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name: gcs-medarbejdere-data description: | Activates when querying employee and workplace safety data from GCS. Use this skill for: Arbejdstilsynet inspections, work permits, safety violations, workplace accidents, compliance rates, foreign workers, incident tracking. Keywords: medarbejdere, employees, worker, arbejdstilsynet, inspection, tilsyn, safety, arbejdsmiljø, accident, ulykke, compliance

GCS Medarbejdere (Employees) Data Catalog

Employee and workplace safety data from regulatory inspections and incident reports.

Frontend Metrics Supported

Metric Key	Danish Name	Description
worker_safety_violations	Arbejdsmiljøovertrædelser	Workplace safety violations
foreign_workers	Udenlandske arbejdere	Foreign worker registrations
work_accidents	Arbejdsulykker	Workplace accident count
inspection_frequency	Tilsynsfrekvens	Inspection frequency rate
compliance_rate	Overholdelsesrate	Overall compliance rate

Available Datasets

Gold Layer

Arbejdstilsynet Inspections (536 rows)

Path: gs://$GCS_BUCKET/gold/arbejdstilsynet_inspections/*/data.parquet

Column	Type	Description	Example
date	date	Inspection date	2024-05-15
case_count	int	Number of cases	3
decision	string	Inspection decision	Påbud
work_env_issue	string	Work environment issue	Ergonomi
cvr_number	string	Company CVR	31373077
company_name	string	Company name	Landbrugsbedrift A/S
industry	string	Industry classification	Landbrug
severity_score	float	Severity (0-10)	7.5
company_compliance_rate	float	Historical compliance (0-1)	0.85
is_repeat_offender	bool	Previous violations	false
inspector_id	string	Inspector identifier	AT-123
follow_up_date	date	Follow-up scheduled	2024-08-15
fine_amount_dkk	float	Fine if applicable	25000.0
corrective_deadline	date	Deadline for correction	2024-06-30

Schema (introspected):

date: date32
case_count: int64
decision: string
work_env_issue: string
cvr_number: string
company_name: string
industry: string
severity_score: double
company_compliance_rate: double
is_repeat_offender: bool
inspector_id: string
follow_up_date: date32
fine_amount_dkk: double
corrective_deadline: date32
[29 columns total]

Silver Layer

Work Permits

Path: gs://$GCS_BUCKET/silver/work permits/*/data.parquet

Column	Type	Description
cvr_number	string	Company CVR
permit_type	string	Type of permit
nationality	string	Worker nationality
issue_date	date	Permit issue date
expiry_date	date	Permit expiry date
worker_count	int	Number of workers

Worker Safety Reports

Path: gs://$GCS_BUCKET/silver/worker safety/*/data.parquet

Column	Type	Description
cvr_number	string	Company CVR
report_date	date	Report date
incident_type	string	Type of incident
injury_severity	string	Severity level
days_lost	int	Workdays lost
body_part_affected	string	Injured body part
activity_during	string	Activity at time

Stable Fires (Incidents)

Path: gs://$GCS_BUCKET/silver/stable fires/*/data.parquet

Column	Type	Description
incident_date	date	Date of fire
location	binary	Location (WKB)
farm_type	string	Type of farm
animals_affected	int	Animals impacted
cause	string	Fire cause
damage_estimate_dkk	float	Estimated damage

Transport Accidents

Path: gs://$GCS_BUCKET/silver/transportation accidents/*/data.parquet

Column	Type	Description
incident_date	date	Accident date
location	binary	Location (WKB)
vehicle_type	string	Type of vehicle
cargo_type	string	Cargo description
injuries	int	Number injured
fatalities	int	Number of fatalities

Bronze Layer

DMA Permits

Path: gs://$GCS_BUCKET/bronze/dma/*/data.parquet

Column	Type	Description
cvr_number	string	Company CVR
permit_number	string	Permit ID
permit_type	string	Permit category
valid_from	date	Start date
valid_until	date	End date
conditions	string	Permit conditions

Common Queries

Get Inspection History for CVR

import pyarrow.parquet as pq
from google.cloud import storage
import io

client = storage.Client()
bucket = client.bucket('$GCS_BUCKET')

# Read arbejdstilsynet inspections
blob = bucket.blob('gold/arbejdstilsynet_inspections/2025-01-10/data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
df = pq.read_table(buffer).to_pandas()

# Filter by CVR
cvr = '31373077'
company_inspections = df[df['cvr_number'] == cvr]

print(f"Total inspections: {len(company_inspections)}")
print(f"Total cases: {company_inspections['case_count'].sum()}")
print(f"Average severity: {company_inspections['severity_score'].mean():.2f}")
print(f"Compliance rate: {company_inspections['company_compliance_rate'].iloc[-1]:.2%}")

Calculate Industry Compliance Rates

# Aggregate by industry
industry_stats = df.groupby('industry').agg({
    'cvr_number': 'nunique',
    'case_count': 'sum',
    'severity_score': 'mean',
    'company_compliance_rate': 'mean',
    'is_repeat_offender': 'sum'
}).reset_index()

industry_stats.columns = ['industry', 'companies', 'total_cases',
                          'avg_severity', 'avg_compliance', 'repeat_offenders']
industry_stats = industry_stats.sort_values('avg_compliance', ascending=True)

Find Repeat Offenders

# Companies with multiple violations
repeat_offenders = df[df['is_repeat_offender'] == True]

# Group by company
offender_summary = repeat_offenders.groupby(['cvr_number', 'company_name']).agg({
    'case_count': 'sum',
    'severity_score': 'mean',
    'fine_amount_dkk': 'sum'
}).reset_index()
offender_summary = offender_summary.sort_values('case_count', ascending=False)

Severity Analysis by Issue Type

# Analyze by work environment issue category
issue_analysis = df.groupby('work_env_issue').agg({
    'case_count': 'sum',
    'severity_score': 'mean',
    'fine_amount_dkk': 'sum'
}).reset_index()
issue_analysis = issue_analysis.sort_values('severity_score', ascending=False)

Monthly Inspection Trends

import pandas as pd

# Convert to datetime
df['inspection_month'] = pd.to_datetime(df['date']).dt.to_period('M')

monthly_stats = df.groupby('inspection_month').agg({
    'cvr_number': 'nunique',
    'case_count': 'sum',
    'severity_score': 'mean'
}).reset_index()
monthly_stats.columns = ['month', 'companies_inspected', 'total_cases', 'avg_severity']

Calculate Fines by Municipality

# Join with CVR address data for geographic analysis
# (requires joining with okonomi/cvr_enrichment data)
from gcs_data_catalog.okonomi import read_cvr_data

cvr_geo = read_cvr_data()
inspections_with_geo = df.merge(
    cvr_geo[['cvr_number', 'municipality']],
    on='cvr_number',
    how='left'
)

municipal_fines = inspections_with_geo.groupby('municipality').agg({
    'fine_amount_dkk': 'sum',
    'case_count': 'sum'
}).reset_index()

Foreign Worker Analysis

# Read work permits
blob = bucket.blob('silver/work permits/2025-01-10/data.parquet')
buffer = io.BytesIO()
blob.download_to_file(buffer)
buffer.seek(0)
permits = pq.read_table(buffer).to_pandas()

# Count by nationality
nationality_counts = permits.groupby('nationality').agg({
    'worker_count': 'sum'
}).reset_index()
nationality_counts = nationality_counts.sort_values('worker_count', ascending=False)

# Companies with most foreign workers
company_workers = permits.groupby('cvr_number').agg({
    'worker_count': 'sum'
}).reset_index()
company_workers = company_workers.sort_values('worker_count', ascending=False)

Decision Types

Decision (Danish)	English	Severity
Påbud	Order/Requirement	Medium
Forbud	Prohibition	High
Vejledning	Guidance	Low
Afgørelse	Decision	Varies
Strakspåbud	Immediate Order	High
Rådgivningspåbud	Advisory Order	Medium

Work Environment Issues

Common categories in work_env_issue:

• Ergonomi - Ergonomic issues (lifting, posture)
• Kemisk arbejdsmiljø - Chemical hazards
• Psykisk arbejdsmiljø - Psychological work environment
• Ulykker - Accident prevention
• Støj - Noise exposure
• Maskinsikkerhed - Machine safety
• Bygge og anlæg - Construction safety

Join Keys

This Dataset	Join Column	Target Dataset	Target Column
arbejdstilsynet	cvr_number	subsidies	cvr_number
arbejdstilsynet	cvr_number	field_production	cvr_number
arbejdstilsynet	cvr_number	pesticide_disaggregation	cvr_number
work_permits	cvr_number	arbejdstilsynet	cvr_number
worker_safety	cvr_number	arbejdstilsynet	cvr_number

Data Quality Notes

Arbejdstilsynet Inspections

• Update frequency: After inspection completion
• Coverage: All inspected agricultural businesses
• Source: Danish Working Environment Authority
• Caveat: Not all farms inspected - risk-based selection

Work Permits

• Update frequency: Monthly
• Coverage: All registered foreign worker permits
• Source: SIRI (Danish Immigration Service)

Incidents (Fires, Accidents)

• Update frequency: After incident reporting
• Coverage: Reported incidents only
• Caveat: Underreporting possible

Compliance Score Interpretation

The company_compliance_rate field (0-1 scale):

• 0.90-1.00: Excellent compliance history
• 0.75-0.89: Good compliance, minor issues
• 0.50-0.74: Moderate compliance, attention needed
• 0.00-0.49: Poor compliance, likely repeat offender

Related Skills

• okonomi/ - Company financial data for CVR context
• landbrugsareal/ - Land area and farm size context
• miljo/ - Environmental compliance (related violations)
• husdyr/ - Livestock operations (animal handling safety)

GCS Paths Reference

# List arbejdstilsynet inspection data
gsutil ls gs://$GCS_BUCKET/gold/arbejdstilsynet_inspections/

# List work permit data
gsutil ls gs://$GCS_BUCKET/silver/work\ permits/

# List worker safety data
gsutil ls gs://$GCS_BUCKET/silver/worker\ safety/

# List incident data
gsutil ls gs://$GCS_BUCKET/silver/stable\ fires/
gsutil ls gs://$GCS_BUCKET/silver/transportation\ accidents/

# List DMA permit data
gsutil ls gs://$GCS_BUCKET/bronze/dma/

Agricultural-Specific Safety Concerns

Common issues in agricultural workplace inspections:

1. Machine safety - Tractors, harvesters, feed machinery
2. Chemical exposure - Pesticides, fertilizers, cleaning agents
3. Animal handling - Large animal injuries, zoonotic diseases
4. Ergonomics - Repetitive lifting, awkward postures
5. Confined spaces - Silos, manure pits, grain bins
6. Environmental - Heat stress, cold exposure, UV radiation