Collaborative noise data collected from smartphones

Abstract

Noise stands for an important human health and environmental issue. Indeed, noise causes annoyance and fatigue, interferes with communication and sleep, damages hearing and entails cardiovascular problems (WHO, 2011) [1]. From an environmental point of view, noise implies a lessening of both the richness and abundance of the animal species, an alteration of the communication, which can threaten the reproduction and predation, etc. (Newport et al., 2014; Shannon et al., 2014) [2], [3]. Consequently, effects related to environmental noise result in a huge cost for society, with 2.2 billion euros in France, for example, for the year 2013 (Bourges and Diel, 2015) [4]. In this context, the reduction of noise in the environment is a burning issue, which requires, firstly, carrying out an evaluation of noise in the environment, and secondly, to establish action plans to reduce noise annoyance. With the development of the concept of participatory measurement, and considering the extremely large number of people equipped with a smartphone while being "in mobility", the use of smartphones is potentially a relevant solution to realize a large-scale environmental noise evaluation. The data presented hereinafter are collected from the Android NoiseCapture application and shared from the OnoMap Spatial Data Infrastructure (SDI). The NoiseCapture approach consists in measuring noise along a path, and then to share data with the community. This approach has been developed within the framework of the European ENERGIC-OD project, which aims at deploying a set of Virtual Hubs (VH) to share heterogeneous data with third parties, in respect with the European INSPIRE, and at developing new and original services that can be useful for the community. The noise data that are acquired by volunteers around the world (citizen observations), are organized in three files, containing the path of measures (a set of points), standardized noise indicators, noise description and other useful variables (GPS accuracy, speed…). These data can be very relevant later to propose an environmental noise evaluation, through simple or complex treatments.

Keywords: Crowdsourcing; GIS; Noise; OGC; SDI; Smartphones; VGI.

Fig. 1

Screenshots of the NoiseCapture application (from left to right: measurement / perception / acoustic indicators / map).

Fig. 2

OnoMap architecture and tools.