Readings and lectures

Each class session has a set of required readings that you should complete prior to the ‘computer lab’ portion of the class.

The lecture slides are special HTML files made with the R package xaringan (R can do so much!). On each class session page you’ll buttons for opening the presentation in a new tab or for downloading a PDF of the slides in case you want to print them or store them on your computer:

View all slides in new window Download PDF of all slides

The slides are also embedded on each page. You can click in the slides and navigate through them with ← and →. If you type ? (or shift + /) while viewing the slides you can see a list of slide-specific commands (like f for fullscreen or p for presenter mode if you want to see my notes).

Evaluating Model Performance

We have reached the end of this introduction to the use of R as a Geographic Information System. You have (hopefully) learned a bit about the nature of spatial data, how to manipulate and visualize spatial data, and finally, how to build predictive models based on spatial data.

Dec 6, 2021

Modelling the probability of occurrence in space

Much of the motivation for this course stems from the need to develop spatially explicit predictions about the likelihood that a species or event occurs in places we haven’t sampled. In ecology, we call these models by several different names: species distribution models, resource selection functions, habitat selection models.

Nov 29, 2021

Introduction to point pattern analysis

Much of the development in statistical techniques for spatial data is rooted in the analysis of point processes. This is, perhaps, unsurprising given that the the point is the simplest of our different data models and the least ambiguous to reference spatially.

Nov 15, 2021

Quantifying spatial patterns

Now that you can load, manipulate, and visualize spatial data in R, we can begin exploring workflows for incorporating these data into statistical analysis. We’ll begin by looking at methods for identifying and characterizing spatial patterns in your data.

Nov 8, 2021

Telling stories with interactive graphics

This week is a bit of a departure from some of the ‘strictly GIS’ components of the course to explore some more creative approaches to data (spatial or otherwise) visualization. Using the idea of the storymap as motivation, we’ll take a look at some of the growing list of tools that R provides to integrate high-powered data analysis with interactive web applications into an intuitive, visually appealing webpage.

Nov 1, 2021

Interactive visualizations with R

Now that you’ve had a chance to practice building a few maps and learning some of the core ideas behind the Grammar of Graphics, we can extend those ideas into the development of interactive webmaps and more expansive data visualizations that can be served on the internet and accessed by collaborators and members of the public.

Oct 25, 2021

Truth and Cartography: Spatial Data Visualization

We’ve spent the last few weeks learning about operations to compile geographic information into databases for visualization and analysis. Because analysis requires you to know something about your data and because visualization is a great way to explore your data (especially when there’s a lot of it), we’ll turn to that next.

Oct 18, 2021

Workflows for combining raster and vector data

Now that you’ve gotten some experience building databases using tabular and vector data, we’ll take a look at combining data models (i.e., using vectors and rasters together). We’ll rely largely on terra::rasterize, terra::zonal, and terra::extract to summarize data in ways that allow them to be combined with other tabular and vector data.

Oct 11, 2021

Building spatial databases based on co-location

Today we’ll continue our development of attributes (or covariates) in our spatial databases. We’ll look at developing attributes that describe various geographic properties along with joining and subsetting based on locations.

Oct 5, 2021

Building spatial databases based on attributes

Today we’ll begin exploring typical workflows for spatial analysis by working with attribute data. Attributes generally provide additional information about a location that we can use for visualization and analysis. Unlike spatial operations that we’ll explore next week, attribute data do not all require geographic information (but they do need some means of relating to a geography).

Sep 27, 2021