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Identification of Strategic Fields for Developing Smart City in Busan Using Text Mining

텍스트 마이닝을 이용한 스마트 도시계획 수립을 위한 전략분야 도출연구: 부산 사례를 바탕으로

  • 채윤식 (부산과학기술기획평가원 전략기획본부) ;
  • 이상훈 (한남대학교 경영학과)
  • Received : 2018.08.21
  • Accepted : 2018.11.20
  • Published : 2018.11.28

Abstract

The purpose of this study is to analyze bibliographic information of Busan and other cities' reports for urban development initiative and identify the strategic fields for future smart city plan. Text mining method is used in this study to extract keywords and identify the characteristics and patterns of information in urban development reports. As a result, in earlier stage, Busan city focused on service creation for industrial development but there are lack of discussions on the linkage of information systems with ICT technology. However, recent urban planning in Busan contained various contents related to integrated connections of infrastructure, ICT system, and operation management of city in the specific fields of traffic, tourism, welfare, port/logistics, culture/MICE. This results of study is expected to provide policy implications for planning the future urban initiatives of smart city development.

본 연구의 목적은 텍스트 마이닝 기법을 활용하여 부산 및 기타 지자체의 도시계획 보고서에 포함되어 있는 서지정보를 분석하고 새로운 스마트도시계획의 수립을 위한 전략 분야를 도출하는 것이다. 텍스트 마이닝 분석은 구조화되어 있지 않은 문서로부터 키워드를 추출하고 획득한 정보의 특성과 패턴을 발견하는 기법으로 최근 지식관리 측면에서 많이 사용되고 있다. 본 분석을 통해 초기의 부산 도시계획은 개별 산업분야 고도화에 초점이 맞춰져 있을 뿐 각 분야별 정보시스템의 연계에 대한 논의가 적은 것으로 나타났지만 최근 계획에서는 도시통합운영관리와 관련한 물리적 인프라와 ICT시스템과 관련한 내용이 다수 포함되어있는 것으로 나타났다. 특히, 타 지자체에 비해 항만/물류, 문화, 전시 분야가 특유의 서비스영역으로 도출되었지만 도시안전, 데이터공유, 신재생에너지 분야에 대한 계획은 부족한 것으로 나타났다. 본 연구는 향후 새로운 스마트 도시계획 수립을 위한 정책적 시사점을 제공할 것으로 기대한다.

Keywords

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Fig. 1. Result of visualizing network analysis onBusan U-City Plan

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Fig. 2. Result of visualizing network analysis on Busan Information Plan

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Fig. 3. Result of visualizing network analysis on U-City Plan of other cities

Table 1. Result of network clustering analysis on Busan U-City Plan

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Table 2. Centrality measures of network clusters analysis on Busan U-City Plan

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Table 3. Result of network clustering analysis on Busan Information Plan

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Table 4. Centrality measures of network clusters analysis on Busan Information Plan

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Table 5. Result of network clustering analysis on U-City Plan of other cities

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Table 6. Centrality measures of network clusters on U-City Plan of other cities

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References

  1. C. Harrison et al. (2010). Foundations for Smarter Cities. IBM Journal of Research and Development, 54(4), 1-16. https://doi.org/10.1147/JRD.2010.2089310
  2. R. Giffinger & H. Gudrun. (2010). Smart Cities Ranking: An Effective Instrument for the Positioning of the Cities?. ACE: Architecture, City and Environment, 4(12), 7-26.
  3. M. L. Marsal-Llacuna, J. Colomer-Llinas & J. Melendez-Frigola. (2015). Lessons in Urban Monitoring Taken from Sustainable and Livable Cities to Better Address the Smart Cities Initiative. Technological Forecasting and Social Change, 90, 611-622. https://doi.org/10.1016/j.techfore.2014.01.012
  4. J. H. Lee, M. G. Hancock & M. C. Hu. (2014). Towards an Effective Framework for Building Smart Cities: Lessons from Seoul and San Francisco. Technological Forecasting and Social Change, 89, 80-99. https://doi.org/10.1016/j.techfore.2013.08.033
  5. S. Wasserman & K. Faust. (1994). Social Network Analysis: Methods and Applications (Vol. 8). Cambridge University Press.
  6. D. H. Baek & H. C. Jin. (2007). A Feasibility Study on the Location based Services under Ubiquitous Environment. Journal of the Society of Korea Industrial and Systems Engineering, 30.
  7. J. H. Lee, R. Phaal & S. H. Lee. (2013). An Integrated Service-device-technology Roadmap for Smart City Development. Technological Forecasting and Social Change, 80(2), 286-306. https://doi.org/10.1016/j.techfore.2012.09.020
  8. H. Chourabi et al. (2012, January). Understanding Smart Cities: An Integrative Framework. In System Science (HICSS), 2012 45th Hawaii International Conference on (pp. 2289-2297). IEEE.
  9. J. Park & S. Yoo. (2017). Critical Understanding of Current Implication of Smart City Focusing on Information and Communication Technology, Governance, Sustainability and Urban Development. Korean Association of Space & Environment Research, 27(1), 128-155.
  10. S. M. Rue. (2014). Smart City Trend Analysis and Case Study. Korea Society for Information Management, 12(1), 19-28.
  11. S. H. Lee, J. H. Han, Y. T. Leem & T. Yigitcanlar. (2008). Towards Ubiquitous City: Concept, Planning, and Experiences in the Republic of Korea. In Knowledgebased Urban Development: Planning and Applications in the Information Era (pp. 148-170). IGI Global.
  12. T. Bakici, E. Almirall & J. Wareham. (2013). A Smart City Initiative: The Case of Barcelona. Journal of the Knowledge Economy, 4(2), 135-148. https://doi.org/10.1007/s13132-012-0084-9
  13. D. Toppeta. (2010). The Smart City Vision: How Innovation and ICT can Build Smart, "livable", Sustainable Cities. The Innovation Knowledge Foundation, 5, 1-9.
  14. D. Washburn, U. Sindhu, S. Balaouras, R. A. Dines, N. Hayes & L. E. Nelson. (2009). Helping CIOs Understand "Smart City" Initiatives. Growth, 17(2), 1-17.
  15. T. W. Miller. (2005). Data and Text Mining: A Business Applications Approach (pp. 917-2199). New Jersey: Pearson Prentice Hall.
  16. C. H. Park, S. H. Lee & T. H. Kim. (2017). Development and Application of a Architecture for Smart City Planning : Focused on Improvement of the Ubiquitous City Planning Guideline. Journal of Korea Planning Association, 52(4), 187. https://doi.org/10.17208/jkpa.2017.08.52.4.187
  17. M. Dixon. (1997). An Overview of Document Mining Technology. Unpublished Paper.
  18. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth & R. Uthurusamy. (1996). Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI Press.
  19. G. Piateski & W. Frawley. (1991). Knowledge Discovery in Databases. MIT press.
  20. B. Y. Yoon & Y. T. Park. (2004). A Text-mining based Patent Network: Analytical Tool for High-technology Trend. The Journal of High Technology Management Research, 15(1), 37-50. https://doi.org/10.1016/j.hitech.2003.09.003
  21. R. Feldman & J. Sanger. (2007). The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, New York, NY: Cambridge University Press.
  22. F. Janssens. (2007). Clustering of Scientific Fields by Integrating Text Mining and Bibliometrics. (Ph.D. Thesis) Faculty of Engineering, Katholieke Universiteit Leuven, Belgium.
  23. P. Calado et al. (2006). Link‐Based Similarity Measures for the Classification of Web Documents. Journal of the American Society for Information Science and Technology, 57(2), 208-221. https://doi.org/10.1002/asi.20266
  24. F. Janssens, V. Tran Quoc, W. Glanzel & B. D. Moor. (2006, October). Integration of Textual Content and Link Information for Accurate Clustering of Science Fields. In Proceedings of the I International Conference on Multidisciplinary Information Sciences & Technologies (InSciT2006). Current Research in Information Sciences and Technologies. Volume I (pp. 615-619). Springer.
  25. D. Sullivan. (2001). Document Warehousing and Text Mining: Techniques for Improving Business Operations, Marketing, and Sales. John Wiley & Sons, Inc.
  26. A. Zanasi. (2005). Text Mining Tools. Text Mining and its Applications to Intelligence, CRM and Knowledge Management. WIT Press, Southampton Boston, 315-327.
  27. S. Ananiadou & J. McNaught. (2006). Text Mining for Biology and Biomedicine. London: Artech House.
  28. R. N. Kostoff, H. J. Eberhart & D. R. Toothman. (1998). Database Tomography for Technical Intelligence: A Roadmap of the Near-earth Space Science and Technology Literature. Information Processing & Management, 34(1), 69-85. https://doi.org/10.1016/S0306-4573(97)00066-6
  29. R. N. Kostoff, D. R. Toothman, H. J. Eberhart, & J. A. Humenik. (2001). Text Mining Using Database Tomography and Bibliometrics: A review. Technological Forecasting and Social Change, 68(3), 223-253. https://doi.org/10.1016/S0040-1625(01)00133-0
  30. S. P. Borgatti, A. Mehra, D. J. Brass & G. Labianca. (2009). Network Analysis in the Social Sciences. Science, 323(5916), 892-895. https://doi.org/10.1126/science.1165821
  31. L. C. Freeman, D. Roeder & R. R. Mulholland. (1979). Centrality in Social Networks: II. Experimental Results. Social Networks, 2(2), 119-141. https://doi.org/10.1016/0378-8733(79)90002-9
  32. J. Y. Lee. (2006). A Novel Clustering Method for Examining and Analyzing the Intellectual Structure of a Scholarly Field. Korea Society for Information Management, 23(4), 215-231. https://doi.org/10.3743/KOSIM.2006.23.4.215
  33. S. I. Lim, Y. M. Lim, & J. Y. Lee. (2014). Study on the Trends of U-City and Smart City Researches Using Text Mining Technology. Journal of the Korean Society for Geospatial Information System, 22(3), 87-97. https://doi.org/10.7319/kogsis.2014.22.3.087
  34. H. W. Jang & J. H. Lee. (2015). Construction Trend and Market Classification of Global Smart City. Journal of the Korean Urban Geographical Society, 18(2), 55-66.
  35. M. Deakin. (2013). Smart Cities: Governing, Modelling and Analysing the Transition. Routledge.
  36. T. Nam & T. A. Pardo. (2011, June). Conceptualizing Smart City with Dimensions of Technology, People, and Institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times (pp. 282-291). ACM.
  37. M. Park. (2018), Determinant of the Elderly Poverty Using Decision Tree Analysis, Journal of Digital Convergence, 16(7), 63-69 https://doi.org/10.14400/JDC.2018.16.7.063
  38. J. Oh & S. Choi. (2018). An Analysis of the Characteristics of Companies Introducing Smart Factory System Using Data Mining Technique, Journal of the Korea Convergence Society, 9(5), 179-189. https://doi.org/10.15207/JKCS.2018.9.5.179
  39. T. Jeong, Y. Shin & M. Yim. (2012). A Study on Promotion Strategy of Categorized Mobile Apps using Datamining, Journal of Digital Convergence, 10(5), 339-349. https://doi.org/10.14400/JDPM.2012.10.5.339
  40. Y. Ji & W. Lee. (2018). A Case Study on Sharing & Using of National Scientific Data, Journal of the Korea Convergence Society, 9(8), 9-15. https://doi.org/10.15207/JKCS.2018.9.8.009
  41. A. Coe, G. Paquet & J. Roy. (2001). E-governance and Smart Communities: A Social Learning Challenge. Social Science Computer Review, 19(1), 80-93. https://doi.org/10.1177/089443930101900107
  42. K. E. Portney & J. M. Berry. (2010). Participation and the Pursuit of Sustainability in US Cities. Urban Affairs Review, 46(1), 119-139. https://doi.org/10.1177/1078087410366122