한국지구과학회지 (Journal of the Korean earth science society)

  • 제25권1호
  • /
  • Pages.10-21
  • /
  • 2004
  • /
  • 1225-6692(pISSN)
  • /
  • 2287-4518(eISSN)
한국지구과학회 (The Korean Earth Science Society)
권호 표지

Quantitative Assessment of Input and Integrated Information in GIS-based Multi-source Spatial Data Integration: A Case Study for Mineral Potential Mapping

  • Kwon, Byung-Doo (Department of Earth Science Education, Seoul National University) ;
  • Chi, Kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Lee, Ki-Won (Department of Software Systems, Information Engineering Division, Hansung University) ;
  • Park, No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
  • 발행 : 2004.01.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, spatial data integration for geoscientific application has been regarded as an important task of various geoscientific applications of GIS. Although much research has been reported in the literature, quantitative assessment of the spatial interrelationship between input data layers and an integrated layer has not been considered fully and is in the development stage. Regarding this matter, we propose here, methodologies that account for the spatial interrelationship and spatial patterns in the spatial integration task, namely a multi-buffer zone analysis and a statistical analysis based on a contingency table. The main part of our work, the multi-buffer zone analysis, was addressed and applied to reveal the spatial pattern around geological source primitives and statistical analysis was performed to extract information for the assessment of an integrated layer. Mineral potential mapping using multi-source geoscience data sets from Ogdong in Korea was applied to illustrate application of this methodology.

키워드

참고문헌

  1. An, P., Moon, W.M., and Rencz, A, 1991, Application of fuzzy set theory to integrated mineral exploration. Canadian Journal of Exploration Geophysics, 27, 1-11
  2. Bonham-Carter, G.F. and Agterberg, F.P., 1988, Integration of geological data sets for gold exploration in Nova Scotia. Photogrammetric Engineering & Remote Sensing, 54, 1585-1592
  3. Bonham-Carter, G.F., 1994, Geographic information systems for geoscientists: modeling with GIS. Pergamon, New York, 398 p.
  4. Chung, C.F. and Fabbri, A.G., 1993, The representation of geoscience information for data integration. Nonrenewable Resources, 2, 122-139 https://doi.org/10.1007/BF02272809
  5. Chung, C.F. and Fabbri, A.G., 1999, Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering & Remote Sensing, 65, 1389-1399
  6. Heckerman, D., 1986, Probabilistic interpretations for MYCIN's certainty factors. In: Kanal, L.N. and Lemmer, J.F. (eds.), Uncertainty in artificial intelligence. Elsevier, New York, 167-196.
  7. Lee, K. and Kwon, B.-D., 1995, Application of artificial neural networks to spatial integration of raster-based geological data. Journal of the Korean Earth Science Society, 16, 358-367
  8. Moon, W.M., 1990, Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing, 28, 711-720 https://doi.org/10.1109/TGRS.1990.572988
  9. Park, N.-W., Chi, K.-H., Chung, C.F., and Kwon, B.-D., 2003, Application of spatial data integration based on the likelihood ratio function and Bayesian rule for landslide hazard mapping. Journal of the Korean Earth Science Society, 24, 428-439
  10. Shortliffe, E.H. and Buchanan, B.G., 1975, A model of inexact reasoning in medicine. Mathematical Biosciences, 23, 351-379 https://doi.org/10.1016/0025-5564(75)90047-4