• ✔ OIDC-based npm publishing (no secrets)
• ✔ Verified supply-chain provenance
• ✔ Automated quality gates (lint, test, audit)
• ✔ Reusable CI workflows

Direct pushes to master should be avoided. See CONTRIBUTING.md to enable the repo’s pre-push hook that blocks direct pushes to master locally.

• Monorepo publishing with npm workspaces
• OIDC-based trusted publishing (no secrets)
• Canary & RC release channels
• SBOM generation (CycloneDX)
• CodeQL static analysis
• Dependabot automated updates

This pipeline enforces supply-chain integrity and production-grade quality gates.

Geospatial Intelligence · Data Visualization · Operational Insight

The Geo-Analytics API/Dashboard is a data-driven analytics platform designed to transform raw geographic and regional datasets into clear, actionable intelligence. It enables organizations to analyze performance by location, identify trends, and support strategic decision-making through interactive visualizations and automated data workflows.

This project reflects a traditional analytics discipline—clean data in, meaningful insight out—implemented with modern tooling and forward-looking design.

This dashboard is built to answer questions executives and operators actually ask:

• Which regions are outperforming or underperforming?
• How does demand, activity, or revenue vary geographically?
• Where should resources, sales efforts, or infrastructure be prioritized?
• How do regional trends evolve over time?

• Business intelligence & operations teams
• Sales & marketing analytics
• Logistics & territory planning
• Public-sector or urban analytics
• Portfolio and case-study demonstrations

• Interactive regional maps (choropleths, markers, heat layers)
• Location-based comparisons and drill-downs

• Region-level performance metrics
• Trend analysis across time and geography

• Data ingestion and transformation using Python
• Structured outputs for repeatable analysis

• Clean datasets optimized for visualization layers
• Exportable formats (CSV / JSON)

• Modular design for future API integration
• Ready for cloud deployment or embedding into BI tools

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

Open interactive docs at http://localhost:8000/docs.

curl -s "http://localhost:8000/api/datasets" | jq
curl -s "http://localhost:8000/api/regions" | jq
curl -s "http://localhost:8000/api/analytics/regions?dataset_id=sample&value_col=revenue&agg=sum" | jq
curl -s "http://localhost:8000/api/analytics/trends?dataset_id=sample&date_col=date&value_col=revenue&freq=M" | jq
curl -s "http://localhost:8000/api/analytics/executive-summary?dataset_id=sample&metric=revenue&value_col=revenue" | jq

curl -s -X POST "http://localhost:8000/api/datasets/json?name=my-dataset" \
  -H "Content-Type: application/json" \
  -d '[{"region":"North","lat":1.0,"lon":2.0,"date":"2025-01-01","revenue":100}]' | jq

curl -s -X POST "http://localhost:8000/api/datasets/csv?name=my-csv" \
  -F "file=@regional_data.csv" | jq