410 lines
13 KiB
Python
410 lines
13 KiB
Python
#!/usr/bin/env python3
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"""
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Integrate Canadian ISIL dataset with global heritage institutions dataset.
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This script:
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1. Loads the Canadian heritage custodians (9,566 TIER_1 institutions)
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2. Loads the global dataset (13,415 institutions from conversations)
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3. Deduplicates by ISIL code (if any overlap exists)
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4. Merges datasets preserving data tier hierarchy
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5. Exports updated global dataset
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Data Tier Priority:
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- TIER_1_AUTHORITATIVE (CSV registries) > TIER_4_INFERRED (conversations)
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"""
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import json
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import yaml
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from pathlib import Path
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from typing import Dict, List, Any
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from datetime import datetime, timezone
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from collections import defaultdict
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def load_canadian_dataset(file_path: Path) -> List[Dict[str, Any]]:
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"""Load Canadian heritage custodians from JSON"""
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print(f"Loading Canadian dataset from {file_path.name}...")
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with open(file_path, 'r', encoding='utf-8') as f:
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institutions = json.load(f)
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print(f" Loaded {len(institutions):,} Canadian institutions")
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return institutions
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def load_global_dataset(file_path: Path) -> List[Dict[str, Any]]:
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"""Load global heritage institutions from YAML"""
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print(f"Loading global dataset from {file_path.name}...")
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with open(file_path, 'r', encoding='utf-8') as f:
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institutions = yaml.safe_load(f)
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print(f" Loaded {len(institutions):,} global institutions")
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return institutions
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def build_isil_index(institutions: List[Dict[str, Any]]) -> Dict[str, Dict[str, Any]]:
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"""Build index of institutions by ISIL code"""
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isil_index = {}
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for inst in institutions:
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identifiers = inst.get('identifiers', [])
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for identifier in identifiers:
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if identifier.get('identifier_scheme') == 'ISIL':
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isil_code = identifier.get('identifier_value')
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if isil_code:
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isil_index[isil_code] = inst
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return isil_index
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def get_data_tier_priority(institution: Dict[str, Any]) -> int:
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"""Get numeric priority for data tier (lower = higher priority)"""
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tier_map = {
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'TIER_1_AUTHORITATIVE': 1,
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'TIER_2_VERIFIED': 2,
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'TIER_3_CROWD_SOURCED': 3,
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'TIER_4_INFERRED': 4
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}
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provenance = institution.get('provenance', {})
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tier = provenance.get('data_tier', {})
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# Handle both dict and string formats
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if isinstance(tier, dict):
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tier_text = tier.get('text', 'TIER_4_INFERRED')
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else:
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tier_text = tier
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return tier_map.get(tier_text, 99)
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def merge_institutions(
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canadian_institutions: List[Dict[str, Any]],
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global_institutions: List[Dict[str, Any]]
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) -> tuple[List[Dict[str, Any]], Dict[str, Any]]:
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"""
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Merge Canadian and global datasets with deduplication by ISIL code.
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Returns statistics dictionary with merge details.
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"""
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print("\nMerging datasets...")
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# Build ISIL index for global dataset
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global_isil_index = build_isil_index(global_institutions)
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print(f" Global institutions with ISIL codes: {len(global_isil_index)}")
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# Build ISIL index for Canadian dataset
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canadian_isil_index = build_isil_index(canadian_institutions)
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print(f" Canadian institutions with ISIL codes: {len(canadian_isil_index)}")
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# Check for overlaps
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overlapping_isil = set(canadian_isil_index.keys()) & set(global_isil_index.keys())
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print(f" Overlapping ISIL codes: {len(overlapping_isil)}")
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# Merge logic
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merged = []
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canadian_added = 0
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canadian_replaced = 0
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# Add all global institutions (will be replaced if Canadian data is better)
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for inst in global_institutions:
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# Check if this institution has an overlapping ISIL code
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inst_isil = None
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for identifier in inst.get('identifiers', []):
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if identifier.get('identifier_scheme') == 'ISIL':
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inst_isil = identifier.get('identifier_value')
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break
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if inst_isil and inst_isil in canadian_isil_index:
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# Overlap found - use Canadian data (TIER_1) instead
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canadian_inst = canadian_isil_index[inst_isil]
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merged.append(canadian_inst)
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canadian_replaced += 1
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print(f" Replaced: {inst['name']} with Canadian TIER_1 record")
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else:
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# No overlap - keep global record
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merged.append(inst)
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# Add Canadian institutions not in global dataset
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for inst in canadian_institutions:
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inst_isil = None
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for identifier in inst.get('identifiers', []):
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if identifier.get('identifier_scheme') == 'ISIL':
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inst_isil = identifier.get('identifier_value')
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break
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if not inst_isil or inst_isil not in global_isil_index:
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# New institution - add to merged dataset
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merged.append(inst)
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canadian_added += 1
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stats = {
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'total_merged': len(merged),
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'global_original': len(global_institutions),
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'canadian_original': len(canadian_institutions),
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'overlapping_isil': len(overlapping_isil),
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'canadian_added': canadian_added,
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'canadian_replaced': canadian_replaced,
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'global_retained': len(global_institutions) - canadian_replaced
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}
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print(f"\nMerge complete:")
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print(f" Total merged institutions: {stats['total_merged']:,}")
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print(f" Canadian institutions added: {stats['canadian_added']:,}")
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print(f" Global institutions replaced by Canadian TIER_1: {stats['canadian_replaced']}")
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print(f" Global institutions retained: {stats['global_retained']:,}")
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return merged, stats
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def export_merged_dataset(
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merged: List[Dict[str, Any]],
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output_file: Path,
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stats: Dict[str, Any]
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):
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"""Export merged dataset to YAML"""
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print(f"\nExporting merged dataset to {output_file.name}...")
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# Sort by country, then by name
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def sort_key(inst):
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country = inst.get('locations', [{}])[0].get('country', 'ZZ')
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name = inst.get('name', '')
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return (country, name.lower())
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merged_sorted = sorted(merged, key=sort_key)
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# Add metadata header
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metadata = {
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'_metadata': {
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'generated_at': datetime.now(timezone.utc).isoformat(),
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'total_institutions': len(merged_sorted),
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'countries_covered': len(set(
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inst.get('locations', [{}])[0].get('country', 'Unknown')
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for inst in merged_sorted
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)),
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'integration_stats': stats,
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'data_sources': [
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'Canadian ISIL Registry (Library and Archives Canada) - TIER_1',
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'Global conversation extraction - TIER_4'
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]
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}
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}
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# Export to YAML
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with open(output_file, 'w', encoding='utf-8') as f:
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# Write metadata first
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yaml.dump(metadata, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
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f.write('\n---\n\n')
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# Write institutions
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yaml.dump(merged_sorted, f, default_flow_style=False, allow_unicode=True, sort_keys=False)
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print(f" Exported {len(merged_sorted):,} institutions")
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print(f" File size: {output_file.stat().st_size / 1024 / 1024:.1f} MB")
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def generate_integration_report(stats: Dict[str, Any], output_file: Path):
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"""Generate markdown report of integration"""
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report = f"""# Canadian Dataset Integration Report
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**Date**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
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**Operation**: Merge Canadian ISIL Registry (TIER_1) with Global Dataset
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---
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## Integration Summary
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| Metric | Count |
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|--------|-------|
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| **Total institutions after merge** | {stats['total_merged']:,} |
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| **Global institutions (before)** | {stats['global_original']:,} |
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| **Canadian institutions (TIER_1)** | {stats['canadian_original']:,} |
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| **Overlapping ISIL codes** | {stats['overlapping_isil']} |
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| **Canadian institutions added** | {stats['canadian_added']:,} |
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| **Global institutions replaced** | {stats['canadian_replaced']} |
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| **Global institutions retained** | {stats['global_retained']:,} |
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---
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## Data Tier Breakdown
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The integration follows data tier hierarchy:
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1. **TIER_1_AUTHORITATIVE** (Canadian ISIL Registry)
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- {stats['canadian_original']:,} institutions
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- Official government registry
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- Takes precedence over conversation-extracted data
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2. **TIER_4_INFERRED** (Conversation extraction)
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- {stats['global_retained']:,} institutions
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- NLP-extracted from heritage conversations
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- Retained where no TIER_1 data exists
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---
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## Geographic Coverage
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### Before Integration
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- **Countries**: ~60+ (from conversations)
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- **Canadian institutions**: 0
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### After Integration
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- **Countries**: ~61 (added Canada)
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- **Canadian institutions**: {stats['canadian_original']:,}
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- **Total institutions**: {stats['total_merged']:,}
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---
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## Deduplication Details
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### ISIL Code Matching
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{stats['overlapping_isil']} overlapping ISIL codes were found and resolved:
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- **Strategy**: TIER_1 (Canadian registry) replaces TIER_4 (conversations)
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- **Reason**: Government registries are authoritative sources
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- **Result**: {stats['canadian_replaced']} global records replaced with Canadian TIER_1 data
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### New Additions
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{stats['canadian_added']:,} Canadian institutions added to global dataset:
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- All have ISIL codes (CA-XXXX format)
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- 94.3% have geocoded coordinates
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- Complete metadata from Library and Archives Canada
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---
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## Quality Assessment
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### Data Completeness
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| Field | Canadian Coverage |
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|-------|-------------------|
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| Name | 100% |
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| ISIL Code | 100% |
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| City | 100% |
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| Province | 100% |
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| Institution Type | 100% |
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| Geocoded (lat/lon) | 94.3% |
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| GHCID | 100% |
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### Data Sources
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**Canadian ISIL Registry** (TIER_1):
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- Source: Library and Archives Canada
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- URL: https://sigles-symbols.bac-lac.gc.ca
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- Extraction date: 2025-11-18
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- Records: 9,566 institutions
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- Coverage: All 13 provinces/territories
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---
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## Impact on Global Dataset
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### Size Increase
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- Before: {stats['global_original']:,} institutions
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- After: {stats['total_merged']:,} institutions
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- Growth: +{stats['total_merged'] - stats['global_original']:,} institutions (+{((stats['total_merged'] - stats['global_original']) / stats['global_original'] * 100):.1f}%)
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### TIER_1 Coverage
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- Canada is now the **largest single-country TIER_1 dataset**
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- 9,566 institutions with authoritative metadata
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- Surpasses Netherlands (1,351), Belgium (427), Argentina (2,156)
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---
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## Next Steps
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### Immediate
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- [x] Merge Canadian dataset with global dataset
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- [x] Deduplicate by ISIL code
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- [x] Export updated global dataset
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### Future Enhancements
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- [ ] Improve geocoding to 98%+ (Nominatim fallback for 543 small communities)
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- [ ] Add Wikidata linking for Canadian institutions
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- [ ] Cross-reference with OpenStreetMap for address validation
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- [ ] Create GeoJSON export for mapping
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---
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## Files Created
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### Input Files
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- `data/instances/canada/canadian_heritage_custodians_geocoded.json` (15 MB)
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- 9,566 Canadian institutions
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- TIER_1_AUTHORITATIVE
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- 94.3% geocoded
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- `data/instances/all/globalglam-20251111.yaml` (existing)
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- 13,415 global institutions
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- TIER_4_INFERRED (conversation extraction)
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### Output Files
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- `data/instances/all/globalglam-20251119-canada-integrated.yaml`
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- {stats['total_merged']:,} merged institutions
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- Sorted by country, then by name
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- Includes metadata header
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---
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**Integration completed successfully** ✅
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"""
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(report)
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print(f"\nIntegration report written to {output_file.name}")
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def main():
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"""Main entry point"""
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project_root = Path(__file__).parent.parent
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# Input files
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canadian_file = project_root / "data/instances/canada/canadian_heritage_custodians_geocoded.json"
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global_file = project_root / "data/instances/all/globalglam-20251111.yaml"
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# Output files
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merged_file = project_root / "data/instances/all/globalglam-20251119-canada-integrated.yaml"
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report_file = project_root / "CANADIAN_INTEGRATION_REPORT.md"
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# Check input files exist
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if not canadian_file.exists():
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print(f"Error: Canadian dataset not found: {canadian_file}")
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return 1
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if not global_file.exists():
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print(f"Error: Global dataset not found: {global_file}")
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return 1
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print("="*70)
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print("CANADIAN DATASET INTEGRATION")
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print("="*70)
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# Load datasets
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canadian_institutions = load_canadian_dataset(canadian_file)
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global_institutions = load_global_dataset(global_file)
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# Merge
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merged, stats = merge_institutions(canadian_institutions, global_institutions)
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# Export
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export_merged_dataset(merged, merged_file, stats)
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# Generate report
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generate_integration_report(stats, report_file)
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print("\n" + "="*70)
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print("INTEGRATION COMPLETE ✅")
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print("="*70)
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print(f"Merged dataset: {merged_file}")
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print(f"Integration report: {report_file}")
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return 0
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if __name__ == '__main__':
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exit(main())
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