### 统计代写|数据可视化代写data visualization代考|Ways of knowing with data visualizations

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|Introduction

Data visualizations combine at least two modes of representation: numerical data and visual diagrams. For a computer program to be able to process data, it has to be converted to numbers, to the zeros and ones of machine code. In addition, the data need to be visually organized, which often requires dividing them into discrete quantities where lines, size, spatial placement, and other visual elements show certain patterns in the data. Each of these two modes of expression, the numeric and the visual, carries its own affordances and constraints for what they can express.

This anthology has several chapters that use concrete examples to discuss how data visualizations can be biased in their representations of data (Ricker, Kraak, \& Engelhardt, this volume; D’Ignazio \& Bhargava, this volume) or how data visualizations can work against the typical abstraction they entail to include individuals’ stories (Alamalhodaei, Alberda, \& Feigenbaum, this volume). My emphasis in this chapter is on examining the underlying mechanisms of data visualizations as an assemblage of data and visualizations. My exploration sits alongside existing critical work on data visualizations in feminist scholarship (D’Ignazio \& Klein, 2016; Hill, Kennedy, \& Gerrard, 2016), in the digital humanities (Drucker, 2011, 2014; Gitelman, 2013), and in critical algorithm studies and other scholarship on the epistemological basis for algorithmic processing of big data (Eubanks, 2018; Gillespie \& Seaver, 2015; Noble, 2018).

## 统计代写|数据可视化代写data visualization代考|Visual organization

Organizing objects visually and spatially is something humans and our ancestors have done for a long time. In her essay ‘Visualizing Thought’, Barbara Tversky describes how hominins living three-quarters of a million years ago organized their tools and belongings in different areas of their home. She argues that this is the basic precursor to any kind of visualization: ‘Perhaps the simplest way to use space to communicate is to arrange or rearrange things in it. An early process is grouping things in space using proximity, putting similar things in close proximity and farther from dissimilar things’ (Tversky, 2010, p. 504). We might extend Tversky’s line of reasoning to the modern domestic habit of keeping forks in one partition of a kitchen drawer and knives in another, and argue that this is a way of visually and spatially communicating information about the forks and knives.

The data visualizations we see on computer screens or printed pages, or even early markings on stones or in the sand, are one step removed from the phenomena they represent or organize. If we walk into somebody’s kitchen and open a drawer, we see the knives and forks in the kitchen drawer, but we also experience them in space, and we can touch them and pick them up. Now, imagine a data visualization about kitchen utensils on a screen. It could be very simple, showing the number of knives and forks and other utensils in a kitchen, perhaps organized as a bar chart, perhaps using little pictures of forks stacked up in one bar and knives in another to show the relative quantities. Or imagine a photograph of the kitchen drawer, or an Instagram-style flat lay photograph of all the knives and forks neatly laid out on a table and photographed from above.

Once the knives and forks are transferred from spatially organized objects to a visual representation on a two-dimensional surface, our distance from them increases. We interpret them as separate from us. A photograph of the drawer might not encourage a great deal of analytical dissection of the image, but the neatly organized flat lay photograph and the bar graph prioritize an analytic approach to that which is represented.

In his influential book about the transition from oral to literate cultures, Walter Ong (1982) argues that a fundamental difference between orality and literacy is that the visual nature of writing leads to ideas of objectivity that are impossible in oral culture. When we speak to each other in a face-to-face conversation, we are immersed in the sound, and because the speakers are in the same physical space, face-to-face oral discourse tends to be situated and concrete. Writing, on the other hand, separates the knower from the known. There is a distance between reader and writer. ‘Sight isolates’, Ong writes, while ‘sound incorporates. Whereas sight situates the observer outside what he views, at a distance, sound pours into the hearer’ (1982, p. 45). A typical visual ideal is clarity and distinctness, a taking apart, Ong argues, whereas the auditory ideal, by contrast, is harmony, a putting together (p. 71). He writes: ‘A sound-dominated verbal economy is consonant with aggregative (harmonizing) tendencies rather than with analytic, dissecting tendencies (which would come with the inscribed, visualized word: vision is a dissecting sense)’ (p. 73).

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

Importantly, not only the visual, but also the data themselves share much of this promise of analytical objectivity. Data visualization had a golden age in the nineteenth century, at the same time as nation states began largescale collection of statistical data (Friendly, 20o6). However, it began a few centuries earlier, at the same time as the scientific method was developing, and with it the idea that humans could precisely observe the world and use those observations to understand it. Seventeenth- and eighteenth-century Europe saw an increasing trend towards observation, measurement, and quantification, and different fields developed new ways of measuring and quantifying things that had not previously been seen as interesting. Some of these methods were technological. For instance the invention of the telescope allowed Galileo to make observations about the solar system that would not previously have been possible. In our time, the existence of precise sensors and of computers that can process massive amounts of data allows for certain types of measurement, analysis, and visualization that were not possible a few decades ago.

Social and organizational changes also led to new kinds of quantification. National registries became common during the nineteenth century, for instance, allowing for analysis of trends over time or the comparison of different regions. For example, the first centralized national system of crime reporting was instituted in France in 1825 , and collected information about all charges made in French courts on a quarterly basis (Friendly, 2006, p. 25). More and more information was collected, and by the end of the nineteenth century the French police not only had detailed statistics about crimes, but also systems for documenting and identifying criminals and suspects using a system of ‘anthropometrics’, devised by Alphonse Bertillon and involving very specific measurements of body parts (Kember, 2014). Once one has such a system, once it is possible to gather data that appears to give us knowledge, we end up with what Helen Kennedy calls a ‘desire for numbers’ that can lead to a lack of critical reflection about what those numbers mean and whether we truly need them (2016, p. $5^{1}$ ).

This sense that systematized data have authority is an important aspect of the rhetorical power of data visualizations. While Ong and Tversky emphasized the visual as allowing for an analytical and perhaps objective stance, many have argued that it is the data themselves, or the quantitative nature of data visualizations, that lend them this sense of authority.

## 统计代写|数据可视化代写data visualization代考|Visual organization

Walter Ong (1982) 在他关于从口语文化到文字文化的转变的有影响力的著作中认为，口语和识字之间的根本区别在于，写作的视觉本质导致了在口语文化中不可能实现的客观性观念。当我们面对面交谈时，我们沉浸在声音中，由于说话者处于同一物理空间，面对面的口语话语往往是情境化的和具体的。另一方面，写作将知道者与已知者分开。读者和作者之间有距离。Ong 写道，“视觉是孤立的”，而“声音是融合的”。视觉将观察者置于其所见之外，而在远处，声音则涌入听者”（1982，第 45 页）。Ong 认为，一个典型的视觉理想是清晰和清晰，一种拆开，相比之下，听觉的理想是和谐，是一种组合（第 71 页）。他写道：“以声音为主导的语言经济与聚合（协调）倾向相一致，而不是与分析、剖析倾向相一致（这将伴随着铭刻的、形象化的词：视觉是一种剖析意义）”（第 73 页）。

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## MATLAB代写

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