@article{Hasan-2021-Putting,
title = "Putting Table Cartograms into Practice",
author = "Hasan, Mohammad Rakib and
Mondal, Debajyoti and
Tasnim, Jarin and
Schneider, Kevin A.",
journal = "Advances in Visual Computing",
year = "2021",
publisher = "Springer International Publishing",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G21-15001",
doi = "10.1007/978-3-030-90439-5_8",
pages = "91--102",
abstract = "Given an m{\mbox{$\times$}}n table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m{\mbox{$\times$}}n convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell{'}s weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.",
}
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<abstract>Given an m\timesn table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m\timesn convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell’s weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.</abstract>
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%0 Journal Article
%T Putting Table Cartograms into Practice
%A Hasan, Mohammad Rakib
%A Mondal, Debajyoti
%A Tasnim, Jarin
%A Schneider, Kevin A.
%J Advances in Visual Computing
%D 2021
%I Springer International Publishing
%F Hasan-2021-Putting
%X Given an m\timesn table T of positive weights, and a rectangle R with an area equal to the sum of the weights, a table cartogram computes a partition of R into m\timesn convex quadrilateral faces such that each face has the same adjacencies as its corresponding cell in T, and has an area equal to the cell’s weight. In this paper, we examine constraint optimization-based and physics-inspired cartographic transformation approaches to produce cartograms for large tables with thousands of cells. We show that large table cartograms may provide diagrammatic representations in various real-life scenarios, e.g., for analyzing correlations between geospatial variables and creating visual effects in images. Our experiments with real-life datasets provide insights into how one approach may outperform the other in various application contexts.
%R 10.1007/978-3-030-90439-5_8
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-15001
%U https://doi.org/10.1007/978-3-030-90439-5_8
%P 91-102
Markdown (Informal)
[Putting Table Cartograms into Practice](https://gwf-uwaterloo.github.io/gwf-publications/G21-15001) (Hasan et al., GWF 2021)
ACL
- Mohammad Rakib Hasan, Debajyoti Mondal, Jarin Tasnim, and Kevin A. Schneider. 2021. Putting Table Cartograms into Practice. Advances in Visual Computing:91–102.
