Establishing the Data Foundation

Until this point, the GIS project has been focused on the intellectual process of ensuring a sound conceptual foundation. We now come to the practical phase where we must “grab tools and shovel bytes” to establish the physical material of our project.

1. The Technical Environment

The “Project Document” vs. The Database

Before we acquire data, we need a container. We must distinguish between the Data Structure (where bytes live) and the Project Document (where logic lives).

• The Project Document: In desktop GIS (QGIS, ArcGIS Pro), this is a file (.qgz, .aprx) that stores references to data, styling rules, and print layouts. In Python, this is your setup script.

• The Connection: The project document establishes a named connection between the processing environment and the physical data. It does not contain the data itself.

⚠️ Stewardship Alert: Never rely on the Project Document to store your data schema. Your data must exist in a robust format (like GeoPackage or PostGIS) independent of the software used to view it.

2. Acquiring and Creating Data

Having defined your Domain of Discourse (DoD) in Phase 2, you must now populate your schema. In SemanticGIS, we distinguish between three fundamental origins of data:

A. Sourcing Existing Data

The “Found” World. You are not creating new data, but integrating data that already exists—whether it is a ready-made map layer (Geospatial Infrastructure) or a raw Excel list (Register Data).

• Workflow: Download, Document Provenance, and Transform (Filter/Clip/Join).

• Key Task: See Handling Address and Contact Data for a guide on cleaning tabular registers.

B. Sensor-Based Capture

The “Measured” World. Data created by machines measuring physical reality. This is resource-intensive and requires strict calibration.

• Field Data Collection: Using GPS/GNSS or mobile apps (QField/Mergin) to record location and attributes in the field.

• Remote Sensing: Using drones, satellites, or LiDAR to capture raw sensor readings that must be processed into geometry.

C. Human-Led Creation

The “Interpreted” World. Data created by the human mind interpreting a source.

• Heads-up Digitizing: The manual process of tracing features (e.g., forests, roads) from an orthophoto or historical map.

• AI-Assisted Extraction: Using algorithms to segment images, followed by human verification.

3. The Outcome: A Clean Database

Regardless of the path taken, this phase concludes when you have a final, documented project database. This database is the “frozen” state of your inputs and serves as the official starting point for Phase 4: Analytical Modelling.