# Regional Risk Analysis AI's impact on work isn't uniform-it depends heavily on regional occupational mix. A metro concentrated in administrative roles faces different pressures than one anchored by healthcare or manufacturing. This guide shows how to analyze regional vulnerability with Cubit. **Tier Required:** Enterprise ## Why Regional Analysis? The same national automation statistics mean different things in different places: * **San Jose**: Heavy tech employment -> augmentation-dominated * **Las Vegas**: Hospitality focus -> mixed exposure profile * **Detroit**: Manufacturing legacy -> physical work protection * **Washington DC**: Government/policy -> knowledge work exposure ## Listing Regions ```python from cubit import CubitClient client = CubitClient("cubit_your_key") # List all regions, sorted by employment regions = client.list_regions(limit=20, sort_by="employment") print(f"{'MSA Name':<45} {'Employment':>12} {'At-Risk Jobs':>12}") print("-" * 72) for region in regions["regions"]: print(f"{region['msa_name'][:45]:<45} " f"{region['total_employment']:>12,.0f} " f"{region['total_at_risk_jobs']:>12,.0f}") ``` ### Filtering by State ```python # Get California metros ca_regions = client.list_regions(state="CA", limit=20) print("California Metropolitan Areas:\n") for region in ca_regions["regions"]: print(f"{region['msa_name']}") print(f" Employment: {region['total_employment']:,.0f}") print(f" At-Risk Wages: ${region['total_at_risk_wages']:,.0f}") print(f" Mean Automation Score: {region['mean_automation_score']:.1f}") print() ``` ### By Risk Level ```python # Find regions with highest automation exposure regions = client.list_regions(limit=50) # Sort by mean automation score high_risk = sorted( regions["regions"], key=lambda r: r["mean_automation_score"], reverse=True )[:10] print("Highest Automation Exposure Metros:\n") for region in high_risk: print(f"{region['msa_name']}: {region['mean_automation_score']:.1f} avg exposure") ``` ## Regional Deep Dive ```python # Los Angeles detailed profile la = client.get_region("31084", include_jobs=True) print(f"Region: {la['msa_name']}\n") print("Summary:") print(f" Total Employment: {la['summary']['total_employment']:,.0f}") print(f" Jobs at Risk: {la['summary']['total_at_risk_jobs']:,.0f}") print(f" Wages at Risk: ${la['summary']['total_at_risk_wages']:,.0f}") print(f" High-Risk Jobs: {la['summary']['total_high_risk_jobs']:,.0f}") print(f" Mean Automation Score: {la['summary']['mean_automation_score']:.1f}") print(f" Mean Resilience Score: {la['summary']['mean_resilience_score']:.1f}") print("\nMost Exposed Occupations:") for job in la["top_exposed_jobs"][:5]: print(f" - {job['title']}") print(f" Employment: {job['employment']:,.0f}") print(f" At-Risk Wages: ${job.get('at_risk_wages', 0):,.0f}") print(f" Automation Score: {job['automation_susceptibility_score']:.0f}") print("\nMost Resilient Occupations:") for job in la["top_resilient_jobs"][:5]: print(f" - {job['title']}") print(f" Employment: {job['employment']:,.0f}") print(f" Resilience Score: {job['human_centric_resilience_score']:.0f}") ``` ## Comparative Analysis ### Metro Comparison ```python def compare_metros(msa_codes: list[str], client): """Compare multiple metros side by side.""" metros = [client.get_region(code) for code in msa_codes] print(f"{'Metro':<35} {'Emp':>10} {'At-Risk':>10} {'Auto':>8} {'Resil':>8}") print("-" * 75) for metro in metros: name = metro["msa_name"].split(",")[0][:35] print(f"{name:<35} " f"{metro['summary']['total_employment']:>10,.0f} " f"{metro['summary']['total_at_risk_jobs']:>10,.0f} " f"{metro['summary']['mean_automation_score']:>8.1f} " f"{metro['summary']['mean_resilience_score']:>8.1f}") # Compare major metros compare_metros(["31084", "35620", "16980", "26420"], client) # LA, NYC, Chicago, Houston ``` **Output:** ``` Metro Emp At-Risk Auto Resil --------------------------------------------------------------------------- Los Angeles-Long Beach-Glendale 5,425,980 10,220 45.0 60.9 New York-Newark-Jersey City 9,234,000 1,456,000 51.2 50.3 Chicago-Naperville-Elgin 4,012,000 756,000 50.8 49.2 Houston-The Woodlands-Sugar Land 2,987,000 534,000 49.5 51.1 ``` ### State Aggregation ```python def aggregate_state(state_code: str, client): """Aggregate regional data to state level.""" regions = client.list_regions(state=state_code, limit=100) total_employment = sum(r["total_employment"] for r in regions["regions"]) total_at_risk = sum(r["total_at_risk_jobs"] for r in regions["regions"]) total_wages_at_risk = sum(r["total_at_risk_wages"] for r in regions["regions"]) # Employment-weighted average scores weighted_auto = sum( r["mean_automation_score"] * r["total_employment"] for r in regions["regions"] ) / total_employment weighted_resil = sum( r["mean_resilience_score"] * r["total_employment"] for r in regions["regions"] ) / total_employment return { "state": state_code, "metros": len(regions["regions"]), "total_employment": total_employment, "at_risk_jobs": total_at_risk, "at_risk_wages": total_wages_at_risk, "mean_automation": weighted_auto, "mean_resilience": weighted_resil } # Compare states for state in ["CA", "TX", "NY", "FL"]: data = aggregate_state(state, client) print(f"{state}: {data['total_employment']:,} workers, " f"{data['at_risk_jobs']:,} at risk, " f"Auto: {data['mean_automation']:.1f}") ``` ## Building Regional Dashboards ### Map Visualization Data ```python import json def get_map_data(client, limit=100): """Prepare data for map visualization.""" regions = client.list_regions(limit=limit) features = [] for region in regions["regions"]: # MSA codes can be mapped to geographic boundaries features.append({ "msa_code": region["msa_code"], "name": region["msa_name"], "employment": region["total_employment"], "at_risk_jobs": region["total_at_risk_jobs"], "at_risk_pct": region["total_at_risk_jobs"] / region["total_employment"] * 100, "automation_score": region["mean_automation_score"], "resilience_score": region["mean_resilience_score"], # Color scale value (0-1) "risk_intensity": min(region["mean_automation_score"] / 70, 1) }) return features map_data = get_map_data(client) # Use with Mapbox, Leaflet, or D3.js ``` ### React Dashboard Component ```tsx interface RegionData { msa_code: string; msa_name: string; total_employment: number; total_at_risk_jobs: number; total_at_risk_wages: number; mean_automation_score: number; mean_resilience_score: number; } function RegionalDashboard() { const [regions, setRegions] = useState([]); const [selectedState, setSelectedState] = useState(''); const [sortBy, setSortBy] = useState('employment'); useEffect(() => { const params = new URLSearchParams({ limit: '100', sort_by: sortBy }); if (selectedState) params.append('state', selectedState); fetch(`/api/regions?${params}`) .then(r => r.json()) .then(data => setRegions(data.regions)); }, [selectedState, sortBy]); const totalAtRisk = regions.reduce((sum, r) => sum + r.total_at_risk_jobs, 0); const totalWagesAtRisk = regions.reduce((sum, r) => sum + r.total_at_risk_wages, 0); return (
); } function RegionTable({ regions }: { regions: RegionData[] }) { return ( {regions.map(region => ( ))}
Metro Area Employment At-Risk Jobs Auto Score Resilience
{region.msa_name} {region.total_employment.toLocaleString()} {region.total_at_risk_jobs.toLocaleString()} {region.mean_automation_score.toFixed(1)} {region.mean_resilience_score.toFixed(1)}
); } ``` ## Use Cases ### Economic Development Planning ```python def identify_priority_regions(client, risk_threshold: float = 55): """Find regions that need transition support investment.""" regions = client.list_regions(limit=200) priority = [ r for r in regions["regions"] if r["mean_automation_score"] > risk_threshold and r["total_employment"] > 100000 # Significant workforce ] # Sort by total wages at risk priority.sort(key=lambda r: r["total_at_risk_wages"], reverse=True) print("Priority Regions for Transition Investment:\n") for r in priority[:10]: print(f"{r['msa_name']}") print(f" Wages at Risk: ${r['total_at_risk_wages']:,.0f}") print(f" Automation Score: {r['mean_automation_score']:.1f}") print() return priority priority_regions = identify_priority_regions(client) ``` ### Sector Concentration Analysis ```python def analyze_sector_concentration(msa_code: str, client): """Analyze which sectors drive regional exposure.""" region = client.get_region(msa_code, include_jobs=True) # Group jobs by major group from collections import defaultdict sectors = defaultdict(lambda: {"employment": 0, "at_risk": 0}) for job in region.get("top_exposed_jobs", []) + region.get("top_resilient_jobs", []): # Extract major group from SOC code (first 2 digits) major = job["soc_code"][:2] + "-0000" sectors[major]["employment"] += job["employment"] sectors[major]["at_risk"] += job.get("at_risk_jobs", 0) return dict(sectors) sectors = analyze_sector_concentration("31080", client) ``` ## Next Steps * [Career Transitions](/guides/career-transitions.md) - Finding pathways for at-risk workers * [Custom Scoring](/guides/custom-scoring.md) - Apply regional weightings * [API Reference: Regions](/api-reference/regions.md) - Full endpoint documentation --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.maidenlabs.tools/guides/regional-analysis.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.