Monthly Archives: May 2013

I’d like to recommend two excellent critical papers on user-generated geographic content and the geosocial web. The first one is by Muki Haklay and raises important issues on the democratizing effects of the Web 2.0 and neography, while the second one by Crampton et. al. takes up the issue and suggests possible solutions to improve the study and analysis of geosocial media.

In his study [1], Haklay argues that neographic theory and practice assume an instrumentalist view of technology, i.e. that technology is value-free and that there is a clear seperation between the means and the ends. Obviously, Haklay does not agree with this view and argues that there is less empowerment and democratization to be found than commonly assumed. In order to realize the full potential of neographic tools and practices, anyone implementing neogeographic tools or practices needs to take into account economic and political aspects. There is a substantial body of work supporting Haklay, including the research by Mark Graham [2], which I recommended in my last post. Patrick Meier on iRevolution has a in-depth commentary of Haklay’s paper [3] and provides a somewhat more optimistic interpretation. My own point of view is running along similar lines as Haklay’s, in that the contemporary digital divides are a continuation of old power divides that participatory GIS sought to overcome in the 90s. And while I have no ill will towards companies that add value to user-generated content, I am highly skeptical of such “involuntary crowdsourcing”, in which the crowd provides freely the raw material but in the end has to pay for access to derived products [4]. There is some similarity to the argument for Open Government Data – why should the tax payers (and tax paying companies) pay again for the use of the data, when they already payed for the creation of it?

Crampton et al. [5] investigate critically the hype around the “Big Data” geoweb. They remind the reader of (a) the limitations inherent in “big-data”-based analysis and (b) shortcomings of the simple spatial ontology of the geotag. Concerning (a), the data used often has limited explanatory value or informational richness, something our research has shown as well [6]. Further,  geocoded social media are still a non-representative sample, no matter how many of them one has collected. Concerning (b), Crampton et al. point out a number of problems with the geotag, e.g. that it is difficult to ascertain whether it refers to the origin of the content or the topic of the content, its lineage and accuracy, and its oversimplification of geography by limiting place geometry to points or lat/lon pairs (see also [7]). As a consequence of their analysis, the authors suggest that studies of the geoweb should try to take into account:

  1. social media that is not explicitly geographic
  2. spatialities beyond the “here and now”
  3. methodologies that are not focused on proximity only
  4. non-human social media
  5. geographic data from non-user generated sources.

I have to admit that I am a little bit proud to say that our research has addressed three of those suggestions: We haven’t limited our sample to geo-coded social media, instead we have re-geo-coded even those with existing coordinates to ensure that we capture the places the social media was about. We have also gone beyond the “here and now” by spatio-temporal clustering data. Finally, a core concept of our approach is the enrichment of the social media data with explicitly geographic data from non-user generated (i.e. authoritative) sources (a paper describing the details has just been accepted but not published yet, an overview can be found here [8]).

Crampton et. al. conclude their paper with the important reminder that caution is needed regarding the surveillance potential of such research, with intelligence agencies around the world focusing more and more on open source intelligence (OSINT). Indeed it seems that even in Really Big Data, our spatial behaviour is unique enough to allow identification [9].