Promising Data for Public Empowerment

“Promising Data for Public Empowerment” examines tensions that arise in the drive toward using complex environmental information to support local needs for knowledge about polluting industries. These tensions are examined in a study of the volunteer water monitoring groups responding to pollution threats from natural gas extraction in the Marcellus Shale regions of New York, Pennsylvania, and West Virginia. This study is one component of my dissertation research, and is supported by funding from the RPI Humanities, Arts, and Social Sciences (HASS) Fellowship, as well as from a Doctoral Dissertation Improvement Grant from the National Science Foundation (Award #1331080).

Promising Data

The scientific study of environmental pollution has undergone two major shifts in the recent decades. On one hand, computing technologies have enabled the development of complex data management systems, or Environmental Information Systems (EISs), to analyze natural and built environments; on the other hand, non-scientists, including environmental activists and community groups, are increasingly involved in gathering and analyzing evidence of pollution in local communities. However, little research has been done on the relationship between these two social changes. In STS literature on citizen science and environmental justice, scholars argue that grassroots environmental movements alter the balance of power between lay citizens, regulators, and scientists by participating in the knowledge production cycle (Irwin 1995; Fischer 2000; Allen, 2003; Corburn, 2005). This shift can open doors to more democratic decision making and impact how regulatory agencies respond to scientific controversies (Brown, 2007; Frickel & Moore, 2006).

The literature on critical GIS suggests that new EIS technologies significantly alter the role of participating communities. Volunteers must be willing to work with processes requiring extensive recording of meta-data, and follow stringent standards to ensure the accuracy of collected data (Gouveia & Fonseca 2008; Corbett & Rambaldi, 2009). More direct volunteer engagements with data also introduce difficulties, such as doubts in the scientific and regulatory community as to whether or not untrained volunteers can produce viable science and use complicated technologies. Meanwhile, literature in the information sciences (Bollier, 2010; Boyd & Crawford, 2011) suggests that the amplified use of data-intensive systems can change how meaning and importance is attached to data. By linking volunteer collected data to information on industrial activity, EISs may have the potential to foster new methods in empowering threatened communities to create new narratives linking industrial pollution to environmental impacts (Elwood, 2006). However, data may also lose its contextual meaning within these systems, or become susceptible to interpretive distortions (Bowker, 2005; Edwards, 2010; Edwards et al. 2011).

Public Empowerment

Little attention has been paid to the ways in which citizen science groups participate in collecting data and leverage their data in EISs without the support of scientific and regulatory institutions. In the absence of such research, it is difficult to understand the implications for citizen science groups of utilizing bigger and more complex data in making knowledge claims. Recent studies in STS suggest that by focusing on accumulating scientific data alone, these groups may limit their capacity to represent community needs when engaging scientific issues (Ottinger 2010; Ottinger & Zurer, 2011). If amplifications of data offer only limited success for environmental advocacy groups, then it is critical to understand how these notions are established and put into practice. In doing so, it is possible to formulate alternative pathways for leveraging the assets of data collected by citizen science groups, but in ways that remain attuned to the concerns of environmentally threatened communities.

This research therefore offers an application of recent scholarship in STS, environmental informatics, and critical-GIS, by exploring these issues in a grounded empirical case study. The research examines the relationship between EISs and citizen science through an in-depth ethnographic study of volunteer water quality monitoring organizations responding to pollution threats from natural gas development in New York and Pennsylvania. Citizen science groups in these regions are generating extensive amounts of data for use in online databases and GIS systems. They hope these systems will lend greater legitimacy to their claims about environmental change. The study also focuses on capacity building organizations (CBOs) that play an important role in developing volunteer monitoring protocols, aggregating data, and determining how data will be used in public data portals. These organizations and their affiliates are attempting to meet the needs of diverse regions and communities. The research addresses three primary questions:

1. Why are communities affected by shale gas development increasingly compiling their observations in large-scale environmental information systems?
2.  How is the turn to large-scale EISs affecting the practices of water monitoring groups that express diverse social and environmental objectives (for example, through pressures to standardize the methods of collecting and managing water quality)?
3. What design features make some EISs better suited than others to serve the localized concerns and preferences of community-based water protection groups? 
4. To what extent are emerging EISs supporting community efforts to gain influence in decision-making about shale gas development and water protection?

This research has potential to inform the technical practices and organizational strategies of civil society groups dealing with a range of environmental justice issues in the Marcellus Shale and elsewhere. It will make valuable contributions to the volunteer water monitoring community in particular by offering knowledge back to organizations and volunteer groups who participate in the study. In its completed stage, the research will be useful for illustrating the mechanisms by which citizen science networks might produce high-quality data in ways that continue to empower local communities. This study will also be useful to regulators and research scientists who are seeking effective ways to interface with information systems that are coming online to support citizen science groups.

References

• Irwin, A. (1995). Citizen science: a study of people, expertise and sustainable development. Routledge.
• Fischer, F. (2000). Citizens, experts and the environment: The politics of local knowledge. Chapel Hill.
• Allen, B. L. (2003). Uneasy alchemy: citizens and experts in Louisiana’s chemical corridor disputes. MIT Press.
• Corburn, J. (2005). Street Science: Community Knowledge and Environmental Health Justice. Environmental Health. Cambridge: MIT Press.
• Brown, P. (2007). Toxic exposures : contested illnesses and the environmental health movement. New York: Columbia University Press.
• Frickel, S., & Moore, K. (Eds.). (2006). The new political sociology of science: Institutions, networks, and power. University of Wisconsin Press.
• Corbett, J., & G. Rambaldi. (2009). “Representing our Reality”: Geographic Information Technologies, Local Knowledge and Change. In S. Elwood & M. Cope (Eds.), Qualitative GIS: A Mixed‐Methods Approach, 70‐90. Thousand Oaks, CA: Sage Publications.
• Gouveia, C., & Fonseca, A. (2008). New approaches to environmental monitoring: the use of ICT to explore volunteered geographic information. GeoJournal, 72(3-4), 185–197.
• Bollier, D. (2010). The promise and peril of big data. Aspen Institute, Communications and Society Program.
• Boyd, D., & Crawford, K. (2011). Six Provocations for Big Data. SSRN Electronic Journal, 1–17.
• Elwood, S. (2006). Beyond Cooptation or Resistance: Urban Spatial Politics, Community Organizations, and GIS-based Spatial Narratives. Annals of the Association of American Geographers 96(2): 323–341.
• Bowker, G. C. (2005). Memory practices in the sciences (pp. 269-298). Cambridge, MA: MIT Press.
• Edwards, P. N. (2010). A vast machine: Computer models, climate data, and the politics of global warming. MIT Press.
• Edwards, P. N., Mayernik, M. S., Batcheller, a. L., Bowker, G. C., & Borgman, C. L. (2011). Science friction: Data, metadata, and collaboration. Social Studies of Science, 41(5), 667–690.
• Ottinger, G. (2010). Constructing Empowerment Through Interpretations of Environmental Surveillance Data. Surveillance & Society, 8(2), 221–234.
• Ottinger, G., & Zurer, R. (2011). Drowning in data. Issues in Science and Technology, 71–82.