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AI Automation Index
Will AI Replace Geographers?
Task-level automation exposure for Geographers: 12 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
51%
weighted task index
12 of 12 tasks scored
Median Salary
$102,040 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
1,400 people hold this job — size, not growth
Projected 2025–35
-2.1%
employment change
100 openings a year · BLS line item
Pay by state
BLS OEWS publishes 7 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$79,170 – $114,920
Highest medians
Maryland $114,920
California $114,460
Washington $112,260
Virginia $108,020
Missouri $102,040
Lowest medians
Texas $79,170
Illinois $92,640
Missouri $102,040
Virginia $108,020
Washington $112,260
State figures come from the 19-3092 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 51%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 24%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 52%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 8%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 0/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
12 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 50%
6 tasks
Medium exposure (≥ 35%) 35%
4 tasks
Low exposure (below 35%) 15%
2 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#5503 · Core · 60% automatable
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.
visual inspection — Generates maps from data; cartographic judgment human
Prepare maps.
#5509 · Core · 44% automatable
Gather and compile geographic data from sources such as censuses, field observations, satellite imagery, aerial photographs, and existing maps.
data analysis — Geographic data compilation, human checks quality
Compile geographic or related data. Collect geographical or geological field data.
#5508 · Core · 55% automatable
Teach geography.
text generation — Instruction content AI-assisted; live teaching human
Instruct college students in social sciences or humanities disciplines.
#5504 · Core · 72% automatable
Write and present reports of research findings.
text generation — Reports drafted; presentations need human delivery
Prepare scientific or technical reports or presentations.
#5514 · Core · 62% automatable
Provide geographical information systems support to the private and public sectors.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#5511 · Core · 42% automatable
Study the economic, political, and cultural characteristics of a specific region's population.
information retrieval — Literature synthesis aided; fieldwork and interpretation human
Conduct anthropological or archaeological research.
#5507 · Core · 42% automatable
Analyze geographic distributions of physical and cultural phenomena on local, regional, continental, or global scales.
information retrieval — Literature synthesis aided; fieldwork and interpretation human
Conduct anthropological or archaeological research.
#5505 · Core · 62% automatable
Develop, operate, and maintain geographical information computer systems, including hardware, software, plotters, digitizers, printers, and video cameras.
text generation — Scientific code drafted; domain validation by humans
Develop software or applications for scientific or technical use.
#5506 · Core · 78% automatable
Locate and obtain existing geographic information databases.
information retrieval — Collect archival data from sources
Collect archival data.
#5513 · Core · 10% automatable
Collect data on physical characteristics of specified areas, such as geological formations, climates, and vegetation, using surveying or meteorological equipment.
situated mobility — Field data collection requires being in the field
Collect geographical or geological field data.
#5510 · Core · 10% automatable
Conduct field work at outdoor sites.
situated mobility — Field data collection requires being in the field
Collect geographical or geological field data.
#5512 · Core · 62% automatable
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.
decision support — Drafts advice; accountability and context owned by human
Advise others on business or operational matters.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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