Analytical Estimation of Map Readability

Abstract

Readability is a major issue with all maps. In this study, we evaluated whether we can predict map readability using analytical measures, both single measures and composites of measures. A user test was conducted regarding the perceived readability of a number of test map samples. Evaluations were then performed to determine how well single measures and composites of measures could describe the map readability. The evaluation of single measures showed that the amount of information was most important, followed by the spatial distribution of information. The measures of object complexity and graphical resolution were not useful for explaining the map readability of our test data. The evaluations of composites of measures included three methods: threshold evaluation, multiple linear regression and support vector machine. We found that the use of composites of measures was better for describing map readability than single measures, but we could not identify any major differences in the results of the three composite methods. The results of this study can be used to recommend readability measures for triggering and controlling the map generalization process of online maps.

A Web-Based Visual and Analytical Geographical Information System for Oil and Gas Data

“…Visualization results in OGVES still need to be improved. Since oil and gas data are big spatiotemporal data, the improvement of the layout readability for a GIS interface and the use of proper spatiotemporal data structures will be further explored in the future [48,49].…”

Is deep learning the new agent for map generalization?

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“… expert interviews (Kilpelainen, 2000;Plazanet et al, 1998)  traces and logs of interactive generalization from experts (Weibel et al, 1995;Taillandier et al, 2011)  analysis of a generalized map (Sester, 2000)  choice by an expert of the best result among several possibilities (Plazanet et al, 1998;Harrie et al, 2015) But compared to other scientific domains, such as automatic text summarization (Touya, 2015), machine learning techniques were a little underemployed in map generalization in the past fifteen years. In the recent years, the success of deep learning brought back the attention of researchers in map generalization (Ma, 2017;Sester et al, 2018) and this new interest is the very essence of this paper.…”

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