Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 2
Proc. Int. Cartogr. Assoc., 2, 158, 2019
https://doi.org/10.5194/ica-proc-2-158-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 2, 158, 2019
https://doi.org/10.5194/ica-proc-2-158-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2019

10 Jul 2019

Visual Analytics for Regional Economic Environment Factors Based on a Dashboard Design

Chenyu Zuo1, Linfang Ding1,2, and Liqiu Meng1 Chenyu Zuo et al.
  • 1Chair of Cartography, Technical University of Munich, Germany
  • 2KRDB Research Centre, Faculty of Computer Science, Free University of Bozen-Bolzano, Italy

Keywords: visual analytics, spatiotemporal analysis, choropleth maps, small multiples

Abstract. Economic environment is vital for commercial investment, city planning and company strategy planning in urban areas. Mastering the economical trend may help the entrepreneurs, government officers and individuals in their decision-making process. In this study, we explore multiple geo-economic datasets using visual analytics methods for understanding the economic environment. More specifically, we user time-series Gross Domestic Product (GDP) data as an economic indicator of economic development and land use data to support the spatial analysis at a refined geographic scale. The spatiotemporal patterns of the regional economic environment are revealed both qualitatively and quantitatively. The work has a three-fold contributions: (1) we apply a grid-based spatial interpolation model to derive GDP values at a file granularity based on land use data; (2) we design a novel interactive dashboard for the GDP data exploration, which serves as a visual analytical tool between data and users; (3) we combine quantitative analysis with visualizations to strengthen the qualitative analysis. The feasibility of visual analytics methods and the dashboard design are tested in one of the most developed regions, Jiangsu Province, China. Both expected and unexpected economical patterns were extracted.

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