Using a case study that examines a stream of tweets related to late-night celebration events following the victory of the University of Kentucky Wildcats NCAA championship game Crampton et al. challenge us to look beyond the obvious when dealing with geotagged big data. Although the #LexingtonPoliceScanner hashtag identified the tweets related to the incidents that took place that night, a simple map showing the hotspots of twitter traffic over space and time merely scratch the surface of what is possible – leaving many unanswered question, multiple unexplored avenues.

It is important to note that this example featured the use of user generated content, introducing the risk of false information, and repeated informations facilitated through retweets: in essence noise. The location of the tweet is unclear as less that 1% of tweets were provided with GIS coordinates from where the tweet was posted, exposing a gaping hole in misleading information, location however could be derived from the user-defined location information, While the advent of big data is a sea of opportunity I would repeat the cautiousness of the authors with regards to the confidence with which the information can be used. Before the use of any data, patterns and trends need to be extracted through the exercise of data mining, a process that the authors argue can be enhanced through the addition of ancillary data to inform the data drawn from the big data source.

While the article clearly demonstrates the ways in which big data requires a broader look in order to tackle the research questions it can answer, I question choice of the case study presented as an example to illustrate this. I find that other examples would be far more appropriate in representing what big data really entails.

The example however provided the opportunity to present the concept of social networks and the idea of measuring distance not only over physical space but also through a social network structure. Social networks such as twitter offers the ease of access to UGC data that moves through the social network structure. I do wonder what potential would lie in another data set example removed from the category of geotagging.

– Othello


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