In times of global pandemics, the relevance of and attention towards spatial health data is increasing. Scientists, politicians, and citizens study and often trust in maps showing e.g. the spread of COVID-19 cases. Even political decisions may be taken with the help of these maps. In reality, however, the visualized patterns of spatial expansion or local data values are often misunderstood due to abstract, inconsistent, or incomplete information or inadequacies of communication. The resulting uncertainty can cause confusion or even mistrust in these scientific findings.

The project unGESUND aims to determine and model appropriate visualizations of uncertainty in health data. By combining multiple disciplines from cartography, health und urban research, as well as journalism, various prototypes are being developed to communicate uncertain health data.

An additional focus is on the transformation of health data into arbitrary spatial units via areal interpolation. We use an existing tool (CoGran) to bring formerly independent data together, relate them, and evaluate resulting uncertainties.

Project partners are the Universitätsklinikum Eppendorf (PD Dr. Jobst Augustin), University of Applied Sciences Hamburg (Prof. Christoph Kinkeldey) and colleagues from HCU (Prof. Jörg Pohlan, Prof. Jörg Noennig).

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