Literature DB >> 20975124

Necklace maps.

Bettina Speckmann1, Kevin Verbeek.   

Abstract

Statistical data associated with geographic regions is nowadays globally available in large amounts and hence automated methods to visually display these data are in high demand. There are several well-established thematic map types for quantitative data on the ratio-scale associated with regions: choropleth maps, cartograms, and proportional symbol maps. However, all these maps suffer from limitations, especially if large data values are associated with small regions. To overcome these limitations, we propose a novel type of quantitative thematic map, the necklace map. In a necklace map, the regions of the underlying two-dimensional map are projected onto intervals on a one-dimensional curve (the necklace) that surrounds the map regions. Symbols are scaled such that their area corresponds to the data of their region and placed without overlap inside the corresponding interval on the necklace. Necklace maps appear clear and uncluttered and allow for comparatively large symbol sizes. They visualize data sets well which are not proportional to region sizes. The linear ordering of the symbols along the necklace facilitates an easy comparison of symbol sizes. One map can contain several nested or disjoint necklaces to visualize clustered data. The advantages of necklace maps come at a price: the association between a symbol and its region is weaker than with other types of maps. Interactivity can help to strengthen this association if necessary. We present an automated approach to generate necklace maps which allows the user to interactively control the final symbol placement. We validate our approach with experiments using various data sets and maps.

Year:  2010        PMID: 20975124     DOI: 10.1109/TVCG.2010.180

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  2 in total

1.  Visualization of Big Spatial Data using Coresets for Kernel Density Estimates.

Authors:  Yan Zheng; Yi Ou; Alexander Lex; Jeff M Phillips
Journal:  IEEE Trans Big Data       Date:  2019-04-29

2.  Detecting spatio-temporal hotspots of scarlet fever in Taiwan with spatio-temporal Gi* statistic.

Authors:  Jia-Hong Tang; Tzu-Jung Tseng; Ta-Chien Chan
Journal:  PLoS One       Date:  2019-04-16       Impact factor: 3.240

  2 in total

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