349 27 Critical statistical literacy and interactive data visualisations Jim Ridgway, James Nicholson, Sinclair Sutherland and Spencer Hedger Introduction: conceptions of statistical literacy One can trace advocacy for what we might now call ‘critical statistical literacy’ (CSL) at least as far back as the eighteenth century. Writing early in the French Revolution, Condorcet (1792/1994) proposed the idea of savoir liberateur – knowledge about governance and social inequality that would engage citizens, and would motivate them to reconstruct society
123 9 Enhancing museum visits through the creation of data visualisation to support the recording and sharing of experiences Ian Gwilt, Patrick McEntaggart, Melanie Levick-Parkin and Jonathan Wood Introduction This project explores the use of a practice-led research methodology in the design of generative data visualisations that can be used to record and reveal the details of an empiric museum visit. The object of capturing this visitor information is to assist in the future design and development of tools for the creation of interactive museum
As policy and funding associated with informal/unpaid/family caring develops, more attention has been given to data that support and inform such policy. In particular, evidence around aspects of inequality is often expressed in geographical variations between places in terms of numbers and rates. In general, to date, research on informal caring has focused little on how such variations can be visualised and analysed. This short article looks at the mapping of data from Irish censuses between 2002 and 2016 to: first, explore and visualise patterns of caring, including high-intensity caring. A second broad aim is to use different spatial techniques, including location quotients and clustering, to provide more robust visualisations of spatial variations. Finally, some putative links are but forward between the variable geographical distributions of caring and changes in legislation and policy for carers in Ireland during that same period.
223 14 Disseminating research and evaluation Chapter summary This chapter includes: • Some advice on summarising research or evaluation • An overview of the barriers to dissemination • Advice on presenting in person • Some key points about sharing findings online • Information about data visualisation • A review of some common dissemination methods • Disseminating workplace and academic research • A brief discussion of dissemination ethics Introduction The point of disseminating your research or evaluation is to share the knowledge you have gained through the
the field means that the digital tropes presented here are not mutually exclusive. For example, the bridge between the first trope, networks, and the second trope, big data, is traversed through the field of data visualization. Digital trace data on social media, for example, is often encoded with relational data such as followers or friendship networks. These networked or relational structures between data points are commonly used to identify and amplify key themes within social sentiment, such as studies of affect, emotion and social tone (Rill et al, 2014
Whereas the previous three chapters have focused on the relationship between quantitative realism and data bounds in general terms, Chapter 6 and Chapter 7 circle back to the Trade-Off data bound introduced in Chapter 2 . This chapter focuses on a data visualization that came to visually represent Trade-Off : the graph showing the number of cases, hospitalizations or deaths per day across the entire pandemic. This ‘humped’ graph – capturing how cases rose and fell across 2020 – provides a way into a discussion about the affective qualities of data
The human condition during COVID-19 has been communicated through a barrage of news stories about the pandemic, and much of that news has been visual. Line charts, bar charts and data visualizations have become key to public communication about the pandemic. Likewise, photographs are also used to represent the virus and illustrate the pandemic’s impact on aspects of everyday life, like working, going to school or socializing at the pub. Writing in 2020, Julia Sonnevend argued that visual representations of COVID-19 ‘are entry points for public discussion and
government data for exploring the voluntary sector. Relevant data sources having been explored, the tools and approaches to spatial data visualisation are shown and then critically explored in the light of established methodological and ethical concerns. Finally, innovative approaches are encouraged in order to further our spatial understanding of the voluntary sector. While the chapter draws heavily on the work of academic voluntary sector scholars, it is hoped that it will not only stimulate spatial thinking but also provide practical guidance for practitioners and
International Relations and Research Office in DCC informed AIRO that it was in the process of putting together a tender document for the possible development of a ‘Data Visualisation of a Dublin Indicator Database’. Just at the point where we had made a start on creating a city dashboard, it appeared that DCC was thinking of commissioning one. Five days later, we had our first meeting with a DCC staff member in their offices. Both parties explained what they were hoping to achieve. We agreed that we would continue to develop a prototype dashboard for Dublin, and DCC would
