• Edcley Silva UFPE
  • Nivan Ferreira UFPE
  • Fabio Miranda NYU


Currently, technological advances have revolutionized the way natural phenomena are studied. Natural phenomena can be represented through distributions of geographic data that are a rich source of information and can be explored in different ways. One of them is the representation of uncertainty through the distribution of probability. Modeling the uncertainty of this type of distribution and representing it in geographic visualization is complicated because maps (common types of geographic visualization) need the visual environment to represent geographic space and there are not many ways to represent any other information. One of the ways often used as a solution is statistical summarization such as mean, but summarizing the data alone may can hide the data’s behavior and generates ambiguity. The concealment of the uncertainty of the data in visualization can be justified by the way the uncertainty is represented that may not be understood by the user. Technical proposals have been proposed to represent distributions, but generally they only represent the presence and spread of uncertainty recently others approaches based on probability of proportion of data, animation and interaction have proposed to make quantification of probability, but have not been used or compared formally for geographic data. The objective was qualitatively compare main approaches to visualize probability distributions on a geographical scenario (includes factors such as distance, size and variation), using the recent proposed approaches in the context of abstract data, analytical tasks and user study. The results show which approach has the better performance in the presented cases.


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SILVA, Edcley; FERREIRA, Nivan; MIRANDA, Fabio. A COMPARATIVE STUDY OF TECHNIQUES OF VISUALIZATION OF DISTRIBUTIONS FOR GEOGRAPHICAL DATA. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 91-97. DOI: https://doi.org/10.5753/sibgrapi.est.2020.12989.