| dc.contributor.author | Sehgal, Gunjan | en_US |
| dc.contributor.author | Sharma, Geetika | en_US |
| dc.contributor.editor | Natalia Andrienko and Michael Sedlmair | en_US |
| dc.date.accessioned | 2016-06-09T09:32:09Z | |
| dc.date.available | 2016-06-09T09:32:09Z | |
| dc.date.issued | 2016 | en_US |
| dc.identifier.isbn | 978-3-03868-016-1 | en_US |
| dc.identifier.issn | - | en_US |
| dc.identifier.uri | http://dx.doi.org/10.2312/eurova.20161120 | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10 | |
| dc.description.abstract | In this paper, we propose an art-based approach to visual analytics.We argue that while artistic data visualizations have mainly been designed to communicate the artist's message, certain artistic styles can be very effective in exploratory data analysis as well and data visualizations can benefit from more than just the aesthetics inspired by art. We use the ancient Warli style of tribal paintings, found in western India to demonstrate the use of artistic styles for visual analytics over open data provided by the Indian government. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.3 [Computer Graphics] | en_US |
| dc.subject | Picture/Image Generation | en_US |
| dc.subject | Line and curve generation | en_US |
| dc.title | An Art-based Approach to Visual Analytics | en_US |
| dc.description.seriesinformation | EuroVis Workshop on Visual Analytics (EuroVA) | en_US |
| dc.description.sectionheaders | Multivariate Data Analysis | en_US |
| dc.identifier.doi | 10.2312/eurova.20161120 | en_US |
| dc.identifier.pages | 25-29 | en_US |
