Stop the noise!: Enhancing meaningfulness in participatory sensing with community level indicators

Saskia Coulson; Melanie Woods; Michelle Scott; Drew Hemment; M Balestrini

doi:10.1145/3196709.3196762

Stop the noise! Enhancing meaningfulness in participatory sensing with community level indicators

Saskia Coulson, Melanie Woods, Michelle Scott, Drew Hemment, M Balestrini

Edinburgh College of Art

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

In this paper we examine ways to make data more meaningful and useful for citizens in participatory sensing. Participatory sensing has evolved as a digitally enabled grassroots approach to data collection for citizens with shared concerns. However, citizens often struggle to understand data in relation to their daily lives, and use them effectively. This paper presents a qualitative study on the development of a novel approach to Community Level Indicators (CLIs) during two participatory sensing projects focused on noise pollution. It investigates how CLIs can provide an infrastructure to address challenges in participatory sensing, specifically, making data meaningful and useful for non-experts. Furthermore, we consider how this approach moves towards an ambition of achieving change and impact through participatory sensing and discuss the challenges in this way of working and provide recommendations for future use of CLIs.

Original language	English
Title of host publication	Proceedings of the 2018 Designing Interactive Systems Conference
Place of Publication	United States
Publisher	Association for Computing Machinery (ACM)
Pages	1183-1192
Number of pages	10
ISBN (Print)	978-1-4503-5198-0
DOIs	https://doi.org/10.1145/3196709.3196762
Publication status	Published - 8 Jun 2018

Keywords / Materials (for Non-textual outputs)

Community Level Indicators, Noise pollution, Participatory Sensing, Research Methods, Co-Design

Access to Document

10.1145/3196709.3196762

Cite this

@inproceedings{78c61c1abf7d46ae85b543164b8be0fa,

title = "Stop the noise!: Enhancing meaningfulness in participatory sensing with community level indicators",

abstract = "In this paper we examine ways to make data more meaningful and useful for citizens in participatory sensing. Participatory sensing has evolved as a digitally enabled grassroots approach to data collection for citizens with shared concerns. However, citizens often struggle to understand data in relation to their daily lives, and use them effectively. This paper presents a qualitative study on the development of a novel approach to Community Level Indicators (CLIs) during two participatory sensing projects focused on noise pollution. It investigates how CLIs can provide an infrastructure to address challenges in participatory sensing, specifically, making data meaningful and useful for non-experts. Furthermore, we consider how this approach moves towards an ambition of achieving change and impact through participatory sensing and discuss the challenges in this way of working and provide recommendations for future use of CLIs.",

keywords = "Community Level Indicators, Noise pollution, Participatory Sensing, Research Methods, Co-Design",

author = "Saskia Coulson and Melanie Woods and Michelle Scott and Drew Hemment and M Balestrini",

note = "This research was supported by European Commission within the H2020 ICT 2015 Research and Innovation action under the grant agreement number 688620.",

year = "2018",

month = jun,

day = "8",

doi = "10.1145/3196709.3196762",

language = "English",

isbn = "978-1-4503-5198-0",

pages = "1183--1192",

booktitle = "Proceedings of the 2018 Designing Interactive Systems Conference",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

}

Stop the noise! Enhancing meaningfulness in participatory sensing with community level indicators. / Coulson, Saskia; Woods, Melanie; Scott, Michelle et al.
Proceedings of the 2018 Designing Interactive Systems Conference. United States: Association for Computing Machinery (ACM), 2018. p. 1183-1192.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Stop the noise!

T2 - Enhancing meaningfulness in participatory sensing with community level indicators

AU - Coulson, Saskia

AU - Woods, Melanie

AU - Scott, Michelle

AU - Hemment, Drew

AU - Balestrini, M

N1 - This research was supported by European Commission within the H2020 ICT 2015 Research and Innovation action under the grant agreement number 688620.

PY - 2018/6/8

Y1 - 2018/6/8

N2 - In this paper we examine ways to make data more meaningful and useful for citizens in participatory sensing. Participatory sensing has evolved as a digitally enabled grassroots approach to data collection for citizens with shared concerns. However, citizens often struggle to understand data in relation to their daily lives, and use them effectively. This paper presents a qualitative study on the development of a novel approach to Community Level Indicators (CLIs) during two participatory sensing projects focused on noise pollution. It investigates how CLIs can provide an infrastructure to address challenges in participatory sensing, specifically, making data meaningful and useful for non-experts. Furthermore, we consider how this approach moves towards an ambition of achieving change and impact through participatory sensing and discuss the challenges in this way of working and provide recommendations for future use of CLIs.

AB - In this paper we examine ways to make data more meaningful and useful for citizens in participatory sensing. Participatory sensing has evolved as a digitally enabled grassroots approach to data collection for citizens with shared concerns. However, citizens often struggle to understand data in relation to their daily lives, and use them effectively. This paper presents a qualitative study on the development of a novel approach to Community Level Indicators (CLIs) during two participatory sensing projects focused on noise pollution. It investigates how CLIs can provide an infrastructure to address challenges in participatory sensing, specifically, making data meaningful and useful for non-experts. Furthermore, we consider how this approach moves towards an ambition of achieving change and impact through participatory sensing and discuss the challenges in this way of working and provide recommendations for future use of CLIs.

KW - Community Level Indicators, Noise pollution, Participatory Sensing, Research Methods, Co-Design

UR - https://discovery.dundee.ac.uk/en/publications/stop-the-noise-enhancing-meaningfulness-in-participatory-sensing-

U2 - 10.1145/3196709.3196762

DO - 10.1145/3196709.3196762

M3 - Conference contribution

SN - 978-1-4503-5198-0

SP - 1183

EP - 1192

BT - Proceedings of the 2018 Designing Interactive Systems Conference

PB - Association for Computing Machinery (ACM)

CY - United States

ER -

Stop the noise! Enhancing meaningfulness in participatory sensing with community level indicators

Abstract / Description of output

Keywords / Materials (for Non-textual outputs)

Access to Document

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Citizen Sensing: A Toolkit

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