Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 2
Proc. Int. Cartogr. Assoc., 2, 100, 2019
https://doi.org/10.5194/ica-proc-2-100-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 2, 100, 2019
https://doi.org/10.5194/ica-proc-2-100-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  10 Jul 2019

10 Jul 2019

A comprehensive workflow for automating thematic map geovisualization from univariate big geospatial point data

Larissa Pillay1, Gertrud Schaab2, Serena Coetzee1, and Victoria Rautenbach1 Larissa Pillay et al.
  • 1Centre for Geoinformation Science, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, South Africa
  • 2Faculty for Information Management and Media, Karlsruhe University of Applied Sciences, Karlsruhe, Germany

Keywords: thematic mapping, big data, point features, geospatial data, workflow

Abstract. The increase in massive volumes of point data that are continuously being generated calls for more powerful solutions to analyze and explore this data. Very often, such data includes a direct or indirect reference to a location on the Earth and can then be referred to as ‘big geospatial data’. Maps are one of the best ways to assist humans with understanding geospatial relationships in such data. In this paper, we present a comprehensive workflow for generating all possible thematic map types from two-dimensional univariate big geospatial point data. The objective is twofold: to facilitate and support thematic map automation, and to make this information accessible to software developers. The workflow illustrates processing steps, design choices and dependencies between them based on the characteristics of input data. Processing steps and design choices that can be automated and those requiring human intervention are identified. The scope of the workflow in this paper was restricted to two-dimensional univariate geospatial point data and planar and true geometrical map depictions. The results presented in this paper support the development of geovisualization and geovisual analytics tools for big geospatial data.

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