Geo Data Engineering

Geospatial Data Engineering

Purpose: get spatial data into a clean, validated, analysis-ready state with a repeatable pipeline — the stage where most real-world GIS time is spent and most silent errors are born.

Format selection

Acquisition playbook

• OpenStreetMap: small areas → osmnx; large extracts → Geofabrik PBF + pyrosm/osmium. Respect tag heterogeneity: always inspect tag value distributions before filtering.
• Buildings/places at scale: Overture Maps (GeoParquet on S3/Azure, query with DuckDB spatial — often the fastest path).
• Satellite/raster: STAC APIs via pystac-client + odc-stac — see remote-sensing-analysis; planetary archives → google-earth-engine.
• Boundaries: authoritative national source first; Natural Earth / GADM / geoBoundaries for global work — record which, versions differ materially.
• Record every acquisition: source URL, query parameters, retrieval date, license. Put it in a DATA_SOURCES.md next to the data.

CRS engineering

• Store in EPSG:4326 or source CRS; analyze in a projected CRS suited to the extent: local UTM zone (gdf.estimate_utm_crs()), national grid, or equal-area (EPSG:6933/Mollweide) for cross-region area stats.
• Datum shifts matter at sub-meter precision: transformations between datums need the right transformation grid (pyproj.network.set_network_enabled(True) when accuracy matters).
• Never strip or overwrite a CRS to "fix" misaligned layers — diagnose which layer is wrong with a known landmark instead.

Cleaning pipeline

Run scripts/clean_vector.py (or import its clean_vector() function) as the standard hygiene pass: drops empty/null geometries, repairs invalid ones with make_valid, de-duplicates, reprojects, and prints an accounting report so silent data loss is impossible.

Then: normalize text attributes (trim, collapse whitespace, locale-aware casefold — beware Turkish İ/ı, German ß), coerce dtypes explicitly, and show value_counts() of every categorical you will later filter on.

Scale strategies

• Fits in RAM: GeoPandas + Shapely 2 vectorized ops. Ensure the spatial index is used (sjoin, query_bulk) — hand-rolled loops are O(n²).
• Bigger than RAM, single machine: DuckDB spatial extension over GeoParquet (predicate pushdown + spatial SQL), or dask-geopandas.
• Served / concurrent / transactional: PostGIS — see postgis-spatial-sql.
• Rasters: windowed reads (rasterio.windows), chunked xarray + dask; never read() a 50 GB mosaic into memory.

Pipeline standards

• Idempotent steps with explicit inputs/outputs on disk; re-running never corrupts state.
• Checkpoint after expensive stages (download, big join) in GeoParquet/GPKG.
• Log an accounting line per stage: rows/features/pixels in → out.
• Deterministic ordering before writing (sort by stable key) so diffs are meaningful.

Pitfalls checklist

• CSV opened without declaring lon/lat columns' CRS.
• Shapefile column names silently truncated on export.
• Encoding mojibake from legacy files (try encoding="utf-8" then cp1252).
• Mixed geometry types in one layer (Polygon + MultiPolygon breaks some tools — normalize with .explode() or promote to Multi*).
• Antimeridian and pole-crossing geometries after naive reprojection.
• Downloaded "latest" data with no recorded version/date — unreproducible.

Execution contract

• Workflow: inventory sources and contracts; acquire with provenance; inspect CRS, schema, geometry, and scale; clean deterministically; validate; write an analysis-ready artifact.
• Decision rules: select formats and engines from size, geometry, concurrency, and downstream access needs; never infer CRS or destructive repairs silently.
• Verification protocol: reconcile feature or pixel counts at every stage, assert CRS and geometry invariants, sample outputs spatially, and rerun to confirm idempotence.
• Failure modes: quarantine ambiguous CRS, mixed units, invalid encodings, lossy format conversions, or unexplained row loss instead of guessing.
• Deliverables: validated dataset, machine-readable schema and CRS, provenance manifest, accounting log, rejected-record report, and reproducible pipeline.
• Source freshness: consult the authoritative source registry before using version-sensitive formats or APIs and record the checked date.