US dataswitch to UK
Geographers
creating and modifying maps, graphs or diagrams, providing geographical information systems support to the private and public sectors and analyzing geographic distributions of physical and cultural phenomena on local. If that's your week, this page is about your job.
The honest answer
This job is splitting in two: creating and modifying maps, graphs or diagrams is work AI now does quickly and cheaply, and conducting field work at outdoor sites is work it can't touch.
Which half fills your week decides your exposure. The ledger below shows which rows you can move toward.
Your week, as this page understands it
Study the nature and use of areas of the Earth's surface, relating and interpreting interactions of physical and cultural phenomena. Conduct research on physical aspects of a region, including land forms, climates, soils, plants, and animals, and conduct research on the spatial implications of human activities within a given area, including social characteristics, economic activities, and political organization, as well as researching interdependence between regions at scales ranging from local to global. The job title says “geographers”. The real job is the part underneath: conducting field work at outdoor sites. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But geographers is not one task. It is 12 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conducting field work at outdoor sites, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 46%
- changing shape
- 16%
- staying human
- 39%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 49 out of 100 (44–55 allowing for uncertainty): partial exposure, across 12 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for geographers is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
5 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Creating and modifying maps, graphs or diagrams
This is reading one thing and writing another: maps, graphs or diagrams in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Create and modify maps, graphs, or diagrams, using geographical information software and related equipment, and principles of cartography, such as coordinate systems, longitude, latitude, elevation, topography, and map scales.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Mapping software and well-documented cartographic rules mean AI produces usable maps with review.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Gathering and compiling geographic data from sources
This is reading one thing and writing another: geographic data in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Gather and compile geographic data from sources such as censuses, field observations, satellite imagery, aerial photographs, and existing maps.” (O*NET task statement)
How this row was scored
Exposure score: 62 out of 100 (58–66 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Pulling together census, satellite and map data is search-and-assemble work software does well.
The five ratings: output a model can produce 3/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Writing and presenting reports of research findings
This is reading one thing and writing another: reports of research findings in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Write and present reports of research findings.” (O*NET task statement)
How this row was scored
Exposure score: 70 out of 100 (63–77 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Writing up findings is standard drafting work; presenting them still involves a live audience.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Analyzing geographic distributions of physical and cultural phenomena on local
This is reading one thing and writing another: geographic distributions of physical in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Analyze geographic distributions of physical and cultural phenomena on local, regional, continental, or global scales.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Spatial analysis follows documented methods that software and AI carry out reliably.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Changing shape
2 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Studying the economic, political and cultural characteristics of a specific region's population
The software now makes the first pass at the economic, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Study the economic, political, and cultural characteristics of a specific region's population.” (O*NET task statement)
How this row was scored
Exposure score: 43 out of 100 (36–50 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Published sources cover a lot, but understanding a specific region takes local knowledge.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Providing geographical information systems support to the private and public sectors
The software now makes the first pass at geographical information systems support, but the deciding part still needs a person. So the job becomes checking and deciding rather than producing.
importance 4 · CoreSource: “Provide geographical information systems support to the private and public sectors.” (O*NET task statement)
How this row was scored
Exposure score: 49 out of 100 (42–56 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Much mapping support is documented technique, though each organization's data and quirks need a person.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Staying human
5 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Teaching geography
The value here is that a specific person handles geography and stands behind it. That is earned, not computed.
importance 4 · CoreSource: “Teach geography.” (O*NET task statement)
How this row was scored
Exposure score: 34 out of 100 (27–41 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Geography material is abundant, but teaching a class still depends on the teacher present.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 4/4.
Conducting field work at outdoor sites
This work happens in the physical world: field work, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Conduct field work at outdoor sites.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Field work means being outdoors at the site.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 1/4.
Developing, operating and maintaining geographical information computer systems, including hardware, software, plotters, digitizers, printers and video cameras
This work happens in the physical world: geographical information computer systems, including hardware, software, plotters, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Develop, operate, and maintain geographical information computer systems, including hardware, software, plotters, digitizers, printers, and video cameras.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (22–36 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; mistakes that are cheap to catch; work that happens in the physical world.
The rating behind it: Software setup can be automated, but plotters, digitizers and servers are physical equipment.
The five ratings: output a model can produce 2/4 · needs a body in a room 2/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Collecting data on physical characteristics of specified areas
This work happens in the physical world: data, in a real place. Software cannot follow it there.
importance 4 · CoreSource: “Collect data on physical characteristics of specified areas, such as geological formations, climates, and vegetation, using surveying or meteorological equipment.” (O*NET task statement)
How this row was scored
Exposure score: 0 out of 100 (0–4 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world.
The rating behind it: Field measurements require someone at the site with the instruments.
The five ratings: output a model can produce 0/4 · needs a body in a room 4/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 2/4.
Show the other 2 tasks
Locating and obtaining existing geographic information databases
shifting to AIThis is reading one thing and writing another: geographic information databases in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Locate and obtain existing geographic information databases.” (O*NET task statement)
How this row was scored
Exposure score: 83 out of 100 (79–87 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Finding and obtaining existing datasets is a search task AI is well suited to.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Providing consulting services in fields
staying humanThe value here is that a specific person handles services and stands behind it. That is earned, not computed.
importance 3 · CoreSource: “Provide consulting services in fields such as resource development and management, business location and market area analysis, environmental hazards, regional cultural history, and urban social planning.” (O*NET task statement)
How this row was scored
Exposure score: 30 out of 100 (23–37 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Analysis can be drafted, but consulting clients buy judgment from a person they can question.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
What this job pays, and how many people do it
- Median pay
- $102,040a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 1,400in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: maps, graphs or diagrams in, a record out. The rows above are exactly that shape: creating and modifying maps, graphs or diagrams and gathering and compiling geographic data from sources. What it cannot do is be there in the room, and that is still where field work gets done. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
The exposed part of your job is the biggest part, and I am not going to dress that up: creating and modifying maps, graphs or diagrams is work today's tools do quickly and cheaply, and that is not coming back.
So, given all that: 46% of this job's task weight sits in rows the software is already learning, 16% in rows that change shape rather than disappear, and 39% in rows it is nowhere near. That is the position, measured across 12 scored tasks. It is not a forecast about you.
What you have that the software does not is conducting field work at outdoor sites, plus the years of knowing when something looks wrong before you can say why. That is the raw material for everything below.
This week: one thing
Sit on the machine's side of the desk. Pick one real piece of maps, graphs or diagrams you would normally do yourself, let whatever software you already have take the first pass at it, and then go through what it produced and write down every single thing it got wrong. One evening this week. Do not fix anything yet. Just catch it.
- What you end up holding
- a written list of the machine’s mistakes, in your handwriting
- How long it takes
- an evening, or an hour if you pick one job rather than one client
If you can’t run software on your employer’s or your clients’ data: Do the same hunt on paper. Take one printed piece of maps, graphs or diagrams, work through it the way you always do, and mark every point where you made a call rather than followed a rule. Same evening, same list, nothing to log into and nobody to ask permission from. That list is the same artifact: it is the judgment written down.
Over the next 90 days
Change one sentence about what you do. Not on a CV. Out loud, to whoever pays you. From “my job is creating and modifying maps, graphs or diagrams” to “I check what the software does and tell you what it means.” Your error list from this week is what makes that sentence true instead of a claim, so use it: show it once, to one person, inside the next ninety days. Same skills, priced as judgment rather than as typing.
Over the next 12 months
Walk toward the end of this job that answers for things, and get it recognised. Pick the one part of conducting field work at outdoor sites you are already best at, and spend the year making it formal: a qualification, a named responsibility, a specialism people ask for by name. Price it honestly: that is evenings, it is months rather than weeks, and the seats are competitive because everyone in your position is looking at the same door. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to geographers (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was sociologists: only about 6% of its durable work is work you already do. Your own job splits about 46/54: that share of the list sits in the top exposure band and the rest does not. On this evidence the honest move is inside the job rather than out of it. Become the person who owns “teach geography”, and let the exposed end go.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Sociologists
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already write and present reports of research findings, and their equivalent is to present research findings at professional meetings. Across both published task lists that is about 6% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 6% of the durable side of that job. That is a different job, not a next step.
Social Science Research Assistants
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already write and present reports of research findings, and their equivalent is to present research findings to groups of people. Across both published task lists that is about 5% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 5% of the durable side of that job. That is a different job, not a next step. I will not move you off one melting floe onto another: 72% of its own task list already scores in the top exposure band (63/100 in this release), so the same software is eating it. It is a pay cut, in those words: $61,990 against your $102,040, 39.2% less (OEWS May 2025 (both)). Retraining to earn less is a decision, not advice.
Geography Teachers, Postsecondary
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already teach geography, and their equivalent is to prepare and deliver lectures to undergraduate or graduate students on topics. Across both published task lists that is about 3% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 3% of the durable side of that job. That is a different job, not a next step.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 46% of its task weight, across 12 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
“It’s too late for me to become something else”
You are not starting from zero, and the page shows why: conducting field work at outdoor sites is work the software cannot do and you already do it. The move above is a repricing of what you know, not a new career. Nobody who has just left college has that.
“I should learn to code”
Almost certainly not. The value in your job is moving toward checking, deciding and answering for the output, not toward writing the software. A weekend of Python will not change your position; the error list from this week will.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Social and humanities scientists is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Social and humanities scientists. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for geographers, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 46% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for geographers. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for geographers launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
We could not save that. The fault is ours, not yours, and your address was not stored. Please try again later.
No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Geographers?
- Not as a job, but it is already doing parts of the work. Across the 12 official task statements scored for Geographers (United States, SOC 19-3092), 46% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 49 out of 100 (range 44–55, band: partial). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Geographers” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Create and modify maps, graphs, or diagrams, using geographical information software and related equipment, and principles of cartography, such as coordinate…” (83/100, very high); “Locate and obtain existing geographic information databases” (83/100, very high); “Analyze geographic distributions of physical and cultural phenomena on local, regional, continental, or global scales” (83/100, very high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Geographers” stay human?
- About 39% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Collect data on physical characteristics of specified areas, such as geological formations, climates, and vegetation, using surveying or meteorological equip…” (0/100, minimal); “Conduct field work at outdoor sites” (0/100, minimal); “Develop, operate, and maintain geographical information computer systems, including hardware, software, plotters, digitizers, printers, and video cameras” (29/100, low). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Geographers” do about AI?
- Start from the ledger rather than the headline: 46% of this job's weighted core work is exposed, and roughly 39% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Geographers calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 12 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
Worth knowing about these figures
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.