Just Good Enough Data: Figuring Data Citizenships through Air Pollution Sensing and Data Stories
Gabrys, Jennifer; Pritchard, Helen and Barratt, Benjamin. 2016. Just Good Enough Data: Figuring Data Citizenships through Air Pollution Sensing and Data Stories. Big Data & Society, 3(2), pp. 1-14. ISSN 2053-9517 [Article]
|
Text
Gabrysetal_JustGood_2016.pdf - Published Version Available under License Creative Commons Attribution. Download (900kB) | Preview |
Abstract or Description
Citizen sensing, or the use of low-cost and accessible digital technologies to monitor environments, has contributed to new types of environmental data and data practices. Through a discussion of participatory research into air pollution sensing with residents of northeastern Pennsylvania concerned about the effects of hydraulic fracturing, we examine how new technologies for generating environmental data also give rise to new problems for analysing and making sense of citizen-gathered data. After first outlining the citizen data practices we collaboratively developed with residents for monitoring air quality, we then describe the data stories that we created along with citizens as a method and technique for composing data. We further mobilise the concept of ‘just good enough data’ to discuss the ways in which citizen data gives rise to alternative ways of creating, valuing and interpreting datasets. We specifically consider how environmental data raises different concerns and possibilities in relation to Big Data, which can be distinct from security or social media studies. We then suggest ways in which citizen datasets could generate different practices and interpretive insights that go beyond the usual uses of environmental data for regulation, compliance and modelling to generate expanded data citizenships.
Item Type: |
Article |
||||||
Identification Number (DOI): |
|||||||
Keywords: |
citizen sensing, citizen data, environmental data, data practices, data stories, data citizenships |
||||||
Related URLs: |
|
||||||
Departments, Centres and Research Units: |
|||||||
Dates: |
|
||||||
Item ID: |
19511 |
||||||
Date Deposited: |
16 Jan 2017 13:18 |
||||||
Last Modified: |
17 Nov 2020 12:06 |
||||||
Peer Reviewed: |
Yes, this version has been peer-reviewed. |
||||||
URI: |
View statistics for this item...
Edit Record (login required) |