Overlays

Content for Monday, October 24, 2022

Now that you have some experience with analyzing point patterns, we’ll extend those ideas to begin developing new multivariate algorithms. We’ll start with some simple additions of covariates to our kernel density estimators. Then we’ll move into overlay analysis to set the stage for more complicated regression models.

Resources

Overlay analysis provides an overview of the logic of overlay analysis.
Spatial Operations and Vector Overlays from Manual Gimond provides an intro to using vector data in overlays. Easily converable to sf syntax.
Predicting site location with simple additive raster sensitivity analysis using R from Ben Markwick has a complete example of using a weights of evidence approach to overlays.

Objectives

By the end of today you should be able to:

integrate a covariate into KDE’s
Describe the utility and shortcomings of overlay analysis
Describe and implement different overlay approaches

Slides

The slides for today’s lesson are available online as an HTML file. Use the buttons below to open the slides either as an interactive website or as a static PDF (for printing or storing for later). You can also click in the slides below and navigate through them with your left and right arrow keys.

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