krl-geospatial-tools 0.2.0

Geospatial analysis and mapping tools for the Khipu Intelligence Suite

KRL Geospatial Tools

Version: 1.0.0
License: Proprietary
Python: ≥3.9
Status: Production

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SECTION A — Executive & Strategic Overview

What This Repository Does

KRL Geospatial Tools provides spatial analysis capabilities for economic data. It implements:

1. Spatial Econometrics — Spatial regression, autocorrelation, spillover analysis
2. Interactive Mapping — Choropleth maps and geographic visualization
3. Geographic Processing — Shapefiles, GeoJSON, spatial joins
4. Geocoding — Address to coordinates and reverse geocoding

Current Maturity Level: PRODUCTION

Criterion	Status
Core spatial methods	✅ Yes
Interactive maps	✅ Yes
Test coverage	⚠️ 16.2% (749/4632 lines)
Documentation	✅ Partial
PyPI published	⚠️ Not yet

Strategic Dependencies

• Upstream: krl-premium-backend (API access)
• Downstream: None
• Peer: krl-network-analysis (network + spatial overlap)

Known Gaps Blocking Scale

1. Test coverage at 16.2% — Critical gap
2. Large codebase — 4632 lines with minimal tests

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SECTION B — Product, Marketing & Sales Intelligence

Spatial Capabilities (Verified)

Capability	Status
Spatial regression	✅
Spatial autocorrelation	✅
Choropleth maps	✅
Shapefile processing	✅
GeoJSON support	✅
Geocoding	✅
Spatial clustering	✅
Distance calculations	✅

Capabilities Safe to Reference in Sales

✅ Can claim:

• "Spatial econometrics for economic data"
• "Interactive choropleth maps"
• "Geographic data processing"
• "Spatial autocorrelation analysis"

❌ Cannot claim:

• "Enterprise-grade reliability" — 16% test coverage

Differentiators

1. Economic focus — Designed for economic spatial analysis
2. KRL integration — Works with KRL data connectors
3. Interactive outputs — Browser-based map visualization

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SECTION C — Engineering & Development Brief

Tech Stack

Component	Technology
Language	Python ≥3.9
Geospatial	geopandas, shapely
Econometrics	pysal, libpysal
Visualization	folium, plotly
Data	pandas, numpy

How to Run

pip install -e ".[dev]"
pytest tests/ -v --cov=src

Known Refactor Targets

1. Increase test coverage — From 16% to 80%+
2. Add integration tests — End-to-end spatial workflows

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SECTION D — Operational & Governance Notes

Maintenance Risks

Risk	Severity	Mitigation
Test coverage 16%	HIGH	Add comprehensive tests
Large untested codebase	HIGH	Prioritize critical paths

Ownership

• Team: KR-Labs Engineering
• Escalation: [e-mail]

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Last updated: December 14, 2025 — Forensic audit verified 16.2% coverage