Pub Date : 2018-10-01DOI: 10.1109/VISAP45312.2018.9046053
F. Samsel, L. Bartram, Annie Bares
As the complexity of scientific data and the needs to communicate the science have grown, the requirements for visualization design and use have become more sophisticated. We increasingly need more effective ways of communicating the science across multiple audiences, including non-experts in the field. The challenges of enriching the representation have moved from the more naive ideas of making it “aesthetically attractive” to more profound constructs of visual language: how to enhance nuances in the data, and how to support more expressive visualizations that elicit different cognitive and communicative affect to tell the science story. In this paper, we describe how artistic color techniques drawn from paintings can be operationally applied to produce more evocative and informative scientific visualization. We illustrate how the color use in a painting can reveal structure and information priority and elicit affect using examples from current work with our scientific visualization colleagues. Our results highlight the value of engaging with artists in long-term, multidisciplinary science teams, but also emphasize the comprehension gaps that exist across the disciplines and the need for methods and techniques that bridge them so they are accessible to a wider range of data scientists. Our color extraction methods and results are a small example of such bridging techniques.
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Pub Date : 2018-10-01DOI: 10.1109/visap45312.2018.9046050
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Pub Date : 2018-10-01DOI: 10.1109/VISAP45312.2018.9046054
Heike Otten, Lennart Hildebrand, T. Nagel, M. Dörk, Boris Müller
We present a hybrid visualization technique that integrates maps into network visualizations to reveal and analyze diverse topologies in geospatial movement data. With the rise of GPS tracking in various contexts such as smartphones and vehicles there has been a drastic increase in geospatial data being collect for personal reflection and organizational optimization. The generated movement datasets contain both geographical and temporal information, from which rich relational information can be derived. Common map visualizations perform especially well in revealing basic spatial patterns, but pay less attention to more nuanced relational properties. In contrast, network visualizations represent the specific topological structure of a dataset through the visual connections of nodes and their positioning. So far there has been relatively little research on combining these two approaches. Shifted Maps aims to bring maps and network visualizations together as equals. The visualization of places shown as circular map extracts and movements between places shown as edges, can be analyzed in different network arrangements, which reveal spatial and temporal topologies of movement data. We implemented a web-based prototype and report on challenges and opportunities about a novel network layout of places gathered during a qualitative evaluation.
{"title":"Shifted Maps: Revealing spatio-temporal topologies in movement data","authors":"Heike Otten, Lennart Hildebrand, T. Nagel, M. Dörk, Boris Müller","doi":"10.1109/VISAP45312.2018.9046054","DOIUrl":"https://doi.org/10.1109/VISAP45312.2018.9046054","url":null,"abstract":"We present a hybrid visualization technique that integrates maps into network visualizations to reveal and analyze diverse topologies in geospatial movement data. With the rise of GPS tracking in various contexts such as smartphones and vehicles there has been a drastic increase in geospatial data being collect for personal reflection and organizational optimization. The generated movement datasets contain both geographical and temporal information, from which rich relational information can be derived. Common map visualizations perform especially well in revealing basic spatial patterns, but pay less attention to more nuanced relational properties. In contrast, network visualizations represent the specific topological structure of a dataset through the visual connections of nodes and their positioning. So far there has been relatively little research on combining these two approaches. Shifted Maps aims to bring maps and network visualizations together as equals. The visualization of places shown as circular map extracts and movements between places shown as edges, can be analyzed in different network arrangements, which reveal spatial and temporal topologies of movement data. We implemented a web-based prototype and report on challenges and opportunities about a novel network layout of places gathered during a qualitative evaluation.","PeriodicalId":405454,"journal":{"name":"2018 IEEE VIS Arts Program (VISAP)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/VISAP45312.2018.9046052
Aaron Hill, Clare Churchouse, M. F. Schober
In data visualization, the representation of uncertainty and error estimation is often difficult to display effectively. Constraints on the number of dimensions that can be expressed visually as well as limitations of statistical graphing software often lead to data visualizations that inadvertently omit and/or poorly convey the uncertainty and vulnerability of the underlying data.This research is based on more than 400 works of fine art from museum collections and galleries across several countries, curated and analyzed for inspiration and information on potentially effective ways to visually communicate uncertainty, ambiguity, and vulnerability. We chose these artworks because we feel they have a unique ability to convey uncertainty using a range of approaches and techniques. This paper includes observations from the analysis, examples of compelling works of art from the research, and an exploration of ways these works might inform data visualization practice, specifically for the visual display of uncertainty.
{"title":"Seeking New Ways to Visually Represent Uncertainty in Data: What We Can Learn from the Fine Arts","authors":"Aaron Hill, Clare Churchouse, M. F. Schober","doi":"10.1109/VISAP45312.2018.9046052","DOIUrl":"https://doi.org/10.1109/VISAP45312.2018.9046052","url":null,"abstract":"In data visualization, the representation of uncertainty and error estimation is often difficult to display effectively. Constraints on the number of dimensions that can be expressed visually as well as limitations of statistical graphing software often lead to data visualizations that inadvertently omit and/or poorly convey the uncertainty and vulnerability of the underlying data.This research is based on more than 400 works of fine art from museum collections and galleries across several countries, curated and analyzed for inspiration and information on potentially effective ways to visually communicate uncertainty, ambiguity, and vulnerability. We chose these artworks because we feel they have a unique ability to convey uncertainty using a range of approaches and techniques. This paper includes observations from the analysis, examples of compelling works of art from the research, and an exploration of ways these works might inform data visualization practice, specifically for the visual display of uncertainty.","PeriodicalId":405454,"journal":{"name":"2018 IEEE VIS Arts Program (VISAP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115085453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/VISAP45312.2018.9046055
Raphael Arar
Nostalgia is an installation that draws attention to the computational challenges of understanding human emotion. Through affective computing and machine learning, the underlying system attempts to translate the components of the sentiment’s qualitative makeup in quantitative terms. In Nostalgia, participants are asked to submit text-based memories, which are then used to calculate, predict and ultimately visualize relative nostalgia scores based on the aggregate of stories collected. However, given the ambiguities and complexity of human self-expression and the necessary precision of computational intelligence, Nostalgia highlights the entanglements of achieving emotional understanding between humans and machines.
{"title":"Nostalgia: A Human-Machine Transliteration","authors":"Raphael Arar","doi":"10.1109/VISAP45312.2018.9046055","DOIUrl":"https://doi.org/10.1109/VISAP45312.2018.9046055","url":null,"abstract":"Nostalgia is an installation that draws attention to the computational challenges of understanding human emotion. Through affective computing and machine learning, the underlying system attempts to translate the components of the sentiment’s qualitative makeup in quantitative terms. In Nostalgia, participants are asked to submit text-based memories, which are then used to calculate, predict and ultimately visualize relative nostalgia scores based on the aggregate of stories collected. However, given the ambiguities and complexity of human self-expression and the necessary precision of computational intelligence, Nostalgia highlights the entanglements of achieving emotional understanding between humans and machines.","PeriodicalId":405454,"journal":{"name":"2018 IEEE VIS Arts Program (VISAP)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133057602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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