Editor's Note: In a field that evolves as rapidly as geospatial information science and technologies, the idea of “getting a GIS job” may not be as straight-forward as it sounds. What are employers looking for, and how do you know that your training and education will get you there? Join Directions Magazine as we continue a short series of articles examining these topics.
Behind the keystrokes involved in the production of a single GIS-based map stands all of the data, hardware, software, standards and regulations, GI science research and the human interactions that have taken place to make that production process possible. What is the role of openness in that world, and how does openness affect those seeking GIS-related jobs?
Open GIS, as it has been labeled, includes the open dimensions of data, software, hardware, standards, research, publication, funding and education. Daniel Sui, in his 2014 Transactions in GIS article "Opportunities and impediments for Open GIS," envisions this as the approach that can be successful and effective at addressing the thorniest and most wicked problems we face in the world. Open GIS involves solutions for big data and scalable applications that align well within a broader Open Science context. This is an important characteristic of the Open GIS movement, recognizing that science, defined broadly, has already undergone a paradigm shift towards openness. Open access to research, data, codes and methods makes it easier for more scientists to both replicate and expand knowledge. Since many of the issues that science is addressing rely on the contributions of the geospatial sciences, being able to collaborate in that world will require parallel – rather than sequential – changes in our world too.
For many, the first association is with open source software, whether it is free or not. With open source software, one is able to access and modify the computer code that makes the program run. Why would you want to do that? Well, let’s use the sausage analogy. You are curious to see what has gone into the production process so that you can modify the ingredients, or the mixing process itself, to adjust the outcome. This doesn’t mean that all users of open source software categorically disavow the Styrofoam-plated, plastic-wrapped links readily available at the local market. It’s just a different experience, whether you are the one with your sleeves rolled up and doing the work in the kitchen or simply an appreciative consumer of a product that can be changed to be just right for you.
In practice there are dozens of different reasons, and combinations of reasons, for why OSS is part of the world of GIS computing today. From an educational perspective it can be empowering for students to understand what is happening and why. In this way the fundamentals of geospatial science and data analyses are disentangled from software manipulation. As Vaclav Petras and his colleagues from North Carolina State University explain in their article, "Integrating free and open source solutions into geospatial science education," published in the ISPRS International Journal of Geo-Information: “... to help students gain an understanding of the manner in which geospatial algorithms are implemented, we will ask students to find and study the segments of code in GRASS GIS that underlie the geospatial analyses discussed in a lecture, such as computing slope and aspect or bilinear interpolation between raster cells.”
Persistence to learn at this level has its rewards. In his article, "Teaching introductory GIS programming to geographers using an open source Python approach," published in the Journal of Geography in Higher Education, Stewart Etherington wrote: “I acknowledge that some geographers will want to learn specific ArcGIS Python programming skills. However, having learnt how to program Python in an open source context, it is easy to then program with ArcGIS – but the reverse is certainly not true.”
Thus his strategy is to cover programming as a general skill, Python as the language, and GIS as a specific application. As was noted earlier, the skill set of programming together with a capacity to problem-solve is a particularly powerful and pertinent combination for those eager to procure – and maintain – GIS employment.
OS tools and packages are already common in GIS. Programmers will intersect with database developers while using PostGIS, PostgreSQL or MySQL. Those involved with mobile- and web-based applications may encounter OpenLayers or TileMill. Cartographers and others using forms of information visualization are heavily investing their efforts and resources with Mapbox, Leaflet or Carto, among many others. QGIS, GRASS GIS, and gvSIG are just a few of the more extensive and developed GIS packages. Collectively, these knowledge areas are extensive and GIS students would be challenged to acquire expertise across all of them. Likewise, it is challenging for faculty in formal academic programs to maintain a curriculum – and their own knowledge – as current and comprehensive as it could be in these areas. One program, New Maps Plus at the University of Kentucky, has designed its digital mapping curriculum specifically around these emerging technologies, many of which also happen to be open source.
Regardless of how you acquire the expertise, the most important tactic will be to ensure that your knowledge is well-aligned with the needs and practices of your intended work or industry. Those who develop data with the intent to distribute will need to know all about open data standards. Globally, the business sector is already steeped with open source software, so step aboard if you will be interacting in this domain. Are you a researcher or data analyst and expect to continue this activity via GIS? If you haven’t already, prepare to dive into R. The versatility and power of R is evident by the explosive growth in its usage within the geospatial sciences. Esri knows this too and formally joined the R community in recent days.
The open geospatial world is full of vim and vigor. Especially outside of the U.S., designing projects to minimize costs and maximize long-term access is key. This is one reason why the lowly shapefile has had such an enduring lifespan. In practice, however, it’s a mix of proprietary and open, commercial and free, private and public, restricted and not, that gets a job done. The complexity of today’s work precludes simple, one-track solutions to meet all needs. The first thing you can do to prepare yourself is to do your homework. Don’t do yourself a disservice by ignoring or disrespecting whatever is the industry standard in your area. But then appreciate the nuances. Who’s using what and for what tasks? What are the expectations and allowances for your own professional development, especially if you will need to get yourself up to speed with a new data format or application? Will there be local support, or will you be expected to figure things out on your own? Let’s hear it for online user communities.
The second thing is to be as ready as you can be for that new learning. Think at the meta level and extract yourself from the weeds whenever necessary. If you know how a spatial database works in general, you’ll be better poised to figure out specifics from one package to another. Document your efforts and keep track of your steps. Relax your anxieties around Github, even if it’s just to teach yourself how to read through things on it. Appreciate the distinctions between open, proprietary, free, commercial and the hybrids. Unless you work in a complete bubble, it’s likely you’ll eventually interact with the world of OS.
Etherington, Thomas R. 2016. Teaching introductory GIS programming to geographers using an open source Python approach. Journal of Geography in Higher Education 40 (1): 117–30.
Petras, Vaclas; Petrasova, Anna; Harmon, Brendan; Meentemeyer, Ross; and Mitasova, Helena. 2015. Integrating free and open source solutions into geospatial science education. ISPRS Int. J. Geo-Inf. 4, 942-956; doi:10.3390/ijgi4020942.
Sui, Daniel. 2014. Opportunities and impediments for Open GIS. Transactions in GIS, 18(1): 1-24. doi: 10.1111/tgis.12075.