Literature DB >> 26357164

Spatial Text Visualization Using Automatic Typographic Maps.

S Afzal1, R Maciejewski, Yun Jang, N Elmqvist, D S Ebert.   

Abstract

We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.

Year:  2012        PMID: 26357164     DOI: 10.1109/TVCG.2012.264

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


  2 in total

1.  Words analysis of online Chinese news headlines about trending events: a complex network perspective.

Authors:  Huajiao Li; Wei Fang; Haizhong An; Xuan Huang
Journal:  PLoS One       Date:  2015-03-25       Impact factor: 3.240

2.  Automatic and intelligent content visualization system based on deep learning and genetic algorithm.

Authors:  Murat İnce
Journal:  Neural Comput Appl       Date:  2022-01-15       Impact factor: 5.606

  2 in total

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