I am a Geoinformatics graduate with a strong interest in using spatial data, remote sensing, and geospatial technologies to solve real-world problems. Throughout my university journey, I developed a foundation in GIS, spatial analysis, cartography, and Earth observation. However, I realised that professional geospatial work requires more than technical knowledge; it requires the ability to work with complex datasets, understand practical challenges, and develop solutions that create value for users.
My attachment at GeoPsy Research, under the supervision of Francis Oloo, provided an opportunity to transition from academic GIS workflows into real-world applications. Working on environmental monitoring, renewable energy assessment, and spatial information systems exposed me to the realities of professional geospatial projects, including data preparation, workflow design, analysis, and communicating results effectively.
The experience reshaped my understanding of GIS. I began to view GIS not only as a tool for producing maps, but as an integrated approach for analysing systems, supporting decisions, and developing solutions across different sectors.
Understanding Environmental Change Through the Mataranka Wetland Persistence Project
One of the major projects I worked on during my attachment was the Mataranka Wetland Persistence Project, focused on understanding long-term wetland dynamics in the Mataranka wetland system, Northern Territory, Australia.
The objective of the project was to assess wetland persistence over time and investigate the environmental factors influencing patterns of inundation. Instead of simply identifying whether wetlands were present or absent, the project aimed to understand how hydrological processes, climate variability, vegetation condition, and groundwater behaviour influenced wetland dynamics.
The analysis covered a multi-year period from 2014 to 2024 and integrated multiple environmental variables, including:
- Surface water extent dynamics.
- Landsat-derived vegetation condition indicators.
- Precipitation patterns.
- Groundwater indicators.
- Water balance estimates.
Google Earth Engine was used for satellite data processing and time-series generation, while Python supported statistical analysis, anomaly detection, correlation analysis, and visualization.
Working on this project highlighted an important lesson: remote sensing is not only about detecting change from satellite imagery. The real value comes from understanding the environmental processes driving those changes.
Developing GIS Decision-Support Systems: Somalia Renewable Energy DSS
Another significant project during my attachment was the development of a Renewable Energy Decision Support System (DSS) for Somalia.
This project demonstrated how GIS can move beyond traditional mapping and become a tool for strategic planning and decision-making. The objective was to identify areas with high renewable energy potential by integrating spatial datasets related to solar and wind resources, terrain characteristics, accessibility, and suitability factors.
The project involved developing a multi-criteria spatial analysis workflow combining:
- Solar potential indicators such as Global Horizontal Irradiance (GHI).
- Wind resource variables including wind speed and wind power density.
- Terrain constraints.
- Land characteristics.
- Infrastructure considerations.
The suitability analysis used weighted criteria to evaluate renewable energy potential and identify areas suitable for future development.
The analytical outputs were transformed into an interactive web-based GIS application that allowed users to explore renewable energy potential spatially.
The system integrated several technologies:
PostGIS was used to manage spatial datasets, including administrative boundaries and regional statistics.
FastAPI provided the backend architecture for connecting spatial queries, databases, and application services.
Leaflet and QGIS2Web workflows supported interactive visualization of solar and wind potential layers.
Raster processing workflows enabled efficient handling and visualization of renewable energy datasets.
This project taught me that a successful GIS product is not only about producing accurate spatial analysis. It also requires designing systems where users can interact with information and make informed decisions.
Additional Earth Observation Experience: Mangrove Monitoring and Land Cover Analysis
During my attachment, I also gained experience in ecosystem monitoring through mangrove analysis and land cover classification workflows.
These projects involved using satellite imagery and remote sensing techniques to understand environmental changes. Spectral indicators such as vegetation and water-related indices were used to support ecosystem assessment and land cover characterization.
Working with Earth observation data strengthened my understanding of how satellite imagery can be transformed into meaningful environmental information for monitoring ecosystems and detecting landscape changes.
Lessons From GeoPsy Research
GIS is about solving problems, not just producing maps. Before working on real projects, it is easy to view GIS mainly as a tool for map production. However, professional GIS requires understanding the underlying problem and selecting appropriate spatial methods to answer meaningful questions.
Data quality determines analysis quality. Real-world datasets often require significant preparation before analysis. Across the Mataranka and Somalia DSS projects alike, data cleaning, validation, and understanding the limitations of each source were essential steps in producing reliable geospatial outputs — no analysis is more trustworthy than the data feeding it.
Reproducibility matters. Developing workflows that can be repeated, updated, and improved is critical. Automation and structured processes make geospatial analysis more reliable and scalable.
Communication is part of GIS. A technically correct analysis has limited impact if the results cannot be communicated effectively. Interactive dashboards, visualisations, and clear reporting are important parts of modern GIS.
Looking Forward
The lessons gained during my attachment at GeoPsy Research continue to shape how I approach geospatial projects today, from the value of understanding environmental processes behind the data, to designing systems that help people make decisions, not just view maps.
I’m grateful to Francis Oloo for his guidance and mentorship throughout the attachment, which laid the foundation for how I think about GIS as a discipline.
Key takeaway: My attachment at GeoPsy Research represented a transition from learning GIS concepts to applying them in real-world contexts. It showed me that GIS is not simply about creating maps; it is about transforming spatial data into knowledge that helps people understand problems, make decisions, and create meaningful solutions
