Data Visualization (2nd Edition)

Name: Data Visualization (2nd Edition)
ISBN: 9787121357275

陈为Publishing House of Electronics Industry · ChinaDec 2020

4.3

Word count: 469K words
Chapters: 298
ISBN: 9787121357275

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Summary

The book consists of 15 chapters in four parts. The Fundamentals part explains the basic theories and concepts of data visualization, introducing data models and visualization foundations from the perspective of human perception and cognition. The Spatiotemporal Data part presents visualization methods for data with spatial coordinates or temporal information, such data being collected by devices in real physical space or generated by scientific computing simulations. The Non-spatiotemporal Data part describes the visualization of unstructured and nongeometric abstract data, which exist in physical space and are also basic expressions of social space and cyber-information space. The User part introduces methods, techniques, and tools commonly needed in practical applications of visualization for various types of data, such as interaction and visualization evaluation methods, as well as visualization and application systems in specific domains. From a researcher's perspective, the book introduces the definition, methods, utility, and tools of data visualization. It can serve both as a guidebook for beginners and as a reference for visualization research and the use of visualization tools.

Editor's review

Data Visualization (2nd Edition) is a professional textbook published by Publishing House of Electronics Industry in 2020, led by Professor Chen Wei of Zhejiang University. As a systematic introduction and advanced guide to data visualization, its value lies in the deep integration of academic frontiers with engineering practice. Compared with the first edition, the second edition has been fully updated to keep pace with the latest visualization research of the past five years, adding cutting-edge chapters on immersive visualization and the combination of visualization and artificial intelligence, reflecting the field's shift from static charts to interactive, intelligent development.

In terms of structure, the book is clearly organized into seven major sections, including basic theory, spatiotemporal data, geographic information and high-dimensional data. It covers classic theories such as visual encoding and perception principles, and also explains in depth visualization methods for complex data such as network relationships and textual information. Professor Chen Wei and his team paid special attention to practicality: the book contains many real cases from research and industry, with abundant illustrations and code examples (some chapters provide Python implementations), so that readers can not only understand why a design works but also master how to implement it.

For learners, the book's outstanding advantage is its balance: it avoids the abstraction and dryness of purely theoretical books while going beyond the superficial limits of tool manuals. After systematic study, readers can build a complete knowledge framework from data characteristic analysis and visual mapping design to interaction logic construction. This framework thinking is especially important for professionals in data analysis, product design, business intelligence and other fields, helping them fundamentally improve their ability to mine data value and convey complex information through visualization.

Overall, this is an excellent work that is rigorous yet thought-provoking, complete in system and keeping pace with the times. It is not only an ideal textbook for related university majors but also an important reference for industry practitioners to consolidate their foundations and broaden their horizons.

Who it's for

A good fit for

Senior undergraduates or graduate students in computer science, statistics, design and related majors who need to systematically learn data visualization theories and methods
Engineers engaged in data analysis, business intelligence, interaction design or front-end development who wish to deepen their understanding of visualization principles to improve their work
Researchers in research institutions, especially those who need to process complex data and seek effective ways to present it
Self-learners and enthusiasts with a strong interest in data storytelling and information graphics who have some programming or mathematical background

Not a good fit for

Pure tool users who only want to quickly learn the basic operations of a specific charting tool (such as Excel or Tableau) without understanding the underlying principles
Readers with no programming background or an aversion to mathematical logic who are only looking for light popular-science reading

Reading tips

Read the chapters in order, and pay special attention to the basic theory in the first four chapters, which are the foundation for understanding the later application chapters
While reading, use the book's cases and try to reproduce or adapt the examples with Python or other tools mentioned in the book; practice greatly deepens understanding
Follow the latest research developments of Professor Chen Wei's team or related academic websites, and cross-check the book's classic theories against frontier industry progress

About the publisher

Publishing House of Electronics Industry China · Founded in 1982

The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.

版权信息
作者简介
内容简介
Foreword
序言
再版序
第1版前言
基础篇
第1章数据可视化简介
1.1 可视化释义
1.2 可视化简史
1.3 数据可视化详解
1.3.1 数据科学的发展
1.3.2 数据可视化的意义
1.3.3 数据可视化分类
1.3.4 数据可视化与其他学科领域的关系
1.4 数据可视化研究挑战
参考文献
第2章视觉感知与认知
2.1 视觉感知和认知
2.1.1 视觉感知和认知的定义
2.1.2 视觉感知处理过程
2.1.3 格式塔理论
2.1.4 相关实验
2.2 颜色
2.2.1 颜色刺激理论
2.2.2 色彩空间
2.3 视觉编码原则
2.3.1 相对判断和视觉假象
2.3.2 标记和视觉通道
2.3.3 视觉通道的概念
2.3.4 视觉通道的特性
参考文献
第3章数据
3.1 总览
3.2 数据基础
3.2.1 数据分类
3.2.2 数据集
3.2.3 数据相似度与密度
3.3 数据获取、清洗和预处理
3.3.1 数据获取
3.3.2 数据清洗
3.3.3 数据精简
3.3.4 其他常用的数据预处理步骤
3.4 数据组织与管理
3.4.1 数据整合与集成
3.4.2 数据库与数据仓库
3.5 数据分析与挖掘
3.5.1 探索式数据分析
3.5.2 联机分析处理
3.5.3 数据挖掘
3.6 数据科学与可视化
3.6.1 数据工作流
3.6.2 可视数据挖掘
3.7 数据科学的挑战
参考文献
第4章数据可视化基础
4.1 数据可视化基本框架
4.1.1 数据可视化流程
4.1.2 数据可视化设计
4.2 可视化中的数据
4.2.1 数据认知
4.2.2 数据类型
4.3 可视化的基本图表
4.3.1 原始数据绘图
4.3.2 简单统计值标绘
4.3.3 多视图协调关联
4.4 可视化设计原则
4.4.1 数据到可视化的直观映射
4.4.2 视图选择与交互设计
4.4.3 信息密度——数据的筛选
4.4.4 美学因素
4.4.5 动画与过渡
4.4.6 可视化隐喻
4.4.7 颜色与透明度
4.5 可视化理论发展
4.5.1 图形符号学
4.5.2 关系数据的图形表示
4.5.3 图形语法
4.5.4 基于数据类型的研究
4.5.5 基于数据状态模型的研究
4.5.6 多维关系数据库可视化分析系n统
参考文献
时空数据篇
第5章空间标量场可视化
5.1 一维标量场可视化
5.2 二维标量场可视化
5.2.1 颜色映射
5.2.2 等值线
5.2.3 高度图
5.3 三维标量场数据可视化
5.3.1 空间数据表达
5.3.2 空间数据特征计算
5.3.3 间接体绘制
5.3.4 规则三维标量场的直接体可视化
5.3.5 不规则体数据的体可视化
参考文献
第6章大规模多变量空间数据场可视化
6.1 大规模空间标量场数据的实时可视化
6.1.1 大规模空间标量场数据的单机绘制
6.1.2 大规模空间标量场数据的并行绘制
6.1.3 时变空间标量场数据加速绘制方法
6.2 时变异构空间数据场的特征追踪与可视化
6.2.1 时变空间标量场数据的特征提取
6.2.2 异构数据的特征融合
6.2.3 时变空间标量场数据的特征追踪
6.3 空间向量场数据可视化
6.3.1 图标法
6.3.2 几何法
6.3.3 纹理法
6.3.4 拓扑法
6.4 空间张量场数据可视化
6.4.1 张量场的数学描述
6.4.2 基于几何的方法
6.4.3 基于纹理的方法
6.4.4 基于拓扑的方法
6.4.5 高阶张量场可视化
6.5 多变量空间数据场可视化
6.5.1 多变量空间数据场的特征表达与关联分析
6.5.2 多变量空间数据场的可视化与交互
参考文献
第7章时变数据可视化
7.1 时间属性的可视化
7.1.1 线性和周期时间可视化
7.1.2 日历时间可视化
7.1.3 分支和多角度时间可视化
7.1.4 时间属性的动态可视化
7.2 多变量时变型数据可视化
7.2.1 基于线表示的可视化
7.2.2 基于图结构的可视化
7.2.3 时间序列数据的可视化交互
7.3 流数据可视化
7.3.1 流数据可视化模型
7.3.2 流数据处理技术
7.3.3 流数据可视化案例
7.3.4 并行流计算框架
参考文献
非时空数据篇
第8章层次和网络数据可视化
8.1 层次数据
8.1.1 层次数据的可视化
8.1.2 节点-链接法
8.1.3 空间填充法
8.1.4 其他方法
8.2 网络数据
8.2.1 网络和图
8.2.2 网络数据可视化
8.2.3 网络数据的地图隐喻可视化
8.2.4 超图及其可视化
8.2.5 动态网络数据可视化
8.2.6 图可视化的视觉效果
8.2.7 图可视化中的交互
8.2.8 网络数据可视化的挑战
参考文献
第9章文本和文档可视化
9.1 文本可视化释义
9.1.1 文本信息的层级
9.1.2 文本可视化的研究内容与任务
9.1.3 文本可视化流程
9.2 文本信息分析基础
9.2.1 分词技术和词干提取
9.2.2 数据模型
9.3 文本内容可视化
9.3.1 基于关键词的文本内容可视化
9.3.2 时序性的文本内容可视化
9.3.3 文本特征的分布模式可视化
9.3.4 文档信息检索可视化
9.3.5 软件可视化
9.4 文本关系可视化
9.4.1 文档相似性可视化
9.4.2 文本内容关联可视化
9.4.3 文档集合关系可视化
9.5 文件情感分析可视化
9.5.1 顾客评价可视化
9.5.2 情感变化可视化
9.5.3 情感差异可视化
9.6 总结
参考文献
第10章跨媒体数据可视化
10.1 图像
10.1.1 图像网格
10.1.2 基于时空采样的图像集可视化
10.1.3 基于相似性的图像集可视化
10.1.4 基于海塞图的社交图像可视化
10.1.5 基于故事线的社交图像可视化
10.2 视频
10.2.1 视频摘要
10.2.2 视频抽象
10.3 声音与音乐
10.3.1 声乐波形可视化
10.3.2 声乐结构的可视化
10.4 超媒体
10.4.1 社交媒体可视化
10.4.2 社交网络可视化
10.5 数字生活可视化
参考文献
第11章复杂高维多元数据的可视化
11.1 高维多元数据
11.1.1 空间映射法
11.1.2 图标法
11.1.3 基于像素图的方法
11.1.4 基于动画的方法
11.2 非结构化与异构数据的可视化
11.2.1 非结构化数据
11.2.2 异构数据
11.3 大尺度数据的可视化
11.3.1 基于并行的大尺度数据高分辨率可视化
11.3.2 大尺度数据的分而治之可视化与分析
11.4 数据不确定性的可视化
11.4.1 不确定性的基本定义
11.4.2 不确定性的来源
11.4.3 不确定性的可视化方法
参考文献
用户篇
第12章可视化中的交互
12.1 交互准则
12.1.1 交互延时
12.1.2 交互成本
12.1.3 交互场景变化
12.2 交互分类
12.2.1 按低阶交互操作分类
12.2.2 按交互操作符与空间分类
12.2.3 按交互任务分类
12.3 交互技术
12.3.1 选择
12.3.2 导航
12.3.3 重配
12.3.4 编码
12.3.5 抽象/具象
12.3.6 过滤
12.3.7 关联
12.3.8 概览+细节
12.3.9 焦点+上下文
12.4 交互与硬件设备
12.4.1 交互环境
12.4.2 交互设备
参考文献
第13章可视化效果评测与用户实验
13.1 评测流程
13.2 评测方法
13.2.1 用户实验（User Studies）
13.2.2 专家评估（Expert Review/Heuristic Evaluation）
13.2.3 案例研究（Case Studies and Use Cases）
13.2.4 指标评估（Metrics）
13.2.5 众包（Crowdsourcing）
13.2.6 标注（Labeling）
13.3 用户实验
13.3.1 确定实验目标
13.3.2 准备实验
13.3.3 进行实验
13.3.4 分析结果并讨论
13.3.5 评测案例分析
13.4 总结
参考文献
第14章面向领域的数据可视化
14.1 高性能科学计算
14.1.1 高性能科学可视化的挑战
14.1.2 重要信息的提取和显示
14.1.3 原位可视化
14.1.4 未来挑战
14.2 生命科学
14.2.1 临床医学影像
14.2.2 其他影像
14.2.3 电生理信号
14.2.4 OMICS组学
14.2.5 深度学习
14.3 其他科学与艺术
14.3.1 气候学与气象中的可视化
14.3.2 面向艺术的表意性可视化
14.4 网络与系统安全的可视化
14.4.1 基于可视变换的虫洞攻击可视化
14.4.2 可信计算的可视化
14.4.3 安全日志数据的可视化
14.4.4 智能电网数据的可视化
14.5 商业智能可视化
14.5.1 商业智能
14.5.2 商业智能中的数据可视化
14.5.3 云端商业智能
14.5.4 未来趋势
14.6 金融数据可视化
14.6.1 金融数据来源
14.6.2 金融数据分析的自动化方法
14.6.3 金融数据可视化方法
14.6.4 金融数据可视分析
参考文献
第15章可视化研究与开发资源
15.1 可视化软件
15.1.1 医学可视化软件
15.1.2 科学可视化软件
15.1.3 信息可视化软件
15.1.4 可视分析软件
15.2 可视化开发工具
15.2.1 应用程序开发工具
15.2.2 Web应用开发工具
15.3 数据分析和数据挖掘软件与开发工具
15.4 可视化数据集资源
15.5 可视化信息资源
15.6 海外可视化研究机构

FAQ

What prior knowledge does this book require?

Ideally, readers should have a foundation in advanced mathematics and linear algebra and some initial knowledge of at least one programming language (such as Python). Some algorithms and implementations in the book involve these. Without a programming background, readers can focus on understanding design principles and visual encoding theory.

What is the main difference between this book and other data visualization books on the market?

This book focuses more on the systematic knowledge system and core principles of visualization. It is more academic, and its depth and breadth exceed many practical manuals that focus on a single tool. It aims to develop readers' ability to design visualization solutions for any data and problem.

Are the code examples in the book complete? Are they suitable for direct use in projects?

Most code in the book consists of examples of key algorithms or processes intended to illustrate principles. They are highly valuable for reference but are usually not ready-to-use complete project code. Readers need to adapt and extend them according to their own data and environment.

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About the author

陈为 Author

Chen Wei is an author about whom limited public information is available. His representative work is Data Visualization (2nd Edition), which is classified under programming and design in the library. Judging from the subject matter, Chen Wei focuses on data visualization, an interdisciplinary field concerned with turning abstract data into intuitive graphics and conveying information effectively through charts, color, and layout. Writing of this kind usually combines theory with hands-on tools, serving both readers who need to handle data and practitioners who want to improve their visual expression. Because little public information can be verified, details of his personal background, education, and positions cannot be confirmed and are not speculated upon here. Based on the available work, Chen Wei's writing emphasizes methodological organization and practical application, centering on the clarity and readability of data presentation, and is suitable as reference reading for related courses or self-study.