🗺 Advanced GIS in R

Table of Contents

As both a student and instructor in statistics, spatial analysis, and general data classes, the common approach is to spend a vast amount of time and effort on describing the characteristics of the systems or software that is being used. This is a very short-sited approach because it is often not too long before the next ‘big thing’ in analysis comes down the street and everyone jumps on the next bandwagon. What is needed is to train you in being practitioners of your work, to understand the basis and context of all the kinds of analyses we use in geospatial data analysis, and not be ‘trained in this piece of software’. For example. Consider your skills as a writer. When you sit down to write a document, how do you go about it? Yellow legal pad (like my father), fire up Microsoft Word (like most academics), or use a word processor? The process of writing has absolutely nothing to do with what the medium is that you are using, it is more fundamental than that. This class takes the same approach in the analysis of geospatial data. Many classes in GIS spend the vast majority of time training you on how to use a single software package—the Microsoft Word of Vectors and Points if you will. That is not how this course is designed. Here you will be learning about how to integrate geospatial data into your normal data analysis workflows. There is nothing special about points, lines, polygons, or rasters, that make them so unique that they must be analyzed differently than your ecological, morphological, chemical, or other kinds of data that Environmental Scientists work with every day. This class is designed to produce practioneers of geospatial data analysis.

Learning Objectives

  • Understand geospatial data in the form of rasters, points, lines, and polygons.
  • Perform easy transformations between various datum and projections.
  • Statistical concepts of spatial point process.
  • Develop professional cartographic skills leading to publication-quality graphical output.
  • Deploy interactive shiny dashboards.

Course Overview

The following diagram describes the basic workflow for any data science activity.

In this class, we will work on all of these components using the open-source R language.

  • Collect - Getting data from an external source into a format that you can use is often the most time-consuming step in the analysis. The content of this class will provide training in data import from local, online, and database sources.
  • Visualize - Visualizing data is key to understanding. In the image below, notice that the variables X and Y in all the displayed data sets have equivalent means, standard deviations, and correlation up to 2 decimal places! We will emphasize visualization, both static and dynamic, throughout this class.
  • Transform - Pulling data into your analysis ecosystem is not sufficient. Often the data need to be reformatted and reconfigured before it is actually usable.
  • Model - The application of models to subsets of data is often the step that takes the least amount of time and effort. However, the application of a model to data is not the endpoint. The model must be visualized and, many times, the underlying data or derivate data must be transformed and submitted to subsequent models.
  • Communication - The effort we put into research and analyses is meaningless without effective communication of your data and findings to a broad audience. Here we will focus on how to develop effective data communication strategies and formats.

Sections in this Coruse

  • Markdown

    Learning Objectives The content of this lecture will cover the use and manipulation of data vectors in R. At the end of the lecture, participants should be able to:

  • Tidyverse

    Plotting in GGPlot2 is to built-in graphics as ______________ is to built-in data workflows. A) Tidyverse B) Tidyverse C) Tidyverse D) Tidyverse Learning Objectives In this topic, we will focus on how the following base analysis verbs are leveraged using the tidyverse libraries.

  • Spatial Points

    Everything is related to everything else, but near things are more related to each other. Learning Objectives For this topic you will learn to: Describe the need for projections.

  • Rasters

    It is just a specific kind of matrix representing data distributed continuously across some spatial extent. Learning Objectives In this topic, we dive into raster objects. Rasters are essentially just matrix objects with some extra geospatial meta data added to it.

  • Joins

    Most data is sequestered in distributed systems, here is how we work with them. Learning Objectives In this topic, we dive into the general topic of data joins, common in database as well as spatial analyses.

  • Lines & Polygons

    Most data is sequestered in distributed systems, here is how we work with them. Learning Objectives In this topic, we dive into lines and polygon objects as well as tackle the ever present “shapefile”.

  • Cartography
  • Leaflet
  • Shiny
  • Habitat Classification
  • Autocorrelation
  • Waterhsed Analyses

Meet your instructor



Are there prerequisites?

The following requirements pertain to students in this course:

How often do the courses run?

This course is taught during the spring of each odd-numbered year.