Analytical Estimation of Map Readability

Summary, in English

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.

• Author: Lars Harrie; Hanna Stigmar; Milan Djordjevic
• Department/s: Dept of Physical Geography and Ecosystem Science
• Publishing year: 2015
• Language: English
• Pages: 418-446
• Publication/Series: ISPRS International Journal of Geo-Information
• Volume: 4
• Issue: 2
• Links: http://dx.doi.org/10.3390/ijgi4020418
• Document type: Journal article
• Publisher: MDPI AG
• Topic: Physical Geography
• Keywords: cartography; map readability; usability; user test; supervised learning
• Status: Published
• ISBN/ISSN/Other: ISSN: 2220-9964