MACDWSA–Data visualisations–Excellent and failure
Today the world composed of information construction, the design of information has become urgent problems designers. Information visualization around “readable logic available” the purpose of passing information, will be boring tedious data into worthy of consumer products, improve the user experience.
I think the visual art design is the use of graphics and image processing technology, to convert the data to graphics or image on a screen, and interact processing methods and techniques. It involves and set design and other fields, it’s research data representation, data processing, decision analysis and so on a series of problems of integrated technology.
And I think the visual design is a new data mining method, avoid many complicated data list.
Case in point
This data comes from ‘The New York Times offers four ways to slice Obama’s 2013 federal budget proposal. The $3.7 trillion in spending can be analyzed by type of spending, changes to discretionary spending, department totals and of course all together. In all of the charts, the data is represented as bubbles. The size equals the proposed amount and the color indicates a cut or an increase in spending from 2012. Although each chart has an individual layout, the transitions between them is extremely smooth and helps the user to make sense of the underlying data.’（Wiederkehr，2012）
Good point 1：Step by step
Data visualization consists of a number of steps, such as can do data collection or to a different business units to collect, and then to deal with these data, data filtering, equivalent to data processing in advance. Next is the data mining and analysis, find out useful information of a large amount of data.After data mining, analysis out, if has a string of data or a table, so it is difficult to attract people, it is hard to impress the customer. Therefore, for the customer, should direct contact to Represent (performance), Refine (improve), Interact (interactive) three stages. Will display of data visualization, data vivid rise, this is a data visualization is simple but very important process.
Said to visual display, usually for any technical or door industry, is derived from one of the most basic things, extract data in nature is a principle, not to say that we may feel visualization, oneself by imagining to display.
This data is very good, after the designer by analyzing listed are not simple, but with the round instead of the shape of the bubble, the contact data, more space also can give a person a lot of imagination.
Good point 2：The data visualization, what’s the visual?
Graphic elements is numerous, slope length, can reflect the data change, graphic size, contrast, color, shape and so on all can be visualized. ‘By height can distinguish quantity size, by color can distinguish between categories. If you want to transfer the most important information to the user, the corresponding coordinate diagram, captions, classification is very important. ‘ Csdn.net，（2014）
This example has a brilliant place is color, red, green, gradient, with the weight of the numerical color expresses the time is very different.
Not perfect example
Mistake 1: display all of the data
Customer and internal users need to be specific, relevant answers, and the sooner the better. Can you give their answers with the closer they want, the more energy when they are looking for the answer. The more irrelevant information on the page when they look for answers, the more arduous. No related data (no matter how effective) is noise.
A good design method is to display only interesting or important indicators. Give priority to what is important, what is unexpected, and what is feasible, and fade out everything else. It’s also important to dig deeper into the data, but not the dashboard reveal the place of these things. Broad generalizations of operational data from it will be easier to deal with some reports.
Mistake 2: show the wrong data
This error is as dangerous as the first error. Display a subset of the information is ok, as long as the data between the are related. For example, if you care about sales, you might also care about each region’s sales and sales of the change over time. Consider how to use these data to make decisions.
Show some closely related diagrams can be used as a show too much information in a chart, and did not show a good compromise between enough information. A few clean, clear charts are usually better than a single, complex data visualization.
Mistake 3: poor data representation
Even when you are in the correct data mapping, you can still make mistakes, most types of fancy graphics are rarely seen, because they are not good. Most of the visualization demand, can through the bar chart and line charts, scatter plot, and (if well done) pie chart to deal with.
Think about the important relationship between the data fields, and consider mark the fields on the axis. By category first, then on time or importance or size to sort data fields. (in the absence of other standards, the alphabet is the most useful). Use color categories, not level; You can be illustrated by use of brightness and saturation level. Use label and other marks selective attention and won’t mess up.
Csdn.net, (2014). CSDN.NET. [online] Available at: http://www.csdn.net/article/2013-05-30/2815495-CMDN-28th-ThoughtWorks-chenxianjun [Accessed 16 Nov. 2014].
Wiederkehr, B. (2012). Four Ways to Slice Obama’s 2013 Budget Proposal on Datavisualization.ch. [online] Datavisualization.ch. Available at: http://datavisualization.ch/showcases/four-ways-2013/ [Accessed 16 Nov. 2014].