Urban contexts: A geospatial approach to identifying in-situ measurement sites for urban acoustic environments

Kurzfassung

The ecological validity of soundscape research relies, among other issues, on the selection of measurement sites that are contextually significant to the space under investigation. Traditional site selection methods in environmental studies, while insightful, often lack a structured approach, leading to inconsistency and potential bias in identifying optimal sites for acoustic measurements. To overcome this, we present a novel data-driven approach using Enclosed Tessellation (ET) and unsupervised learning. Our method classifies urban areas to identify locations that best capture the diversity of environmental and acoustic conditions based on urban form, function, and environmental and topographic variables across a city-region. We use purposive sampling to locate these most appropriate sites for acoustic measurements adapted to specific research questions. This approach not only ensures a meaningful range of environmental conditions but also increases the reliability and validity of research findings, thereby improving our understanding of urban health and well-being.