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A bubble chart is a type of data visualization in which several circles (bubbles) are shown in a two-dimensional plot. It is a generalization of the scatter plot, with bubbles substituting the dots. A bubble chart is most typically used to display the values of three numeric variables, with each observation’s data represented by a circle (“bubble”), and the horizontal and vertical locations of the bubble displaying the values of two additional variables.
A bubble chart is a form of chart that displays data in three dimensions. Each entity and its triplet (v1, v2, v3) of related data is shown as a disc, with two of the vi values expressed through the disk’s xy position and the third through its size. Bubble charts may help people comprehend social, economic, medical, and scientific linkages.
Bubble charts are a type of scatter plot in which the data points have been replaced with bubbles. “You may use a bubble chart instead of a scatter chart if your data comprises three data series that each contain a set of numbers,” according to Microsoft Office guidelines. The numbers in third data series govern the size of the bubbles.
Each bubble in the bubble chart below represents a nation, its area is proportionate to its population size, the color symbolizes the continent, and the horizontal and vertical positions correspond to GDP per capita and life expectancy, respectively.
Conducting exploratory research seems tricky but an effective guide can help.
It is possible to be misled when using bubbles to represent scalar (one-dimensional) values. The human visual system perceives the size of a disc in terms of its diameter rather than its area. This is why, as the third input element, most charting software requires the radius or diameter of the bubble (after horizontal and vertical axis data). The use of area to scale the size of bubbles can be deceptive.
This scaling problem can lead to significant misinterpretations, especially when the data range has a wide range. And, because many people are unfamiliar with — or do not pause to examine — the problem and its influence on perception, those who are aware of it must often exercise caution when interpreting a bubble chart because they cannot assume that the scale correction was done. It is consequently critical that bubble charts not only be accurately sized, but also clearly labelled, to demonstrate that area, rather than radius or diameter, conveys the data.
The phrase “bubble chart” is also used in architecture to refer to a preliminary architectural design of a plan created using bubbles.
A “bubble chart” in software engineering can refer to a data flow, a data structure, or any other diagram in which entities are represented by circles or bubbles and relationships are represented by links drawn between the circles.
A “bubble chart” in information visualization may refer to a technique in which a collection of numeric numbers is represented by densely packed circles with areas proportionate to the quantities. Unlike a standard bubble chart, such displays do not attribute significance to x- or y-axis positions, instead attempting to pack circles as densely as possible to maximize space use. Fernanda Viegas and Martin Wattenberg invented bubble charts, which have subsequently become a popular way to present data. The New York Times has employed circular packing charts, which are included in popular visualization toolkits such as D3.
Search Engine Marketers can quickly assess the impact of a high Cost Per Click rate on ad position, clicks, and website conversions. A bubble chart will assist in determining whether there have been substantial improvements in these aspects. The size of the bubble will be determined by charting the number of clicks on the x-axis, the cost per ad on the y-axis, and the rise in conversion rate.
Keyword researchers can utilize Word Graphs to assess the density of a specific keyword or hashtag when working on Word Graphs. The Google yearly report of the most searched terms is one example. This information may be turned into a bubble chart, with the most frequently searched terms taking up the greatest space on the globe. This will make it much easily identifiable.
A bubble chart may be used to study the impact of promotions and advertisements on your business. The x-axis should show the number of ads created, the y-axis should show the cost of each ad, and the circle radius should show the money earned.
One common error is to scale the diameters or radii of the points to the values of the third variable. When this type of scaling is used, a point with twice the value of another point will have four times the area, making its value appear much bigger than it is.
Instead, ensure that the regions of the bubbles correspond to the values of the third variable. In the identical case, a point with twice the value of another point should have sqrt(2) = 1.41 times the diameter or radius of the smaller point, so that its area is twice that of the smaller point.
Because overlaps are much simpler to occur when all points have a small size, bubble charts are typically made with transparency on points. This overlapping also implies that the amount of data points that may be plotted while making a plot understandable is limited.
Another suggestion is to put a caption or other key on your plot to indicate how different bubble sizes correlate to different values of your third variable. The tick marks on the axes make it very simple to assess and compare numbers based on horizontal or vertical lengths and locations. A bubble size key serves the same purpose as the tick marks for the third variable.
If you’re thinking about utilizing a bubble chart to communicate information to others, be sure it can show a clear trend by using point size as an indicator of worth. Experiment with the order in which variables are plotted while creating your chart. The two most significant variables or relationships should be placed on the vertical and horizontal axes. Use extra, simpler plots instead of a bubble chart if the third variable does not contribute significantly to the story given by the chart.
If a variable has negative values, it cannot be allocated directly to point size as an encoding: how can a form have a negative area? Negative values require additional information to be encoded into shape size. For example, you may have full circles representing positive values and empty circles representing negative values. You might also have good points in one color and negative points in a unique, other color.
Simple Bubble Chart is the simplest fundamental bubble chart version. In reality, the basic bubble chart is not a subset of the bubble chart. The bubble chart is in its original form.
The labelled bubble chart is similarly quite basic, with few modifications. The difference between this chart and the simple bubble chart is that the bubbles on this chart include labels. These labels serve as stand-ins for legends. They aid in determining the variable each bubble represents. Bubble labelling is only possible when working with a three-variable bubble chart with a small number of data sets, as seen in the figure below. When the dataset is enormous, it normally takes a long time, and the display becomes congested.
This bubble chart variant is utilized when dealing with more than three variables. Colors can be used to assist depict various sets of data.
In this situation, chart shows sphere-like bubbles rather than circle-like bubbles. The third parameter in the dataset determines the radius of the sphere. All of the previous options are also possible with this 3-dimensional bubble chart.
However, using a 3-D bubble chart to show data is not always recommended. This will have an impact on the aesthetic appeal of the bubble chart.
If the two positional variables reflect geographical coordinates (i.e. latitude and longitude), we may create a bubble map by superimposing bubbles over a map in the backdrop. A bubble map is a fascinating extension of the scatter map that can help with the latter’s possible overplotting difficulties. If a scatter map has so many points in a location that the number is difficult to see, we might replace it with a single bubble that indicates the total number of points inside the region.
Packed circle charts (also known as circular packing or bubble clouds) have the appearance of a bubble chart on the surface. While bubbles in a packed circle chart show numerical values or frequencies, this is the sole variable: the bubbles are crowded together in a dense arrangement with no true spatial axis.
A packed circle chart is similar to a bar chart composed of discs in several ways. However, this reveals one of the packed circle chart’s flaws: the unordered bubble sizes make it impossible to obtain accurate numbers or a ranking. Because of their usage of location to encode value, you’ll likely be better off going with a bar chart, lollipop chart, or dot plot to convey information. The one advantage of packed circles is that they can be significantly more compact than presenting each category in a continuous line if there are many groupings to depict. Smaller values, on the other hand, may be grouped into an “other” group to save space in a more traditional chart.
Circular packing is most frequently seen in a hierarchical framework, where smaller circles are packed inside bigger circles to demonstrate how a whole is broken into components at several degrees of division. Even here, the circular shape for proportions is fairly inefficient when compared to other chart types such as the treemap, therefore the circular packing chart’s benefit is firmly in aesthetics rather than functionality.
Bubble charts are without a doubt one of the most difficult yet helpful charts available. Aside from the fact that it can display 3D data, there are several other advantages to utilizing it. Some of the benefits of utilizing this style of chart are as follows:
The bubble charts, as attractive as they are, are far from ideal. Look for the junction of 11.5 percent Net Margin and 2 percent Market Share in the bubble chart below. A larger bubble is covering a smaller one. That is entirely possible. A cursory scan at the chart, on the other hand, may be problematic since we would miss this. Careful color selection might perhaps aid in the discovery of these situations. This also suggests that bubble charts may not be the best option for huge data sets due to too many overlaps.
Furthermore, bubble charts in their most basic form only present a snapshot in time. Time-series analysis must be performed in a unique method. The good news is that Cognos 10 includes sliders. These sliders may be used to go through history and quickly identify changes in the data.