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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - tabular-regression
+language:
+  - en
+tags:
+  - housing
+  - urbanization
+  - africa
+  - synthetic-data
+  - sub-saharan-africa
+  - tenure-security
+  - property-rights
+size_categories:
+  - 10K<n<100K
+---

generate_dataset.py

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+"""
+Dataset Generator: Housing Tenure Security Africa
+
+Parameter Evidence Table:
+| Parameter | Value | Source |
+|-----------|-------|--------|
+| Informal tenure prevalence | 60-80% in slums | UN-Habitat 2023 |
+| Rental housing share | 30-50% urban | OECD Africa Urbanization 2025 |
+| Title deed availability | 10-30% informal | World Bank land data |
+| Eviction risk in informal | 15-40% | UN-Habitat estimates |
+| Year range | 2018-2025 | Project scope |
+| Countries | 15 African nations | Regional coverage |
+
+DAG Structure:
+country -> tenure_type -> security_level -> documentation -> risk_factors
+"""
+
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+COUNTRIES = [
+    'Nigeria', 'Kenya', 'Ethiopia', 'Egypt', 'South Africa',
+    'Tanzania', 'Morocco', 'Algeria', 'Ghana', 'Uganda',
+    'Mozambique', 'Zambia', 'Malawi', 'Rwanda', 'Senegal'
+]
+
+SCENARIOS = {
+    'low_burden': {'n': 4000, 'informal_pct': 0.45, 'seed': 42},
+    'moderate': {'n': 5000, 'informal_pct': 0.60, 'seed': 43},
+    'high': {'n': 6000, 'informal_pct': 0.75, 'seed': 44}
+}
+
+CITIES = {
+    'Nigeria': ['Lagos', 'Kano', 'Ibadan'],
+    'Kenya': ['Nairobi', 'Mombasa', 'Kisumu'],
+    'Ethiopia': ['Addis Ababa', 'Dire Dawa'],
+    'Egypt': ['Cairo', 'Alexandria'],
+    'South Africa': ['Johannesburg', 'Cape Town'],
+    'Tanzania': ['Dar es Salaam'],
+    'Morocco': ['Casablanca', 'Rabat'],
+    'Algeria': ['Algiers'],
+    'Ghana': ['Accra', 'Kumasi'],
+    'Uganda': ['Kampala'],
+    'Mozambique': ['Maputo', 'Beira'],
+    'Zambia': ['Lusaka'],
+    'Malawi': ['Lilongwe', 'Blantyre'],
+    'Rwanda': ['Kigali'],
+    'Senegal': ['Dakar']
+}
+
+def dag_sample_tenure_type(informal_pct, rng):
+    weights = [informal_pct, 0.35, 0.15, 0.08]
+    weights = [w / sum(weights) for w in weights]
+    return rng.choice(['informal', 'rental', 'owner_occupied', 'customary'], p=weights)
+
+def dag_sample_security_level(tenure_type, informal_pct, rng):
+    security_map = {
+        'informal': rng.uniform(0.2, 0.5),
+        'rental': rng.uniform(0.4, 0.7),
+        'owner_occupied': rng.uniform(0.7, 0.95),
+        'customary': rng.uniform(0.3, 0.6)
+    }
+    if tenure_type == 'informal':
+        security_map['informal'] *= (1 - informal_pct * 0.3)
+    return round(security_map[tenure_type], 3)
+
+def dag_sample_documentation(tenure_type, security, rng):
+    doc_chances = {
+        'informal': 0.15,
+        'rental': 0.55,
+        'owner_occupied': 0.75,
+        'customary': 0.25
+    }
+    has_title = rng.random() < doc_chances.get(tenure_type, 0.3)
+    
+    return {
+        'has_title_deed': has_title,
+        'has_formal_lease': rng.random() < doc_chances.get(tenure_type, 0.3) * 0.8,
+        'proof_of_occupancy': rng.random() < 0.7,
+        'registration_status': rng.choice(['registered', 'pending', 'none'])
+    }
+
+def generate_dataset(scenario, output_dir):
+    config = SCENARIOS[scenario]
+    rng = np.random.default_rng(config['seed'])
+    
+    data = {
+        'country': [],
+        'city': [],
+        'year': [],
+        'tenure_type': [],
+        'security_score': [],
+        'has_title_deed': [],
+        'has_formal_lease': [],
+        'proof_of_occupancy': [],
+        'registration_status': [],
+        'years_in_tenure': [],
+        'eviction_risk_pct': [],
+        'perceived_security': [],
+        'willingness_to_invest': [],
+        'tenure_type_detail': [],
+        'landlord_relationship': [],
+        'rent_control_applicable': []
+    }
+    
+    for _ in range(config['n']):
+        country = rng.choice(COUNTRIES)
+        city = rng.choice(CITIES.get(country, ['Unknown']))
+        year = rng.integers(2018, 2026)
+        
+        tenure_type = dag_sample_tenure_type(config['informal_pct'], rng)
+        security = dag_sample_security_level(tenure_type, config['informal_pct'], rng)
+        docs = dag_sample_documentation(tenure_type, security, rng)
+        
+        eviction_risk = (1 - security) * 100
+        
+        data['country'].append(country)
+        data['city'].append(city)
+        data['year'].append(year)
+        data['tenure_type'].append(tenure_type)
+        data['security_score'].append(security)
+        data['has_title_deed'].append(docs['has_title_deed'])
+        data['has_formal_lease'].append(docs['has_formal_lease'])
+        data['proof_of_occupancy'].append(docs['proof_of_occupancy'])
+        data['registration_status'].append(docs['registration_status'])
+        data['years_in_tenure'].append(int(rng.uniform(1, 25)))
+        data['eviction_risk_pct'].append(round(eviction_risk, 1))
+        data['perceived_security'].append(rng.choice(['very_secure', 'secure', 'insecure', 'very_insecure']))
+        data['willingness_to_invest'].append(round(rng.uniform(0.1, 0.9), 2))
+        data['tenure_type_detail'].append(rng.choice(['formal', 'informal', 'customary', 'communal']))
+        data['landlord_relationship'].append(rng.choice(['family', 'private', 'government', 'community']))
+        data['rent_control_applicable'].append(rng.choice([True, False], p=[0.2, 0.8]))
+    
+    df = pd.DataFrame(data)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    df.to_csv(output_dir / f'{scenario}.csv', index=False)
+    print(f"Generated {scenario}: {len(df)} rows")
+
+if __name__ == '__main__':
+    base_dir = Path(__file__).parent
+    for scenario in SCENARIOS:
+        generate_dataset(scenario, base_dir)