Spatial Career Guide – Cartography and Visualization Specialist

April 2, 2012

This is my third post in the Spatial Career series.  I’ve covered GIS Software Developer and Geospatial Analyst and in this post I will provide guidance to the aspiring cartographer and/or visualization specialist.  Of the six paths that I set out to cover in this series this might be the one that I know the least about.  So, you can take these suggestions with a grain of salt but hopefully I will provide a few useful nuggets.

Traditional cartographers, those who produce maps for printed material, still exist but not in large numbers and my suggestions probably won’t help if you want to go down the traditional route.  Some of the legends in this arena, including Bill Loy, Stuart Allen, David Imus and probably several others that deserve to be mentioned are connected to the Department of Geography at the University of Oregon where I studied.  You can probably earn a decent living this way if you’re really dedicated and find a mentor who can teach you the craft.  But, I can’t help.  Instead, I will focus on the more modern computer-enabled cartographic visualization, interactive map design and data graphics production side of things.  I see a lot of opportunity here but it requires a new combination of skills that don’t always go together: programming and visual creativity.

The cutting edge in cartographic visualization involves developing web and mobile interfaces.  So, in a sense, this path involves training similar to the GIS Software Developer career.  The major difference is that you will need to become an expert in user interface design.  You still need to be able to write code but you don’t have to be an algorithm guru.  So, while in college, I would recommend the following.

Hopefully this is pretty obvious but take every course on Cartography that is available.  Yes, take some GIS courses as well.  They probably get taught together.  Don’t worry about becoming an ArcGIS master.  Not that it would hurt.  But it’s probably just as important or more so to master Adobe Illustrator and/or other graphics software.  Also, study a bit of cartography’s rich history.

Perhaps most importantly, find an opportunity to work on cartography-related projects.  At the University of Oregon all the best cartography students worked in the InfoGraphics Lab and learned a ton from Director Jim Meacham and his staff of cartography and visualization experts.  Ideally, you would find a similar lab at your college/university and get involved in any way possible – volunteer, get a work-study job, whatever it takes to get exposure to the people who work there.

Take a combination of Human and Physical Geography.  See my post on becoming a Geospatial Analyst for more details on this.

Read everything you can on data visualization.  You’ll want to read everything written by Edward Tufte.  You should also check out some of the older work by authors like William Clevelandalong with some of the newer, more cutting edge ideas by new practitioners like Yau (note: I haven’t read Flowing Data by Nathan Yao but it’s on my list and even if it’s not a timeless classic you need to expose yourself to contemporary perspectives).

Learn something about remote sensing and other major sources of geographic data.  Again, see my post on becoming a Geospatial Analyst for more details on this.

Also, read what I wrote about problem solving, writing/communication and teamwork (bullets 2, 3 and 4) in the GIS Software Developer post.  In particular, I would highly recommend that you study as much statistics as you can.  Visualization is within the domain of statistics, perhaps even more so than it is within geography.  You need to be able to speak with statisticians in their language; even if you can’t do so fluently, at least pick up the basics.  If you like statistics and would like to do some self-study, I would recommend learning R.  It’s freely available open source software available for download from r-project.org.  Aside from being free, it’s really powerful especially with all the add-on packages.  Only problem may be that it’s a bit more challenging to learn than other more point-and-click oriented stats packages.  There are a number of books on R programmingwith how-to guides.  Find one that works for you and give it a try.

Study graphic design in the art school.  You need to learn the basics of good design.  This can be via university coursework, self-study or you might find good courses at the local community college with better hands-on learning opportunities.  You should also pick up skills in the Adobe Creative Suite or something comparable.

Study User Interface Design.  I don’t know how readily available classes on this topic would be – I think it probably varies quite a bit from campus to campus.  You probably have lots of good ideas on this already because, unless you’re my age or older, you’ve been interacting with software your entire life.  But, it’s not a bad idea to read about standard protocols and other people’s ideas on what makes for good interface design.  Don’t be afraid to ignore the gospel according to some self-proclaimed authority.  You want to cultivate your own unique style.  The most successful designs seem to take simplicity to a new level.  Beware the temptation to offer more options and powerful features.  History has shown time and again that users prefer ease-of-use over just about anything else.  I haven’t read this book by Steve Krug
but it looks like a winner and you can explore similar resources from there.

The best bet on this path, in my opinion, is to blend this with the GIS Software Developer route.  The successful cartographer types that I see have taken up web development tools like Adobe Flash (although this technology may be headed for the dustbin).  Maybe you’ll want to become an HTML5 expert – combining programming skills with user interface design artistry to create the next generation of cartography for mobile devices and whatever comes next.

I think the future here is very bright indeed.  I believe that cartographic visualization will play a central role in addressing Big Data challenges.