Big Data: A Bubble in the Making? (geocomplexity)

Coming off of the heels of our discussion last week’s seminar, I can’t help (for better or for worse) to read the articles about geo-complexity through the lens of uncertainty. In particular, I am reminded of when Professor Sieber challenged me to make an argument for why uncertainty could be good, and I proposed that some level of geographic uncertainty is likely to mitigate the worst effects of spatially occurring trends of discrimination (e.g.: red-lining, gerrymandering, etc.), while also accommodating a diversity of geographic experiences and ontologies. In his article “Asia on the Move: Research Challenges for Population Geography”, Graeme Hugo discusses geocomplexity as it pertains to conceptualizing and analyzing human migration in Asia. I wonder–somewhat contrary to conventional wisdom–if we are headed to a world of more geographic uncertainty, in spite of the emergence of big data and the discussion of a “major and focused multidisciplinary research effort” in order to circumvent the “huge gaps in our knowledge of the patterns, causes and consequences of international migration in Asia” (Hugo 95).

Hugo points out that census data is predicated on the assumption that “individuals and families have a single place and country of residence”, and therefore are increasingly difficult to use for studying migration patterns. As discussed in the paper, ease of travel has accommodated several migration patterns which involve living part-time both in the nation of origin and the nation of desitnation. Although Hugo presents several secondary sources for understanding migration trends, he notes nonetheless that understanding migration patterns is complicated by the increasing volume of migration, as well as the “increasing heterogeneity of the international labour flows and the people involved in them” (Hugo 103). It is that remark about the “heterogeneity” of labour flows that intrigues me.

If the motives behind labour migration are increasingly divergent, what implications does that have on studying migration patterns at all, even if we develop techniques to use secondary/alternative sources to mitigate the issue of geocomplexity? In my opinion, this will mean that certain assumptions held by human geographers will become invalid; in the case of migration and geocomplexity, this will mean that we cannot assume the migration was necessarily driven by economic necessity. Increasingly, rich, middle-class, and poor people are drawn to migrate for a variety of reasons, and even if we grasp exactly how many people move around, we will not be able to make assumptions as to why, or even the nature or duration of their migration.

To frame this another way, even if the quantity of data we are collecting is increasing, I believe the certainty, validity, and utility of it is often decreasing. In the same way we’ve discussed the limitations of making sweeping demographic assumptions about VGI (e.g.: people post information on social media selectively and aspirationally), so to are their limitations of capturing migration patterns in any region of the world. The reasons for migration are increasingly heterogenous and simply having numbers tells us nothing. In my opinion, this is bad news for Uber, Facebook, or any other company whose stock market value is intimately tied to the anticipated value of their amassed datasets. But it’s good news for anyone who’s worried about their privacy and ability to be profiled by their data footprint. It’s certainly contrary to the general thrust of this course, but I think our ability to be profiled based on data footprints is overstated.


Comments are closed.