Geometry 및 Topology측면에서 일관성을 유지한 방법을 이용한 연방과 지방정부의 공간데이터 융합

Geometrically and Topographically Consistent Map Conflation for Federal and Local Governments

  • 발행 : 2004.12.01

초록

공간데이터자원이 많아 질수록 그들끼리 불일치가 일어날 확률은 높아지고 있다. 이러한 불일치는 같은 지역을 커버하는 같은 종류의 공간데이터사이에서도 일어날 수 있다. 그러므로, 이런 공간데이터를 효율적으로 연결시켜 Geometry 및 Topology측면에서 일관성을 지닌 새로운 공간데이터를 생성시키는 일의 중요성은 증가 할 것이다. 이러한 공간데이터중의 하나로서 미국 인구통계국의 TIGER파일을 예로 들 수 있다. 현재 인구통계국 지도들은 지방정부의 지도 레이어들과 공간적으로 일치 하지 않기 때문에 인구적, 경제적인 많은 유용한 정보가 지방정부의 레이어들과 연계되어 공간적으로 충분히 활용되어지고 있지 않고 있다. 그러므로, 인구통계국 지도의 위치정보는 좀 더 정확한 위치정보를 가지고 있는 지방정부의 레이어들과 융합되어 Geometry 및 Topology측면에서 새로운 정보로 대체되어져야 한다. 이 논문은 참고맵을 이용하여 Geometry 및 Topology측면에서 일관성을 지닌지도를 만들기 위한 개념적인 프레임과 두가지 맵모델을 제시한다. 첫번째 모델은 셀 모델인데 맵은 0셀, 1셀, 그리고 2셀로 구성되어진다. 두번째 모델은 수학적으로 다른 원형을 가진 물체는 지도 일반화후에도 유사성을 가지고 있다는 것이다. 새롭게 제시된 계층적인 맵 융합은 물리적, 수학적, 논리적 경계에 바탕을 두고 있고 복잡성과 계산적인 부담을 감소시킬 수 있다. 반복성을 가진 맵 융합 원리는 인구통계지도를 예로하여 형성되었다. 이것들은 속성 매치, 의미있는 노드발견, 지도학적인 0-cell 매치. 지도학적인 1-cell 매치, 그리고 맵 변형으로 구성된다.

As spatial data resources become more abundant, the potential for conflict among them increases. Those conflicts can exist between two or many spatial datasets covering the same area and categories. Therefore, it becomes increasingly important to be able to effectively relate these spatial data sources with others then create new spatial datasets with matching geometry and topology. One extensive spatial dataset is US Census Bureau's TIGER file, which includes census tracts, block groups, and blocks. At present, however, census maps often carry information that conflicts with municipally-maintained detailed spatial information. Therefore, in order to fully utilize census maps and their valuable demographic and economic information, the locational information of the census maps must be reconciled with the more accurate municipally-maintained reference maps and imagery. This paper formulates a conceptual framework and two map models of map conflation to make geometrically and topologically consistent source maps according to the reference maps. The first model is based on the cell model of map in which a map is a cell complex consisting of 0-cells, 1-cells, and 2-cells. The second map model is based on a different set of primitive objects that remain homeomorphic even after map generalization. A new hierarchical based map conflation is also presented to be incorporated with physical, logical, and mathematical boundary and to reduce the complexity and computational load. Map conflation principles with iteration are formulated and census maps are used as a conflation example. They consist of attribute embedding, find meaning node, cartographic 0-cell match, cartographic 1-cell match, and map transformation.

키워드

참고문헌

  1. Anson, R.W., 1993, Basic Cartography, 2., International Cartographic Association
  2. Brassel, K. E. and Weibel, R., 1988, A review and conceptual framework of automated map generalization, Int. J. Geographic Information Systems, 2(3),229-244
  3. Broome, F. R. and Meixler, D. B., 1990, The TIGER data base structure, Cartographic And Geographic Information Systems, 17(1),39-47
  4. Buttenfield, B. and McMaster, R., 1991, Map Generalization: Making Rules for Knowledge Representaion, Longman, London
  5. Car, A., 1994a, Modelling a Hierarchy of Space Applied to Large Road Network, IGIS'94: Geographic Information Systems, International Workshop onAdvanced Research in GIS, Monte Verita, Ascona, Switzerland
  6. Car, A., 1994b, General Principles of Hierarchical Spatial Reasoning, The Case of Wayfinding, Proceeding of Sixth Int. Symposium on Spatial Data Handling, 646-664
  7. Car, A., 1996,General Principles of Hierarchical Spatial Reasoning, Ph. D. Dissertation, Institute for Geoinformation, Technical University of Vienna
  8. Callahan, G. and Broome, F., Year Unknown, The Joint Development ofa National 1:100,000-Scale Digital Cartographic Data Base
  9. Census Bureau, 2000, TIGER/Line Files 2000 Technical Documentation, http://www.census.gov
  10. Corbett, J., 1979, Topological principles in cartography, Technical Paper 48, U.S. Bureau of the Census
  11. Cuenin, P., R., 1972, Cartographie Generale, Eyrolle
  12. Douglas, D. H. and Peucker, T. K, 1973, Algorithms for the reduction of the number of points required to represent a digitized line or its character, The Canadian Cartographer, 10(2), 112-123
  13. Gross, J, and Yellen, J., 1998, Graph Theory and Its Application, CRC Press
  14. Hangouet, J. F., 1995,Computation of the hausdorff distance between plane vector polylines, AutoCarto, 12, 1-10
  15. Hake, G., 1975, Kartographie, Sammlung Goschen Band
  16. Kang, H. S., 2002, Analytical Conflation of Spatial Data from Municipal and Federal Government Agencies, Ph. D. Dissertation, The Ohio State University
  17. Katoh, N., Ibaraki, T., and Mine, H., 1978, An $O(Kn^2)$ Algorithms for K shortest simple paths in an undirected graph with nonnegative arc length, The Transactions of The IECE of Japan, J61-A(12),1199-1206
  18. Keates, J. S., 1989, Cartographic Design and Produnction, second edition, Longman, Scientific & Technical
  19. Marx, R. W., 1984, Developing an Integrated Cartographic/Geographic Data Base for the United States Bureau of The Census, Bureau of the Census
  20. McMaster, R. B., 1992, Generalization in Digital Cartography, Association of American Geographers
  21. Mohar, B. and Thomassen, C., 2001, Graphs On Surfaces, Johns Hopkins University Press
  22. Ramirez, R., 1993, Development of a Cartographic Language, Lecture Notes in Computer Science, 716, Springer-Verlag, 92-112
  23. Robinson, A. H., Morrison, J. L., Muehrcke, Phillip C., Kimerling A. Jon, and Guptill, Stephen C., 1995, Elements of Cartography, Sixth Edition, John Wiley&Sons
  24. Saalfeld, A., 1986, Shape representation for linear features in automated cartography, Technical Papers of the 1986 ACSM-ASPRS Annual Convention, 1, 143-152
  25. Saalfeld, A., 1993, Conflation : Automated Map Compilation, Dissertation, University of Maryland
  26. Saalfeld, A., 1998, Topologically Consistent Line Simplification with the Douglas-Peucker Algorithm, the presented paper from Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University
  27. Thompson, M. M., 1988, Maps for American, Third Edition, U.S.G.S
  28. Timpf, S., 1998, Hierarchical Structures in Map Series, Ph. D. Dissertation, Institute for Geoinformation, Technical University of Vienna
  29. Topfer, F. and Pillewizer, W., 1966, The principles of selection, a means of cartographic generalization, Cartographic Journal, 3(1),10-16