Remark

These lecture notes are in early access and will continue to evolve based on classroom experience and feedback.

1. Foundations: Questions, Data Models, and Reference Systems

Geospatial data engineering is the practice of acquiring, storing, checking, transforming, and analyzing data that has a location. It supports questions such as Which homes are near a clinic?, What overlaps a flood zone?, and How much area is protected?

This chapter builds a mental model before introducing complicated tools. We first turn a broad question into a reproducible pipeline, then compare vector and raster data, and finish by inspecting a first layer and managing its coordinate reference system (CRS).

Learning path

1. Question → pipeline: define the decision, inputs, operations, and output.

2. Data models: choose vector for discrete features and raster for continuous fields.

3. Quality and communication: validate layers, document provenance, and design maps.

4. CRS and inspection: understand units, validate geometry, and record assumptions.

Before you begin

You should know Python imports, variables, functions, and basic pandas filtering. You do not need prior GIS experience. The key habit is to ask: where is it, relative to what, and in which units?

• 1.1. What is Geospatial Data Engineering?
• 1.2. Geospatial Data Models — Vector and Raster
• 1.3. Vector Data Models
• 1.4. Raster Data Models
• 1.5. Sources of Geospatial Data
• 1.6. Creating New Geospatial Data
• 1.7. Spatial Data Quality and Metadata