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We’ve looked at functional modeling in some detail. Now it’s time to look at how to linearize those models, with a particular focus on the parametric case.

This topic takes you through: 1) why this matters; 2) what are the key equations I’m going to need for linearizing parametric models?; 3) deriving those key equations; and 4) an illustrative numerical example that makes things tangible.

By the time you’re done, you will have what you need to set up what you need for pretty much any parametric geospatial estimation problem on your own.

Unlike most of our other topics so far, this one contains an animated whiteboard video. This means that you can check out the lecture and examples on your own time and at any time – now as you take the course, or in a year’s time when you need to check in again for something you’re coding, or even further down the road.

Completing this will also earn you a mini-badge (if you are signed in).


1. Watch the video in the lesson below and do the self-assessments (under “Resource content”) below. This kind of online format allows me to be more thorough in places, but as a guide, the topic taken together would likely be covered in a 50 or 75 minute lecture period, depending on how much discussion took place.

2. We won’t be covering this material in class so be sure to add to your “perfect set of lecture notes” as you see fit. As always, these are carefully designed to help you do well on the related quiz and final exam. Be sure your notes make everything handy because just like skipping a class, you likely won’t have time on an exam or on the job to watch the videos for the first time.

3. Once you’ve finished, you’ll pick up the associated mini-badge (as long as you’re signed in). I don’t mark the mini-badges in the course, as you know. They’re just a way for you and me to know when you’ve finished the task. As usual, the content itself will be audited through the self-assessments. And we’re always happy to help with those, so don’t hesitate.