Points-of-Interest Mining From People’s Pho-Taking Behavior

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Icjai Lee, Guochen Cai and Kyungmi Lee presented their article “Points-of-interest mining from people’s pho-taking behaviour” at the 46^th Hawaii international conference on system sciences in 2013. Although the authors have not specified the actual question they are addressing in the article, it is clear that they are attempting to address the question on whether the presence of geo-tagged photos in social sites produce interesting points-of-interest patterns, taking Flickr for Queensland as an example. With the increasing availability of the number of geo-tagged photos on the social media, photo sharing has become a common phenomenon in these websites (Kennedy, Naaman, Ahem, Nair, et al, 2007).

This has enabled local tourism and related businesses to take the advantage of people’s photo-taking behaviour as a good opportunity to establish lucrative businesses. This study is important as it seeks to determine the effectiveness of geo-tagged photos on local tourism. It seeks to provide information on how social sites can be used as a means of promoting tourism using the people’s photo-taking and photo-tagging behaviour on the internet (Zheng, Li, Zha & Chua, 2011).

The three authors have shown a deep interest and understating in the topic. It is worth noting that all the three authors are scholars at the school of business and IT, James Cook University. This proves their worthiness in developing the article and the argument presented (Popescu, Grefenstette & Moellic, 2009). From an analysis of the article, the authors seem to understand three major issues- the potential of the internet as a tool in promoting tourism, human behaviour in pho-taking and the emergence of a new business niche alongside the new geo-tagging aspects of the social sites. Hers, the three aspects are the key concepts of the argument in the article. They are in related in some way.

For instance, social media, a new way of socialization and communication, supports and encourages geo-tagging of photos on the sites, which people share and enjoy as points-of-interest in the media. In turn, this encourages local tourism in Queensland.

With the research, the authors found that there is an interesting point-s-of-interest pattern associated with the Flickr for Queensland, which has the potential to encourage local tourism. They key indicators of the concepts include the awareness of geo-photo and geo-tagging among the users, the willingness of the people to take geo-photos and use them to tag their friends on the social sites and the willingness of the tourism corporations to attract clients with this form of communication and advertising (Popescu & Grefenstette, 2009).

The conceptual model and the empirical model displayed in this research article is evident, indicated by the authors finding that the hypothesis that Flicr of Queensland has interesting point-of-interest and its ability to develop and promote local tourism. In fact, the hypothesis has been proved right. The evidence given in this article seems to build on the confidence in social media as a tool of doing business. In addition, the authors have attempted to answer a common question on whether social media and its increasing application can effectively promote tourism business.

The authors have used a sophisticated methodology for their research to arrive at the conclusion. In general, the methodology involve data mining to determine important data from Flickr of Queensland in order to find the usage of geo-tagging of photos and its applicability in tourism. This method has allowed the researchers to investigate data from the social website. This empirical research has used algorithm such as clustering to mine data (Kisilevich, Mansmann & Keim, 2010). Therefore, it is worth arguing that the study design is largely a quantitative method that has come out with quantitative data from the social website to explain the phenomenon.

The methodology is quite accurate and effective as it involves sophisticated methods whose efficiency is generally high (Brown, Tauler & Walczak, 2009).

References

Brown, S., Tauler, R., & Walczak, R. (2009). Comprehensive Chemometrics. Elsevier, 2, 577-618.

Kennedy, L., Naaman, M., Ahem, S., Nair, R., et al. (2007). How Flickr Helps Us Make Sense of The World: Context and Content in Community-Contributed Media Collections. In ACM Conference on Multimedia, 4(5), 631-640.

Kisilevich, Mansmann, F., & Keim, D. A. (2010). P-dbscan: a Density Based Clustering Algorithm for Exploration and Analysis of Attractive Areas Using Collections of Geo-tagged Photos. ACM Conference on Multimedia, 231-241.

Lee, I., Cai, G., & Lee, K. (2013). Points-of-Interest Mining from People’s Photo-Taking Behavior. 46th Hawaii International Conference on System Sciences.

Popescu, A., & Grefenstette, G. (2009). Deducing trip related information from flickr. London: In International World Wide Web Conference.

Popescu, A., Grefenstette, G., & Moellic, A. (2009). Mining Tourist Informaiton from User-supplied Collections. Sydney: CIKM Conference.

Zheng, Y., Li, Y., Zha, Z., & Chua, T. (2011). Mining Travel Patterns from GPS-Tagged Photos. MMM Conference, I, 262-272.

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Points-of-Interest Mining From People’s Pho-Taking Behavior

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