive modelling, early warning, emergency preparedness, crisis response, and resilience planning.
Session Objectives
- Increase knowledge of disaster risk and its different dimensions (hazard, vulnerability, exposure, capacity). Provide technical knowledge for effective decision-making across all phases of disaster management. Develop skills to apply spatial analysis for hazard and vulnerability mapping. Enable the development and implementation of comprehensive Disaster Risk Reduction (DRR) actions. Enhance capacity to obtain, analyze, and communicate risk information effectively to stakeholders.
About the Course
Disaster Risk Intelligence: A Data-Driven Approach is an advanced training course designed to equip disaster risk professionals, emergency managers, policymakers, analysts, and resilience practitioners with the skills required to collect, analyze, interpret, and apply data to improve disaster risk assessment, forecasting, preparedness, response, and resilience planning.
Participants will examine data-driven approaches to hazard identification, exposure and vulnerability analysis, risk modelling, predictive analytics, early-warning systems, scenario development, situational awareness, and evidence-based decision-making. The programme also addresses data quality, uncertainty, interoperability, visualization, information sharing, and ethical considerations in disaster risk intelligence.Through practical exercises, case studies, data analysis scenarios, risk dashboards, and decision-making simulations, participants will learn how to translate complex datasets into actionable intelligence that supports proactive disaster risk reduction and more effective emergency management.
Curriculum & Topics
7 Topics | 35 Sessions
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Workshop 1.1:
Defining data-driven disaster risk intelligence and its value in modern DRR.
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Workshop 1.2:
Exploring the evolution of data use in emergency management and humanitarian action.
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Workshop 1.3:
Understanding the lifecycle of disaster data from collection to actionable insight.
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Workshop 1.4:
Overview of the benefits of data analytics for enhancing disaster preparedness and response.
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Workshop 1.5:
Setting the strategic context for evidence-based risk management.
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Workshop 2.1:
Identifying diverse data sources for disaster risk analysis (e.g., climate, demographic, infrastructure, economic) for your module.
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Workshop 2.2:
Learning various data collection methods (e.g., surveys, sensor networks, administrative records).
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Workshop 2.3:
Understanding the importance of disaggregated data for vulnerability assessment.
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Workshop 2.4:
Exploring open data initiatives and platforms for disaster risk information.
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Workshop 2.5:
Addressing data gaps and challenges in data availability.
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Workshop 3.1:
Mastering data cleaning techniques to ensure data quality, consistency, and accuracy for your module.
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Workshop 3.2:
Understanding data integration from disparate sources for a holistic view.
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Workshop 3.3:
Learning data transformation and structuring for analytical readiness.
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Workshop 3.4:
Implementing robust data management practices for large datasets.
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Workshop 3.5:
Ensuring data privacy and security in disaster risk intelligence.
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Workshop 4.1:
Understanding the power of Geographic Information Systems (GIS) for disaster risk assessment for your module.
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Workshop 4.2:
Applying GIS for hazard mapping, exposure analysis, and vulnerability mapping.
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Workshop 4.3:
Utilizing spatial analysis techniques to identify high-risk areas.
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Workshop 4.4:
Integrating various layers of geospatial data (e.g., population, infrastructure, land use).
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Workshop 4.5:
Creating interactive risk maps for decision-makers.
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Workshop 5.1:
Introduction to remote sensing technologies (satellite imagery, drones) for disaster monitoring for your module.
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Workshop 5.2:
Utilizing remote sensing data for early warning of hazards (e.g., floods, wildfires).
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Workshop 5.3:
Assessing post-disaster damage and impact rapidly and remotely.
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Workshop 5.4:
Understanding the types of remote sensing data and their applications.
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Workshop 5.5:
Integrating remote sensing insights into situational awareness.
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Workshop 6.1:
Exploring the application of predictive analytics for disaster forecasting (e.g., drought, epidemic outbreaks) for your module.
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Workshop 6.2:
Understanding the basics of machine learning algorithms for risk modeling.
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Workshop 6.3:
Utilizing AI to identify patterns and anomalies in disaster data.
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Workshop 6.4:
Developing early warning systems based on predictive models.
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Workshop 6.5:
The potential and limitations of AI/ML in disaster risk intelligence.
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Workshop 7.1:
Principles of effective data visualization for communicating complex disaster risk insights for your module.
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Workshop 7.2:
Designing compelling dashboards and infographics for diverse audiences.
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Workshop 7.3:
Storytelling with data to convey the urgency and impact of disaster risks.
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Workshop 7.4:
Presenting data-driven recommendations to policymakers and stakeholders.
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Workshop 7.5:
Building a culture of data literacy within disaster management organizations.
