Abstract
Uncertainty exists widely in geographic data. However, it is often disregarded during data analysis and decision making. Proper visualization of uncertainty can help map users understand uncertainty in geographic data and make informed decisions. The study reported in this paper examines map users’ perception of and preferences for different visual variables to report uncertainty on bivariate maps. It also explores the possible impact that knowledge and training in Geographic Information Sciences and Systems (GIS) may have on map users’ decision making with uncertainty information. A survey was conducted among college students with and without GIS training. The results showed that boundary fuzziness and color lightness were the most preferred visual variables for representing uncertainty using bivariate maps. GIS knowledge and training was found helpful for some survey participants in their decision making using bivariate uncertainty maps. The results from this case study provide guidance for reporting uncertainty on bivariate maps, aiming at encouraging informed decision making.
Highlights
Uncertainty is resulted from not knowing the exact degree of the discrepancy between geographic data and the geographic reality which these data are intended to represent
This study examined the preference of map users for different visual variables for reporting uncertainty on bivariate maps
Symbol boundary fuzziness and color lightness are more appropriate variables for uncertainty visualization on bivariate maps as these variables tend to be interpreted in a similar way by most map readers, and they are the most preferred visual variables
Summary
Uncertainty is resulted from not knowing the exact degree of the discrepancy between geographic data and the geographic reality which these data are intended to represent. In the past thirty years, researchers in Geographic Information Sciences and Systems (GIS) and the related fields have made great progress in defining, measuring, modeling and visualizing uncertainty in geographic data. There are some gaps in literature regarding how map users may prefer the different visualization techniques and the potential impact of map users’ background on their decision making using uncertainty maps. The existing studies failed to address two important questions: (1) which uncertainty visualization method(s) is preferred by map users, and (2) does GIS knowledge and training affect map users’ decision making using uncertainty maps. The study reported in this paper was conducted to examine the preference for the different visualization methods for representing uncertainty in geographic data and to examine if GIS knowledge and training affects map users’ ability to incorporate uncertainty for better decision making. The findings can provide guidelines for visualizing uncertainty on bivariate maps to encourage informed decision making
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