Notes on geospatial software labor narratives
So I’ve written a bit on here about the business models of geospatial software but I haven’t written as much about the labor of creating and maintaining geospatial software and data. I’m circling the point of “OK I really gotta buckle down and write this fucking thing” with the dissertation, and I know labor is an important framing to bring into it but I’m not totally sure how to situate it in the text. For now, I’m just trying to think through the different kinds of geospatial software workers I’ve encountered doing interviews and reading primary sources.
What follows isn’t really a complete taxonomy insofar as I’m just sort of lumping “software industry” jobs all into one category despite that including loads of kinds of labor (engineering tends to get top billing but that’s not even getting into the differences in remote sensing engineering versus routing versus web, then we have to talk about all the non-technical work like sales, marketing, customer service, and business development). For the purposes of my dissertation I’m trying to identify what, if anything, differentiates the work of doing geospatial software from other kinds of software so getting into the difference between frontend and SRE labor and QA isn’t really crucial here I think.
Ground truth/data collection and quality assurance
This is a category of geospatial software labor that requires some amount of training but isn’t necessarily a role that requires prior geospatial software skills. In the United States, a lot of this labor is done by people driving cars around for companies like Tele Atlas and Google. The development of increasingly sophisticated sensors and cameras to cars hasn’t totally deskilled the work of ground truth data collection, but certainly there’s less manual annotation work involved now and I imagine that the end goal of some of the autonomous vehicle work is to further reduce dependency on human labor for that kind of data collection. Even with increased automation of the data collection, I do think it’s important to acknowledge that this is not trivial work: driving for hours on end is physically taxing and drivers take some responsibility for making sure that the sensors and data capture equipment on vehicles are in good working condition. Honestly, this is a sector I only have gotten a little bit of insight into and it would be cool to learn more, especially about what that kind of work looks like today.
Data collection work has also been devalued by the advent of what geographers decided we should call “volunteered geographic information”—people writing reviews of places or local business owners adding information about their store to Google Maps because it’s an important part of marketing a business to customers (in this respect, their geographic information is “volunteered” in the way kids in the Hunger Games “volunteer”—that is, participation in the system is mandatory).
GIS professionals
Not in the sense of the weird not-sure-this-is-a-real-accreditation thing but in the sense of people whose day-to-day work is “doing” GIS. The Ken doll saying My job…it’s just map. “Doing GIS” for work cuts across a lot of different sectors with their own specializations—military, municipal government, utilities, environmental services, I guess real estate, extractive industries, other stuff I am probably forgetting. Basically, anyone who’s in the r/GIS subreddit (a place I lurk a lot for research reasons).
I’ve been thinking a lot about the work that went into making the GIS professional a specific category of worker—as in, one that’s distinct from any other kind of data worker. Esri seems to be a big part of this story. Although they weren’t the only game in town as GIS coalesced as a thing in academic geography, the idea of certificate programs and advanced degree programs for GIS seems to be largely credited to them. The idea of making geospatial software power user a specialist job makes sense in the lineage of like, having in-house or contract cartographers (which itself became increasingly formalized and academic-ized after World War II, partly as US geography programs sought to distinguish themselves within the academy and retain their personal scholarly fiefdoms). The idea of labor specializations reliant on a particular subset of softwares or computational methods also isn’t unusual—graphic design, video games, special effects, architecture, and audio engineering are like this. The emergence of “data science” as a distinct category of worker seems to have been a weird wrinkle in the whole notion of the GIS professional as a labor category, because there’s a lot of overlap between things data science tools do and things desktop GIS software do to the point that specializing in geo might seem a bit limiting.
Whether it actually is professionally limiting isn’t totally clear. The GIS Professional Network (formerly URISA, the Urban and Regional Information Systems Association) does an annual salary survey that provides some insight into the pay scales of GIS specialists. The medians for a lot of them aren’t terrible but like, I’m on track to make the queenly sum of $38,000 as a graduate student this year so my sense of what’s a “good” salary has been fundamentally broken by academia. It’s also a sample of people who engage with a professional association. Anecdotally from reading r/GIS and searching through GIS job listings, the vibe seems to be that analysts or technicians specializing in geospatial make less money than their counterparts in data science or other technical fields. Some of this might be attributed to the fact that a lot of GIS work happens in governments and governments tend to pay less than industry? I don’t really think that’s a good enough answer, especially when casual searches for GIS jobs in defense and GIS jobs in municipal government show pretty big differences in salary ranges. (That being said, one other place that a lot of GIS professionals will laterally move is actually working for Esri. These people become marketing or sales specialists who can use both their knowledge of the technology and of whatever sector they originally came from to build good client relationships.)
When web-based geospatial tools started to become a thing, there was a fair amount of rhetoric around them as “democratizing” technologies that expanded mapping beyond the purview of experts in specialized monopoly software and technical methods. While critics correctly pointed out the flaws in that rhetoric (“democratized” data and software has in practice mostly been to the benefit of new monopoly firms concentrating power), it’s hard not to read a hint of panic in those critiques. Which is a little frustrating for me personally because like oh no, what if someone who’s never studied geography learns how to make maps with javascript instead of ArcGIS Pro and…gets into geography?? This is literally part of the story of how I ended up going to get a geography PhD! But I can understand why Google encroaching on geospatial stuff would have been worrying for some GIS people: when the majority of a person’s career has been based on being an Esri power user or an educator teaching people to be Esri power users, the introduction of new tools destabilizes and potentially devalues that expertise.
I suppose some of this comes back to geography’s own seemingly-constant existential panic about its own relevance (which in the United States is apparently a story about Harvard killing its geography department maybe because the chair of the department insisted on giving his kind of incompetent secret boyfriend a job, which I realize sounds like a Drunk History summarizing of what happened). GIS jobs give geography departments something to point at when asked about the applicability of the degree or the “success” of the department. Whether those jobs are wildly lucrative isn’t really as important as the fact they exist and they’re the place that can provide the requisite training for them.
Geospatial software engineering as its own weirdo niche
Esri is probably the biggest employer of software engineers working on geospatial stuff specifically, though it’s kind of amazing that’s the case when you look at how weirdly run it is. As far as I could find, they still pay everyone at the company an hourly wage instead of salaries, and it’s still entirely privately held so the promise of stocks vesting or whatever isn’t there to entice engineers. I don’t think those salaries are necessarily terrible (especially for Redlands, where the company is based), but they’re not rank-and-file Big Tech or even medium-startup salaries. And there’s certainly documentation of bad labor shenanigans at the company: they were subject to a DOJ investigation in 2022 and a class action lawsuit in 2025 around unequal pay of female employees. It’s also probably pretty isolating to be a software engineer living and working in Redlands, California—I guess you could eventually switch to a job in LA or something, or move, but man.
Most of the problems of doing geospatial on the computer bottom out in the baseline hard tasks of computer science—stuff like graphics, routing, and Euclidean geometry. Remote sensing? I think you mean pixel analysis, my friend. (I jest, and I love my friends who do remote sensing work, you are all smarter and hotter than me.)
Sometimes the trajectory of geospatial software people is getting into software engineering via geospatial—like GIS specialists who learn just enough Python to be a problem and work their way into full-stack dev skillsets. For my interviews I’ve talked to a lot of people from the other trajectory: computer people who got into geospatial software kind of by accident and then stayed in geospatial software. The people who tend to geek out on this stuff are pretty likable, the problem space is sufficiently interesting, and for some engineers it’s gratifying to work on technical problems where the real-world stakes are very materially legible. Unfortunately, working on software with obvious real-world stakes also means having to navigate the morality of a lot of the more financially lucrative categories of geospatial software engineering. Remote sensing has lots of great human rights applications, but the people spending top dollar on it are integrating it into military kill chains. I don’t think this is totally unique to geospatial—people who work on signals processing and compilers can be part of a kill chain too—but it’s definitely more concrete there.
The nicheness of geospatial can also become a problem if a software engineer experiences conflict within one of the handful of companies focused on geospatial. I don’t know the full count of Mapbox workers who left geospatial entirely after the contentious union campaign in 2021, but it’s definitely not zero. It’s a completely understandable move—it would be impossible to avoid running into people from Mapbox at other geo industry events, and given how many people move laterally from one geo company to another it’s very likely that some shitty person in Mapbox leadership who spammed the company Slack with anti-union propaganda could end up a former union organizer’s manager at some new job. That’s not even getting into the possibility of being blacklisted—which, I don’t know if that’s happened to anyone but also, can you name any geospatial software company where the workers are unionized?
I don’t think that the Mapbox union debacle is a uniquely geospatial software story—tech companies pretty famously across the board are anti-union, this is a formative aspect of Silicon Valley history. But one unique aspect of the Mapbox union story is how it illustrates the gap between the commonly used marketing and recruitment narratives of geospatial software companies and the general mismatch of “billion dollar startup exits/massive profits for people at the top of the corporate hierarchy” with “public interest technology in a non-hostile workplace.”
Not really sure how to wrap this one up
The big picture question here, I guess, is how (if at all) do the working conditions of these different kinds of labor affect the design of geospatial technologies and by extension how people understand the world around them through geospatial technologies. I don’t know if I have a great answer! I do think that the labor conditions matter, but it’s harder to draw a direct line from those to product outcomes in software or decisions about how a municipal government releases geospatial data or designs a map. I’m also a little depressed because wow, the world is extremely fucked up these days and working on a dissertation feels absolutely like one of the silliest little indulgences one could possibly engage in.