Data visualization methods, tools, core concepts, and needed to be aware of a deep pit

By Earl Graham,2015-10-02 14:36
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Data visualization methods, tools, core concepts, and needed to be aware of a deep pit

    Data visualization methods, tools, core concepts, and

    needed to be aware of a deep pit

    Big data is one of the hottest topics.And be followed by the sustainable development of data visualization technology, it is used to display and interpretation of large-scale data.But the data visualization technology is not the same.

    Data visualization is one of the most powerful mechanism of data, technical advantage and created a unique for its realization method.As interactive, unique data visualization method gradually to the forefront, and the audience is becoming more and more understand their own likes and dislikes, who draw up the simple pie chart of the day will be gone forever.

    It follows that we will introduce to you the importance of data visualization, different data presentation and industry commonly used tools.You will also look to win data visualization techniques and the central idea behind you need to avoid mistakes.

    You will learn from this article: 1, what is data visualization.2 why, data visualization is important;3, what are the data visualization method;4, win the key idea behind the data visualization;5, complete the data visualization to avoid a deep pit.

    What is data visualization?

    Data visualization is the study of how the data in the form of images or graphic display of science.It is mainly focus on the show, put a lot of information in the form of coherent and short.Although data visualization can also deal with written information, its focus is in the form of pictures and images to give information to the audience.

    In addition, the data visualization technology in the use of the data is not narrow.It can visualize all kinds of information, you can ask others to pass your ideas and assumptions.Now even can choose to add data visualization technology and interactive visualization methods.

    Information of visual expression is an ancient way of thinking and experience sharing.For example, maps and charts is some early important instance data visualization technology.

    Why data visualization is important?

    As mentioned above, the human use data visualization technology has been a long time.Images and diagrams have proved to be a kind of used for communication and the effective way to learn new information.researchShowed that 80% of people can remember what they see, and only 20% of people remember how to read!It can even thoughts and events to the offspring.The development of the technology to further strengthen the data visualization give people the opportunity to.

    Perhaps the most important benefits of data visualization is that it can help people master the data more quickly.You can put a lot of data concentration in a chart, people also can quickly grasp the key point.If in writing, may take a few hours to analyze all the data and establish a data connection.

    In addition, this can show the ability of the large amounts of data is another huge advantage of data visualization.A chart is likely to highlight several aspects, people can form different points of view on data.This naturally for enterprise the road to open up new business.People may be able to found some unexpected things from the data.

    Data visualization had increased its ability to explain information.From the large amounts of data and information to find associations is not easy, but the figure and chart can provide information in a few seconds.To identify the required information.

    All the above can strengthen the communication and effectiveness of people in work and study.Data visualization is widely considered to be a simple and effective method of summary data, so it can improve the way of people to share information and learning.

    The video below are gracefully show a good example of data:

    Video link:

    Data visualization methods

    The development of technology has causedThe explosion of the data.This, in turn, increases the data is showing the way.Generally speaking, data visualization is mainly divided into two different types: explore (exploration), and explain (explanation).Explore type can help people find the story behind the data, and explain the simple and clear explanation of the data type to the audience.

    In addition, there areDifferent methodsCan be used to create these two types.The most common data visualization methods include:

    ; 2 d area, this method USES the geographical spatial data

    visualization technology, often associated with the location of

    events in a particular area.An example of a 2 d area data

    visualization including point distribution, which can display

    information such as the crime in a region.

    ; Temporal - time visualization data in a linear fashion show.Time

    data visualization is the key to have a beginning and an end point

    in time.Time visualization example might be a connection of scatter

    plot, it can display information such as the temperature of an area.

    ; Multidimensional - you can also by the method of Multidimensional

    data in two or more dimensions.This is one of the most commonly used

    method.An example of multi-dimensional visualization is a pie chart,

    it can display information such as government spending.

    ; Hierarchical - level method is used to present multiple sets of

    data.These data visualization in large groups often nested within

    a small group.Examples of hierarchical data visualization can be

    a tree graph, it can show the information such as language groups.

    ; Network - data also can be show in the form of interconnected

    networks.This is another common method of large amounts of data.An

    example of network data visualization methods can be alluvial

    diagram, it can display information such as the change of the

    medical industry.

    The above gives a lot of choice, it not only provides us a lot of opportunities, but also let us feel headache for choosing the right method.

    In addition toA lot of data visualization tools.They can easily collect data, also can streamline data use.

    Some of the most commonly used tools include:

    ; Google charts- Google products in the industry of data are well

    known, Google charts is an easy-to-use tool, especially for the

    first time users.

    ; datawrapper - this is an online tool, it can help you to create

    interactive data visualization.

    ; RAW- the benefits of RAW include it has a large number of ready-made

    type, so you can clearly and easily to show information.The platform

    is open source, so you can provide a custom layout, or using the

    design of the other.

    ; Infogram - Infogram is another tool suitable for novices.It allows

    users to create different charts and Iinfographs, also convenient

    for the system.

    Available tools far more than these, you can find a lot of free and paid software.Had better know more information, to ensure that the software you are using and the visualization of data the collocation.

    To win the key concepts behind the data visualization

    Seen data visualization knows that design is good or bad.If the information is not in the correct and proper way, the benefits of the data visualization is very likely to be offset, the method of the project need to customize.

    No matter what your message is, in the use of data visualization you need to bear in mind that there are some concept.The following is the key concept behind win data visualization technology.

    Know the audience

    Before showing data, the first thing you need to do is to know who will look at the data.Know your audience is crucial, so that in a right way to display the data.

    Although data visualization is usually a way to simplify data, but the level of audience knowledge of the subject differ in thousands ways, needs a good preparation.If you are in a group of professional audience, you can use more professional methods and terminology to explain the data.However, for the same data, general audience may need to be more popular methods to explain.

    Also, know the audience for what do you expect the data is also very important.You need to know what they want to get from the data points, and show you what is the main purpose of the data.In addition, you also need to remember that you show what is the purpose of the data.

    Enough to understand the data

    In addition to master the target audience, you will also need to know the data.If you are not correctly understand the data, is a good chance can't effectively convey the information to the audience.

    But you can't take care of the data contained in all of the information, so to be able to extract key information, and well organized to show them.You also need to ensure that from the data to get the correlation information is correct and not fiction - can never do with wrong data visualization.

    If you correctly understand the data and its associated, you can get from information unique and interesting data correlation.

    Tell a story

    Data visualization should also draw up a story.You do not want the data just to show in the form of a set of information, but can use the information behind the sent data.This can be a different descriptive introduction, or present a particular image for the audience.

    Make up a story often means that the audience get more insights from the data.It can help the audience to understand the new associations and more in-depth information.

    In fact, data visualization technology is an excellent story-telling tools."One picture is worth thousand words" this sentence is good, you should take the advantage of the it.Through data story is not difficult, because you can put the colors, fonts, and presentations as part of the storytelling technique.

    In order to make the data visualization successfully integrated into the story, the understanding of the above mentioned data is a crucial point.

    Keep it simple

    In recent years, the development of data visualization, soon as shown above, sprung up many tools and systems for people to use.Can contact with different unique approach does not mean that all want to use them.Moreover, a large amount of data doesn't mean that all the information is essential.

    All in all, keep your data visualization method is simple and clear.Don't have to deliberately use too much data or excessive use of skills.

    If from the point of view of a story, you must understand what you show each element is an essential part of story.If the data or elements, such as pictures of some of the things, it doesn't matter to the plot of the story, then you should not join in it.

    Data display contains too many elements will actually destroy the final finished product, and the data out of line.Keep in mind that the core of the data visualization is to present a

    large amount of data in a moment.If visualization is difficult, then you have to look at whether or not to use the wrong data presentation or contain too much information.

    Differentiate show platform

    In the end, win a data visualization technology is also to understand the technical aspects.Now people through a variety of different platforms to view and access to information, this must keep firmly in mind.As need to know the target audience, you also need to consider the way people view the data visualization.

    Your visual results need to be able to easily adapt to a variety of platforms, such as mobile device, tablet or computer.If you only by mobile phone users to browse data, then apply to move the display method will more helpful for you, rather than on a laptop.

    In addition to consider the interface options platform, also need to consider the accessibility (the org.eclipse.swt.accessibility) problem.If the data visualization allows poor vision for the proper zoom in and out, can significantly improve the user experience.You can also consider different color options for color blind people.accessibilityAimed at improving the user experience, to ensure that data visualization can be for everyone.

    Data visualization to avoid big mistake

    Although the above key method can help you to generate win the strategy of data visualization, and some common pitfalls to alert.

    The wrong information

    As mentioned above, the data error is the audience the most disgusting things.You must make sure that those who are watching your data access to the correct data.Ensure that people can be used directly in your chart data, without having to confirm the accuracy of the data again, it's your duty.

    Incomplete information

    In addition to ensure the correct information, but also to present the complete data.People must be able to find relevant information, you can't use data visualization to deceive or incomplete information.

    Data visualization can and should tell a story, but the story needs to contain complete and accurate information, not only show what do you think the right data.

    Too simplify data

    Although is a simple way to ensure that data, that doesn't mean you should simplify it.First of all, you need to remember that the audience is who, for professionals do not use

    popular and simplistic language.And if it is a common audience, don't use jargon to populate the text.

    But beyond that, if you failed to clearly show the data, also cannot expect the audience can clearly understand the relationship between them.Cannot because link seems to be clear for you, just omit some information - remember that the audience can only see you shown this part of the data, rather than the complete data set you use!

    Inappropriate visualization

    In the display data, you need to think carefully about data show the way.Such as font, color and image properties such as always.For example, if the display by the specific diseases and cause of death information, use the bright color and pleasant image seems to be not harmonious.

    Not also include the use of appropriate visualization technology makes data is difficult to see and understand.For example, you may be represented in the department with a bubble of different consumption level, but if the bubble size difference is not appropriate, can lead to misdiagnosis and inaccurate.

    Missing annotation

    Excessive simplify may also lead to the lack of annotation.When you provide data, it is easy to assume that the audience already knows what do the every aspect of the image.But adding a simple annotation can improve the user experience, and make sure the audience understand all the data points in the data.

    For example, you may use a diagram to show the enterprise in the past ten years sales of bicycles.If the data in the chart has a big ups and downs, use comments to explain the reasons behind this mutation, to ensure that the audience to grasp this additional information.

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