Acta Geodaetica et Cartographica Sinica ›› 2019, Vol. 48 ›› Issue (6): 756-766.doi: 10.11947/j.AGCS.2019.20180353

• Cartography and Geoinformation • Previous Articles     Next Articles

A visualization method of continuous area cartogram for two or multiple variables

LI Xiang1, WANG Lina2, ZHANG Weidong3, YANG Fei1, YANG Zhenkai1   

  1. 1. Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450052, China;
    2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China;
    3. 61512 Troops, Beijing 100088, China
  • Received:2018-07-23 Revised:2019-03-18 Online:2019-06-20 Published:2019-07-09
  • Supported by:
    The Open Foundation of State Key Laboratory of Geo-information Engineering (No. SKLGIE 2016-Z-4-2);The National Key Research and Development Program (Nos. 2016YFB0502300;2017YFC1200300);The National Natural Science Foundation of China(No. 41671455)

Abstract: Area cartogram is a visualization method that quantitatively represents regional attribute information by using the area size. Area cartogram is more conductive to the bivariate/multivariate mapping because the area size itself participates in the expression of variables. Now, bivariate/multivariate mapping based on area cartogram is difficult to express the basic situation between adjacent regions, and it is also difficult to express the spatial distribution of different geographical phenomena, to detect differences between two or more variables and spatial patterns. A method of a continuous Area cartogram for two or multiple variables has been proposed in this paper. Firstly, compensation of grid density and the progressive heuristics of the integration step are used to improve and optimize the classic algorithm of continuous area cartogram-"the diffusion-based method for producing density equalizing maps". Then, the first variable is visualized by area cartogram and the second or more variables are visualized by interpolating location on a continuous Area cartogram and symbolization. Finally, we use the population density and bank/ATM distribution data in Munich (bivariate mapping), the population density, kindergarten distribution and scale data in Augsburg (multivariate mapping) as case studies. This method is proved to be more effective and superior by the experiment results.

Key words: geovisualization, bivariate/multivariate mapping, continuous area cartogram, the diffusion-based method for producing density equalizing maps

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