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Collaborative noise data collected from smartphones.

Erwan Bocher1, Gwendall Petit2, Judicaël Picaut3

  • 1CNRS, Lab-STICC Laboratory UMR 6285, Vannes, France.

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Environmental noise poses significant health and ecological risks. This study explores using smartphone applications for large-scale noise mapping and management, enabling community-driven data collection for environmental protection.

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CrowdsourcingGISNoiseOGCSDISmartphonesVGI

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Area of Science:

  • Environmental Science
  • Public Health
  • Acoustics
  • Geoinformatics

Background:

  • Environmental noise is a critical issue impacting human health (annoyance, fatigue, cardiovascular problems) and ecosystems (biodiversity loss, altered animal communication).
  • The societal cost of noise pollution is substantial, necessitating effective environmental noise evaluation and reduction strategies.
  • Traditional noise monitoring is limited in scale and scope, highlighting the need for innovative, large-scale data acquisition methods.

Purpose of the Study:

  • To investigate the potential of using smartphone-based participatory sensing for large-scale environmental noise evaluation.
  • To present the NoiseCapture application and OnoMap Spatial Data Infrastructure (SDI) as tools for citizen noise data collection and sharing.
  • To demonstrate how community-sourced noise data can contribute to environmental noise assessment and management.

Main Methods:

  • Utilized the Android NoiseCapture application for volunteer-based noise measurements along defined paths.
  • Collected and organized noise data, including location, standardized indicators, and descriptive variables, via the OnoMap SDI.
  • Leveraged the European ENERGIC-OD project framework for data sharing and service development.

Main Results:

  • Demonstrated a scalable approach to environmental noise data acquisition through citizen observations worldwide.
  • Structured noise data into standardized files, enabling diverse analytical treatments for environmental noise evaluation.
  • Highlighted the potential of integrating mobile technology with spatial data infrastructures for comprehensive noise monitoring.

Conclusions:

  • Smartphone-based participatory sensing offers a viable and cost-effective solution for extensive environmental noise mapping.
  • The NoiseCapture and OnoMap platforms facilitate community engagement in noise monitoring, contributing valuable data for environmental management.
  • This approach supports the development of new services for understanding and mitigating noise pollution impacts.