GEO-INFER-DATA

Instructions

Core Capabilities

• Connectors: PostgreSQL/PostGIS, SQLite/SpatiaLite, REST APIs, file I/O
• Formats: GeoJSON, Shapefile, GeoParquet, GeoTIFF, CSV with coordinates
• ETL pipelines: Extract → Transform → Load with spatial awareness
• Caching: Spatial tile caching, query result caching
• Validation: Schema validation, coordinate bounds checking

Key Imports

from geo_infer_data.connectors.database import DatabaseConnector
from geo_infer_data.core.pipeline import ETLPipeline
from geo_infer_data.formats.geojson import GeoJSONLoader

Examples

from geo_infer_data.formats.geojson import GeoJSONLoader
from geo_infer_data.core.validation import CoordinateValidator

loader = GeoJSONLoader()
features = loader.load("buildings.geojson")
print(f"Loaded {len(features)} features")

# Validate coordinates against WGS84 bounds
validator = CoordinateValidator()
valid, invalid = validator.validate(features)
print(f"Valid: {len(valid)}, Out-of-bounds: {len(invalid)}")

from geo_infer_data.core.pipeline import ETLPipeline

pipeline = ETLPipeline(name="census_ingest")
pipeline.extract(source="postgresql://db/census", query="SELECT * FROM tracts")
pipeline.transform(operations=["reproject_to_4326", "validate_bounds"])
pipeline.load(target="geoparquet", path="output/census.parquet")
pipeline.run()

Guidelines

• SQL uses parameterized queries (:param placeholders) — never string interpolation
• All coordinate data validated against WGS84 bounds
• Test: uv run python -m pytest GEO-INFER-DATA/tests/ -v

Integrations

• SPACE → Spatial indexing of loaded datasets
• GIT → Version control for spatial data
• API → Data source for spatial query endpoints
• IOT → Sensor data ingestion pipelines
• EXAMPLES → Example ETL workflows