FAMILY_SPATIAL_HETEROGENEITY - Experiment Results

Overview

This document tracks experimental runs for spatial heterogeneity intelligence using field-level variability analysis, hot spot detection, and multi-scale spatial patterns to predict potato prices. Tests whether spatial analysis can surpass the 20.5% improvement achieved by temporal satellite intelligence.

Experimental Status

• Status: 🎯 READY FOR IMPLEMENTATION
• Created: 2025-08-20
• Foundation: Builds on FAMILY_GROWING_SEASON_DYNAMICS 20.5% breakthrough
• Innovation: Spatial patterns within potato regions predict market outcomes
• Target: 22-27% improvement over strongest baseline
• Priority: High - Advanced spatial intelligence development

Foundation Success

FAMILY_GROWING_SEASON_DYNAMICS Achievement: - Breakthrough Performance: 20.5% improvement over persistent baseline - Key Innovation: Multi-index approach (EVI vs NDVI) revealed hidden patterns - Validated Method: Real satellite data with proper baseline methodology - Temporal Focus: Growing season trajectory analysis proved predictive

Spatial Intelligence Innovation

Core Hypothesis: Field-level variability and spatial heterogeneity patterns contain predictive information invisible to spatially-averaged analysis.

Key Insight: Just as combining EVI + NDVI revealed patterns neither index showed individually, analyzing spatial variation patterns reveals market information that aggregate analysis misses.

Data Validation

• ✅ Zarr store available: lake_31UFU_small.zarr (36GB, 10m resolution for spatial analysis)
• ✅ Spatial resolution: 10m pixels enable field-level heterogeneity analysis
• ✅ Field boundaries: BRP parcel data for exact field delineation
• ✅ Multi-year coverage: 2015-2024 for robust spatial pattern identification
• ✅ Price data accessible: BoerderijApi NL.157.2086 weekly prices
• ✅ Standard baselines: All 4 standard baselines (persistent, seasonal_naive, ar2, historical_mean) ready

Advanced Spatial Analysis Strategy

Spatial Intelligence Framework

Moving from temporal to spatial satellite intelligence:

1. Field-Level Focus: Individual field performance vs aggregate regional patterns
2. Heterogeneity Metrics: Coefficient of variation, spatial autocorrelation, clustering
3. Multi-Scale Integration: Pixel → Field → Region → National spatial patterns
4. Hot Spot Detection: Identify spatial clusters of high/low performance
5. Neighbor Effects: Account for spatial autocorrelation in agricultural performance

Performance Targets

• Variant A (22% target): Field-level variability and statistical dispersion analysis
• Variant B (25% target): Hot spot/cold spot detection and spatial clustering
• Variant C (27% target): Multi-scale spatial intelligence integration

Technical Innovation

• Spatial Cross-Validation: Prevent spatial leakage in train/test splits
• Local Spatial Statistics: LISA, Getis-Ord Gi*, Moran's I for cluster detection
• Distance-Based Features: Spatial weight matrices and neighbor correlation
• Scale-Dependent Patterns: Different spatial signals at different scales

Experiment Results: [TO BE UPDATED AFTER IMPLEMENTATION]

Data Versions: - Satellite data: lake_31UFU_small.zarr (10m resolution, 2015-2024) - Price data: BoerderijApi NL.157.2086 - Parcel data: BRP consumption potato boundaries (field-level) - Git SHA: [TO BE FILLED]

Spatial Analysis Results: - [TO BE FILLED AFTER IMPLEMENTATION]

Statistical Tests: - DM test vs strongest baseline: [TO BE FILLED] - Spatial autocorrelation tests: [TO BE FILLED] - Cross-validation (spatial): [TO BE FILLED] - Baseline comparison: ALL 4 standard baselines tested

Baseline Comparison: - Model: MAE = [TO BE FILLED] - Persistent baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Seasonal naive baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - AR2 baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Naive baseline: MAE = [TO BE FILLED] (improvement: [TO BE FILLED]) - Strongest competitor: [TO BE IDENTIFIED] - Primary improvement: [TO BE CALCULATED] vs [strongest_baseline_name]

Decision Log - 2025-08-20

Summary: FAMILY_SPATIAL_HETEROGENEITY prepared for implementation to explore spatial intelligence beyond temporal satellite analysis.

Key Innovation Decisions: 1. Spatial Focus: Move from temporal trajectory analysis to spatial pattern recognition 2. Field-Level Granularity: Use BRP parcel boundaries for exact field-level analysis 3. Heterogeneity Emphasis: Coefficient of variation and spatial clustering as key features 4. Multi-Scale Approach: Integrate pixel, field, regional, and national scale patterns 5. Spatial Statistics: Apply LISA, Getis-Ord, and Moran's I for rigorous spatial analysis

Implementation Strategy: - Variant A: Statistical measures of field-level variability (22% target) - Variant B: Hot spot/cold spot spatial clustering analysis (25% target) - Variant C: Multi-scale spatial pattern integration (27% target)

Technical Requirements: - Spatial cross-validation to prevent data leakage - Spatial weight matrices for neighbor relationships - Distance-based feature engineering - Multi-resolution spatial analysis

Success Metrics: - Exceed 22% improvement over strongest baseline - Beat FAMILY_GROWING_SEASON_DYNAMICS 20.5% temporal record - Pass spatial autocorrelation statistical tests - Demonstrate practical significance for regional market analysis

Status: ✅ READY FOR SPATIAL INTELLIGENCE IMPLEMENTATION

Codex Validation — 2025-11-10

Files Reviewed

• hypothesis.yml
• hypothesis.md
• experiment.md

Findings

1. No implementation exists. The family contains only narrative documentation; there is no runner, dataset builder, or notebook that ingests the cited satellite/parcel data.
2. Real-data requirement unmet. Because no code exists, nothing in the repository proves that Sentinel, BRP, or Boerderij feeds have been pulled or pre-processed.
3. Baseline superiority untested. Without any run logs, we cannot tell whether the proposed spatial heterogeneity signals outperform a pure price model.

Verdict

NOT VALIDATED – This family is still conceptual; it cannot be considered validated until executable code ingests real data and demonstrates statistically significant gains over the mandated baselines.