### 统计代写|数据可视化代写data visualization代考|The relationships between graphs, charts, maps and meanings, feelings, engagements

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|Helen Kennedy and Martin Engebretsen

Today we are witnessing an increased use of data visualization in a range of domains and genres. In journalism, education, and public information as well as in workplaces, diverse forms of graphs, charts, and maps are used to explain, persuade, and tell stories. At best, visual representations of statistics and other, often quantitative data can convey complex facts and patterns quickly and effectively. At worst, they can appear confusing or manipulative. In an era in which more and more data are produced and circulated through online networks, and digital tools make visualization production increasingly accessible, it is important to study the conditions under which such visual texts are generated, disseminated and thought to benefit processes of sense-making, learning, and engaging.

Data visualization is not new. The graphical representation of numeric information has roots in early map-making, and grew in importance with the widespread use of data and statistics for planning and commerce in the nineteenth century (Friendly, 2008). Still, in our contemporary society, several factors contribute to give data visualization a social relevance on a scale we have not seen before. One of these factors, as Kennedy, Hill, Aiello, and Allen (2016b, p. 715 ) put it, is that ‘ [… data are becoming increasingly valued and relied upon, as they come to play an ever more important role in decision-making and knowledge about the world’.

In other words, more data are generated, gathered, stored, and made accessible than ever before. Data gathering takes place in many domains, often by law, including commerce, education, health, transport, and cultural and social life. These data offer insights into societal patterns otherwise invisible and unnoticed. Such documentation has been conducted for decades, but technological and other developments have led to its sharp

increase, and data are now being gathered in huge volumes as a result of new techniques of measurement. These combined phenomena, sometimes called ‘datafication’ (Mayer-Schönberger \& Cukier, 2013, p. 78) are understood as a transformation disrupting the social world in all its forms (Couldry, 2016).
Furthermore, to make data accessible to publics, rather than remaining a useful source only for experts and decision-makers, a range of actors have campaigned to open up public data, to make them reusable for a variety of activities and democratic purposes. Open data initiatives and related campaigning activities contribute to accelerate the spread of data visualization, which often serve as a main entry point to data for non-experts.

## 统计代写|数据可视化代写data visualization代考|What do we mean by data and data visualization

In a scientific context, data are generally understood to result from the generation, collection, observation, or registration of objects, events, or processes suitable to serve some analytical purpose. Similarly, in the context of data visualization, data can be anything that can be subjected to categorization, abstraction, and translation into graphical representation: persons, places, documents, relations, sentences, salaries, to mention some examples. A main distinction is between qualitative data and quantitative data. While qualitative data are valued for the uniqueness of each individual unit, be it a poem, a sentence, or an interview, quantitative data are valued for characteristics shared by all or many units in a dataset. It is their shared characteristics that make them objects for counting or measuring, and thus for numeric representation and statistical processing.

Both qualitative and quantitative data can be visualized. It is possible to visualize semantic structures in a novel, or networks of relationships between the works in an art collection, as seen, for example, in the work of Stefanie Posavec (http://stefanieposavec.com/). Most, but not all, of the contributions in this book focus on the visualization of quantitative data, for the reasons given above – that is, because their proliferation and increasing openness, and the enhanced availability of related tools, make them a socially and culturally significant phenomenon.

Numeric data can be structured or unstructured. Structured data have been subjected to statistical treatment and are typically represented as numbers in a table, with columns and rows presenting units and variables and numeric values positioned in cells. A common example is the datasets accessible from national statistics institutes (NSIs) which are often presented

to the public in tabular form. Unstructured data have not been subjected to any statistical or structuring processing, and appear as ‘raw’ data in an analogue or digital register, until the data are structured by someone with an intention to use them for some specific purpose. An automatic registration of cars driving through a tollbooth is one example.
‘Big data’ is a fashionable concept, although its use is rarely accompanied by a shared understanding of what it means or how it differs from ‘small’ data. Big data have been said to be characterized by three Vs: volume, variety, and velocity. More recently, additional Vs have been proposed, such as variability and value (http://whatis.techtarget.com/definition $/ 3 \mathrm{Vs}$; see also Kitchin, 2014 for additions which don’t begin with V). When we talk about datasets consisting of thousands of rows of data, or new streams of data created every second, we are talking about big data. Data harvested from a social media platform, or from the activities on the finance market, are some examples. But exactly when data become big is hard to define.

## 统计代写|数据可视化代写data visualization代考|How can dataviz produce meanings, feelings, and engagements

In this book, we relate the social power of data visualizations to their abilities to produce meanings, feelings, and engagements in their users and audiences. Processes of socially situated meaning-making are best described in the field of social semiotics, first developed by the Australian linguist Michael Halliday $(1978)$, later adapted to visual and multimodal artefacts by Gunther Kress and Theo van Leeuwen $(1996)$ and others. In social semiotic theory, the meaning of semiotic material (which can include words, images, colours, and more) can be traced in three different dimensions, each relating to an aspect of the situation of communication. These are:

1. The field (or topic) of discourse. How does the semiotic material represent the world or ideas about the world? This is known as ideational meaning.
2. The participants involved in the process of communication. How does the semiotic material reflect, establish, or change the social relations between the participants? This is known as interpersonal meaning.
3. The semiotic resources activated in the process. How do all the elements of the semiotic material unite in a textual whole? This is known as compositional meaning.

In many situations, the semiotic material in question will be identified as ‘a text’, such as a multimodal webpage with words, images, and colours organized in a specific user interface. In other contexts, meaning is made through semiotic resources not conventionally identified as texts, such as buildings, clothes, and sculptures. Such artefacts nonetheless carry meaning based on certain culturally and historically formed conventions. The artefacts that this book is concerned with, data visualizations, will normally be produced, distributed, and used in ways comparable to other multimodal and mediated text types.

Semiotic interpretation and aesthetic experience (that is, our sensory impressions, as well as judgements based on taste) go hand in hand in our encounters with texts and other cultural artefacts, and where one stops and the other begins is hard to identify. Our encounters with form, colour, and composition are informed by bodily experience as well as aesthetic judgement, and so the aesthetic (as well as the semiotic) aspects of data visualization need to be taken into account. Also relevant to a discussion of meaning in data visualization is the issue of ‘knowledge regimes’, or epistemology. What aspects of reality are privileged in a semiotic text based on visualized, numeric data? What kinds of truth are foregrounded, and what knowledge, values, and attitudes result? Data visualizations may seem to reflect reality in a more direct way than words because they are based on numbers, which seem trustworthy (Porter, 1995). But this does not mean that they are more true, in the sense that they offer a more objective representation of the world. This issue informs several contributions to this book.

## 统计代写|数据可视化代写data visualization代考|What do we mean by data and data visualization

“大数据”是一个时髦的概念，尽管它的使用很少伴随着对其含义或它与“小”数据有何不同的共同理解。据说大数据具有三个 V 的特征：数量、多样性和速度。最近，已经提出了额外的 V，例如可变性和价值 (http://whatis.techtarget.com/definition/3在s; 另请参阅 Kitchin，2014 以了解不以 V 开头的添加）。当我们谈论由数千行数据组成的数据集或每秒创建的新数据流时，我们谈论的是大数据。从社交媒体平台或金融市场活动中收集的数据就是一些例子。但是，很难准确定义数据何时变大。

## 统计代写|数据可视化代写data visualization代考|How can dataviz produce meanings, feelings, and engagements

1. 话语的领域（或主题）。符号学材料如何代表世界或关于世界的观念？这被称为观念意义。
2. 参与沟通过程的参与者。符号学材料如何反映、建立或改变参与者之间的社会关系？这被称为人际意义。
3. 在这个过程中被激活的符号资源。符号材料的所有元素如何结合成一个文本整体？这被称为组合意义。

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。