Visualizing natural hazard data and uncertainties - Customization through a web-based cartographic information system

Abstract

Communication of natural hazard assessment results is crucial to protect people and infrastructure from devastating impacts of extreme events. While hazard maps provide important information on potential impacts, their interpretation and the general knowledge exchange between stakeholders is often difficult. Web-based information systems contain the potential to support hazard management tasks by fast distribution and customization of hazard visualizations through interactive functionality. However, cartographic principles are often ignored in existing web-based visualizations which leads to poor graphical results and consequently to an impairment of the information flow. While these issues need to be solved, a new task is already waiting: the integration of uncertainty information into hazard visualizations. Since many hazard management activities rely on hazard assessment results, communication of associated uncertainties among experts is vital. \nThe challenge of this research is to overcome these existing shortcomings by combining high quality cartographic visualizations of natural hazard data as well as associated uncertainties with interactive functionality. The resulting web-based cartographic information system will convene the needs of natural hazard specialists by offering a high level of customization: the suggested visualizations include various cartographic techniques such as the application of textures, bars, and interpolated surfaces. The possibility to interactively select particular data sets, customize colors, choose dimensions, query attribute data, and include uncertainty information facilitates the interpretation of complex data and finally the communication among natural hazard specialists.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 3 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.