### 统计代写|数据可视化代写data visualization代考|Inventorizing data visualization resources

statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化data visualization方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据可视化data visualization代写方面经验极为丰富，各种代写数据可视化data visualization相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|数据可视化代写data visualization代考|Inventorizing data visualization resources

As a first step in our social semiotic approach, we must therefore begin by inventorizing the semiotic resources that are typical of data visualization across media and contexts. As van Leeuwen (2005) explains, ‘[t]o make an inventory we first need a collection’ (p. 6 ). In other words, we must identify and catalogue resources that are representative of data visualization as a whole. This is a particularly challenging task, both because uses of data visualization cut across a vast range of social spheres, and because the existing empirical base to systematically describe key data visualization resources is still thin.
To begin building an inventory of data visualization resources and their possible combinations, we can draw from existing social semiotic and multimodal studies of data visualization and of related semiotic objects. In the study led by Helen Kennedy mentioned earlier, we identify four key data visualization conventions, namely two-dimensional viewpoints, clean layouts, geometric shapes and lines, and the inclusion of data sources (Kennedy et al., 2016). By the same token, in their recent book on visual analysis, Ledin and Machin (2018) examine different types of ‘data presentation’ through a social semiotic lens, including lists, bullet points, line graphs, bar charts, and flow charts. In this analysis, they identify a set of semiotic resources, namely paradigms, spatialization, vertical and horizontal orientation, graphic shapes and icons, temporality, and causality. Similar analyses of related semiotic objects like diagrams, infographics, and PowerPoint can also be useful in building an inventory of data visualization resources. This is not only because some of these are used in data visualization (e.g. diagrams) or are, at times, confused with data visualizations (e.g. infographics), but also because these analyses offer a discussion of findings and concepts that are useful for a social semiotic analysis of data visualization. What diagrams, infographics and PowerPoint have in common with data visualization is that they are all often used to relay ‘hard’ facts and key strategic points, usually with the aim to maximize an organization’s outputs and increase its competitiveness.

## 统计代写|数据可视化代写data visualization代考|Situating data visualization resources

Precisely for this reason, the next step of our social semiotic framework entails an attempt to situate data visualization resources in their social and cultural contexts. As Jewitt et al. (2016) explain, one of the main aims of social semiotics is ‘to understand the social dimensions of meaning, its production, interpretation and circulation, and its implications’ (p. 58 ). Both historical and ethnographic methods are often invoked as key to a social semiotic understanding of meaning-making. Cultural and social histories of a variety of resources-like, for example, colour-are used productively to locate their origins, understand the material, cultural, and political forces that shaped them, and trace their changes over time (see, for example, the history of the colour blue by Michel Pastoureau, 2001). However, fieldwork, and ethnographic research in particular, has often remained an ideal among social semioticians. One exception is my own work, in which

I have adopted a multi-sited ethnographic approach to investigate the practices, motivations, and outputs of image-makers like photographers and graphic designers (Aiello, 2012a, 2012b). As Marcus (1995) writes, when the object of ethnographic investigation is in the realm of discourse and modes of thought, then the circulation of signs, symbols, and metaphors guides the design of ethnography’ (p. 108). Because of this focus on the social lives of signs, rather than of particular sites or communities, a social semiotic approach will entail a focus on data visualization as it is produced and used across different social and geographical locales.

This said, there is also much to be learned from existing and ongoing ethnographic studies of particular sites and settings in which data visualization is produced, used, or consumed. Alongside Helen Kennedy’s collaborative work on designers’ intentions and ordinary people’s responses with regard to data visualization, there is also a growing body of work on the production and uses of data visualizations in newsrooms (see Engebretsen et al., 2018). In this regard, a social semiotic approach to data visualization can also benefit from sociological research on digital and data journalism, in that it offers detailed accounts of the material resources, skills, and tools that are available to those who make decisions about data visualizations across news media (Fink \& Anderson, 2015). This said, when interviewing participants, it is important that researchers ask questions not so much about the intentions, motivations, feelings, and overall actions of participants in relation to data visualization, but more specifically about how they use or interpret particular semiotic resources. This can be done through elicitation or reconstructive methods, where participants are asked to comment on particular texts (in this case, specific visualizations) that the researcher shares with them or asks them to share during the interview. Ultimately, asking questions about ‘the set of semiotic choices that typify a given context’ (van Leeuwen, 2005, p. 14) contributes both to understanding the context itself and the reasons why specific semiotic resources come to be the way they are. In situating visualization resources in their contexts, particularly through ethnographic fieldwork, researchers will often also come across ‘new’ resources, which will thus go to enrich and extend their initial inventory.

## 统计代写|数据可视化代写data visualization代考|Transforming data visualization resources

The knowledge generated through the descriptive and interpretive stages of the social semiotic approach to data visualization leads to an understanding of visualization resources as part of broader cultural processes and

power relations. A third and final stage in this framework focuses both on the politics and potentials of data visualization. Major semiotic resources and their combinations can be transformed to break away from dominant ‘visual sensibilities’ and therefore also promote particular forms of social action and social change. As I highlighted earlier in the chapter, the goal of social semiotics is to interrogate as well as redefine sign-making. This is not considered to be a neutral process, but rather as having both power-laden origins and powerful implications.

It can therefore be useful to combine both critical and creative ends to understand how data visualization may be both part of what Fairclough (1995) has termed the ‘technologization of discourse’ and what van Leeuwen (2008) more recently defined as ‘the new writing’, or the new dominant language of multimodal communication. On the one hand, data visualization may be seen as part of a powerful impetus towards the standardization of semiotic resources for ‘the engineering of social change’ (Fairclough, 1995, p. 3). In other words, broader shifts in discursive practices are often aimed at changing the ways in which given institutions-e.g. news media, universities, and governments-and publics think and act in relation to particular issues. For example, Fairclough $(1992,1996)$ focused extensively on how language was used to promote and normalize both marketization and managerialism in public institutions like schools, universities, and hospitals. Through an analysis of how data visualization resources may be increasingly codified within and across institutions, and how such processes of semiotic codification may be tied to broader structures of power, we can begin to provide an evidence-based, sustained critique of the politics of data visualization. In this regard, for example, Ledin and Machin (2016a, 2016b, 2018) are currently building a body of work on how the discourses of performance management and marketized steering are recontextualized into increasingly ubiquitous ‘strategic diagrams’. These are used to translate values like competitiveness and accountability ‘into graphic shapes’ with ‘a clear logic of cause and effect’ (Ledin \& Machin, 2016a, p. 323).

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