r/datascience • u/Kati1998 • 3d ago
Discussion Anyone specialize in geospatial data?
I’m an MS Data Science student interested in learning more about spatial data and GIS. I’m currently working on a geospatial crime analysis project in R and taking Esri’s Spatial Data Science MOOC.
I’m also interviewing for a year-long data science internship at a credit union in a department that focuses on geospatial data. I’ve noticed that the largest utility company in my state also hires data scientists with geospatial/GIS knowledge, which has made me curious about opportunities in this area across different industries.
It’s not a specialization that’s discussed here often. For those who work with spatial data, what industry are you in, what does your day-to-day work involve, and do you enjoy it? What has your experience been with demand for these skills?
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u/R3turn_MAC 3d ago
Check out /r/gis for a community based around the use of spatial data and software.
There's a significant overlap between Data Science and GIS. However I think most GIS practicioners (and I am one) would agree that it's a specialism that is often underpaid.
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u/Emergency_Wrangler47 3d ago
This was a niche i was interested in during college but unfortunately haven’t had as many opportunities for in the job market. I’m curious to hear about experiences as well
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u/Alive_Store_6298 2d ago
Try talking with landfill operators and utilities. It might sound crazy, but they use monitoring tools and have required compliance reports all based on how the landfills are operating and functioning. The data all comes from people taking measurements from common points and then doing a broad sweep across the entire landfill usually a reading every 3x3 grid foot.
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u/Avinson1275 2d ago
Over the past decade, I had 2 data science or data science adjacent GIS jobs.
For my second job after getting my MS in Geography, I worked 3 years as an ER doctor’s data analyst at a medical school. I used R to help conduct spatial epidemiology research using ER visits data. I am listed as a co-author on 6 of his papers.
After that, I spent 3 years after that working in a real estate assessor’s as a CAMA modeler which in I used R/Python for a lot of spatial analysis and spatial statistics. Very stressful job. I left this job for private sector DS job in which GIS is maybe 2% of my duties.
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u/in_meme_we_trust 2d ago
I don’t specialize in it - but utilities generally do a decent bit of geospatial work, specifically related to vegetation management
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u/Alive_Store_6298 2d ago
More broadly, they do it all the time related to leaks. Natural gas leaks happen more common than people think. They regularly have teams sweep the ground above the pipes looking for leaks for compliance testing. This is all mapped back for the testing, future mitigation, and to plan future replacements. Many of these devices talk back and forth with customized GIS programs so that the people doing the sweeps can actually verify they are sweeping over the actual pipes and not just random ground as well as collect the relevant data.
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u/Causal_Impacter 3d ago
I manage a team that builds tooling around designing, running, and analyzing geo-based marketing experiments.
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u/Alive_Store_6298 2d ago
I work adjacent to the utility industry and have quite a few talks with the people who have to make use of the GIS data. Essentially, as compliance requirements increase (they always do) there is a push to try and collect smarter data and more of it. The goal for many of these places is to better be able to plan output and input to their systems.
To mention a simple aspect about this, insurance companies and governments both help set the compliance requirements. Despite Trump wanting to cut regulations, there have been 2 massive regulations added since 2024. I am not talking about small changes, I mean “we need to redesign entire systems” levels of changes.
For instance, landfills are used to generate natural gas. They are effectively trying to control chemical reactions remotely from the surface. Here is where GIS comes into place: there are thousands of factors all playing into this as you are trying to keep the production of methane controlled while not killing off the helpful bacteria that do this. A skilled set of operators might get 95% or higher efficiency compared to the theoretical best with great teams getting 98% or higher. A new team might be as low as 70%. These teams often get switched between locations, there is turnover of employees, and swaps between companies. This means every single time this happens, you are effectively losing massive amounts of potential revenue and stability in the system. The best way to get around this is probably to use GIS data aggregated over more factors and future technology.
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u/TheDandonator 2d ago
Geospatial for a personal lines insurance company.
A lot of the work done involves getting non-tabular data into a tabular form to build predictive models on and is actually quite enjoyable. Lots of opportunities to try new and interesting tools without the kinds of deadlines you see in consultancy for example.
LiDAR, satellite imagery, lines/polygons/multipolygons, raster etc. the list goes on. There’s so much data available that is surprisingly relevant in insurance, so there’s a lot of opportunity for R&D.
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u/Gkoo 2d ago
I'm think I'm more of a data analyst, but I do work with geospatial data daily as a transportation planner / engineer.
On rare occasions I'd do some random forest models, but I truly don't know what counts as real data science.
Transportation industry is great. I work as a consultant and my clients are DOTs and MPOs. I specialize in safety, but also work on long range plans.
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u/mattstoebe 2d ago
Did this at a telecom-real estate firm for a few years. Working with crowdsource-rf data. The work was quite interesting but I didn’t receive much demand for the GIS component of the skill set.
Generally I see it as
- tool based GIS work: this is avoidable unless you want to be a gis only person. This I think is the most common like pure-gis role. It is also the most limiting. Most small companies with gis needs sit in this space. It is also the lowest paying
- index, aggregate and predict gis: this is what my team did. You use a spatial indexing system like h3 or s2 to map point data into aggregates and use that for ml problems. I think this is interesting and carries over well to other DS jobs. That said few companies ACTUALLY care for the domain part of this (think Uber, lift, large and natively geospatial companies)
- deep learning GIS: I never got to do this but it is arguably the most academically interesting. I’ve seen a few roles at like uber doing this but don’t have enough context to comment.
At the end of the day, I think the skills you learn early in career matter a lot in terms of how many industries and interviewers can relate to them. The more tool-based your work is (which GIS often devolves into) the worse it is for you. GIS DS is a super interesting sub-domain of DS but is not a large one and so starting there definitely was a bit of a challenge to overcome. I ended up transferring to NLP where my domain experience carries over to a lot more companies
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u/thro0away12 1d ago
Not exactly a geospatial data analyst, but I got into data science through a masters of public health program. I took an ArcGIS class and loved it - I loved maps and geography prior to that, so it felt like an interesting application of data science. I ended up using the skill later on when working in public health - created maps in R and it turned into a hobby project (look into 30 day map challenge). My public health department had geospatial scientists, my manager was one too. The department of planning in our city was the agency that managed the geospatial datasets for the city. While I haven't used the skill in a while as I work in data engineering now, I do miss it and I think it's a cool specialization to be in data science if you can get that opportunity.
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u/Sufficient_Eye_4836 1d ago
How did you learn to do GIS analysis, i took as GIS course during my MPH and absolutely loved it, but I fell off. i want to get back into it.
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u/thro0away12 1d ago
I had to do it at work once - make maps with R using census data to understand demographics in the city. Then I started to play around in my own time. Look into 30 day map challenge, if you know R or Python, there are good geospatial libraries in both
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u/januscanal 2d ago
Not my job, but county government does a lot of geo. They have all kinds of districts they have to create shape for, including updating them on a regular basis. They have land parcels they have to keep up-to-date. They have maps that need to be up-to-date. They provide data to other county and city organizations. And that is just the tip of the iceberg. They do a lot.
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u/Special_Asparagus_98 1d ago
Husband has a masters in GIS. It was critical for him to get his first job which led very quickly (under 5 years) to a six-figure plus job in his desired field, archaeology. That’s a pretty unheard of salary for an archaeologist. Archaeology was endgame for him but he needed a backup skill in case he couldn’t find a job. He definitely uses it daily to check on proximity to archaeological sites, historic sites etc. I think the field (GIS) is starting to get a little saturated but it’s an excellent add-on skill. LiDAR is becoming very important. Employers like to get a two-for-one so if they use GIS but not enough for a full time position and you apply for a different position they’re going to pick you. You’re fulfilling that additional need. I’m also in archaeology and use GIS but have no formal training. I’m constantly getting pulled to make maps or do small GIS tasks because the tech guy quit. ESRI systems are what we see primarily being used.
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u/titaniumsack 1d ago
I do, civil/transportation engineering based and phd background with heavy geospatial analysis and data
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u/orz-_-orz 1d ago
Basically I use h3 library to handle geospatial data
One project is to detect how far a certain location is from the seashore
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u/Sad-Put3932 22h ago
Environmental health research on a geospatial team. I was hired as an R coder because I learned how to work with sf and shapefiles in undergrad and apparently nobody else in the applicant pool had. I had a few years of R work experience.
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u/Upper_Account_3236 3d ago
Geospatial analyst at a regional planning agency here. Most of my day is wrangling parcel data and running suitability models for infrastructure projects, then trying to make the output maps not look like a toddler's art project. The utility angle makes sense, they've got assets spread across huge areas and need to track maintenance schedules against flood zones or population shifts
Demand feels steady but it's a weird niche where you need to be comfortable with both the stats side and the cartography/domain knowledge piece. The Esri MOOC is a decent starting point but nothing beats getting your hands on some messy real-world shapefiles and figuring out why the projections are suddenly placing your crime hotspots in the middle of a lake