Abstract
Abstract. In this study, we first develop a hypothesis that existing quantitative visual complexity measures will overall reflect the level of cartographic generalization, and test this hypothesis. Specifically, to test our hypothesis, we first selected common geovisualization types (i.e., cartographic maps, hybrid maps, satellite images and shaded relief maps) and retrieved examples as provided by Google Maps, OpenStreetMap and SchweizMobil by swisstopo. Selected geovisualizations vary in cartographic design choices, scene contents and different levels of generalization. Following this, we applied one of Rosenholtz et al.’s (2007) visual clutter algorithms to obtain quantitative visual complexity scores for screenshots of the selected maps. We hypothesized that visual complexity should be constant across generalization levels, however, the algorithm suggested that the complexity of small-scale displays (less detailed) is higher than those of large-scale (high detail). We also observed vast differences in visual complexity among maps providers, which we attribute to their varying approaches towards the cartographic design and generalization process. Our efforts will contribute towards creating recommendations as to how the visual complexity algorithms could be optimized for cartographic products, and eventually be utilized as a part of the cartographic design process to assess the visual complexity.
Highlights
Several algorithmic approaches have been proposed in interdisciplinary scientific literature to measure the complexity levels of visual displays (Batty et al, 2014; MacEachren, 1982; Moacdieh & Sarter, 2015; Rosenholtz et al, 2007)
We present results from one of these visual complexity algorithms, which we applied to various map types (i.e., 2D cartographic maps, hybrid maps, aerial/satellite images, shaded relief maps)
Because many visual complexity measures are based on psychophysiological literature on how attention works, we hypothesize that these complexity measures will overall reflect the levels of map generalization
Summary
Several algorithmic approaches have been proposed in interdisciplinary scientific literature to measure the complexity levels of visual displays (Batty et al, 2014; MacEachren, 1982; Moacdieh & Sarter, 2015; Rosenholtz et al, 2007). The question arises whether these algorithmic measures match the semantically enhanced cartographic generalization approaches, given that ‘simplification’ operations to reduce complexity dominate the generalization processes. In this project, to assess and validate quantitative approaches to measuring visual complexity in a cartographic context, we compare results from a selected set of visual complexity algorithms. For the 2D cartographic map types, we extended the selection and applied the algorithm for a set of maps with various levels of generalization We believe such a comparison would be helpful in validating and improving the aforementioned algorithmic measures, and optimizing them for geographic visualizations, which, in turn, could be used as interim measures for cartographic design as the display size and the zoom levels change
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