Classes

The world presents us with its unbounded complexity, and it is up to each of us to deal with this complexity in our own way. While simplification may save us from sensory overload and decision paralysis, these same simplifications generate friction and discomfort. I truly believe that a well-trained geographer has many of the skills and toolsets needed to slide through that friction to address these complexities and answer the most pressing questions this world has to offer. GIScience and Technology is a critical subset of those tools, and a great way to help frame and analyze the space around you. I’ve gotten a lot of utility and benefit from learning, using, and teaching these topics and it is my hope that I can pass some of that knowledge along to you. I’ve placed a lions share of my teaching portfolio here for the benefit of all and my own selfish desire to streamline my digital footprint. Feel free to send me typos, mis-formatted pages, suggestions, edits, other resources, and otherwise follow along. I’ve found GIS to be a profoundly useful tool that helps me add structure, reason, and logic to this otherwise variable world, and my hope is that by the time you’ve left this site you’ll have taken away something useful as well.

I’ve attempted to standardize my formatting but you have landed on the accumulation of almost every class I’ve taught, so there might be a few inconsistencies. I also write this site in notepad++, so spellchekc and local testing can only take me so far. If you find errors (spelling, formatting, or otherwise) or if something needs clarification or fixing please let me know.

Each lab will start off with an objectives section that outlines the analyses or skills the lab aims to teach. Following this, the requisite data and question word document is attached. I also include a handy table of contents when appropriate.

After that, the tutorial starts in earnest. Major steps you take in an analysis will generally have a level 3 heading, and look like so:

Substeps or other milestones are in a level 4 heading like so:

Writing for technical documentation can be a little awkward. If you need to click or select something I attempt to Bold them. This includes toobar and options clicks. If I want you to write something out explicitly I’ll “typically quotation it”. If you are clicking on an option or choosing settings or sub tabs I’ll italizie it.

Notes (formatted like this) typically serve as parentheticals, or image credits where appropriate.

Questions in the word documents you have to answer are also repeated in the tutorials like so

The lab is a huge part of most of my teaching. Lectures can take you pretty far, but what you can accomplish on your own is most of what you will be hired for. I have made efforts to make these labs as consistent, organized, and followable as I can but inevitably I will have missed something or you will encounter an error. Fortunately for you, you happen to be sitting in front of one of the most powerful tools the world has ever assembled. I refer of course, to our overlords of Google, who make the internet searchable. Being able to effectively Google is critical to success, so if you ask me a question regarding what went wrong with your analysis, the conversation will generally play out like so:

• I found an error, what did I do wrong?
  • What was the error?
• It was XYZ
  • What did you Google?

When in doubt, you can always try to execute something and then reverse engineer your way back. These are just PC’s and nothing we’ll do is mission critical. The worst thing you’ll do is cause the computer to BSOD, and although those aren’t great, they are not the end of the world and your work should already be backed up.

It will be covered more in class, but unless explicitly stated otherwise labs will be due a week after the lab session meets.

One of the foundational goals I have in teaching is to inspire and equip you to tackle your own questions. GISystems and GISScience are an accessible and relevant means of connecting spatial concepts to actionable practices and real-world and digital skills. Although software is the vehicle used to teach these concepts, it’s those underlying skills and concepts I hope to transfer to you. This ability to think critically about space (critical spatial thinking) is one of the most in demand skills you can possess and is widely considered to be a key facet of intelligence and a desirable cognitive capability. I define critical spatial thinking as follows:

Critical spatial thinking is the ability to observe and form a query; place that query in the context of the core spatial concepts; devise an appropriate means of testing that hypothesis; executing that test accurately; communicate those results in textual, verbal or visual form; and objectively reflect on that process.

In teaching the acquisition of GIScience skills though GISystems, we can deploy two primary means, one can take a graphical user interface (GUI) based approach to introducing GIS, or take a more programmatic approach. GUI GIS includes programs such as Google Maps and Earth, QGIS and ESRI Arc products, whereas programmatic approaches to GIS include Python, R, JavaScript, and Matlab. There are tradeoffs to each, and it’s worth peeling them apart.

Anything built in a digital system is inherently less free and less flexible than we can conceptualize internally. These limitations are not necessarily even imposed by hardware, they can often simply be too unwieldy to implement in a digital environment. Likewise, a GUI driven implementation will always have fewer degrees of freedom than a programmatic implementation. This loss of flexibility is further compounded by the nature of a GUI driven system in general; one cannot expose too many of the underlying parameters without overwhelming the end user. Finally, although not limited to GUI programs, many of the more advanced GUI driven systems must be paid for, creating additional barriers of access and raising ethical and moral issues related to academia teaching a private company’s software, and building dependence on computational crutches.

Programmatic approaches, in contrast, require the ability to program which takes quite a while to build competency in, and progress made while learning is not particularly obvious. Furthermore, it is often harder to find that initial motivation/inspiration in programming, whereas GUI driven systems provide immediate feedback and a tangible goal to visualize. I find it far easier to inspire the desire to program when individuals have a self-developed goal in mind; the learning will follow. However, a skilled GIScientist must learn to program eventually, and in that respect, a programmatic approach to learning GIS might save time in the long run. A strictly programmatic approach also foregoes the process of learning common interface accesses paths of a GIS GUI, but such things are trivially straightforward to learn and by the time one has acquired the ability to think critically, they also likely possess the capabilities for self-directed learning.

These differences in approach are summarized in the generalized conceptual diagram shown below. Users who start on the GUI driven track rapidly acquire skills in spatial literacy, but progress quickly plateaus while acquiring computational skills. This is due in parts to the abstraction GUI driven programs provide over the computational domain and the inherent limitations of GUI platforms to design custom tools. Therefore, these users need to spend additional time learning how to program, and in some cases unlearning poor habits that GUI driven tools can create. Eventually, users overcome this learning curve, and move on to become critically spatially literate. In contrast, users who start learning GIS programmatically have a much slower rise to spatial literacy as they overcome the early hurdles associated with programming and visualization, and consequently acquire spatial literacy later than their GUI taught counterparts. However, they have none of the later learning curve, and rapidly transition from spatial literacy and using GIS as a tool to toolmaking. Consequently, they acquire critical spatial and data literacy somewhat sooner.

One more critical aspect left unaddressed between these two approaches to teaching GISystems is the retention rates and success (in terms of the number of students who matriculate and go on to practice GIScience). To provide the most accessible experience and archive possible, this site and the classes within will include approaches to labs in both ESRI (ArcMap and ArcPro), QGIS, Python, R, and Google Earth Engine as time and funding allows. Although I obviously hope you become a seasoned GIScience practitioner, my goal is that you leave my class with a deeper appreciation and understanding for the nuances of spatial analysis and phenomenon, and that you gain practical problem solving skills you can deploy in your own careers.

One of the many skills I hope that you pick up as you progress through my class, and something I continually try to improve, is my ability to effectively manipulate and interpret data. You can’t hope to do this when you have files scattered all over your desktop and hard drive with 7 file names which contain variations of “…final…”. I don’t have many regrets but one of the largest is that I was not more organized digitally when I started my graduate career and this is something you have the chance to avoid now. Use a file storage format and naming convention that you will 1) use consistently, and 2) makes sense to you. I use a variation of the following.

I typically don’t opt for a week by week folder structure, I am very unlikely to remember what week something happened even a month later, so it makes little sense in my mind to organize in that fashion. YMMV

I dislike touching on this subject as I find it a bit counterproductive to the goal behind attending university, but it seems warranted given that is likely how you’ve ended up on this page :) There are many things I love about teaching, but gatekeeping is not one of them. A 4 year degree is a great signpost on your resume that says you are a well rounded and capable individual with the requisite background and theoretical foundation necessary to excel in your chosen field. However, I dislike that my say so (in the form of a pass or fail grade) can act as that barrier to your perceived success or failure. Even more so, a grade is one of the last things you as a learner should be concerned about. If you find yourself chasing points, in my mind you’ve missed the whole reason to learn in the first place. You should be concerned about whether you understand the steps and rational behind the material, and how well you are able to apply that understanding to new situations. Although I have no wish to contribute to grade inflation via grade leniency, grading these labs as laid it out here is much like a positivistic science in that there is a right answer and a wrong answer, and I do not grade on a curve. I will of course push you to do your best and go the extra step, but many of the labs are cut and dry when it comes to assigning a points grade. If you all do well, you get an A, and I’d rather not explore the alternative end of what that range is.

Finally, although it should be obvious at this point in your academic careers, under no circumstances is cheating tolerated. This includes but is not limited to plagiarism in papers, using previous students’ course material in quizzes and tests, and submitting other students’ work as your own. Not only does this detract from the overall integrity of the department and the school (the lesser of the evils in my mind), but cheating in these classes sets you up for disappointment and misery further down the line. You’ll have failed to adequately learn foundational concepts and the advanced skills that employers are looking for, and concepts here form the foundation for virtually all material as you move deeper into the field. In short, it is counterproductive to the very concept of attending college in the first place. I am always available through email, slack, and office hours (or by appointment) and am here to help, so don’t do yourselves the disservice of cheating through what should otherwise be interesting and simulating material.