Big Data & Spatial Relationships

I found this article, ‘Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb’ by Crampton et al., to be an interesting introduction to the topic of Big Data. I thought it was fascinating how they attempted to progress past the simpler research methods previously used to analyse big data and build more robust and detailed frameworks. They made it clear that, while studying big data was useful, there is a need for more nuanced avenues of research which could help advance the field of big data and the geoweb. One such idea that I found interesting was that, as the authors claimed, there is often a paradoxical lack of data, or at least value in the data, that is being analyzed. For example, only 34% of the tweets studied were geocodable by user-defined location information (Pg. 134). They also note that people can use different location data or tweet about events that are not happening in their stated location. Examining how much of an impact this has on the accuracy of studying big data would be interesting to look at.

On the topic of locational relationships, the maps provided on page 135 got me thinking about the potential for people to tweet/create content on the Internet about issues that happen a large physical distance away from them. Does this data still follow Tobler’s First Law of Geography? If a larger number of people in New York tweet about events in Ferguson than people in Ferguson, what sorts of implications does this have in terms of analyzing big data’s spatial patterns? With the power to consume information about distant events, does proximity even matter anymore? When studying big data, I would imagine that this would be an important question to answer.




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