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

Abstract

<p><strong>Abstract.</strong> 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.</p>

Conveying map finesse: thematic map making essentials for today’s university students

“…Traditionally, maps were designed and produced by cartographic professionals. Due to technological advances, developers of software tools and their users, often without any cartographic expertise, are now in control of map making (Pillay et al, 2019). Already in 1997, Dorling and Fairbairn acknowledged that IT developments were leading to a diminishing importance of the craft skills required to understand map making when software tools for map making started to emerge.…”

Towards an Open Source Python Library for Automated Exploratory Spatial Data Analysis

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Training students in Python programming skills and WPS wrapping for geoprocessing tasks by using examples of less commonly applied thematic mapping methods

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