Not just noise monitoring: rethinking citizen sensing for risk-related problem-solving

Figures & data

Table 1. Comparison of citizen sensing with adjacent concepts.

Initiative and major sources	Citizen sensing (Gabrys 2017; Jiang et al. 2016; Making Sense 2018)	Community-based monitoring (Fernandez-Gimenez, Ballard, and Sturtevant 2008; J-PAL Policy Briefcase 2015)	Citizen science (Den Broeder et al. 2017; Van Brussel and Huyse 2018; Cooper et al. 2017)	Participatory env. sensing (Kotovirta, Toivanen, R. Tergujeff, and Huttunen 2012; Turreira-García et al. 2018; Abbot and Guijt 1998)	Mobile crowd sensing (Ganti, Fan, and Hui 2011)	Citizen observatories (EC EASME website; WeObserve platform; Liu et al. 2014)	Less adjacent: Participatory digital culture (Karaganis 2007)
Key feature	Citizen-sensor interaction for tracking environmental risk	Monitoring performed by the community	Participation of lay people in science	Measurement of environmental phenomena (location-based applications)	Crowd of sensing citizens	Citizen-based Earth observation applications	Open source software production and social Internet applications
Primary aim and orientation	Producing citizen- sensed data to verify or challenge institutional data	Achieving community engagement and community-building	Supplementing scientific analysis, rebuilding people’s trust in science	Mostly gathering of information in a cost-effective way	Sharing data to measure and map phenomena of common interest	Mostly enhancing the management of land and natural resources	Matching the production, distribution, and use of Internet services
Selection of sensing actors	Self-selection: citizens as laymen and as concerned people	Based on representation of the community and its needs	Mostly performed by institutional actors: the selected citizen-scientists	Often performed by institutional actors: local community (owning a location aware device)	Self-selection: individuals with sensing and computing devices	Often performed by institutional actors: local community engaged in an observatory project	Self-selection: potentially all users of the Internet
Origin of the initiative	Distrust; scarce transparency; information monopoly; emotional triggers related to risk	The lack of data on a problem; community involvement in data gathering	The need to reduce the gap between science and society	The need to gather large environmental measurements	The concern over a collective problem, need for a large number of data points	The need to perform in-depth observations over time	The wish to collectively benefit from advantages of the Internet
Degree of democratization of power (1st dimension)	High: in general grassroots-driven, spontaneous	Medium: institutionally driven, planned, but focus on community representation	Medium: often institutionally driven, planned	Low: mostly institutionally driven, planned	High: in general grassroots-driven, spontaneous	Medium: often institutionally driven, planned, recently EU-funded	High: grassroots- driven, spontaneous
Co-production with institutional actor (2nd dimension)	Eventually in next step	Strong	Strong	Weak	Eventually in next step	Medium	No
Focus on technological innovation (3rd dimension)	Medium	Low	Medium	High	High	High	Medium

Note: The text in bold corresponds to aspects of partial overlap/coincidence of adjacent fields with citizen sensing.

Table 2. Preliminary performance matrix: problem-solving potential of citizen sensing in AMS and LHA cases.

Performance: stages [a]	Evidence: perceptions/actions [b]
Initial conditions enabling problem-solving [risk perception, multi-actor risk management, distrust] Preparatory stages corresponding to “Origin of the initiative” in Table 1	1. Perceived malfunctioning and dogmatic attitude of the institutional response to the risk problem (citizens) 2. Perceived inconsistencies in the institutional approach to the problem (citizens) [both 1st dimension]
Steps towards problem-solving [multi-actor problem-solving, positive social capital and mutual understanding and legitimacy] Stages corresponding to “Co-production with institutional actor” and “Focus on technical innovation” in Table 1	3. Creating a high quality citizen sensor system displaying the urgency of the problem (citizens) [3rd dimension] 4. Achieving data validity and reliability leading to the contesting of information monopoly (citizens) [3rd dimension]) 5. Drawing attention to the risk problem of own community and broader public (citizens) [3rd dimension] 6. Emphasizing the citizens’ entitlement to be properly informed about risks (citizens) [1st dimension] 7. Building mutual understanding on a shared problem (citizens and institutions) [2nd dimension]
Partial problem-solving [problem-solving through civic engagement, participation and co-production (trusted dialogue), and social capital (integration of initiatives)] Stages corresponding to “Primary aims and orientation”, “Degree of democratization of power”, and “Co-production” in Table 1	8. Adequately challenging institutional strategies to improve institutional risk governance (citizens and institutions) [1st dimension] 9. Integrating the citizen initiative with institutional systems of governance and achieving a trusted dialogue by dropping dogmatic attitudes (citizens and institutions) [apparently only AMS] [2nd dimension]
Full problem solving [convergence, problem-solving through civic engagement, participation and co-production, and social capital] Stage corresponding to “Primary aims”, “Democratization of power” and “Co-production” in Table 1	10. Recognition of the problem, the value of the citizens’ contribution, and implementing practical interventions to mitigate or solve the problem (citizens and institutions) [1st and 2nd dimension]

Note [a]: text in brackets is “assumed performance”, derived from specific theoretical perspectives.

[b]: the list of evidence on performance (perceptions/actions) is not necessarily a sequence.

Gabrys, J. 2017. “Citizen Sensing, Air Pollution and Fracking: From ‘Caring about Your Air’ to Speculative Practices of Evidencing Harm.” The Sociological Review 65 (2_suppl): 172–192. doi:10.1177/0081176917710421.

 Web of Science ®Google Scholar

Jiang, Q., F. Kresin, A.K. Bregt, L. Kooistra, E. Pareschi, E. van Putten, H. Volten, and J. Wesseling. 2016. “Citizen Sensing for Improved Urban Environmental Monitoring.” Journal of Sensors Art. ID 5656245: 1–9. doi:10.1155/2016/5656245.

 Web of Science ®Google Scholar

Making Sense Project. 2018. Citizen Sensing: A Toolkit. ISBN/EAN: 978-90-828215-0.

 Google Scholar

Fernandez-Gimenez, M. E., H. L. Ballard, and V. E. Sturtevant. 2008. “Adaptive Management and Social Learning in Collaborative and Community-Based Monitoring: A Study of Five Community-Based Forestry Organizations in the Western USA.” Ecology and Society 13 (2): 4. http://www.ecologyandsociety.org/vol13/iss2/art4/

 Web of Science ®Google Scholar

J-PAL Policy Briefcase. 2015. The Power of Information in Community Monitoring. Cambridge, MA: Poverty Action Lab.

 Google Scholar

Den Broeder, L., L. Lemmens, S. Uysal, K. Kauw, J. Weekenborg, M. Schönenberger, S. Klooster-Kwakkelstein, et al. 2017. “Public Health Citizen Science; Perceived Impacts on Citizen Scientists: A Case Study in a Low-Income Neighbourhood in The Netherlands.” Citizen Science: Theory and Practice 2 (1): 1–17.

 Google Scholar

Van Brussel, S., and H. Huyse. 2018. “Citizen Science on Speed? Realising the Triple Objective of Scientific Rigour, Policy Influence and Deep Citizen Engagement in a Large-Scale Citizen Science Project on Ambient Air Quality in Antwerp.” Journal of Environmental Planning and Management 1–18. doi:10.1080/09640568.2018.1428183.

 Web of Science ®Google Scholar

Cooper, C. B., L. R. Larson, K. Krafte Holland, R. A. Gibson, D. J. Farnham, D. Y. Hsueh, P. J. Culligan, and W. R. McGillis. 2017. “Contrasting the Views and Actions of Data Collectors and Data Consumers in a Volunteer Water Quality Monitoring Project: Implications for Project Design and Management.” Citizen Science: Theory and Practice 2 (1): 1–14.

 Google Scholar

Kotovirta, V., T. Toivanen, R. Tergujeff, and M. Huttunen. 2012. “Participatory Sensing in Environmental Monitoring: Experiences.” In Proceedings of the Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 155–162. Washington, DC: IEEE.

 Google Scholar

Turreira-García, N., J. F. Lund, P. Domínguez, E. Carrillo-Anglés, M. C. Brummer, P. Duenn, and V. Reyes-García. 2018. “What’s in a Name? Unpacking ‘Participatory’ Environmental Monitoring.” Ecology and Society 23 (2): 24. https://doi.org/10.5751/ES-10144-230224

 Web of Science ®Google Scholar

Abbot, J., and I. Guijt. 1998. “Changing Views on Change: Participatory Approaches to Monitoring the Environment”. SARL Discussion Paper. No. 2. London: IIED. https://pubs.iied.org/6140IIED/

 Google Scholar

Ganti, R. K., Y. Fan, and L. Hui. 2011. “Mobile Crowdsensing: Current State and Future Challenges.” IEEE Communications Magazine 49 (11): 32–39. doi:10.1109/MCOM.2011.6069707.

 Web of Science ®Google Scholar

Liu, H., M. Kobernus, D. Broday, and A. Bartonova. 2014. “A Conceptual Approach to a Citizens’ Observatory: Supporting Community-Based Environmental Governance.” Environmental Health 107 (13): 1–13.

 Google Scholar

Karaganis, J., ed. 2007. Structures of Participation in Digital Culture. New York, NY: Social Science Research Council.

 Google Scholar