### 统计代写|数据可视化代写data visualization代考|Phantasmagrams and affect

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• 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|Phantasmagrams and affect

Sometimes, data visualizations are used to make predictive claims or arguments that can shape our understanding of the world. This can happen in a conceptual manner, as when Quetelet used data visualizations to develop the idea of the average as ideal, or in a more concrete way, as when a data series shows an increase or a decrease and the visualization suggests that this

trend will continue into the future. This predictive use of data visualization is becoming more automated in systems such as those offered by Palantir and other companies for risk prediction. For instance, in predictive policing, police departments have a live map of their district with percentages and colour codes showing places where there is a high risk of certain crimes occurring, based on data analysis of past crimes as well as data such as local weather reports and the school calendar. When data visualizations make claims about the future, they can also affect the future, and we should be wary of how they do so.

Michelle Murphy has used the term phantasmagram to describe the way that zoth-century economic and demographic models became not just descriptions of how the world works, but projections that lived lives of their own. She compares them to the phantasmagoria of the nineteenth century, ‘ghostly simulations made by whirling magic lanterns that stimulated fright and awe’ (Murphy, 2017, p. 53). She argues that demographic models are phantasmagrams, models that created new ways of seeing the world:
Through the work of Keynes and other similarly minded macroeconomists, the national economy was explicated as a new aggregate kind, a collective blur of activity that nonetheless could be modeled as a set of predictable correlations, tendencies, forces, and rates representable in equations and graphs. When interest rates go up, investment goes down, employment drops, output falls. With equations and diagrams, mathematical modelling in the 1930 performatively discerned ‘the economy’ as a constellation of such interrelationships within a closed system whose boundary was the nation-state. (Murphy, 2017, p. 18)

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

This chapter is an overview of social semiotics as a productive framework for research on data visualization. It provides conceptual instruments that can be used to explore the relationship between the formal properties of data visualization and the meanings and practices that these may promote or hinder among users. In particular, the chapter argues that a social semiotic framework can be used to inventorize, situate, and transform visualization resources. Overall, it links descriptive, interpretive, and critical objectives to generate a framework aimed at understanding how data visualization ‘works’ from a formal standpoint, what meanings are consistently associated with particular semiotic resources, and how both key semiotic ‘rules’ and dominant meanings may be questioned and changed.

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

This chapter is a focused critical overview of social semiotics as a productive framework for research on data visualization. It aims to provide conceptual instruments that can be used to explore the relationship between the formal properties of data visualization and the kinds of responses, engagements, and practices that these may promote or hinder among users. Over the last decade or so, and in the wake of digitalization and datafication, data visualization has emerged rapidly as what Engebretsen and Weber (2017) have defined as a ‘super-genre’ that is used to accomplish a wide variety of communicative tasks across an increasing number of professional and

institutional communities of users. Beer and Burrows $(2013)$ highlight that, as a whole, we have witnessed the rise of a ‘visualization of culture’ and a ‘culture of visualization’ across spheres of social activity and cultural production, as ‘there are not many things that have yet to be visualized and archived’ (p. 62). Different kinds of data visualization have become privileged signs to mark the rationality of particular processes and promote specific attitudes towards various aspects of everyday life, ranging from policymaking to personal productivity. As Ledin and Machin (2018) point out, often diagrams, charts, and other types of visualization are used not only to illustrate how things are but also, above all, ‘how things should be done’ (p. 335).

Precisely because of the increasing social significance of this phenomenon, there is a growing body of academic literature centred on critical, practical, and combined approaches to the formal and overall aesthetic qualities of data visualization. Generally speaking, these approaches offer very useful insights to examine data visualization design from an ideological, professional, or praxis-based standpoint. On the one hand, it has become increasingly urgent to examine what Kennedy and Hill (2017) define as the ‘visual sensibilities’ (p. 2) that are at work in the ways in which ordinary people respond culturally and engage emotionally with data and their visualizations. On the other hand, professional and institutional uses of data visualization techniques must be examined in the light of their underlying histories, conventions, and changes over time and across contexts. For these reasons, a detailed appraisal of data visualization’s main semiotic resources, or its tools for meaning-making, is key to empirical research in this field. Unlike other currently more widespread approaches to data visualization research rooted in cultural and social theory, a social semiotic approach focuses keenly on the formal properties of visualizations together with their semiotic and social affordances.

## 统计代写|数据可视化代写data visualization代考|Phantasmagrams and affect

Michelle Murphy 使用 phantasmagram 一词来描述 zoth 世纪的经济和人口模型不仅仅是对世界如何运作的描述，而是对他们自己生活的预测。她将它们与 19 世纪的幻影相提并论，“通过旋转的魔法灯笼制造出令人恐惧和敬畏的幽灵模拟”（墨菲，2017 年，第 53 页）。她认为人口模型是幻象，创造了看待世界的新方式的模型：

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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。