Format

Send to

Choose Destination
PLoS One. 2013 Dec 11;8(12):e81638. doi: 10.1371/journal.pone.0081638. eCollection 2013.

Awareness and learning in participatory noise sensing.

Author information

1
Department for Artificial Intelligence and Applied Computer Science, University of Würzburg, Würzburg, Germany ; L3S Research Center, Leibniz Universität, Hannover, Germany.
2
Physics Department, Sapienza University, Rome, Italy.
3
CSP Innovazione nelle ICT, Turin, Italy.
4
Extreme Citizen Science Research Group, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom.
5
Complex Networks and Systems Lagrange Laboratory, Institute for Scientific Interchange Foundation, Turin, Italy ; Physics Department, Sapienza University, Rome, Italy.
6
Department of Electrical Engineering/Computer Science, University of Kassel, Kassel, Germany ; L3S Research Center, Leibniz Universität, Hannover, Germany.
7
Complex Networks and Systems Lagrange Laboratory, Institute for Scientific Interchange Foundation, Turin, Italy.

Abstract

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

PMID:
24349102
PMCID:
PMC3859489
DOI:
10.1371/journal.pone.0081638
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center