Data exists in almost every facet of our lives, and understanding these data sources is crucial to getting the most out of the information made available. While analysts can truly delve into their datasets and get the most insight from them, most of us need a visual component to better understand these analytics. That’s where charts and graphs come to gain a better understanding of these facts. A spline chart is just one of the templates that allow for better data visualization.
Understanding Spline Charts
You may have heard of a line chart, but what is a spline chart? It’s actually a line chart that uses curves instead of straight lines. It is designed to emphasize trends over a particular time period but in a smoother and more gradual way. Spline charts are a clear, easy way to provide a graphical representation of one or more time-dependent variables. A spline is the name for this curve made up of two or more polynomial curves joined together. These are used in multiple ways now, especially for computer-aided design (CAD) systems.
There are different varieties of spline charts. A spline area chart, for example, is a mapped area chart colored in to pinpoint multiple variables, resembling mountains to illustrate these concepts. A multiple spline chart graphs a number of variables. While they all must be related and mapped on the same scale, this helps to show comparisons in datasets. Range spline area charts show two closely related variables mapped onto the same chart. This is the best approach for showing maximums and minimums within this information. Lastly, there is the stacked spline area chart that allows viewers to follow fluctuations of related variables.
Why use these charts?
A spline chart is used because it’s much more fluid than a normal line chart. This natural-looking curve is not only more natural-looking, but it plots data to show those smooth and gradual changes. This is used to develop insights and analytics within a certain frame. Those illustrating this data can plot time along the X-axis and the variable of interest on the Y-axis to show clear trajectories. Splines cover almost any industry to show off subtle differences that a straight-line graph might not be able to convey. This makes spline charts far more accurate.
Having multiple lines on a chart allows viewers to easily see the differences in chart data between related variables to spot patterns. A retailer may turn to these charts to monitor sales of particular products over time across different branches. Each branch would have its own spline, making it easy to spot trends like seasonal variation or success from one location compared to another. While a line chart can show off the sales of a certain dollar amount reached, a spline chart provides more of an impression as it relates to the development of those sales over a set time frame.
Best Practices for Splines
Data analysts have a number of standard practices to follow in order to create an effective spline chart. With a clear title, viewers are able to easily garner what the information they are about to see refers to. Any new chart should have clearly labeled axes, starting at zero with an even scale. Splines should also use markers to plot exact values along the curve, making for a clear way to pinpoint the exact facts a particular viewer may be seeking.
Be sure to use a strategy when it comes to your datasets to make sure that information is not misrepresented by the spline. The last thing you want is a lack of clarity when presenting findings, especially when looking to make better business decisions.
