Text-based Spatial and Temporal Visualizations and their Applications in Visual Analytics

Textual labels are an essential part of most visualizations used in practice. However, these textual labels are mainly used to annotate other visualizations rather than being a central part of the visualization. Visualization researchers in areas like cartography and geovisualization have studied the combination of graphical features and textual labels to generate map based visualizations, but textual labels alone are not the primary focus in these representations. The idea of using symbols in visual representations and their interpretation as a quantity is gaining more traction. These types of representations are not only aesthetically appealing but also present new possibilities of encoding data. Such scenarios regularly arise while designing visual representations, where designers have to investigate feasibility of encoding information using symbols alone especially textual labels but the lack of readily available automated tools, and design guidelines makes it prohibitively expensive to experiment with such visualization designs. In order to address such challenges, this thesis presents the design and development of visual representations consisting entirely of text. These visual representations open up the possibility of encoding different types of spatial and temporal datasets. We report our results through two novel visualizations: typographic maps and text-based TextRiver visualization. Typographic maps merge text and spatial data into a visual representation where text alone forms the graphical features, mimicking the practices of human map makers. We also introduce methods to combine our automatic typographic maps technique with spatial datasets to generate thema-typographic maps where the properties of individual characters in the map are modified based on the underlying spatial data. Our TextRiver visualization is composed of collection of stream-like shapes consisting entirely of text where each stream represents thematic strength variations over time within a corpus. Such visualization enables additional ways to encode information contained in temporal datasets by modifying text attributes. We also conducted a usability evaluation to assess the potential value of our text-based TextRiver design

An Epidemiology of Information: Data Mining the 1918 Influenza Pandemic

An Epidemiology of Information: Data Mining the 1918 Influenza Pandemic seeks to harness the power of data mining techniques with the interpretive analytics of the humanities and social sciences to understand how newspapers shaped public opinion and represented authoritative knowledge during this deadly pandemic. This project makes use of the more than 100 newspaper titles for 1918 available from Chronicling America at the United States Library of Congress and the Peel’s Prairie Provinces collection at the University of Alberta Library. The application of algorithmic techniques enables the domain expert to systematically explore a broad repository of data and identify qualitative features of the pandemic in the small scale as well as the genealogy of information flow in the large scale. This research can provide methods for understanding the spread of information and the flow of disease in other societies facing the threat of pandemics

Not Just the Flu: The Impacts of ASIAFLUCAP Influenza Policy Recommendations on Southeast Asia During the SARS-CoV-2 Pandemic

During 2008 to 2011, a multi-year influenza pandemic study (ASIAFLUCAP) took place in six Southeast Asian countries: Thailand, Indonesia, Vietnam, Taiwan, Cambodia, and Laos, to analyze their healthcare system capacities and determine appropriate policy recommendations in order that they might be better equipped for future influenza pandemics. This research expands upon that project to see if the countries that implemented higher numbers of ASIAFLUCAP policy recommendations prior to or in the SARS-CoV-2 pandemic fared better than those countries which did implemented fewer recommendations. It finds that results are mixed across the sample, with no clear association between a country’s adoption of ASIAFLUCAP policy recommendations and its subsequent SARS-CoV-2 response and overall situation

Sentiment analysis of COVID-19 cases in Greece using Twitter data.

Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics for the last two decades, using different sources from social media to search engine records. More recently, studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of pandemics. Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the sentiment impact of COVID-19 cases in Greece in real time as related to cases. Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year and were examined towards two sentimental lexicons: one in English language translated into Greek (using the Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness, Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data. Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of COVID-19. The correlation coefficient (R2 ) for the Vader lexicon is &#8722; 0.07454 related to cases and &#8722; 0.,70668 to the tweets, while the other lexicon had 0.167387 and &#8722; 0.93095 respectively, all measured at significance level of p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the interest in COVID-19 declined after a certain time

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117