Geospatial data science is data science that treats location as a core variable. It combines geographic data (coordinates, boundaries, aerial imagery, sensor readings) with data engineering, statistics and machine learning to answer not only what is happening or will happen, but where. Examples include predicting which water mains are most likely to fail, identifying streets at risk of flooding, or choosing the best sites for EV charging points.
The term is often used loosely, as a synonym for big data analysis, machine learning or AI. In this article I explain the seven stages of a geospatial data science project and what a geospatial data scientist actually does.
The Geospatial Data Science lifecycle
A Geospatial Data Science (GDS) process can be described in seven stages:
- Defining the business goal;
- Data mining (Data Engineering);
- Data cleaning (Data Engineering);
- Data exploration (Data Analysis);
- Feature Engineering (Data Analysis);
- Predictive modelling (GDS/AI/ML/DL);
- Data Visualisation.
Figure 1: the seven stages of geospatial data science.
Stage 1: Defining the business goal
The first step is universal for any project involving data. Define the purpose. What do you want to know and why? Geospatial data science is not an end in itself, but a means to make data-driven decisions.
As a Consultant, this is where I can add value for Avineon Tensing clients. Just mastering the technical side is not enough. As an external force, I can look at your organisation and your goals with an open mind. I can help you properly separate goals from resources. Besides, very often much more is possible than you think.
Stages 2 and 3: Data Engineering
Data engineering is the first technical step in all data-related processes. Step one of the Data Engineering-phase is collecting the data needed for the specific project. There is often a lot involved in unlocking the right data from the right sources and in the right formats. Therefore, look very critically at what you need and especially what you don't need. This greatly benefits efficiency later in the process.
After compiling a suitable dataset, it is time to clean up data. In practice, values are always missing, different tables exist that mean the same thing and inconsistency is often present.
For geospatial projects, data mining usually means combining sources that were never designed to fit together: asset registers, sensor feeds, aerial or satellite imagery, and open data such as Ordnance Survey or Copernicus datasets. Each has its own coordinate system, accuracy and update cycle, so aligning them is often the hardest part of the project.
Avineon Tensing primarily uses FME (an ETL tool optimised to work with geodata) to complete the Data Engineering phase. As a Geospatial Data Scientist, I can rely on over 80 FME-certified colleagues who are fully specialised in Geospatial Data Engineering.
Figure 2: FME is our software of choice for geospatial data engineering.
Stages 4 and 5: Data Analysis
After completing the Data Engineering phase, it's time to start working with the data. Based on hypotheses, you test whether you can extract all the desired insights from the available dataset. You can do this using test visualisations. When you determine that your data selection is complete, you can continue to the next stage. Sometimes it happens that your Data Engineering steps need some fine-tuning before you go any further.
During the Feature Engineering step, you create new features based on existing data. These are values that are relevant to your model but are not included as separate variables. I'll take gross profit as an example: sales - purchase value. If you need profit as a separate feature to generate your predictive model, include it in your dataset during the Feature Engineering phase.
Executing the first five steps carefully is essential before you start the predictive modelling phase. First, because you will be faced with large lists of errors. Second, because the result will fall under the heading garbage in, garbage out. The insights that come out of your model are likely to show extreme outliers, may be way too positive (or negative), or they are just incompatible with reality.
Stage 6: Predictive modelling
Data science is mostly associated with the predictive modelling stage. Predicting trends based on past (and present) data. Artificial Intelligence, Machine Learning and Deep Learning are tools that enable predictive modelling.
Predictive modelling is impossible without carefully going through the previous steps. A model is only as good as the data behind it.
Stage 7: Data visualisation
Data visualisation is what you (usually) do it all for. The goal is to show insights to a wide range of stakeholders in a way they understand, whether that's a dashboard, a heatmap or a 3D visualisation.
What does a geospatial data scientist do?
A geospatial data scientist turns location data into predictions and recommendations. In practice, the role sits alongside two others:
- GIS analyst: what is where; mapping, spatial queries, cartography; ArcGIS, QGIS.
- Geospatial data engineer: is the data ready and reliable; collecting, cleaning, transforming and loading spatial data; FME, PostGIS, Python.
- Geospatial data scientist: what will happen, and where; feature engineering, statistical and machine learning models, spatial patterns; Python (GeoPandas, scikit-learn), R, DuckDB.
Most projects need all three. Data engineering (stages 2 and 3) usually takes up most of the effort, which is why our data scientists work alongside our FME-certified data engineers.
Geospatial data science in practice
- Agentic flooding describes councils facing a surge of AI-written objections and requests; we indexed around 700 Dutch spatial planning documents page by page, adding semantic search and zoning lookup by address.
- For SAIL Amsterdam 2025, we integrated live drone, vessel, traffic and crowd data with FME into a digital twin giving emergency services one common operational picture.
- In Ghana, we worked with Ghana Water Ltd. on sustainable water management, building GIS workflows for service connections, water quality sampling and network mapping while training the in-house GIS team.
The geographical component
The world of geographic information systems has carved its own niche. Working with location data is just that little bit different. This also applies to Geospatial Data Science. Properly integrating data sources without a geographical component with geometric data is the main challenge. Extensive knowledge of geographic data is absolutely necessary to create predictive models that can tell you not only what is going to happen, but also where.
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