🧮 Environmental Data Literacy
Table of Contents
This course is taught in the Fall Semester of each academic year and topics will be added as the semester unfolds.
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.
To understand analytics, one needs to recognize the entire workflow, independent of what kind of data is being collected.. Here is a brief graphical depiction of how analysis actually works—in the real world.
Sections in this Coruse
This topics introduces you to the concept of reproducible research and RMarkdown.
This lecture covers basic vectors and understanding of introductionary analysis of numerical data.
This topic coveres strings and character data as well as the enigmatic factor data type.
Here we introduce the use of the data.frame and tibble for keeping, manipulating, and storing large amounts of mixed data types.
If a picture is worth a thousands words, then this topic is an introductionary compendium… If you can’t visualize it, you can understand it.
For making graphics in R that ‘don’t suck’
Basic query language approaches for data.tables and tibble containers.
Either you use tidyverse or you hate yourself—it is that simple.
Encapsulating repeatedly used code in functions to make your life suck less.
Joins and relations. Or, How we really keep very large data sets.
Points, lines, polygons, & Rasters.
Basic mapping skills in R.
The nature of statistical inferences.
The analysis of coincident change—pirate attacks and alligators.
Building a linear (or non-linear) model of the functional relationships between two sets of continuous variables.
So you want to see if the mean of these are different from the mean of those? No Problem!
Meet your instructorrodney
Are there prerequisites?
The following requirements pertain to students in this course:
How often do the courses run?
This course is offered each Fall semester and is intended to be taken by all incoming graduate students in their first semester.