Rotterdam faces a rapidly growing challenge with naastplaatsing, the improper disposal of municipal solid waste next to municipal containers,. Residents increasingly describe waste accumulations around containers as a major nuisance, contributing to unhealthy residues, degraded streetscapes, and declining confidence in municipal services. NietRnaast (nRn), the city’s dedicated response team, handles hundreds of thousands of incidents annually; these are all recorded as administrative actions detailing the type of waste detected (if any), the amount of waste, the time of the intervention, and sometimes a picture of the container(s) with waste. Still, structural pressures (population density, container malfunctions) and behavioral dynamics (“waste attracts waste”) continue to reinforce the phenomenon.

This thesis investigates whether the phenomenon of naastplaatsing can be modeled to understand its root causes and to improve the operational response of the Departmente of Waste Management (Stadsbeheer) of the Municipality of Rotterdam. Combining stakeholder mapping, multivariate data analysis (MVDA), machine learning (ML), and heuristic routing, the study provides a comprehensive analysis of one of Rotterdam’s most persistent urban challenges.