Don Boyes is a professor at the University of Toronto in the Geography Department. His popular GIS courses reach many of UT’s 93,000 students. Don’s been around the GIS crowd since 1988 ̶ he knows a thing or two. His university position came after completing his masters, followed by his Ph.D. and spending time in the western Arctic doing consultancy.
It depends on what your goals are in your career. Where do you see yourself in 10 years?
Suppose you got your undergrad, and you want to dive deeper into a particular aspect of GIS. In that case, doing a master’s degree is probably not a bad idea. Plus, it’s reasonably marketable. If you’re looking for jobs, the fact that you have one is something employers see as attractive. As a bonus, you can complete your master’s relatively quickly in a couple of years.
A Ph.D. is a whole other thing. It’s an enormous commitment and requires much sacrifice in terms of time and money. Are you comfortable putting off things at an age when your friends are starting a family or making good money? It’s a long haul commitment, and at the very minimum, it takes four years, but often five or six to complete.
Are you heading into academia? Do you want to do consulting? Work for the government?
You need to know what you’re getting into and what you’ll get out of it.
I started my Ph.D. because I was unemployed and had nothing else to do. I enjoyed learning and being at university. I thought I’d just start and see what I think of it. I started that way because it was such a daunting thing to get into. I took one step at a time; I ended up enjoying it, and I finished it. But it’s not for everybody.
The what and the how.
A decade ago, we had one set of data to work with. All students completed the same exercise and solved the problem the same way.
With the open data movement, there’s an ubiquity of data. We can let students pick their own data on topics that interest them. In my introductory course, they find their own data for a geographic area they’re interested in, perhaps where they live or where they’d love to travel. They make connections to their own interests and lives. The more they’ll see the relevance of what they’re learning, the more they’re motivated.
My job is to teach them how to find and understand the data and evaluate good versus bad. What’s an authoritative source, what’s metadata, and how can this get messy?
Students today don’t realize that we didn’t have access to all these amazing software and tools just a few years ago. The entry barriers are so much lower today.
Still, we need to start with the old-fashioned basics. What’s a map? What’s a coordinate system? What’s a map projection?
Then we can move onto more interesting things such as Big Data, Data Science, machine learning, and artificial intelligence.
Yes. You have to start with the fundamentals.
We’re still a long way off from AI taking over our work. At this stage, we still need humans to understand the context of a problem and understand all the things that could go wrong when exploring data.
AI and machine learning can absolutely help in that process. They’re opening up new potential; they can automate things and help us see things we couldn’t before. But there’s still a place for understanding what it is you’re trying to do.
Take a simple example. I teach a section on raster weighted overlay. I go through the basics of how it works and how you set the weights for different layers. I give examples, the typical constraint analysis for citing a landfill or store, or whatever it is. But I always emphasize the fact that the weightings make it all work.
The weightings are based on expert knowledge.
You have to know why you’re assigning weights to something before you can put it all together. Otherwise, it’s garbage in garbage out.
Understanding the context, the perspective, and the variables is still relevant today.
I get undergrads from all over the university.
I also teach an introductory course, specifically for graduate students. They’re even more interested because they’re already doing research, trying to figure out how to use GIS in their work. I get students from architecture, civil engineering, Near and Middle Eastern studies… you name it, the different disciplines are using it.
These students need an open, welcoming environment, so they see that they can learn this. I start with “What’s a map?” and we work our way up to more advanced topics so they can make informed decisions. They learn critical thinking and how to use the tools.
I taught my first online course at the University of Toronto about eight years ago. It was a fun experiment. I’m a techie person with an interest in photography and video. I thought this could be a great way to try something different and give students options on how and when they learn.
I started off with a basic PowerPoint presentation voiced over. It wasn’t exciting or great, but it wasn’t bad either. I experimented with a hybrid approach where I live-streamed my lesson from the lecture hall and students had a choice to attend in person or via online streaming. I also recorded these sessions and treated them as an asynchronous or time-shifted online course.
At the University of Toronto, I have an introductory GIS course, followed by the intermediate, advanced, and capstone courses ̶ a mixture of online and hybrid delivery.
A year and a half ago, I launched a series of four Coursera courses through their specialization program.
Coursera started in 2012. They were part of a movement called Massive Open Online Courses (or MOOCs). There was a lot of hype about them. Their competitor was MIT and Harvard, which created a nonprofit called edX.
The idea was to allow people worldwide open access to learning content and courses hosted by “prestigious universities.” They wanted to democratize the learning process by allowing everybody to learn.
Over the years, they’ve evolved, and MOOCs went through the hype cycle. At one stage, the New York Times declared that they would be a massive disrupter to higher education. That didn’t turn out to be the case. In the last two or three years, the term MOOCs is used less and less.
Coursera and others market themselves as online course providers. The massive and open aspects are not emphasized as much anymore.
Coursera has around 40 million learners, as they call them on the platform. With over 150 university partnerships, they offer thousands of courses.
An instructor at a university can create a version of their university course. The university partners with Coursera, who becomes the hosting platform. Coursera helps to structure and put the course together in a way they think works best. Then they market those courses to students.
Yes and no.
Similar to classroom teaching, there are good online courses and not so good ones. How’s the course put together? What’s the syllabus? What’s the instructor like? Your experience may also be relative tohow you’re taking the course.
Coursera has a vast number of courses and materials you can take for free. For some premium content, you pay a fee. Monthly subscriptions and different avenues make this affordable.
The courses are designed to be taught on a massive scale to thousands or hundreds of thousands of learners.
When developing such a course, the instructor needs to design it to scale and make it as automated as possible. That may translate into watching videos, completing multiple-choice quizzes, or doing a peer-reviewed mini-project at the end of the course. You still get to learn a lot this way.
It’s still fundamentally different from a university course where you get to interact with your peers, your instructor, and the teaching assistants. Feedback on your assignments is more customized and in-depth. The level of assignments is more rigorous.
They’re different things, and they’re meant for different purposes. It’s always worth trying it out and seeing if it works for your unique situation.
Online learning with Coursera is not a replacement for university learning. It’s excellent fortechnical subjects, though, like programming, learning Python, R, and Data Science.
People use it to upskill for professional development. But some aspects can be complicated. In my courses, students use ArcGIS, which they install themselves. Anyone who’s used ArcGIS knows that it can be tricky to install.
Imagine somebody sitting at home, trying to figure out how to install it on their own, and they get an error, or something goes wrong.
This scenario is course dependent, but this is how online education can be challenging.
The good part is that learners learn with real software, real applications, and try things out for themselves. Courses like Python have modules built into the Coursera website, where learners are doing the coding inside that page. You don’t have to install anything, and it works well.
There are variations in how the courses are implemented, but it can most certainly be a useful way to learn.
An expert on the subject decided on the key topics, the concepts, the order of things, and the best way to make it easy for you to learn. It’s almost like a library of materials that students can access and learn from in many ways. The same way nobody reads a textbook in one sitting, learners benefit from on-demand access to useful, digestible, and easy-to-follow materials.
At the university, I teach a series of courses from introductory to intermediate, to advanced, and to capstone.
We start with about 500 students a year in the introductory course. We get about 120 in the second course. From those, we get down to maybe 50 in the third course. In the capstone, there might be 20 to 25 students.
When I designed the curriculum for that sequence, I didn’t see it as a bad thing that many students take just one GIS course to learn a bit about it.
I developed the first course based on what students need to know and why they want to know it. They’re not going to become a GIS expert. They want to understand the basics so that if they go look at a map in the future, they have a critical understanding of a good one or the thought process that went into it.
It’s tempting to want to teach just the hardcore GIS nerds ̶ the ones that are going to end up in careers in GIS. Yet, I want to help people learn as much GIS as they want to know. That might be a single course, two courses, three or four.
I’m happy to welcome them into two more courses and make that as barrier-free as possible. But they decide how much they want to learn.
In Coursera, the completion rates are lower, somewhere between 10% and 12%. That’s not too bad for Coursera courses. Some think low completion rates are a problem.
I don’t see it that way.
Many people have no intention of completing these courses; they just want to learn a particular thing. Just because you didn’t read the entire textbook, you haven’t necessarily failed at what you were trying to do.
People go to YouTube and learn what they need to know about fixing their leaky faucet. They go in and learn what they need to know. If they like it, they might keep learning and complete the course. Or they might just decide it’s not for them.
My job is to make learning GIS open and inviting. You decide how much you want to learn.
I try to hook them from the beginning with their questions, interests, and what they need to know. Then, I give them a suite of tools and related concepts to get them there.
What kinds of questions can you answer with GIS? If you had a map, and you wanted to know something, how could you do that? What questions could you ask? What would be your thought process?
I won’t bore students with the syllabus or the exams on the very first day. I want to know what questions they have and how they think GIS will help solve those. I make it clear to the students it’s going to be a time-consuming but fun course.
I do spend time just on cartography at the beginning. Students love map design. They don’t realize that until they get into it.
Where should I put the legend? What color should I make this?
They create something that they show their friends and family. They know the basics. Then, as they start to better understand what GIS is, they see the wheels beginning to turn.
What could I use this for? Can I use it for something else, too?
They start looking at all the different data sources out there. It could even be something they’ve seen in a movie.
They map tweets. They use social media.
All I did was let them choose the data and the context. I get to see what they’re interested in and how they see it applying to what they’re learning.
What job title are you looking for?
Analyst? Specialist? Developer? Conservationist? Urban planner? Market research analyst?
What’s your focus?
During those foundational years, all I can do is to expose students to the basics and the various options out there.
Once they go forward, it’s not all about Python, R, and coding. Not everyone wants to be a developer.
What about interpersonal skills, integrity, professionalism, reading, writing, math, communication, critical and analytical thinking, teamwork, and creative thinking?
Employers value people with elastic brains, creative and good at working with other people, and tackling things differently. I want students to have solid skills. I want them to feel confident when they go out.
We launch students into their careers understanding they need to continue learning. They’ll be working independently, applying critical thinking. They’ll need to amass a catalog of skills ̶ technical and soft ̶ to be successful.
Python and programming are popular with graduates. They’re definitely something you see in a lot of job ads.
If you haven’t done programming before, take a course and decide if you like it. But don’t get a job doing coding if that’s not something you enjoy. Try things out, explore different options, and see what’s out there.
What do you like learning?
Some take Python courses, and they think it’s incredible. Others decide it’s not for them.
What marketable skills should you get?
Those that align with your interests.
It’s not exactly new, but it’s changing the fundamental ways we’re working with maps. Back in the days, what we worked on got printed.
How often does a web map get printed today?
Not very. Probably ends up on a PDF. Maybe on a poster.
Everything is web-based now. Maps are communicated and distributed to a mass audience. So is the data; we’re getting it, sharing it, and mapping it on a mass scale.
LIDAR and the advent of drones are incredible. Data is processed fast, and we generate 3D models quickly. Phones have LIDAR scanners in them. I’m amazed by the volume of data that’s available near real-time.
Machine learning and AI, pattern, and object recognition are exciting. We haven’t even touched the surface on so many things.
Undoubtedly, Don puts his students at the center of their learning, just like Google Maps put us at the center of the map. We are finally moving away from one size fits all educational methods.
In the long line of expert guests who appeared on the podcast, Don’s another one who reminds us of essential skills being vital in GIS. Can you explain your ideas to people? Can you make them understand how the thing you’re working on benefits them?
In GIS, we are in the service industry, and we’re part of the solution, not the entire solution. We shouldn’t make GIS sound magical or outer worldly. People won’t understand it and, therefore, won’t implement it in their critical infrastructure, and that’s a lose-lose situation.
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Instead of one long antenna that sends one big pulse at one time and then collects the pulses that come back, SAR have a much smaller antenna that sends lots of pulses in quick succession over time as the satellite goes through space.It “listens” to the pulses that come back to it when it moves through its orbit.That’s why the name synthetic is applied to the radar.
I liked the geospatial component of things. I enjoyed solving a problem and then seeing the result. It wasn’t just a Microsoft Excel model or some database table. It was something I could visualize in GIS software. If there had been a path to becoming a more in-depth GIS analyst at this company, I might have stayed on it.
What is a voxel?. It’s a 3D volumetric pixel, a cube. But voxels are nothing new. They’ve been used extensively in two key areas within computing. Computer games render worlds and use voxels instead of polygons. Minecraft is a good example — it’s a voxel rendered world. Gaming companies love voxels for their multi-resolution capability over polygons. Robotics uses voxels for image processing to reduce the size of LIDAR point clouds and to create small dynamic maps — or what we call VOG (Voxel Occupancy Grid) — for robots.