Approach

I want my work to help a landscape change for the better. Both for the environment and the people living in it.

This is done in three ways: understanding a landscape's history, analysing the present environmental and human systems, and modelling the changes to come.

The process matters as much as the result. These four principles run through everything I build.

01

Clarity over complexity

Every result is defendable. You can see how it was reached, not just what it says.

Clarity and simplicity drive the work before anything else. A model no one can follow is a liability no matter the sophistication. Methods are explained, assumptions are documented, and results are validated against reference data wherever possible. The goal is that you can stand behind the analysis in front of your own stakeholders, because you understand how it was achieved.

In practice Lebanon mountain snowlines
02

Combine, don't invent

New solutions come from composing respected methods, not inventing untested ones.

I don't build exotic algorithms from scratch. I take reviewed, respected models with literature and communities behind them, and combine them in new ways to answer a question none of them could answer alone.

Everything runs on open-source tools and open data. Python-first: GeoPandas, Shapely, Rasterio, OSMnx. That keeps the work reproducible, inspectable, and yours to keep. There are no licences, no vendor lock-in, no black boxes you can't audit.

In practice MOSH Antwerp
03

A coherent picture of the whole system

Environmental and human systems, read together rather than in parallel.

My background is physical geography, and I am fascinated by how the configuration of a system shapes what happens within it. But most real questions cross disciplines: ecology meets infrastructure, hydrology meets housing. I stitch those layers into one picture, at whatever scale the question lives (a block, a coastline, a whole sea), and look for the effects that land far from the obvious place, because that's usually where the real finding is.

In practice North Sea MSP
04

Build toward change

An analysis should end in action, not just description.

I frame problems as scenarios and trade-offs. What shifts if you prioritise ecology over cost? Capacity over equity? If possible, I turn the result into something people can actually use: an interactive map, a tool with sliders, not just a static report. The measure of success isn't the analysis itself; it's the decision it made possible.

In practice IBX interactive model
See it applied
Selected work →