The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
권호 표지
DOI QR코드

DOI QR Code

Density-Based Estimation of POI Boundaries Using Geo-Tagged Tweets

공간 태그된 트윗을 사용한 밀도 기반 관심지점 경계선 추정

  • Shin, Won-Yong (Dankook University, Department of Computer Science and Engineering) ;
  • Vu, Dung D. (Dankook University, Department of Computer Science and Engineering)
  • Received : 2017.01.08
  • Accepted : 2017.02.07
  • Published : 2017.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Users tend to check in and post their statuses in location-based social networks (LBSNs) to describe that their interests are related to a point-of-interest (POI). While previous studies on discovering area-of-interests (AOIs) were conducted mostly on the basis of density-based clustering methods with the collection of geo-tagged photos from LBSNs, we focus on estimating a POI boundary, which corresponds to only one cluster containing its POI center. Using geo-tagged tweets recorded from Twitter users, this paper introduces a density-based low-complexity two-phase method to estimate a POI boundary by finding a suitable radius reachable from the POI center. We estimate a boundary of the POI as the convex hull of selected geo-tags through our two-phase density-based estimation, where each phase proceeds with different sizes of radius increment. It is shown that our method outperforms the conventional density-based clustering method in terms of computational complexity.

사용자들은 그들의 관심이 관심지점 (POI: Point-of-Interest)과 관련이 있다는 사실을 언급하기 위해 위치 기반 소셜 네트워크에 체크인하거나 그들의 상태를 올리는 경향이 있다. 관심지역 (AOI: Area-of-Interest)을 찾는 기존 연구는 대부분 위치 기반 소셜 네트워크로부터 수집된 공간 태그된 사진과 함께 밀도 기반 군집화 기법을 사용하여 수행되었다. 반면, 본 연구에서는 POI 중심을 포함한 하나의 군집에 해당하는 POI 경계선을 추정하는 데에 초점을 맞춘다. 트위터 사용자들로부터의 공간 태그된 트윗을 사용하여 POI 중심으로부터 도달할 수 있는 적절한 반경을 찾음으로써 POI 경계선을 추정하는 밀도 기반 저복잡도 두 단계 방법을 소개한다. 두 단계 밀도 기반 추정을 통해 선택된 공간 태그의 convex hull로써 POI 경계선을 추정하는데, 각 단계에서 다른 크기의 반경 증가를 가정하여 진행한다. 제안한 방법은 기본 밀도 기반 군집화 방법보다 계산 복잡도 측면에서 우수한 성능을 가짐을 보인다.

Keywords

References

  1. H.-M. An, S.-K. Lee, K.-S. Sim, I.-H. Kim, S.-H. Jin, and M.-S. Kim, "Big-data traffic analysis for the campus network resource efficiency," J. KICS, vol. 40, no. 3, pp. 541-550, Mar. 2015. https://doi.org/10.7840/kics.2015.40.3.541
  2. J. W. Kim and K.-H. Park, "Personalized group recommendation using collaborative filtering and frequent pattern," J. KICS, vol. 41, no. 7 pp. 768-774, Jul. 2016. https://doi.org/10.7840/kics.2016.41.7.768
  3. Y.-H. Kim, Y.-K. Hwang, T.-G. Kang, and K.-M. Jung, "LSTM language model based Korean sentence generation," J. KICS, vol. 41, no. 5, pp. 592-601, May 2016. https://doi.org/10.7840/kics.2016.41.5.592
  4. Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. Magnenat-Thalmann, "Time-aware point-ofinterest recommendation," in 36th Proc. Int. ACM SIGIR'13, pp. 363-372, Dublin, Ireland, Jul. 2013.
  5. M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee, "Exploiting geographical influence for collaborative point-of-interest recommendation," in 34th Proc. Int. ACM SIGIR'11, pp. 325- 334, Beijing, China, Jul. 2011.
  6. J. J. Levandoski, Sarwat, A. Eldawy, and M. F. Mokbel, "LARS: A location-aware recommender system," in Proc. IEEE ICDE2012, pp. 450-461, Washington DC, USA, Apr. 2012.
  7. J.-W. Son, A.-Y. Kim, and S.-B. Park, "A location-based news article recommendation with explicit localized semantic analysis," in Proc. ACM SIGIR'13, pp. 293-302, Dublin, Ireland, Jul. 2013.
  8. J. Liu, Z. Huang, L. Chen, H.-T. Shen, and Z. Yan, "Discovering areas of interest with geo-tagged images and check-ins," in Proc. ACM MM'12, pp. 589-598, Nara, Japan, Oct. 2012.
  9. 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 Proc. COM.Geo2010, Bethesda, MD, USA, Jun. 2010.
  10. M. Ester, H.-P. Keriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," Data Mining Knowledge Discovery, vol. 96, no. 34, pp. 226-231, 1996.
  11. S. V. Canneyt, S. Schockaert, O. V. Laere, and B. Dhoedt, "Detecting places of interest using social media," in Proc. 2012 IEEE/WIC/ACM WI-IAT'12, pp. 447-451, Macau, SAR of the People's Republic of China, Dec. 2012.
  12. A. Skovsgaard, D. Sidlauskas, and C. S. Jensen, "A clustering approach to the discovery of points of interest from geo-tagged microblog posts," in Proc. IEEE MDM'14, pp. 178-188, Brisbane, Australia, Jul. 2014.
  13. D. D. Vu, H. To, W.-Y. Shin, and C. Shahabi, "GeoSocialBound: An efficient framework for estimating social POI boundaries using spatio-textual information," in Proc. Int. ACM SIGMOD GeoRich2016, pp. 1-6, Sanfrancisco, CA, USA, Jun. 2016.
  14. S. Hahmann, R. S. Purves, and D. Burghardt, "Twitter location (sometimes) matters: Exploiting the relationship between georeferenced tweet content and nearby feature class," J. Spatial Inf. Sci., no. 9, pp. 1-36, Sept. 2014.
  15. A. Arampatzis, M. van Kreveld, I. Reinbacher, C. B. Jones, S. Vaid, P. Clough, H. Joho, and M. Sanderson, "Web-based delineation of imprecise regions," Comp. Environ. Urban Syst., vol. 30, no. 4, pp. 436-459, Jul. 2016. https://doi.org/10.1016/j.compenvurbsys.2005.08.001
  16. M. Berg, W. Meulemans, and B. Speckman, "Delineating imprecise regions via shortest-path graphs," in Proc. 19th ACM SIGSPATIAL'11, pp. 271-280, Chicago, Illinois, USA, Nov. 2011.
  17. Y. Takhteyev, A. Gruzd, and B. Wellman, "Geography of Twitter networks," Social Netw., vol. 34, no. 1, pp. 73-81, Jan. 2012. https://doi.org/10.1016/j.socnet.2011.05.006
  18. J. Julshrestha, F. Kooti, A. Nikravesh, and K. P. Gummadi, "Geographic dissection of the twitter network," in Proc. ICWSM-12, pp. 202-209, Dublin, Ireland, Jun. 2012.
  19. W.-Y. Shin, B. C. Singh, J. Cho, and A. M. Everett, "A new understanding of friendships in space: Complex networks meet Twitter," J. Inf. Sci., vol. 41, no. 6, pp. 751-764, Dec. 2015. https://doi.org/10.1177/0165551515600136
  20. A.-C. Lee, G.-E. Seo, W.-Y. Shin, D. Kim, and J. Cho, "Tweet bot detection using geo-location information," in Proc. KICS Winter Conf., pp. 1-2, Jeju Island, Korea, Jun. 2015.