Data Driven Multi-Hazard Risk Visualization

Sharma, Ritesh; Tomson, Ayme; Lobato, Emilio; Kallmann, Marcelo; Padilla, Lace

dc.contributor.author	Sharma, Ritesh	en_US
dc.contributor.author	Tomson, Ayme	en_US
dc.contributor.author	Lobato, Emilio	en_US
dc.contributor.author	Kallmann, Marcelo	en_US
dc.contributor.author	Padilla, Lace	en_US
dc.contributor.editor	Byška, Jan and Jänicke, Stefan	en_US
dc.date.accessioned	2020-05-24T13:49:27Z
dc.date.available	2020-05-24T13:49:27Z
dc.date.issued	2020
dc.identifier.isbn	978-3-03868-105-2
dc.identifier.uri	https://doi.org/10.2312/eurp.20201117
dc.identifier.uri	https://diglib.eg.org:443/handle/10.2312/eurp20201117
dc.description.abstract	This work presents an approach for visualizing aggregate spatial risk data for natural hazards in a way which is not restricted by fixed geographical boundaries and is intended to improve multi-risk awareness in at-risk populations. First, spatial proximity is analyzed to organize occurrences in clusters and the convex hull of each cluster is created in order to define our visualization regions. Then, each region is assigned a risk factor value which is visualized by selecting a color scheme specific to the data variation. The application of this technique is demonstrated using the state of California as a region of interest.	en_US
dc.publisher	The Eurographics Association	en_US
dc.rights	Attribution 4.0 International License
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	]
dc.subject	Human centered computing
dc.subject	Visualization techniques
dc.title	Data Driven Multi-Hazard Risk Visualization	en_US
dc.description.seriesinformation	EuroVis 2020 - Posters
dc.description.sectionheaders	Posters
dc.identifier.doi	10.2312/eurp.20201117
dc.identifier.pages	13-15