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Chapter 3: Software Engineering Basics

Software engineering basics for geospatial work

Geospatial software engineering is still software engineering. The difference is that ordinary engineering concerns such as schemas, APIs, testing, performance, security, and operations must also account for spatial correctness, coordinate systems, data quality, and scale.

Learning Goals

  • Connect general software engineering practices to geospatial systems.
  • Design schemas and APIs that preserve spatial meaning.
  • Test geospatial behavior, not only code execution.
  • Reason about performance, indexes, observability, and data contracts.

Theory

Good geospatial systems separate concerns: storage, transformation, spatial analysis, visualization, and user workflows. They make assumptions explicit through schemas, metadata, tests, and documentation. They also distinguish source data, derived data, cached data, and published data.

The engineer's job is to make spatial behavior predictable. If a route, buffer, overlay, or classification result changes, the team should know whether the cause was new data, new code, a CRS change, a library upgrade, or a model change.

Math

The required math is pragmatic: algorithmic complexity, indexing behavior, floating-point arithmetic, graph weights, interpolation, statistics, and numerical tolerance. Spatial equality is often tolerance-based because coordinates are continuous, transformed, rounded, and sometimes uncertain.

Equation companion: Math and Algorithms Reference

Tools of the Trade

  • Languages: Python, SQL, JavaScript/TypeScript, Java, C++, Rust, R.
  • Engineering tools: Git, Docker, CI/CD, test frameworks, linters, package managers.
  • Data tools: schema validators, Great Expectations, dbt, Airflow, Dagster, Prefect.
  • Spatial tools: PostGIS, GDAL, PROJ, GEOS, Shapely, GeoPandas, QGIS.
  • Observability: structured logs, metrics, tracing, data quality checks, pipeline lineage.

Examples of Real-World Solutions

  • A spatial API validates incoming geometries before writing them to PostGIS.
  • A pipeline records the source, timestamp, CRS, transformation parameters, and checksum for every output layer.
  • A routing service uses unit tests for graph behavior and integration tests for known routes.
  • A map tile service measures tile latency, cache hit rate, feature count, and geometry simplification effects.

Working Practice Examples

  1. Write a JSON schema for a geospatial feature with geometry, CRS assumption, timestamp, and provenance.
  2. Create test cases for point-in-polygon, distance, and spatial join behavior.
  3. Design an API endpoint that accepts a bounding box and returns paginated features.
  4. Add a data quality checklist to a geospatial ETL job.

Common Failure Modes

  • No explicit CRS policy.
  • No data lineage.
  • Tests that ignore edge cases such as antimeridian, poles, invalid polygons, and empty geometry.
  • API responses that return too much geometry at once.
  • Hidden manual GIS steps that cannot be reproduced.

Works Cited

Fowler, Martin. Patterns of Enterprise Application Architecture. Addison-Wesley, 2002.

Kleppmann, Martin. Designing Data-Intensive Applications. O'Reilly Media, 2017.

Ramsey, Paul. PostGIS in Action. 3rd ed., Manning, 2021.

"The Twelve-Factor App." The Twelve-Factor App, https://12factor.net/. Accessed 9 May 2026.

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