Discovering popular point of interests for tourism with appropriate names from social data analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a method for determining an appropriate names of popular POIs (Point of Interests) obtained in a clustering-based social spatial data analysis. The proposed method utilizes several reverse geocoding APIs, such as Foursquare and Google, and selects the most probable name for each cluster. In addition, the author tries to figure out the adequate dataset size when the proposed name assign method is used. Because the proposed name assign method is not affected by the size of dataset. By using the collected data, more than 4 million geo-tagged photos of 5 cities from Flickr, the author confirmed that the proposed method can assign more proper name for the clustering results compared with a conventional tag-based name assign method, even if the size of dataset is small.

Original languageEnglish
Title of host publicationIWWISS 2014 - International Workshop on Web Intelligence and Smart Sensing
PublisherAssociation for Computing Machinery
ISBN (Print)9781450327473
DOIs
Publication statusPublished - Jan 1 2014
Externally publishedYes
Event2014 International Workshop on Web Intelligence and Smart Sensing, IWWISS 2014 - Saint Etienne, France
Duration: Sep 1 2014Sep 2 2014

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2014 International Workshop on Web Intelligence and Smart Sensing, IWWISS 2014
CountryFrance
CitySaint Etienne
Period9/1/149/2/14

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Arakawa, Y. (2014). Discovering popular point of interests for tourism with appropriate names from social data analysis. In IWWISS 2014 - International Workshop on Web Intelligence and Smart Sensing (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2637064.2637100