Becoming Comfortable with Uncertainty

In Hunter and Goodchild’s essay entitled “Managing Uncertainty in Spatial Databases”, one statement in particular regarding uncertainty and error in data really hit home: the idea that people don’t understand the error in their data—and I would add, don’t ask about it, either.  This also recalled for me something Ana presented in her talk about LBS—that people don’t always understand or appreciate the data and technologies they are working with today—even perhaps the experts who know the complexities of data and technology that generate information we work with, and potentially take for granted, today.

So for me, amidst all their discussion of identifying, working with, and explaining/understanding error in data, their goal for future research stood out.  “Future error research cannot stay confined to the academic sector and should be conducted jointly with the user community to reflect the need for solving management aspects of the issue”.  The emphasis they place on being able to manage error is also incredibly important, and should also be at the forefront of this integration of research into the user domain, particularly as the user domain does not necessarily mean experts working in the field, but can be any layperson with access to the Internet.

This argument is particularly important as they express in their semi-ironic Figure 1, demonstrating how the experts are the ones who generally know how to deal with data, and what to ask, but don’t need to ask, whereas the layperson does not know how to deal or what to ask, but unfortunately is the one in need of those answers.  When reading Foody’s take on uncertainty, it further highlighted why lack of understanding in users can be negative, outside of creating questionable results—people may choose not to act at all with information they are uncertain about.  As Foody mentions with the global methane sink, without an understanding of how something works, it leads to uncertainty in models and results, and no action being taken.  This also seems a largely important point when arguing for the increased understanding of not only what uncertainties are present, but also what we can do with uncertainty in data.

This particularly reminds me of another class I am taking, in which we are discussing uncertainty in the media with regards to climate change.  Media sources depict scientific knowledge and models to be wrought with uncertainty—and as the public, policy makers, and other “end-users” don’t understand how scientists work with uncertainty in data and models, they are likely to be unreceptive of results and recommendations.

Of particular interest to me, Foody also discusses uncertainty inherent in geographical data, and the issue of zoning and the placement of administrative boundaries that can influence analysis on population data—it appears with uncertainty it is just another problem where assumptions must be made explicit.



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