Data Visualization Theory and Practice
Data visualization is the strategic use of graphics and interactivity to present data clearly and aesthetically. The history of visualization goes back to ancient times when troglodytes created pictorial representations using their imagination and the tools they had available. Over the years, there have been a number of different attempts to apply visual psychology in the development of information systems. However, the advent of computer software and animation has created a new genre of visual information systems, namely Data visualization, which seeks to address more advanced psychological problems by providing means to represent and explore data in more understandable ways.
This article will provide an overview of the history of visual psychology and its applications to data visualization.
Data visualization has two broad categories: Data visualization with visualization tools and Data visualization as a stand-alone medium. In the former cases, the focus is on the visual aspects of representing data sets; in the latter case, the focus is on the communication aspects of displaying the data sets. Both types share some common examples, but the distinctions become clearer when examining more specific examples. Data visualization with tools refers to the use of matrices, dashboards, plots, histograms, pie charts, etc., to represent the numerical values.
Figure 1 shows a simple bar chart, with x and y coordinates of longitude and latitude plotted against each other. Scientists and engineers have widely used this visual graph to display the location of points of interest. If you hover your mouse over a point on the chart, you will see a corresponding value on the chart. One example of data visualization tools is the Microsoft Office data visualization toolbox. This toolbox allows you to create custom charts and graphs and perform basic analysis of the data sets in the data visualization toolbox.
The data visualization toolbox allows users to perform basic tasks. The user may select a point on a chart and then choose different shapes and colors for points A to C. A typical point chart, for example, could have columns for (A, B, C), each one denoting a different data point on that chart. Another popular data visualization tool is the scatter graph, which could have many different lines connected to points A to C. Again, the user would select a point on the chart, define a shape, and select colors for the lines. A third popular tool is a bar chart, which allows the user to select any arbitrary range of data points and then creates a colorful graphical representation of the data, often in a figure style. These various examples provide only an overview of what is possible in a data visualization toolbox.
Of course, there are many more options for creating a data visualization, from a simple bar or line chart to an entire tableau website or dashboard. One limitation of most of these tools is that they only present data in a way that is very clear and easy to follow. Fortunately, many leading companies now offer hundreds of interactive demos of these tools, along with complete instructions for how to create your visualizations and customize your dashboards. These demos can often be found free on the company’s website.
Perhaps the most important thing to remember when creating visualizations or using a dashboard or chart to represent data is to remember that good data visualization theory is necessary and good data visualization practice. This is because even the simplest visualizations will fail if the user cannot interpret the underlying structure or relationships. For example, an analysis of two data sets, A and B, could be presented in various ways. One might use the common data visualization pattern of using a rectangular grid to display the points, while the other might use a scatter plot to show the points’ location. While it is not essential to learn all of the patterns used in these examples, it is helpful to understand how to create these patterns and how to apply them to different types of data sets.
There are a wide variety of third-party applications that can greatly simplify the process of developing and deploying visualizations. In particular, many popular open-source frameworks such as Vizio, webplyr, and dashboard are excellent choices for quickly and easily creating data visualization tools that work well in just about any programming language. This is also important for those who would like to try out different approaches to building dashboards or visualizations without spending significant amounts of money on third-party development.
The importance of data visualization is evident when you take the time to explore the visualizations and insights you can draw from them. Data visualization can tell stories about relationships that have not been explored in previously researched ways. It can tell stories about trends that have not been well represented in previously published research. And, it can tell stories about relationships that have not been well represented in previously published data sets.