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
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