数据可视化英文PPT概要介绍

1、 By 朱兴宇 &蔡慧敏 Visualization, as a discipline in computer science, is a rather young field of study. The field has made many advances over the past 25 years through tremendous basic and application-driven research efforts, and also successfully transferred some of these advances into products and serv

2、ices for data-intensive applications. Visualization as a problem-solving and knowledge discovery tool has become even more important as we enter the Big Data area. Its applications grow from scientific computing, engineering design, biomedicine, cyber security, and intelligence, to social science, t

3、ransportation studies, and commerce. Visualization will be considered a basic skill, and will likely become part of the standard curriculum in science and engineering. The goal of visualization is to aid our understanding of data by leveraginglev()rd the human visual systems highly-tuned ability to

4、see patterns, spot trends, and identify outliers. Well-designed visual representations can replace cognitive calculations with simple perceptual inferencesnfrns and improve comprehension, memory, and decision making. By making data more accessible and appealing, visual representations may also help

5、engage more diverse audiences in exploration and analysis. The challenge is to create effective and engaging visualizations that are appropriate to the data. Now , we provide a brief tour through the visualization zoo Here we go !The most famous, early example mapping epidemiologicalp,dimldkl data w

6、as Dr. John Snows map of deaths from a choleraklr outbreak in London, 1854, in relation to the locations of public water pumps. Snow observed that cholera occurred almost entirely among those who lived near and drank from the Broad Street water pump. He had the handle of the contaminatedkntmnetd pum

7、p removed, ending the neighborhood epidemic which had taken more than 500 lives.The French engineer, Charles Minard (1781-1870), illustrated the disastrousdzstrs result of Napoleons npulin failed Russian campaign of 1812. The graph shows the size of the army by the width of the band across the map o

8、f the campaign on its outward and return legs, with temperature on the retreat shown on the line graph at the bottom. Many consider Minards original the best statistical graphic ever drawn.The Graphics Department of the NY Times, including Amanda Cox, Shan Carter and many others, has recently create

9、d a number of visualizations of complex phenomena that are at once stunningly beautiful and effective in communicating the essential ideas to a mass audience. Web-based versions now allow them to use dynamic and interactive graphics to go beyond what can be shown in static print versions.This image,

10、 from May 3, 2008, shows the changes in prices from March, 2007 to March, 2007 of various components of an average consumers spending. The circular diagram is broken up into 8 main sectors, whose area is proportional to the percent ofspending, and each of these is subdivided in proportion to sub-cat

11、egories. A bipolar color scale is used to shade each region, using shades toward redish-brown for increases and shades toward blue for decreases.The interactive version of this diagram uses tool-tip boxes to show the details and allows zooming in on components whose labels cannot be shown in a stati

12、c graph. The basic graphic form is an adaptation of a pie chart to a hierarchical data structure, based on the idea of a Voronoi tree-map by Michael Balzer and others at the University of Konstanz.We have now arrived at the end of our tour, and hope that the reader has found examples both intriguing

13、 ntri and practical. Though we have visited a number of visual encoding and interaction techniques, many more species of visualization exist in the wild, and others await discovery. Emerging domains such as bioinformatics and text visualization are driving researchers and designers to continually formulate new, creative representations or find more powerful ways to apply the classics. In either case, the DNA underlying all visualizations remains the same: the principled mapping of data variables to visual fe