A socio-technical model for soundmapping community airplane noise

Abstract

Modern megacities around the world are exemplified by extraordinary population densities, ground transportation omnipresence, and breathtakingly busy airports that operate day and night. And while airports act as critical city infrastructures from economic, employment, and transportation perspectives, assessing noise resulting from the constancy of flyover aircraft is challenging, in part due to the lack of noise measurements. Additionally, community noise reporting yield mostly qualitative data which further renders community noise assessment difficult. Citygram, in partnership with GetNoisy, employs a socio-technical model to enable the creation of data-driven, community-accessible spatio-temporally granular airplane noise maps. The technology layer is comprised of edge compute sensor devices, cloud server, and web-based user dashboard that automatically identifies, tracks, and tags airplane events with timestamps, decibel levels, 3D positions, tail numbers, and speed. The social layer enables community engagement and facilitates rapid sensor network scaling. This socio-technical airplane soundmapping model, we believe, can be applied to practically any neighborhood as it harnesses and integrates state-of-the-art smart noise sensing technologies, environmentally incentivized communities, existing community resources, and participation workflows that are necessary in rendering a scalable and cost-effective airplane noise soundmapping system.