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.
- D.J. Crandall, L. Backstrom, D. Huttenlocher, and J. Kleinberg. Mapping the world's photos. In Proceedings of the 18th international conference on World wide web, pp. 761--770. ACM, 2009. Google ScholarDigital Library
- Q. Hao, R. Cai, X.J. Wang, J.M. Yang, Y. Pang, and L. Zhang. Generating location overviews with images and tags by mining user-generated travelogues. In Proceedings of the 17th ACM international conference on Multimedia, pp. 801--804. ACM, 2009. Google ScholarDigital Library
- Y. Arase, X. Xie, T. Hara, and S. Nishio. Mining people's trips from large scale geo-tagged photos. In Proceedings of the international conference on Multimedia, pp. 133--142. ACM, 2010. Google ScholarDigital Library
- T. Kurashima, T. Iwata, G. Irie, and K. Fujimura. Travel route recommendation using geotags in photo sharing sites. In Proceedings of the 19th ACM international conference on Information and knowledge management, pp. 579--588. ACM, 2010. Google ScholarDigital Library
- M. Clements, P. Serdyukov, A.P. de Vries, and M.J.T. Reinders. Using flickr geotags to predict user travel behaviour. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 851--852. ACM, 2010. Google ScholarDigital Library
- X. Lu, C. Wang, J.M. Yang, Y. Pang, and L. Zhang. Photo2trip: generating travel routes from geo-tagged photos for trip planning. In Proceedings of the international conference on Multimedia, pp. 143--152. ACM, 2010. Google ScholarDigital Library
- M. De Choudhury, M. Feldman, S. Amer-Yahia, N. Golbandi, R. Lempel, and C. Yu. Automatic construction of travel itineraries using social breadcrumbs. In Proceedings of the 21st ACM conference on Hypertext and hypermedia, pp. 35--44. ACM, 2010. Google ScholarDigital Library
- S. Kisilevich, F. Mansmann, P. Bak, D. Keim, and A. Tchaikin. Where would you go on your next vacation? a framework for visual exploration of attractive places. In Advanced Geographic Information Systems, Applications, and Services (GEOPROCESSING), 2010 Second International Conference on, pp. 21--26. IEEE, 2010. Google ScholarDigital Library
- S. Kisilevich, F. Mansmann, and D. Keim. P-dbscan: A density based clustering algorithm for exploration and analysis of attractive areas using collections of geo-tagged photos. In Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application, p. 38. ACM, 2010. Google ScholarDigital Library
- P. Jankowski, N. Andrienko, G. Andrienko, and S. Kisilevich. Discovering landmark preferences and movement patterns from photo postings. Transactions in GIS, Vol. 14, No. 6, pp. 833--852, 2010.Google ScholarCross Ref
- S. Kisilevich, D. Keim, and L. Rokach. A novel approach to mining travel sequences using collections of geotagged photos. In Proceedings of the Thirteenth International Conference on Geographic Information Science. Berlin, Springer-Verlag, pp. 163--82, 2010.Google ScholarCross Ref
- Y. Gao, J. Tang, R. Hong, Q. Dai, T.S. Chua, and R. Jain. W2go: a travel guidance system by automatic landmark ranking. In Proceedings of the international conference on Multimedia, pp. 123--132. ACM, 2010. Google ScholarDigital Library
- Y. Yang, Z. Gong, et al. Identifying points of interest by self-tuning clustering. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, pp. 883--892. ACM, 2011. Google ScholarDigital Library
- Z. Yin, L. Cao, J. Han, J. Luo, and T. Huang. Diversified trajectory pattern ranking in geo-tagged social media. In Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, pp. 980--991, 2011.Google ScholarCross Ref
- A.J. Cheng, Y.Y. Chen, Y.T. Huang, W.H. Hsu, and H.Y.M. Liao. Personalized travel recommendation by mining people attributes from community-contributed photos. In Proceedings of the 19th ACM international conference on Multimedia, pp. 83--92. ACM, 2011. Google ScholarDigital Library
- C. Lee, D. Greene, and P.Cunningham. Detecting grand tours of europe with geo-tags. In NIPS 2011 Workshop on Computational Social Science and the Wisdom of Crowds, 2011.Google Scholar
- A. Majid, L. Chen, H.T. Mirza, I. Hussain, and G. Chen. Mining context-aware significant travel sequences from geotagged social media. In Proceedings of AAAI 2012, 2012.Google Scholar
- H.P. Hsieh, C.T. Li, and S.D. Lin. Exploiting large-scale check-in data to recommend time-sensitive routes. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, pp. 55--62. ACM, 2012. Google ScholarDigital Library
- Y. Cheng. Mean shift, mode seeking, and clustering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 17, No. 8, pp. 790--799, 1995. Google ScholarDigital Library
- W.E. Winkler. String comparator metrics and enhanced decision rules in the fellegi-sunter model of record linkage. Proceedings of the Section on Survey Research, pp. 354--359, 1990.Google Scholar
- W.C. Chen, A. Battestini, N. Gelfand, and V. Setlur. Visual summaries of popular landmarks from community photo collections. In Signals, Systems and Computers, 2009 Conference Record of the Forty-Third Asilomar Conference on, pp. 1248--1255. IEEE, 2009. Google ScholarDigital Library
- David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, Vol. 60, No. 2, pp. 91--110, 2004. Google ScholarDigital Library
- M.A. Carreira-Perpinan. Acceleration strategies for gaussian mean-shift image segmentation. In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1, pp. 1160--1167. IEEE, 2006. Google ScholarDigital Library
- M.A. Jaro. Advances in record-linkage methodology as applied to matching the 1985 census of tampa, florida. Journal of the American Statistical Association, Vol. 84, No. 406, pp. 414--420, 1989.Google ScholarCross Ref
Index Terms
- Discovering Popular Point of Interests for Tourism with Appropriate Names from Social Data Analysis
Recommendations
Personality and location-based social networks
We investigate personality and use of location based social networks.We examine the five-factor personality model against places that users check-in at.Conscientiousness positively correlates with the number of venues visited.Openness positively ...
Discovering Point-of-Interest Signatures Based on Group Features from Geo-social Networking Data
TAAI '13: Proceedings of the 2013 Conference on Technologies and Applications of Artificial IntelligenceIn recent years, location-based social networking services (LBSNSs) have become popular, generating a huge volume of geo-social networking data, such as check-in records and geo-tagged photos. The geo-social networking data provide a new source for ...
Discovering Point-of-Interest Signatures Based on Group Features from Geo-social Networking Data
TAAI '13: Proceedings of the 2013 Conference on Technologies and Applications of Artificial IntelligenceIn recent years, location-based social networking services (LBSNSs) have become popular, generating a huge volume of geo-social networking data, such as check-in records and geo-tagged photos. The geo-social networking data provide a new source for ...
Comments