The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Data Visualization Using Weighted Voronoi Diagrams
|
|
Author(s): Raghuveer Devulapalli (University of Minnesota, USA), Neil Peterson (University of Minnesota, USA)and John Gunnar Carlsson (University of Southern California, USA)
Copyright: 2015
Pages: 24
Source title:
Geo-Intelligence and Visualization through Big Data Trends
Source Author(s)/Editor(s): Burçin Bozkaya (Sabanci University School of Management, Turkey)and Vivek Kumar Singh (Rutgers, The State University of New Jersey, USA & Massachusetts Institute of Technology, USA)
DOI: 10.4018/978-1-4666-8465-2.ch007
Purchase
|
Abstract
A Voronoi diagram is a standard spatial tessellation that partitions a domain into sub-regions based on proximity to a fixed set of landmark points. In order to maintain control over the size and shape of these sub-regions, a weighting scheme is often used, in which each landmark has a scalar value associated with it. This suggests a natural “inverse” problem: given a fixed set of landmark points in a given planar region and a set of “desired” areas, is it possible to calculate a set of weights so that each sub-region has a particular area? In this chapter, the authors give a fast scheme for determining these weights based on theory from convex optimization, which is then applied to a variety of problems in data visualization.
Related Content
|
Salwa Saidi, Anis Ghattassi, Samar Zaggouri, Ahmed Ezzine.
© 2021.
19 pages.
|
|
Mehmet Sevkli, Abdullah S. Karaman, Yusuf Ziya Unal, Muheeb Babajide Kotun.
© 2021.
29 pages.
|
|
Soumaya Elhosni, Sami Faiz.
© 2021.
13 pages.
|
|
Symphorien Monsia, Sami Faiz.
© 2021.
20 pages.
|
|
Sana Rekik.
© 2021.
9 pages.
|
|
Oumayma Bounouh, Houcine Essid, Imed Riadh Farah.
© 2021.
14 pages.
|
|
Mustapha Mimouni, Nabil Ben Khatra, Amjed Hadj Tayeb, Sami Faiz.
© 2021.
18 pages.
|
|
|