Today is the day! It is the last essay in our mini-series covering the eight knowledge areas of geospatial computing. If you missed the earlier essays, you can find links to Part 1 (The computational side) and Part 2 (The geospatial side). This essay will look at the dimensions and intersections of these areas.
Technologies
Across the top of the core areas, we have the technologies dimension. This powerful combination applies parallel computing principles and practices to spatial modeling and analytics. While parallelizing models and methods is nothing new, cyberinfrastructure (CI) is the game changer. Why? It makes two things easier.
First, CI makes it easier for scientists and developers to access advanced computing technologies. Therefore, more people can parallelize and run their spatial models and analytics. This topic was covered in a recent paper written by Manish Parashar, the Office Director for the Office of Advanced Cyberinfrastructure at the National Science Foundation (see the paper in the "Links of the Week" at the bottom of the newsletter).
Second, CI makes it easier for anyone to access those parallelized models and analytics. Providing user-friendly front-ends through web portals and science gateways broadens access even further. In this way, CI creates a multiplying effect. More people can access powerful resources to develop and run parallel models and analytics. Once the codes are ready, they can invite nearly anyone to use them on the same powerful resources through something as simple as a web portal.
But! To achieve this level of greatness requires a developer whose understanding spans spatial modeling and analytics to parallel computing. Or a team. What if you were that developer? The rare few who can gain mastery across this technical dimension tend to have ample career opportunities in academia, government, and industry.
The key challenge is building knowledge and expertise across this entire dimension. So often, we get GIS professionals who are very competent in spatial modeling and analytics but have little to no experience in parallel computing and computational architectures. This knowledge deficiency limits how they can leverage computational infrastructures to scale up their spatial models and spatial analytics. On the flip side. We get computer and computational scientists with significant parallel computation and cyberinfrastructure expertise. But, they have little to no knowledge of spatial models and analytics. Therefore they lack the domain knowledge and ways of thinking to tackle spatial problems.
We can boil this conundrum down to the question many ask me.
"Do you hire a programmer and teach them GIS?"
or
"Do you hire a GISer and teach them programming?"
I think we are asking the wrong question. The right question is: "Why are we not educating and training individuals to do both?" (Hint: We are starting to as a field, which I hope to write about in an upcoming essay). But right now. This minute. There are opportunities for individuals who have deep expertise in both.
Problem Solving
The bottom of the figure is the people and problem-solving dimension. This dimension includes the ability to apply spatial thinking and computational thinking for problem-solving. Individuals with strength along this dimension tend to have intermediate literacy in interdisciplinary communication skills. As a result, they can act as a bridge for different ways of thinking across teams and disciplines. Notice that this dimension does not contain computing or programming, so individuals here can be less technical.
Numerous people have strength in spatial thinking but cannot translate that thinking into computational systems. It can be challenging to take the nuances of spatial processes around the globe and boil them down to the 1s and 0s of a computer. Whether that thinking gets actualized as geospatial data (raster or vector) or geospatial computation (parameters, models, etc.), it can take significant creativity to reimagine how to capture phenomena and processes as data structures and algorithms.
A significant win for spatial problem solving is taking computational thinking principles and applying them to spatial thinking. Take abstraction as an example. We can abstract the complex shape of a city to a single point if the spatial extent is large enough. Everyone is familiar with this level of abstraction if they have seen a state map dotted with points representing all the cities across the state. Furthermore, we can apply computational abstraction in many forms, focusing on the most pertinent details, ignoring some inherent complexities, and then adding more detail when necessary.
Fewer may be familiar with disease modeling, but we can use it as an example. In the simplest case, we can say that we have a population of people susceptible to disease (let's say in a major city). Ten people are infected in the population. Each infected person has a tiny percentage chance of infecting other susceptible people. So the number of infected people grows, and the number of susceptible people shrinks (because they are infected now). This is called a "Compartmental Model" and is simple. However, it is unrealistic because no person interacts with everyone else in a major city. So we can add more details like building a network of connections where infected individuals can only infect people in their network—or dividing people into groups (household groups of families and work groups of co-workers). But! What about schools, public transportation, friends, gyms, grocery stores, ... The list is endless! That is why abstraction is so powerful.
Start with simple. Then add details. Similarly, concepts such as decomposition (breaking down a problem into smaller sub-problems) and pattern recognition (if this strategy worked here, will it work over there?) are powerful and complement spatial thinking in many ways. It just takes practice and exposure to ways to connect them.
It also works in reverse. There are ways to use spatial thinking to complement computational thinking. There will be an essay on this topic in the future.
Disciplinary Silos
I will not spend many words discussing the two scientific dimensions of geographic information science and computational science because many academic programs exist to build literacy along these verticals. Resultantly, many people around the globe already have expertise in one of these dimensions. That is why they are called a GIScientist or computational scientist, after all!
If that is you, then keep reading because one of the goals of this newsletter is to build up individuals like you. So you can build on your background and expertise. Developing literacy in another dimension and building your skills in a new knowledge area takes some time, but not that much. Plus! There are more opportunities and fun topics embedded within these eight knowledge areas. GeoAI. Geospatial Data Science. Edge computing. Domain-specific programming languages (for geospatial problems). So there is plenty to talk about as we venture beyond geographic information.
Links of the week
Democratizing Science Through Advanced Cyberinfrastructure
by M. Parashar in Computer
IPUMS Data
The source of census and survey data around the world.
Shout out to Steve Ruggles, creator of and mastermind behind IPUMS, who won the MacArthur Fellowship (Genius Grant) this year for his life's work: IPUMS!
IPUMS IHGIS
Geospatial + IPUMS data, what could be better?
If you want to learn more about the eight knowledge areas of geospatial computing, you can check out our academic paper that dives into more detail.
Shook, Eric, et al. "Cyber literacy for GIScience: Toward formalizing geospatial computing education." The professional geographer 71.2 (2019): 221-238. [Journal article link]
This essay was delayed due to illness. I know you could barely sleep waiting for this third part. Fortunately, you can rest easy tonight. I finished the essay after I recovered. I apologize if you experienced poor sleep over the past few nights waiting for this essay, and I will try to make it up by sending another one this week. Which reminds me...
This week I'll be on the road! I'm looking forward to my visit to the Department of Geography at the University of Alabama. I will give a talk as part of the Department's colloquium series entitled "Advancing geospatial education and research: A computational approach."
I look forward to meeting faculty, staff, and students and hearing about the extraordinary work at the University of Alabama.