Geo-Crowdsourced Sound Level Data in Support of the Community Facilities Planning. A Methodological Proposal

Gabriella Graziuso, Simona Mancini, Antonella Bianca Francavilla, Michele Grimaldi and Claudio Guarnaccia
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Gabriella Graziuso: Department of Civil Engineering, University of Salerno, 84084 Fisciano, Italy
Simona Mancini: Department of Information and Electric Engineering and Applied Mathematics, University of Salerno, 84084 Fisciano, Italy
Antonella Bianca Francavilla: Department of Civil Engineering, University of Salerno, 84084 Fisciano, Italy
Michele Grimaldi: Department of Civil Engineering, University of Salerno, 84084 Fisciano, Italy
Claudio Guarnaccia: Department of Civil Engineering, University of Salerno, 84084 Fisciano, Italy

Sustainability, 2021, vol. 13, issue 10, 1-18

Abstract: To reduce environmental noise pollution and to safeguard people’s well-being, it is urgently necessary to move towards sustainable urban development and reconcile demographic and economic growth with the protection and restoration of the environment and the improvement of the quality of human lives. This challenge should be a concern to policymakers, who must issue regulations and define the appropriate actions for noise monitoring and management, and citizens, who must be sensitive to the problem and act accordingly. Starting from an analysis of several crowdsourcing noise data collection tools, this paper focuses on the definition of a methodology for data analysis and mapping. The sound sensing system, indeed, enables mobile devices, such as smartphones and tablets, to become a low-cost data collection for monitoring environmental noise. For this study, the “NoiseCapture” application developed in France by CNRS and IFSTTAR has been utilized. The measurements acquired in 2018 and 2019 at the Fisciano Campus at the University of Salerno were integrated with the kernel density estimation. This is a spatial analysis technique that allows for the elaboration of sound level density maps, defined spatially and temporally. These maps, overlaid on a campus facilities map, can become tools to support the appropriate mitigation actions.

Keywords: noise pollution; crowdsourcing data; NoiseCapture; kernel density estimation; spatial analysis; sound density maps (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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