_{Does walking accessibility to textile collection bins affect usage rates in Dutch urban contexts? This is the question that second-year Data Science and Society students explored in collaboration with two external stakeholders: Statistics Netherlands (CBS) and TEXroad. The students analysed data to investigate the relationship between accessibility and usage. Two of the four students who worked on the project shared their reflections, while Barteld Braaksma from Statistics Netherlands and Traci Kinden from TEXroad also spoke about the importance of collaborating with students who are passionate about applying data-driven approaches to socially relevant challenges.}

Could you briefly introduce yourselves and explain how you came to study Data Science & Society at Campus Fryslân?

Jeska: I’m 21 years old and I live in Hallum, a small village near Leeuwarden. When I was trying to decide what to study, I honestly had no idea what direction to take. So I made a big list of every bachelor’s programme in the Netherlands, and that’s how I came across Data Science & Society. What really appealed to me was the combination of learning technical data skills while also looking at the social and ethical impact of data in society.

Faiza: I’m 19 years old and from Meppel, a town in Drenthe. While browsing the University of Groningen website, I came across the Data Science & Society programme at Campus Fryslân. I had been looking at both technical and more socially oriented studies, so this one immediately stood out. It felt like a good mix of both: learning how to work with data while also thinking about how it affects people and society. That combination is what made me choose it.

Which organisation did you collaborate with for your field project, and what was the main focus of your work?

Jeska: We collaborated with Statistics Netherlands (CBS) and TEXroad, a Dutch foundation that helps the circular textile industry work better by using data to create more insight, reduce waste, and drive change. Our work focused on analyzing how walking accessibility to textile collection bins affects usage rates in three Dutch cities, using GIS-based mapping and demographic data. By linking accessibility with neighborhood characteristics, we aimed to help municipalities improve textile collection systems in support of circular economy goals.

Faiza: We worked with CBS (Statistics Netherlands) and TEXroad, a Dutch organisation focused on improving the circular textile system through data. Our project looked at how accessible textile collection points are for people living in different municipalities. We focused on walking distance and how that might influence how often people actually use them. The goal was to give cities some useful insights to help improve their textile recycling setup.

How did you carry out your research?

Jeska: To carry out our research, we first contacted around a dozen Dutch municipalities and several textile collection companies to ask if they could share the locations of their textile collection bins and data on how much textile was collected. Based on the responses we received, we chose to focus on three cities: Leeuwarden, Tilburg, and Amersfoort, because they provided the most complete and usable data. We combined this information with publicly available data about the road network and population characteristics, such as age, income, and education levels. Using mapping software (ArcGIS Pro), we mapped all textile collection bins in the selected cities and calculated walking accessibility zones of 400m, 800m, and 1200m. This helped us understand which areas are well-served and which might need better access. In the final step, we looked at whether there is a link between how easy it is to reach a textile bin and how often people actually use them. We also explored whether access to bins is related to demographic and socio-economic factors, such as population density, income, or education level. This helped us understand not only how well the current system works, but also whether it serves all groups in society fairly.

Faiza: We started by contacting different municipalities and textile collection companies to gather data on where bins were located and how much textile was being collected. Based on the responses, we ended up focusing on Leeuwarden, Tilburg, and Amersfoort because they had the best data available. Then we combined that info with open-source data on demographics and the road network. Using ArcGIS Pro, we created maps showing accessibility zones (400m, 800m, 1200m) and looked at how they lined up with population data like income and education. We also looked at whether easier access meant higher usage, and how this varied across different neighbourhoods.

How did you experience working with professional organisations that relied on your contribution?

Jeska: I really enjoyed the opportunity to work with a professional organization for the first time. It was exciting to work with real data and real people, and it made the project feel much more meaningful. I especially appreciated that our work could actually contribute to something beyond the classroom.

Faiza: Working with organisations like CBS and TEXroad added a different level of pressure and responsibility to the project, but in a good way. Since they were actually interested in our results, we had to be more careful with how we handled data and communicated our findings. That made the project feel more valuable, and it gave me a better understanding of what it’s like to work on data projects that involve external stakeholders and actual expectations.

❝It was exciting to work with real data and real people, and it made the project feel much more meaningful.❞

❝Since they were actually interested in our results, we had to be more careful with how we handled data and communicated our findings.❞

What was the most memorable or meaningful aspect of your project experience? Or What key insights or lessons did you gain from working on the project?

Jeska: One of the most meaningful aspects of the project was how it brought together theory and practice in a real-world setting. I gained a deeper understanding of the broader challenges surrounding textile recycling. At the same time, I developed hands-on skills in geospatial mapping and data analysis. It made the work feel more relevant and showed how data can really help with solving real problems.

Faiza:  A key takeaway for me was how important it is to look at the bigger picture behind the data. It’s one thing to make maps and run accessibility analyses, but it only becomes useful when you actually understand what that means in practice — like which areas are underserved and why that matters for things like recycling behavior. The project helped me connect the technical side of data work with how it can be used to make decisions or improve public services, which made the process feel more grounded.  

In your opinion, what is the societal relevance and potential impact of your research?

Jeska: Our research is socially important because it helps municipalities understand how easy access to textile collection bins affects how people dispose of their clothing waste. With clothing waste growing rapidly and new EU rules pushing for better recycling, our work supports cities in improving their textile collection systems. By showing which neighbourhoods have good or poor access, we provide clear, practical guidance for local governments to make textile recycling fairer and more convenient. This can lead to higher recycling rates, less waste, and stronger progress toward a sustainable, circular economy that benefits both communities and the environment.

Faiza: Our research can help cities and municipalities better understand how accessible their textile recycling systems actually are. By identifying gaps in coverage, especially in certain neighborhoods, local governments can make targeted improvements—like placing more bins where access is low. That kind of small, data-driven change can lead to higher participation in textile recycling, which helps reduce waste and supports broader sustainability goals.