In reply to Ali Al Lawati.

Thank you for writing and for your suggestion. To keep the article as brief as possible, I chose to focus on a simple example. The canonical example where bilinear interpolation is useful is scaling an image. For example, if an image is 300x300 pixels and you want to scale it to 600x600 pixels, you can use bilinear interpolation to fill in values for the new pixels in the larger image.

This is a good opportunity to mention that for most applications, interpolation is not the best way to analyze the data. It is usually better to create a statistical model (either parametric or nonparametric) and then score the model to predict the response at an unobserved location. Statistical models are more powerful than interpolation. Statistical models account for errors in the observed response and enable you to perform inference; interpolation does not.