《人人会建的大数据可视化大屏》重点阐述数据可视化的重要性、基本概念、主要应用和意义，通过需求梳理、数据准备、屏幕测算、合理布局、图表颜色等方面的设计，呈现数据分析的关键指标，增强数据的可读性。同时，以海致科技集团自主研发的伏羲数据可视化平台应用为例，通俗易懂地介绍伏羲数据可视化大屏设计思路、组件功能和制作方法，举例说明公安、金融、交通、能源、司法等领域的数据大屏应用。

《人人会建的大数据可视化大屏》适于各行各业大数据可视化工程师、数据分析人员、大数据爱好者、人工智能从业人员、计算机软件应用专业的在读大学生等参阅。

从17世纪使用的地图和图形，到19世纪初饼图的发明，再到人们利用计算机创建出首批图形图表，使用图片来理解数据的概念已经存在了数个世纪。数据可视化的发展历程可归纳为两个阶段：第一个阶段是科学可视化，第二个阶段是信息可视化。如今，数据可视化已成为一门快速发展的、融合科学和艺术的技术，在智慧城市、社会治理现代化、企业经营决策等方面得到全面应用。

数据可视化是大数据分析的一种应用形态，主要包含数据采集、数据分析、数据治理、数据管理、数据挖掘和数据图表展示等内容。数据可视化指将原始数据转换为图表、图形、图像等，一是可以轻松发现趋势，更快地识别异常值，从而帮助了解业务的表现形态、发展机遇和潜在风险；二是可以使用系统内置的组件进行关联交互，发现更深层次的内容，在数据驱动的基础上快速做出决策；三是可以迅速吸引眼球，突破大脑记忆能力的限制，从而更快发现数据的变化规律、理清思路。

本书重点阐述数据可视化的重要性、基本概念、主要应用和意义，通过需求梳理、数据准备、屏幕测算、合理布局、图表颜色等方面的设计，呈现数据分析的关键指标，增强数据的可读性。同时，本书以海致科技集团自主研发的伏羲数据可视化平台应用为例，通俗易懂地介绍了伏羲数据可视化大屏设计思路、组件功能和制作方法，并举例说明了公安、金融、交通、能源、司法等领域的数据大屏应用。

随着数字中国和行业数字化的深入推进，数据可视化技术的应用将更为广泛。读懂数据、可视化数据是数字时代的刚需。本书希望能为各行各业大数据可视化工程师、数据分析人员、大数据爱好者、人工智能从业人员、计算机软件应用专业的在读大学生等提供一定的帮助。

尽管作者已尽力撰写，但难免存在不足之处，敬请广大读者赐教。

编　者

第1章　数据可视化概述· ··················1

1.1　数据可视化························1

1.1.1　什么是数据可视化··········1

1.1.2　基本概念······················1

1.1.3　主要应用······················1

1.1.4　发展阶段······················2

1.1.5　相关分析······················2

1.1.6　数据可视化的意义··········3

1.2　数据可视化要素·················4

1.2.1　需求准确······················4

1.2.2　数据准确······················5

1.2.3　屏幕准确······················5

1.2.4　布局合理······················5

1.2.5　图表合理······················5

1.2.6　颜色合理······················5

1.2.7　长度合理······················6

1.2.8　可读性强······················6

1.3　数据可视化展现形式··········6

1.3.1　数据大屏······················6

1.3.2　数据看板/仪表盘············7

1.3.3　数据报表······················7

1.4　 伏羲数据可视化平台技术架构···································9

1.4.1　伏羲数据可视化平台产品架构······················9

1.4.2　伏羲数据可视化平台产品优势····················10

1.4.3　伏羲数据可视化平台登录方式····················10

1.4.4　伏羲数据可视化平台主要功能····················11

1.5　 常见数据可视化应用场景举例··································13

1.5.1　公安场景举例··············14

1.5.2　政务场景举例··············14

1.5.3　交通场景举例··············15

1.5.4　金融场景举例··············15

第2章　可视化方案设计流程·········16

2.1　确认需求···························16

2.1.1　明确业务场景··············16

2.1.2　梳理关键指标··············16

2.1.3　确定分析维度··············16

2.1.4　收集整理数据··············17

2.1.5　选定图表类型··············17

2.1.6　了解输出设备··············19

2.2　大屏主题···························19

2.2.1　大屏主题风格··············19

2.2.2　大屏标题····················19

2.3　样式设计···························20

2.3.1　尺寸确定····················20

2.3.2　布局方案· ····················20

2.3.3　边框设计····················24

2.3.4　字体设计····················24

2.4　数据处理···························24

2.4.1　数据处理原则··············24

2.4.2　数据处理方法··············25

2.5　图表制作···························25

2.5.1　图表制作方法··············25

2.5.2　注意事项····················25

2.6　视觉美化···························26

2.6.1　配色规范····················26

2.6.2　文字规范····················26

2.6.3　背景图片····················27

2.6.4　图表边框····················27

2.6.5　UI动效·······················27

2.7　应用发布···························27

2.7.1　大屏预览····················27

2.7.2　大屏发布共享··············28

2.7.3　终端交互····················29

第3章　数据准备及指标计算·········31

3.1　数据准备···························31

3.1.1　梳理所需数据··············31

3.1.2　检查数据质量··············31

3.1.3　做好数据分类··············32

3.2　数据处理···························32

3.2.1　DMC/万象模型处理······32

3.2.2　Excel处理···················32

3.2.3　其他免费数据处理工具··························32

3.3　数据对接···························32

3.3.1　DMC数据库················33

3.3.2　API数据·····················33

3.3.3　Excel、CSV、JSON文件··························34

3.3.4　MySQL、PostgreSQL、Oracle、MongoDB、达梦数据库·················35

3.4　指标计算···························35

3.4.1　数据指标计算方法········35

3.4.2　不同场景常见指标········37

3.4.3　数据指标计算案例········38

第4章　数据大屏基础组件应用·····39

4.1　 数据大屏创建及操作界面概述··································39

4.1.1　大屏创建····················39

4.1.2　常用的工具·················41

4.1.3　编辑页概述·················45

4.1.4　页面设置····················47

4.1.5　要素设计····················48

4.2　图表组件应用技巧·············48

4.2.1　柱形图应用·················48

4.2.2　散点图应用·················70

4.2.3　多类型图表应用···········71

4.2.4　其他常见图表应用········71

4.3　布局组件应用技巧·············72

4.3.1　3D背景组件应用··········72

4.3.2　图文标题组件应用········76

4.3.3　其他布局组件应用········80

4.4　数据组件应用技巧·············80

4.4.1　文本组件应用··············80

4.4.2　数据集组件应用···········91

4.4.3　指标卡组件应用···········96

4.5　可视化大屏管理··············101

4.5.1　图层管理···················101

4.5.2　组件管理···················103

4.5.3　大屏管理···················104

4.5.4　分组管理···················105

第5章　 数据可视化基础大屏实操案例· ························107

5.1　大屏设计原则··················107

5.1.1　信息结构···················107

5.1.2　交互原则···················107

5.1.3　可视化表达················107

5.1.4　大屏规范提取/设计······108

5.2　从0到1制作数据大屏·······109

5.2.1　数据大屏需求梳理·······109

5.2.2　数据大屏主题确定·······109

5.2.3　数据大屏样式设计·······109

5.2.4　数据大屏数据处理·······110

5.2.5　制作大屏···················111

5.2.6　数据大屏应用发布·······111

第6章　数据大屏复杂组件应用···112

6.1　媒体组件应用技巧···········112

6.1.1　图片组件应用·············112

6.1.2　视频组件应用·············115

6.1.3　音频组件应用·············117

6.1.4　直播组件应用·············119

6.2　地图组件应用技巧···········121

6.3　关系组件应用技巧···········126

6.3.1　流程图组件应用··········126

6.3.2　关系图组件应用··········129

6.3.3　树图组件应用·············132

6.4　装饰组件应用技巧···········135

第7章　 数据可视化图表交互应用·······························140

7.1　表单交互应用技巧···········140

7.2　导航交互应用技巧···········144

7.3　容器交互应用技巧···········153

7.3.1　弹窗面板组件应用·······153

7.3.2　动态面板组件应用·······155

7.3.3　引用面板组件应用·······157

7.3.4　轮播切换面板组件应用·························160

7.3.5　轮播选中面板组件应用·························164

7.3.6　Iframe面板组件应用·····165

7.3.7　数据容器组件应用·······166

7.4　动画交互应用技巧···········168

7.4.1　绘制动画应用·············168

7.4.2　动画效果应用·············169

7.4.3　转场动画应用·············169

7.5　语音识别交互应用技巧····170

第8章　 数据可视化复杂大屏实操案例························174

8.1　图表交互设置··················174

8.1.1　设置联动···················174

8.1.2　设置自定义事件··········175

8.2　组件组合交互设置···········175

8.2.1　导航组件与图表交互····175

8.2.2　表单组件与图表交互····177

8.3　动画交互设置··················178

附录··············································180

"适用于智慧城市、数字政府各应用场景中的数据可视化需求

由浅入深地教会你玩转数据可视化，丰富地呈现数据的价值

实用 实战  实效

 "