Four types of ensemble coding in data visualizations

This chapter provides an overview of social network visualization. Network analysis encourages the visual display of complex information, but effective network diagrams, like other data visualizations, result from several best practices. After a brief history of network visualization, the chapter outlines several of those practices. It highlights the role that network visualizations play as heuristics for making sense of networked data and translating complicated social relationships, such as those that are dynamic, into more comprehensible structures. The goal in this chapter is to help identify the methods underlying network visualization with an eye toward helping users produce more effective figures.

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Arrows and their modern versions: narrativity signalled by lines in data visualizations

Journal of Visual Literacy ◽

10.1080/1051144x.2021.1902039 ◽

2021 ◽

pp. 1-20

Author(s):

Verena Elisabeth Lechner

Keyword(s):

Data Visualizations

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Composable data visualizations

Journal of Functional Programming ◽

10.1017/s0956796821000046 ◽

2021 ◽

Vol 31 ◽

Author(s):

TOMAS PETRICEK

Keyword(s):

The Right ◽

Data Visualizations

Let’s say we want to create the two charts in Figure 1. The chart on the left is a bar chart that shows two different values for each bar. The chart on the right consists of two line charts that share the x axis with parts of the timeline highlighted using two different colors.

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COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Information ◽

10.3390/info11060314 ◽

2020 ◽

Vol 11 (6) ◽

pp. 314 ◽

Cited By ~ 17

Author(s):

Jim Samuel ◽

G. G. Md. Nawaz Ali ◽

Md. Mokhlesur Rahman ◽

Ek Esawi ◽

Yana Samuel

Keyword(s):

Machine Learning ◽

The United States ◽

Classification Methods ◽

Reasonable Accuracy ◽

Bayes Method ◽

Inaccurate Information ◽

Public Sentiment ◽

Research Article ◽

Textual Data ◽

Data Visualizations

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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Visualization approaches to support healthy aging: A systematic review

Journal of Innovation in Health Informatics ◽

10.14236/jhi.v23i3.860 ◽

2016 ◽

Vol 23 (3) ◽

pp. 600 ◽

Cited By ~ 4

Author(s):

Uba Backonja ◽

Nai-Ching Chi ◽

Yong Choi ◽

Amanda K Hall ◽

Thai Le ◽

...

Keyword(s):

Older Adults ◽

Health Behaviors ◽

Healthy Aging ◽

A Priori ◽

Aging In Place ◽

Future Research ◽

Health Technologies ◽

Data Visualizations ◽

Informatics Tools

Background: Health technologies have the potential to support the growing number of older adults who are aging in place. Many tools include visualizations (data visualizations, visualizations of physical representations). However, the role of visualizations in supporting aging in place remains largely unexplored.Objective: To synthesize and identify gaps in the literature evaluating visualizations (data visualizations and visualizations of physical representations), for informatics tools to support healthy aging.Methods: We conducted a search in CINAHL, Embase, Engineering Village, PsycINFO, PubMed, and Web of Science using a priori defined terms for publications in English describing community-based studies evaluating visualizations used by adults aged ≥65 years.Results: Six out of the identified 251 publications were eligible. Most studies were user studies and varied methodological quality. Three visualizations of virtual representations supported performing at-home exercises. Participants found visual representations either (a) helpful, motivational, and supported their understanding of their health behaviors or (b) not an improvement over alternatives. Three data visualizations supported understanding of one’s health. Participants were able to interpret data visualizations that used precise data and encodings that were more concrete better than those that did not provide precision or were abstract. Participants found data visualizations helpful in understanding their overall health and granular data.Conclusions: Studies we identified used visualizations to promote engagement in exercises or understandings of one’s health. Future research could overcome methodological limitations of studies we identified to develop visualizations that older adults could use with ease and accuracy to support their health behaviors and decision-making.

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