Tutorial on the use of KUMU for Impact Chain Generation
Abstract
This tutorial teaches how to generate impact chains using the KUMU tool, and how to link it with geospatial information in GeoNode. An impact chain is a conceptual and analytical model used to systematically describe how climate hazards lead to impacts on human or natural systems, by explicitly linking cause–and–effect relationships from the hazard through exposure and vulnerability to final impacts. Impact chains are the result of intensive stakeholder interaction in workshops, combined with forensic analysis, literature review, and modelling of potential multi-hazard impact scenarios. After careful analysis of various options, the PARATUS project selected the KUMU tool as the best option for generating impact chains. KUMU is a web-based platform for visualising complex systems using nodes (factors), links (relationships), and attributes. It is particularly well-suited to impact chains because they are, at their core, causal systems. KUMU allows nodes and links to be tagged with metadata, such as Impact chain components (hazard, exposure, vulnerability, impacts), types of sources and references, and a more detailed description to support interpretation. We also developed a link between the description in KUMU and the map data in GeoNode. GeoNode is an open-source web-based platform for managing, sharing, and visualising geospatial data and maps. It enables organisations to publish spatial datasets with metadata, access them through standard services, and support collaborative, interoperable GIS workflows. In PARATUS, we have developed our own GeoNode for data sharing (https://www.paratus-geonode.eu/#/ ) This description is accompanied by a Template for Impact Chain generation in JSON format.
Fields of Study & Subjects
Disaster Management and Resilience
Infrastructure Resilience and Vulnerability Analysis
Geographic Information Systems Studies
Publication details
- Journal / source
- University of Twente Research Information
- Publication date
- 30 January 2026
- Language
- EN
- Document type
- Dataset