지적과 국토정보 (Journal of Cadastre & Land InformatiX)

  • 제48권1호
  • /
  • Pages.15-33
  • /
  • 2018
  • /
  • 2508-3384(pISSN)
  • /
  • 2508-3392(eISSN)
한국국토정보공사 공간정보연구원 (LX Spatial Information Research Institute)
권호 표지
DOI QR코드

DOI QR Code

LDA를 이용한 국제지적연구의 주제와 추세확인에 관한 연구: 특히 FIG Peer Review Journal을 중심으로

A Study on Identifying Topics and Trends in International Cadastral Research Using LDA: With Special Reference to the FIG Peer Review Journal

  • kim, Yun-Ki (Department of Land Management, Choengju University)
  • 투고 : 2018.04.03
  • 심사 : 2018.06.27
  • 발행 : 2018.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구의 주된 목적은 LDA를 이용하여 국제지적연구의 주제와 연구추세를 확인하는 것이었다. 이러한 연구목적을 달성하기 위해 나는 LDA와 국제지적연구에 관한 선행연구를 검토하였고 이를 기반으로 4 개의 연구 질문을 설정하였다. 이러한 연구 질문에 답을 구하기 위해 나는 FIG Peer Review Journal에 2008년 1월1일 부터 2017년 10월 31일 사이에 발표된 370편의 논문들을 LDA를 이용하여 분석하였다. 분석의 결과 나는 국제지적연구에 12개의 주요 주제가 존재하고 있음을 확인하였다. 그리고 이러한 주제 중에 가장 영향력 있는 주제는 topic 2 (지적정보시스템)로 확인되었으며 또한 topic 5 (토지개발과 토지행정)도 전체 문서에서 중요한 역할을 수행하고 있는 주제로 파악되었다. 이두 주제는 지난 10년 동안 추세선이 매우 활발하게 움직인 가장 인기 있는 주제들로서 앞으로의 지적연구에서도 주도적인 역할을 수행할 것이 틀림없다.

The main purpose of this study was to identify the topics and research trends of international cadastral research using LDA. To achieve this goal, I reviewed the literature on LDA and international cadastral study and formulated four research questions that are topics of cadastral researchers, distribution of topics, the most influential topics and changes of topics over time. To answer these research questions, I analyzed 370 papers published in the FIG Peer Review Journal between January 1, 2008, and October 31, 2017, using LDA. As a result of the analysis, I confirmed that there are twelve major topics in international cadastral research. And the most influential topic of these topics was identified as topic 2(cadastral information systems), and topic 5(land development and land administration) was also confirmed as playing an important role in the overall document. These two topics have been the most popular topics whose trendlines have been very active over the past decade and will play a leading role in future cadastral research.

키워드

참고문헌

  1. Bisnath, S., Saeidi, A., Wang, J. G., & Seepersad, G. (2013). Evaluation of Network RTK Performance and Elements of Certification-A Southern Ontario Case Study. Geomatica, 67(4), 243-251. https://doi.org/10.5623/cig2013-050
  2. Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
  3. Blei, D. M., & Lafferty, J. D. (2006, June). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (pp. 113-120). ACM.
  4. Blei, D. M., & Lafferty, J. D. (2009). Topic models. Text mining: classification, clustering, and applications, 10(71), 34.
  5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
  6. Braam, R. R., Moed, H. F., & Van Raan, A. F. (1991). Mapping of science by combined co-citation and word analysis I. Structural aspects. Journal of the American Society for inBlei, D. M., Ng, A. Y., & Jordan, M. I. (2003). https://doi.org/10.1002/(SICI)1097-4571(199105)42:4<233::AID-ASI1>3.0.CO;2-I
  7. Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?. Journal of the American Society for Information Science and Technology, 61(12), 2389-2404. https://doi.org/10.1002/asi.21419
  8. Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems (pp. 288-296).
  9. Chaney, A. J. B., & Blei, D. M. (2012, March). Visualizing Topic Models. In ICWSM.
  10. Chen, C., & Carr, L. (1999, February). Trailblazing the literature of hypertext: author co-citation analysis (1989-1998). In Proceedings of the tenth ACM Conference on Hypertext and hypermedia: returning to our diverse roots: returning to our diverse roots (pp. 51-60). ACM.
  11. Choi, H. S., Lee, W. S., & Sohn, S. Y. (2017). Analyzing research trends in personal information privacy using topic modeling. Computers & Security, 67, 244-253. https://doi.org/10.1016/j.cose.2017.03.007
  12. Das, S., Sun, X., & Dutta, A. (2016). Text Mining and Topic Modeling of Compendiums of Papers from Transportation Research Board Annual Meetings. Transportation Research Record: Journal of the Transportation Research Board, (2552), 48-56.
  13. Deininger, K., Selod, H., &Burns, A. (2012). The Land Governance Assessment Framework: Identifying and monitoring good practice in the land sector. World Bank Publications.
  14. Ding, Y., Chowdhury, G., & Foo, S. (1999). Mapping the intellectual structure of information retrieval studies: an author co-citation analysis, 1987-1997. Journal of information science, 25(1), 67-78. https://doi.org/10.1177/016555159902500107
  15. Enemark, S. (2009). Sustainable Land Administration Infrastructures to support Natural Disaster Prevention and Management. In The United Nations Regional Cartographic Conference for the Americas.
  16. Estabrooks, C. A., Derksen, L., Winther, C., Lavis, J. N., Scott, S. D., Wallin, L., & Profetto-McGrath, J. (2008). The intellectual structure and substance of the knowledge utilization field: A longitudinal author co-citation analysis, 1945 to 2004. Implementation Science, 3(1), 49. https://doi.org/10.1186/1748-5908-3-49
  17. Franco, J. C. (2010). Contemporary Discourses and Contestations around Pro-Poor Land Policies and Land Governance. Journal of Agrarian Change, 10(1), 1-32. https://doi.org/10.1111/j.1471-0366.2009.00243.x
  18. Gatti, C. J., Brooks, J. D., & Nurre, S. G. (2015). A historical analysis of the field of or/ms using topic models. arXiv preprint arXiv:1510.05154.
  19. Gregoire, D. A., Noel, M. X., Dery, R., & Bechard, J. P. (2006). Is there conceptual convergence in entrepreneurship research? A co-citation analysis of frontiers of entrepreneurship research, 1981-2004. Entrepreneurship theory and practice, 30(3), 333-373. https://doi.org/10.1111/j.1540-6520.2006.00124.x
  20. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235. https://doi.org/10.1073/pnas.0307752101
  21. Hansen K.D., Gentry J., Long L., Gentleman R., Falcon S., Hahne F., & Sarkar D. (2017). Rgraphviz: Provides plotting capabilities for R graph objects. R package version 2.20.0.
  22. Gucevic, J., & Miljkovic, S. (2014). Testing the Operational Quality of the Permanent GNSS stations network within AGROS RTK Service. Geonauka, 2(1), 14-19. https://doi.org/10.14438/gn.2014.02
  23. He, Y., & Hui, S. C. (2002). Mining a web citation database for author co-citation analysis. Information processing &management, 38(4), 491-508. https://doi.org/10.1016/S0306-4573(01)00046-2
  24. Hoffman, M., Bach, F. R., & Blei, D. M. (2010). Online learning for latent dirichlet allocation. In advances in neural information processing systems (pp. 856-864).
  25. Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), 128-136. https://doi.org/10.1016/j.ijinfomgt.2010.07.003
  26. Karpik, A. P., & Musikhin, I. A. (2016). RESEARCH AND PRACTICAL TRENDS IN GEOSPATIAL SCIENCES. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41.
  27. Kim, Y. K. & Lee, S. B. (2006). A Study on Improving Cadastre System and Cadastre-related Laws for Introducing 3D Cadastre. The Korea Spatial Planning Review, 2006.9, 147-162.
  28. Lafferty, J. D., & Blei, D. M. (2006). Correlated topic models. In Advances in neural information processing systems (pp. 147-154).
  29. Liu, Y., Niculescu-Mizil, A., & Gryc, W. (2009, June). Topic-link LDA: joint models of topic and author community. In proceedings of the 26th annual international conference on machine learning (pp. 665-672). ACM.
  30. Manzano-Agugliaro, F., Castro-Garcia, M., Perez-Romero, A. M., Garcia-Cruz, A., Novas, N., & Salmeron-Manzano, E. (2016). Alternative methods for teaching cadastre and remote sensing. Survey Review, 48(351), 450-459. https://doi.org/10.1179/1752270615Y.0000000046
  31. Mcauliffe, J. D., & Blei, D. M. (2008). Supervised topic models. In Advances in neural information processing systems (pp. 121-128).
  32. Neis, P., & Zielstra, D. (2014). Recent developments and future trends in volunteered geographic information research: The case of OpenStreetMap. Future Internet, 6(1), 76-106. https://doi.org/10.3390/fi6010076
  33. Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29(3), 319-336. https://doi.org/10.1002/smj.659
  34. Paulsson, J., & Paasch, J. (2011). 3D property research-a survey of the occurrence of legal topics in publications. In 2nd International Workshop on 3D Cadastres, Delft, The Netherlands, 16-18 November, 2011 (pp. 1-14). International Federation of Surveyors.
  35. Paulsson, J., & Paasch, J. M. (2013). 3D property research from a legal perspective. Computers, Environment and Urban Systems, 40, 7-13. https://doi.org/10.1016/j.compenvurbsys.2012.11.004
  36. Paulsson, J., & Paasch, J. M. (2015). The land administration domain model-a literature survey. Land use policy, 49, 546-551. https://doi.org/10.1016/j.landusepol.2015.08.008
  37. Rizos, C. (2007). Alternatives to current GPS-RTK services and some implications for CORS infrastructure and operations. GPS solutions, 11(3), 151-158. https://doi.org/10.1007/s10291-007-0056-x
  38. Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009, August). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1 (pp. 248-256). Association for Computational Linguistics.
  39. Schildt, H. A., Zahra, S. A., & Sillanpaa, A. (2006). Scholarly communities in entrepreneurship research: a co-citation analysis. Entrepreneurship Theory and Pra. https://doi.org/10.1111/j.1540-6520.2006.00126.x
  40. Schwarz, C. (2017). ldagibbs: A command for Topic Modeling in Stata using Latent Dirichlet Allocation. Forthcoming Stata Journal.ctice, 30(3), 399-415.
  41. Silva, L. (2007). Institutionalization does not occur by decree: Institutional obstacles in implementing a land administration system in a developing country. Information Technology for Development, 13(1), 27-48. https://doi.org/10.1002/itdj.20056
  42. Silva, M. A., & Stubkjaer, E. (2002). A review of methodologies used in research on cadastral development. Computers, Environment and Urban Systems, 26(5), 403-423. https://doi.org/10.1016/S0198-9715(02)00011-X
  43. Steudler, D., Rajabifard, A., &Williamson, I. P. (2004). Evaluation of land administration systems. Land Use Policy, 21(4), 371-380. https://doi.org/10.1016/j.landusepol.2003.05.001
  44. Sun, L., & Yin, Y. (2017). Discovering themes and trends in transportation research using topic modeling. Transportation Research Part C: Emerging Technologies, 77, 49-66. https://doi.org/10.1016/j.trc.2017.01.013
  45. van Oosterom, P. (2013). Research and development in 3D cadastres. Computers, Environment and Urban Systems, 40, 1-6. https://doi.org/10.1016/j.compenvurbsys.2013.01.002
  46. Wang, C., & Blei, D. M. (2011, August). Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 448-456). ACM.
  47. Watkins, D., & Reader, D. (2002). Identifying current trends in entrepreneurship research: A new approach. ARPENT: Annual Review of Progress in Entrepreneurship, 2, 311.
  48. White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of the American society for information science, 49(4), 327-355. https://doi.org/10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-4
  49. Williamson, I., Enemark, S., Wallace, J., &Rajabifard, A. (2010). Land administration for sustainable development (p. 487). Redlands, CA: ESRI Press Academic.
  50. Williamson, I., & Ting, L. (2001). Land administration and cadastral trends-a framework for re-engineering. Computers, Environment and Urban Systems, 25(4), 339-366. https://doi.org/10.1016/S0198-9715(00)00053-3
  51. Zhu, S. (2014). Pain expression recognition based on pLSA model. The Scientific World Journal, 2014.