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ISPRS Int. J. Geo-Inf. 2014, 3(4), 1180-1197; doi:10.3390/ijgi3041180

Uncertainty in Geographic Data on Bivariate Maps: An Examination of Visualization Preference and Decision Making

Department of Geography, Texas State University, San Marcos, TX 78666, USA
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Received: 31 July 2014 / Revised: 13 October 2014 / Accepted: 13 October 2014 / Published: 24 October 2014
(This article belongs to the Special Issue Recent Developments in Cartography and Display Technologies)
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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. View Full-Text
Keywords: bivariate map; uncertainty visualization; decision making; visual variable; Geographic Information Sciences and Systems (GIS) bivariate map; uncertainty visualization; decision making; visual variable; Geographic Information Sciences and Systems (GIS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Scholz, R.W.; Lu, Y. Uncertainty in Geographic Data on Bivariate Maps: An Examination of Visualization Preference and Decision Making. ISPRS Int. J. Geo-Inf. 2014, 3, 1180-1197.

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