boc/landvex-ai-training/taxonomy-guide.md

Landvex AI Training Guide — Urban Taxonomy

Version 1.0 — 2026-06-28

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1. SIX-LAYER ANALYTICAL FRAMEWORK

Purpose

Transform raw field observations into structured intelligence by analysing the same location through six distinct lenses. What appears irrational at one layer often becomes fully rational at another.

The Six Layers

Layer 01 — Physical

Question: What exists here? Data Points:

• Building types, heights, conditions
• Road quality, traffic patterns
• Signage, storefronts, vacancies
• People density, demographics
• Green space, water features

AI Training Labels:

{
  "building_type": ["residential", "commercial", "industrial", "mixed"],
  "building_condition": ["excellent", "good", "fair", "poor", "derelict"],
  "road_quality": ["excellent", "good", "fair", "poor"],
  "pedestrian_density": ["very_high", "high", "moderate", "low", "very_low"],
  "vacancy_rate": "float (0.0-1.0)"
}

Layer 02 — Operational

Question: What is happening? Data Points:

• Business hours, activity levels
• Delivery frequency, logistics
• Customer flows, queue lengths
• Construction, renovation activity
• Event presence, street markets

AI Training Labels:

{
  "business_status": ["open_active", "open_quiet", "closed_temporarily", "closed_permanently", "unknown"],
  "activity_level": ["very_high", "high", "moderate", "low", "very_low"],
  "delivery_frequency": ["constant", "frequent", "occasional", "rare", "none"],
  "construction_activity": ["major", "minor", "none"],
  "event_presence": ["large", "small", "none"]
}

Layer 03 — Economic

Question: How is this financed? Data Points:

• Ownership structure (family, corporate, institutional)
• Revenue streams (visible + inferred)
• Cost structure (rent, labour, materials)
• Profitability indicators
• Informal economy presence

AI Training Labels:

{
  "ownership_type": ["family_owned", "sole_proprietor", "corporate", "institutional", "government", "unknown"],
  "revenue_visibility": ["fully_visible", "partially_visible", "mostly_hidden", "unknown"],
  "informal_economy_presence": ["high", "moderate", "low", "none", "unknown"],
  "profitability_indicator": ["strongly_profitable", "profitable", "break_even", "loss_making", "unknown"]
}

Layer 04 — Institutional

Question: What rules govern this? Data Points:

• Zoning classification
• Lease terms, rent controls
• Permit status, compliance
• Tax regime, incentives
• Regulatory enforcement level

AI Training Labels:

{
  "zoning": ["residential", "commercial", "industrial", "mixed_use", "special"],
  "lease_type": ["long_term", "short_term", "informal", "owner_occupied", "unknown"],
  "permit_status": ["fully_compliant", "minor_violations", "major_violations", "unlicensed", "unknown"],
  "regulatory_enforcement": ["strict", "moderate", "lax", "non_existent", "unknown"]
}

Layer 05 — Social

Question: What networks sustain this? Data Points:

• Family/kinship structures
• Migrant worker presence
• Tourist vs local ratio
• Community organisation
• Social trust indicators

AI Training Labels:

{
  "family_business": ["yes", "no", "unknown"],
  "migrant_worker_presence": ["high", "moderate", "low", "none", "unknown"],
  "customer_composition": ["mostly_tourist", "mixed", "mostly_local", "unknown"],
  "social_trust_indicator": ["high", "moderate", "low", "very_low", "unknown"]
}

Layer 06 — Temporal

Question: How does this change over time? Data Points:

• Time of day patterns
• Day of week patterns
• Seasonal variations
• Economic cycle position
• Construction phase

AI Training Labels:

{
  "time_pattern": ["rush_hour_peak", "daytime_active", "evening_active", "night_active", "always_quiet"],
  "seasonal_variation": ["very_high", "high", "moderate", "low", "none"],
  "economic_cycle": ["expansion", "peak", "contraction", "trough", "unknown"],
  "construction_phase": ["pre_construction", "active", "recently_completed", "mature", "none"]
}

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2. CONTRADICTION DETECTION

Definition

A contradiction occurs when observations from different layers conflict with each other, or when official narratives conflict with observed reality.

Types of Contradictions

Type A — Cross-Layer Contradiction

Example: Physical layer shows "major construction" but Temporal layer shows "no activity for 6+ months" → Likely stalled project

Type B — Narrative-Reality Contradiction

Example: Official report states "commercial vitality increasing" but Operational layer shows "30% vacancy rate" → Overstated growth

Type C — Temporal Contradiction

Example: Rush hour observations show low traffic but Evening observations show high activity → Different economic rhythms than expected

Scoring

Contradiction Index = (Number of detected contradictions / Number of possible cross-layer checks) × 100

Interpretation:

• 0-20: High consistency, reliable data
• 21-40: Minor inconsistencies, verify key assumptions
• 41-60: Significant contradictions, investigate further
• 61-80: Major contradictions, likely data quality issues or hidden dynamics
• 81-100: Critical contradictions, do not rely on single data source

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3. AGGREGATE SCORES

Opportunity Score (0-100)

Weighted combination of:

• Physical accessibility (15%)
• Operational activity (20%)
• Economic diversity (20%)
• Institutional support (15%)
• Social dynamism (15%)
• Temporal stability (15%)

Growth Score (0-100)

Weighted combination of:

• Construction activity (25%)
• Business formation rate (25%)
• Investment flows (25%)
• Population trends (25%)

Commercial Vitality Score (0-100)

Weighted combination of:

• Business density (20%)
• Customer traffic (25%)
• Revenue visibility (20%)
• Lease activity (15%)
• Night-time economy (20%)

Infrastructure Stability Score (0-100)

Weighted combination of:

• Road quality (20%)
• Utility reliability (25%)
• Public transport (20%)
• Digital connectivity (15%)
• Maintenance schedules (20%)

Investment Confidence Score (0-100)

Weighted combination of:

• Regulatory clarity (20%)
• Contract enforcement (20%)
• Currency stability (15%)
• Political risk (20%)
• Exit liquidity (25%)

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4. TRAINING DATA REQUIREMENTS

Minimum Observations per District

• Physical: 50+ geo-tagged images
• Operational: 10+ time-distributed observations
• Economic: 20+ business interviews/observations
• Institutional: Document review + 5+ expert interviews
• Social: 30+ behavioural observations
• Temporal: 4+ observations at different times

Quality Thresholds

• GPS accuracy: <10m
• Image resolution: minimum 12MP
• Time stamp accuracy: <1 minute
• Contributor verification: ID + training completion
• AI review pass rate: >95%

Bias Mitigation

• Rotate observation times (avoid only rush hour)
• Distribute observers across demographics
• Cross-validate with satellite imagery
• Compare with official statistics quarterly

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5. OUTPUT FORMAT

District Intelligence Card

{
  "district_id": "string",
  "city": "string",
  "country": "string",
  "last_updated": "ISO-8601",
  "layer_scores": {
    "physical": {"score": 0-100, "confidence": 0-100},
    "operational": {"score": 0-100, "confidence": 0-100},
    "economic": {"score": 0-100, "confidence": 0-100},
    "institutional": {"score": 0-100, "confidence": 0-100},
    "social": {"score": 0-100, "confidence": 0-100},
    "temporal": {"score": 0-100, "confidence": 0-100}
  },
  "aggregate_scores": {
    "opportunity": 0-100,
    "growth": 0-100,
    "commercial_vitality": 0-100,
    "infrastructure": 0-100,
    "investment_confidence": 0-100,
    "contradiction_index": 0-100
  },
  "contradictions": [
    {
      "type": "A|B|C",
      "severity": "low|medium|high|critical",
      "description": "string",
      "layers_involved": ["string"],
      "recommended_action": "string"
    }
  ],
  "observation_count": integer,
  "contributor_count": integer,
  "data_quality_flag": "green|yellow|red"
}

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6. CONTINUOUS IMPROVEMENT

Feedback Loop

1. Deploy observations
2. AI analyses layers
3. Detect contradictions
4. Human expert review
5. Adjust weights/scoring
6. Retrain models
7. Repeat

Model Update Cadence

• Daily: New observations ingested
• Weekly: Layer scores recalculated
• Monthly: Contradiction index updated
• Quarterly: Full model retraining
• Annually: Framework version update

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7. EXAMPLE: BANGKOK ANALYSIS

Observed Contradictions

1. Physical vs Economic: Luxury mall adjacent to informal market → Different economic systems coexisting
2. Operational vs Temporal: Massage salon empty at noon but full at midnight → Non-standard business hours
3. Institutional vs Social: Strict zoning but informal settlements persist → Enforcement gap

Aggregate Scores (Example)

• Opportunity: 78/100
• Growth: 82/100
• Commercial Vitality: 71/100
• Infrastructure: 65/100
• Investment Confidence: 58/100
• Contradiction Index: 34/100 (moderate inconsistencies)

Key Insight

Bangkok exhibits high opportunity and growth but lower investment confidence due to institutional-social contradictions. The informal economy provides operational resilience but creates regulatory uncertainty for formal investors.

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Document version: 1.0 Last updated: 2026-06-28 Next review: 2026-09-28