SCHEMA.md

BlueLedger Enhanced Dataset Schema v2.0

Overview

This document describes the data structure for the BlueLedger Enhanced Police Officer Directory dataset.

Dataset Structure

Top Level

{
  "dataset_metadata": { ... },
  "officers": [ ... ]
}

dataset_metadata

• name: Dataset name
• version: Semantic version number
• generated_at: ISO 8601 timestamp
• total_officers: Count of officer records
• total_offense_records: Total offense incidents across all officers
• compliance: App store and data standards compliance info
• sources: List of data sources with verification levels

officer Record

officer_id

Unique identifier: BL-{STATE}-{NUMBER} Example: BL-WA-000001

personal_info

• full_name: Complete name as recorded
• name_last: Last name/surname
• name_first: First name/given name

employment

• department: Law enforcement agency name
• state: Full state name
• rank: Officer rank/position
• status: Employment status (Active, Inactive, Terminated, Retired)
• badge_number: Badge/shield number (if available)
• hire_date: Date of hire (ISO 8601)
• separation_date: Date of separation (ISO 8601, null if active)

certification

• certification_status: POST certification status
• certification_number: State POST certification ID
• certification_date: Date certification issued
• expiration_date: Certification expiration date
• state_post_agency: State jurisdiction
• certifying_authority: Full name of POST agency

verification_sources

• primary_gov_source: Official state POST commission
  • url: Official .gov URL (required for app store compliance)
  • agency: Agency name
  • verified: Verification status (boolean)
  • last_verified: Last verification timestamp
• profile_links: Array of secondary sources (CPDP, etc.)

offense_records

• total_count: Total number of recorded incidents
• categories: Breakdown by offense type
  • use_of_force
  • misconduct
  • civil_rights_violation
  • criminal_charges
  • policy_violation
  • excessive_force
  • other
• incidents: Array of incident objects

incident Object

• incident_id: Unique incident identifier
• date: Incident date (ISO 8601)
• category: Incident category
• description: Detailed description
• disposition: Investigation outcome (Sustained, Not Sustained, Exonerated, Unfounded, etc.)
• discipline: Disciplinary action taken
• verification_source: Source documentation
  • type: Source type (Internal Affairs, Court Record, etc.)
  • url: Source URL (preferably .gov)
  • document_id: Official document ID
  • verified: Verification status
• status: Case status (Open, Closed, Under Investigation)
• legal_outcome: Legal proceedings info
  • civil_lawsuit: Boolean
  • criminal_charges: Boolean
  • settlement_amount: Dollar amount if applicable

data_quality

• completeness_score: 0-100 score indicating data completeness
• has_gov_verification: Boolean indicating .gov link presence
• last_updated: Last update timestamp
• data_source: Original data source

Data Standards

App Store Compliance

• ✅ All officer records MUST have a valid .gov verification URL
• ✅ Offense records MUST cite verifiable sources
• ✅ No unverified allegations included
• ✅ Clear data provenance and update timestamps

URL Requirements

• Primary sources MUST be official government (.gov) domains
• State POST commission URLs are the gold standard
• Secondary sources (CPDP, etc.) are supplementary

Date Format

All dates use ISO 8601: YYYY-MM-DD or YYYY-MM-DDTHH:MM:SSZ

Null Values

• null used for unavailable data (not empty strings)
• Distinguished from "Unknown" which indicates active uncertainty

Usage Examples

Python

import json

with open('blueledger_enhanced_v2.json', 'r') as f:
    data = json.load(f)

# Access officers
for officer in data['officers']:
    print(f"{officer['personal_info']['full_name']} - {officer['employment']['department']}")
    print(f"Gov Verification: {officer['verification_sources']['primary_gov_source']['url']}")
    print(f"Total Offenses: {officer['offense_records']['total_count']}")

Pandas

import pandas as pd

# Load CSV
df = pd.read_csv('blueledger_enhanced_v2.csv')

# Filter officers with offenses
officers_with_offenses = df[df['total_offenses'] > 0]

# Group by state
state_summary = df.groupby('state').agg({
    'officer_id': 'count',
    'total_offenses': 'sum'
})

Data Integration Notes

Adding Offense Records

Real offense data should be sourced from:

1. State POST disciplinary databases
2. Court records (PACER, state court systems)
3. Internal affairs reports (via FOIA requests)
4. Verified journalist databases (CPDP, etc.)

Verification Workflow

1. Obtain source document
2. Verify authenticity via .gov source
3. Extract structured data
4. Add verification metadata
5. Update last_verified timestamp

License

Public Domain - Government Data

Contact

For questions or contributions, see README.md