Occupation · SOC 19-3051.00
Develop comprehensive plans and programs for use of land and physical facilities of jurisdictions, such as towns, cities, counties, and metropolitan areas.
Also called: Community Development Planner · Planner · Planning Consultant · Planning Technician · City Planner · Community Planner · Development Technician · Housing Development Specialist · Neighborhood Planner · Regional Planner · Campus Planner · City Designer
Job family: Life, Physical, and Social Science Occupations
A source-stamped Markdown brief of this occupation — paste it into an agent, or fetch
/roles/role-19-3051-00/context.md directly.
A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
93rd-percentile task overlap — yet about 3,400 openings a year (+3.4% projected, BLS) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 83rd | 1.2 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 87th | 1.0 | |
| AI assistant applicability (Microsoft) High | 94th | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.0), with simple added tooling (β 0.5), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
This job mostly cannot be done remotely (Dingel–Neiman) — its hands-on tasks sit outside what software-based AI reaches.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.1 · 30th percentile among occupations · Low
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Research, compile, analyze and organize information from maps, reports, investigations, and books for use in reports and special projects. | 7.1% | |
| Recommend approval, denial, or conditional approval of proposals. | 2.3% | |
| Advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives. | 1.3% | |
| Hold public meetings with government officials, social scientists, lawyers, developers, the public, or special interest groups to formulate, develop, or address issues regarding land use or community plans. | 0.3% | |
| Keep informed about economic or legal issues involved in zoning codes, building codes, or environmental regulations. | 0.2% | |
| Prepare reports, using statistics, charts, and graphs, to illustrate planning studies in areas such as population, land use, or zoning. | 0.2% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | About average · +3.4% by 2034 |
| Projected annual openings | 3,400 |
| Employment 2024 → 2034 | 44,700 → 46,200 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Town and Traffic Planners · 2164 | 41% | Minimal |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 72.7% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives. | — | 0.3% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives. | 93.9% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives. From: Advise planning officials on project feasibility, cost-effectiveness, regulatory conformance, or possible alternatives. · 0.3% of measured AI use
All 25 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Active Listening | 4.1 | |
| Speaking | 4.1 | |
| Reading Comprehension | 4.0 | |
| Critical Thinking | 4.0 | |
| Writing | 3.9 | |
| Active Learning | 3.3 |
| Judgment and Decision Making | 4.1 | |
| Systems Analysis | 4.0 | |
| Complex Problem Solving | 3.8 | |
| Systems Evaluation | 3.8 | |
| Social Perceptiveness | 3.6 | |
| Negotiation | 3.4 | |
| Coordination | 3.3 | |
| Service Orientation | 3.3 | |
| Time Management | 3.3 | |
| Persuasion | 3.1 | |
| Operations Analysis | 3.1 |
| Oral Comprehension | 4.1 | |
| Oral Expression | 4.1 | |
| Written Comprehension | 4.0 | |
| Written Expression | 4.0 | |
| Problem Sensitivity | 4.0 | |
| Speech Clarity | 4.0 | |
| Deductive Reasoning | 3.9 | |
| Inductive Reasoning | 3.9 | |
| Near Vision | 3.9 | |
| Speech Recognition | 3.9 | |
| Fluency of Ideas | 3.6 | |
| Information Ordering | 3.6 | |
| Category Flexibility | 3.5 | |
| Originality | 3.3 | |
| Visualization | 3.1 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 53.
Showing the top 40 of 77.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Architecture and Related Services , Multi/Interdisciplinary Studies , Public Administration and Social Service Professions , Social Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Master's Degree | 56.0% | |
| Bachelor's Degree | 40.0% | |
| First Professional Degree | 4.0% |
The interests and personal qualities O*NET associates with people who do this work.
| Dependability | 8.0 | |
| Attention to Detail | 7.0 | |
| Integrity | 6.0 | |
| Intellectual Curiosity | 5.0 | |
| Cooperation | 4.0 |
| Investigative | 5.3 | |
| Enterprising | 4.5 | |
| Conventional | 4.0 | |
| Realistic | 3.2 | |
| Artistic | 3.1 | |
| Social | 3.0 |
| Management/Administration | 4.4 | |
| Public Speaking | 4.2 | |
| Social Science | 3.9 | |
| Politics | 3.8 | |
| Law | 3.6 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $55,590 |
| 25th percentile | $66,210 |
| Median (50th) | $83,720 |
| 75th percentile | $104,450 |
| 90th percentile | $128,550 |
| People employed | 43,040 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 5,100 | $89,430 |
| Engineering Services · National industry | 2,500 | $92,040 |
| Utilities · Sector | 190 | $122,010 |
| Educational Services · Sector | 150 | $77,870 |
| Other Services (except Public Administration) · Sector | 110 | $79,630 |
| Fossil Fuel Electric Power Generation · National industry | 40 | $76,570 |
| Construction · Sector | — | $104,220 |
| Real Estate and Rental and Leasing · Sector | — | $76,960 |
| Research and Development in the Social Sciences and Humanities · National industry | — | $68,970 |
| Administrative and Support and Waste Management and Remediation Services · Sector | — | $71,880 |
| Temporary Help Services · National industry | — | $66,410 |
| Health Care and Social Assistance · Sector | — | $72,500 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Engineering Services · National industry | 7.75× | 2,500 |
| Professional, Scientific, and Technical Services · Sector | 1.7× | 5,100 |
| Utilities · Sector | 1.17× | 190 |
| Other Services (except Public Administration) · Sector | 0.09× | 110 |
| Educational Services · Sector | 0.04× | 150 |
Part of the Construction , Public Service & Safety and Supply Chain & Transportation career clusters.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Urban and Regional Planners — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 78th percentile of 427 international occupations.
Urban and Regional Planners show 93rd-percentile AI task overlap — and about 3,400 annual U.S. openings
Urban and Regional Planners show 93rd-percentile AI task overlap — and about 3,400 annual U.S. openings • Urban and Regional Planners rank in the 93rd percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 3,400 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be about average (+3.4%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $83,720, across about 43,040 U.S. workers. (BLS OEWS (May 2024)) Source: Singulariki — "Urban and Regional Planners". https://singulariki.com/roles/role-19-3051-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
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Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Urban and Regional Planners." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-19-3051-00
Singulariki. (2026). Urban and Regional Planners. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-19-3051-00
@misc{singulariki-role-19-3051-00,
title = {Urban and Regional Planners},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-19-3051-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.