Data Driven Multi-Hazard Risk Visualization
| 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.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.description.sectionheaders | Posters | |
| dc.description.seriesinformation | EuroVis 2020 - Posters | |
| dc.identifier.doi | 10.2312/eurp.20201117 | |
| dc.identifier.isbn | 978-3-03868-105-2 | |
| dc.identifier.pages | 13-15 | |
| dc.identifier.uri | https://doi.org/10.2312/eurp.20201117 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurp20201117 | |
| 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 |
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