Título: | UNCOVERING FACTORS THAT INFLUENCE HOW DATA VISUALIZATIONS ARE INTERPRETED BY NON-EXPERTS | ||||||||||||
Autor: |
ARIANE MORAES BUENO RODRIGUES |
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Colaborador(es): |
SIMONE DINIZ JUNQUEIRA BARBOSA - Orientador |
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Catalogação: | 23/MAI/2022 | Língua(s): | ENGLISH - UNITED STATES |
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Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=59149&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=59149&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.59149 | ||||||||||||
Resumo: | |||||||||||||
Data visualizations are increasingly common in traditional media and
social networks. However, the visualization literacy of the population did not
follow this growing popularity. It is necessary for those who create the charts
to assemble a visual communication that contains the necessary information
in an attractive and easy-to-understand way. By contrast, it is necessary for
those who consume them to capture information represented by the charts and
extract the analyses of what they see. The importance of visual literacy is the
ability to read a chart, i.e., look at a chart and identify relevant information,
trends, and outliers in a given scenario. In this work, we conducted four studies
to explore factors related to the success of visual data analysis. We identified
issues ranging from data distribution to formulating good questions to enrich
exploration. The first study discovered how people try to make sense of specific
data visualizations through questions they ask when they first encounter a
visualization. In the second study, we explored how data distributions can
affect the effectiveness and efficiency of data visualizations. In the third study,
we investigated when non-experts identify that particular visualization is
not adequate to answer a specific analysis question, when they make good
suggestions for changes to make these visualizations adequate, and when they
evaluated well the adequacy of some suggestions offered to them. In the
fourth study, we created a test to assess people s understanding of the applied
(answering analysis questions supported by a visualization) and conceptual
(questions about function and structure) aspects of data visualization. Our
results provide resources for developing of educational material and tools for
recommending data visualizations to answer specific data-relation questions.
An additional contribution of this work to the results of the studies was the
structuring of a unified list of different visualization tasks that we found in the
literature.
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