On SAGE Insight: Practices of monitoring and modeling air pollution data

Article title: Developing a feeling for error: Practices of monitoring and modeling air pollution data

From Big Data & Society

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. Air pollution is very much a hybrid thing understood as the product of human and non-human relations which have actively changed the material constitution of air. Data generation has also actively shaped what constitutes air, and how air is experienced and engaged with. Occasions of measuring air are proliferating: from government-led monitoring devices. The ethnographic account of air pollution data offered in this paper is an attempt to consider how data emerge as a result of one kind of transformation, that of a purification of data through the working out of error. The author suggests that environmental data practices can be studied through researchers’ materialization of error, which complicate normative accounts of Big Data and highlight the non-linear and entangled relations that are at work in the making of stable, accurate data.

Abstract

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. (All names are pseudonyms.) I examine two different kinds of practices that make air pollution data, focusing on how they relate to particular modes of sensing and articulating air pollution. I begin by describing the interstitial spaces involved in making measurements of air pollution at monitoring sites and in the running of a computer simulation. Specifically, I attend to a shared dimension of these practices, the checking of a numerical reading for error. Checking a measurement for error is routine practice and a fundamental component of making data, yet these are also moments of interpretation, where the form and meaning of numbers are ambiguous. Through two case studies of modelling and monitoring data practices, I show that making a ‘good’ (error free) measurement requires developing a feeling for the instrument–air pollution interaction in terms of the intended functionality of the measurements made. These affective dimensions of practice are useful analytically, making explicit the interaction of standardized ways of knowing and embodied skill in stabilizing data. I suggest that environmental data practices can be studied through researchers’ materialization of error, which complicate normative accounts of Big Data and highlight the non-linear and entangled relations that are at work in the making of stable, accurate data.

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Article details
Developing a feeling for error: Practices of monitoring and modeling air pollution data
Emma Garnett
Big Data & Society
July–December 2016
DOI: 10.1177/2053951716658061

 

     
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