Engineering Approach

The entire project, along with its environment, has been containerized using Docker. This is noteworthy because it makes the entire study fully reproducible and easy to share.

Reproducibility was a significant challenge in this project due to the use of QGIS and PyQGIS. PyQGIS cannot be easily installed in a local environment via common package managers such as pip or conda. Instead, you must install the QGIS application on your local machine and then manually configure the path to the Python packages inside the application, which are OS-dependent. This process reduces the shareability of the code and imposes a significant barrier for end users wishing to run the project. By using a QGIS-based Docker image within the container, this limitation has been completely overcome.

Additionally, this project relies on spatial data, which is best understood when visualized – especially for transformation processes and results. Displaying such data in tables often leads to ambiguity and poor clarity. To eliminate the need for a third-party application, a simple Graphical User Interface (GUI) has been developed, which currently allows the visualization of results and data in GeoJSON format.