A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT

Crisan, Anamaria; Gardy, Jennifer L.; Munzner, Tamara

doi:10.1101/325290

Cited by 5 publications

(22 citation statements)

References 23 publications

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“…reminders for adding labels and legends, suggestions for optimal colour schemes, warnings in case of chart junk). Our results and findings from similar studies in other fields [23,24] can support them in doing so by providing an overview of what is already used, including potential pitfalls. Of note, academic journals play an important part in this process by providing the platform for data visualizations and should be encouraged to promote high quality data visualization practices.…”

Section: Discussionsupporting

confidence: 82%

“…The audience’s background and its familiarity with data visualization (the visual domain context) have to be taken into account in the design process to avoid these obstacles. Example studies that identified the visual domain context by studying the design space can be found in the field of genomic epidemiology and genomic data visualization [23,24]. Although, some recommendations and best practices exist that are helpful in the data visualization creation process, common data visualizations practices in the field of stewardship have yet to be revealed [25,26].…”

Section: Introductionmentioning

confidence: 99%

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The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

Keizer

Luz

Sinha

et al. 2021

Preprint

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Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed. The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations. Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n=54; 36.0%) and line charts (n=42; 28.1%) were preferred visualization types. Visualization problems comprised colour scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve colour use for grouping/stratifying, improve the display of magnitude, and bring visualizations to match scientific standards. Conclusions: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship towards more effective visualizations that improve data insights, knowledge, and clinical decision-making.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

Keizer

Luz

Sinha

et al. 2021

Preprint

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“…With pathogen genomic data, this is no longer true: these data need to be integrated to realize the full value of both. 31,86,87 Fortunately, academic research is addressing this challenge, 88 producing tools for visualizing and analyzing epidemiologic and phylogenetic data together, such as Microreact (microreact.org), 89 Nextstrain (nextstrain.org) 90 or the Interactive Tree of Life (itol.embl.de). 91 More broadly, the emerging field of data science offers novel approaches for integrating, analyzing and visualizing increasingly diverse public health data.…”

Section: Data Integration and Data Sciencementioning

confidence: 99%

Pathogen Genomics in Public Health

et al. 2019

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Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the future potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" via more effective foodborne illness outbreak investigations, better targeted tuberculosis control, and more timely and granular influenza surveillance to inform vaccine strain selection. In this article, we describe how public health agencies are rapidly adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given ongoing development in sequencing and sequencing-related technologies. An important transformation is underway in public health. Next-generation sequencing (NGS, also called "high-throughput sequencing") is reshaping communicable disease surveillance, allowing for earlier detection and more precise investigation of outbreaks. NGS helps characterize microbes more effectively and offers new insights into their ecology and

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“…A novel feature of our approach is that we tailor the recommendation for a specific domain, to tame the combinatorial explosion of possibilities that arise from a broad variety of input data types and output chart types. We leverage the knowledge encapsulated in a recent domainspecific visualization prevalence design space (VPDS), where visual design collections used by experts in a particular domain are both characterized and enumerated [6]. We use this domain-specific information in a few targeted stages of our recommender algorithm, in conjunction with many domain-agnostic decision procedures.…”

Section: Introductionmentioning

confidence: 99%

“…We use this domain-specific information in a few targeted stages of our recommender algorithm, in conjunction with many domain-agnostic decision procedures. The GEViT [6] VPDS that we proposed in previous work arises from genomic epidemiology (genEpi), a very appropriate exemplar domain for data recon with a diverse and heterogeneous set of input data types and output chart types; genEpi analysts often grapple with unfamiliar collections of datasets in situations where access is tightly controlled due to sensitive personally identifiable information.…”

Section: Introductionmentioning

confidence: 99%

GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design Space

Crisan

Fisher

Gardy

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates visually coherent combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating a data source graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with positional and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real Ebola outbreak data. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results.

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A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT

Cited by 5 publications

References 23 publications

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance

Pathogen Genomics in Public Health

GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Visualization Prevalence Design Space

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