On the Use of 'Glyphmaps' for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases

Recent analysis of area-level COVID-19 cases data attempts to grapple with a challenge familiar to geovisualization: how to capture the development of the virus, whilst supporting analysis across geographic areas? We present several glyphmap designs for addressing this challenge applied to local authority data in England whereby charts displaying multiple aspects related to the pandemic are given a geographic arrangement. These graphics are visually complex, with clutter, occlusion and salience bias an inevitable consequence. We develop a framework for describing and validating the graphics against data and design requirements. Together with an observational data analysis, this framework is used to evaluate our designs, relating them to particular data analysis needs based on the usefulness of the structure they expose. Our designs, documented in an accompanying code repository, attend to common difficulties in geovisualization design and could transfer to contexts outside of the UK and to phenomena beyond the pandemic

How to effectively use interactivity to improve visual analysis and communication in groups of novices or experts

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceConseguir passar um ponto de vista claro através da visualização de dados é um dos principais objetivos das organizações dos dias de hoje. O principal objetivo deste projeto foi perceber qual a melhor maneira de utilizar interatividade em diferentes tipos de indivíduos, especialistas e novatos, e descobrir as principais diferenças entre os dois grupos. Isto foi feito através da criação de um protótipo com diversas visualizações interativas, onde em cada uma delas foram utilizadas diferentes técnicas de visualização e interatividade. Após a sua criação, seguiu-se a validação de cada uma delas, de modo a chegar a conclusões sobre os melhores métodos a utilizar para melhorar a análise e comunicação da informação, para os diferentes grupos de indivíduos. O desenvolvimento do protótipo foi realizado com o software R, mais especificamente o pacote Shiny. O estudo contribuiu com uma metodologia para avaliar as diferenças entre grupos de especialistas e de novatos, relativamente ao protótipo de visualização que foi validado recorrendo a 6 medidas quantitativas e qualitativas. Utilizando um teste ANOVA de fator único foi possível concluir que em relação às medidas quantitativas não foram encontradas diferenças com significância estatística e em relação às medidas qualitativas a única medida que mostrou diferenças com significância estatística entre ambos os grupos foi o nível de interação (engagement). Isto significa que esta é a única métrica possível de melhorar para diminuir as diferenças entre ambos os grupos. Em relação às visualizações ambos os grupos, concordaram que as melhores foram o mapa de calor (heatmap) e o gráfico de barras e as piores visualizações foram o mapa coropleto e o gráfico de barras empilhadas. Houve, no entanto, diferenças entre a forma como os diferentes grupos interagiram com os componentes. Por exemplo, a select box foi uma melhor opção para o grupo de novatos, enquanto que a radio box foi a melhor para o grupo de especialistas. Os tooltips e o slider foram adequados para os dois tipos de indivíduos. Também foi comprovado que o pacote Shiny é uma ferramenta capaz de criar visualizações interativas eficazes para diferentes tipos de indivíduos uma vez que, em média, os participantes obtiveram ótimos resultados utilizando medidas qualitativas ou quantitativas. Os resultados deste estudo, permitirão às organizações a adaptação eficiente das suas visualizações a diferentes tipos de audiência.Getting a clear point of view through data visualization is one of the main goals of todays’ organizations. The main objective of this project was to understand the most efficient way to use interactivity in different groups of individuals, experts and novices, and to discover the main differences between these two groups. This was achieved through the creation of a prototype with several interactive visualizations, where in each of them different visualizations and interaction techniques were used. After the creation of the prototype, the next step was the validation of each one of them to reach conclusions on what are the most effective means to improve visual analysis and communication, in different groups of individuals. The development of the prototype was done using the R software, and most specifically the Shiny package. This study contributed with a methodology to evaluate the differences between experts and novices, using the visualization prototype that was validated with 6 quantitative and qualitative metrics. Using an ANOVA single factor test it was possible to conclude that regarding the quantitative measures no statistically significant differences were found. However, regarding the qualitative measures the only measure that had statistically significant differences between both groups was the engagement measure. This means that this is the only metric where results can be improved in order to close the gap between the group of experts and novices. Regarding the visualizations, both groups agreed that the best visualizations were the heatmap and the bar chart and the worst visualizations were the choropleth map and the stacked bar chart. Nevertheless, there were differences between how the different groups interacted with the components. For example, the select box was a better option for the novice’s group, while the radio box was the best for the expert’s group. The tooltips and the slider are adequate for both types of individuals. It was also proved that the Shiny package is a tool that is capable of creating effective interactive visualizations for different types of individuals, since that on average the participants obtained great results using qualitative or quantitative measures. The results of this study will allow organizations to efficiently adapt their visualizations to different types of audiences

A Comparison of Visualizations for Identifying Correlation over Space and Time

International audienceObserving the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation