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격자 기반의 통계정보 표현을 위한 데이터 변환 방법

A Data Transformation Method for Visualizing the Statistical Information based on the Grid

  • Kim, Munsu (Dept. of Geoinformatics, University of Seoul) ;
  • Lee, Jiyeong (Dept. of Geoinformatics, University of Seoul)
  • 투고 : 2015.07.08
  • 심사 : 2015.10.27
  • 발행 : 2015.10.31

초록

본 논문에서는 다양한 형태로 존재하는 통계정보를 일정한 모양과 크기를 갖는 격자로 표현하기 위해 필요한 데이터 변환 방법론에 대하여 제시한다. 격자는 기존 통계지도 서비스에서 활용하고 있는 통계공간단위인 행정구역과 집계구와 비교하였을 때 모양과 크기가 일정하여 통계정보를 객관적으로 파악할 수 있게 하며, 지도 축척 변화에 유연하게 적용될 수 있는 특징이 있다. 한편, 기존 통계지도 서비스에서는 면 보간법을 활용하여 통계공간단위로 변환하고 있는데, 이것을 다양한 형태로 존재하는 통계정보에 적용시키기 위해서는 추가적인 프로세스가 필요하다. 이에 따라, 본 논문에서는 다양한 형태로 존재하는 통계정보의 격자 변환을 위해 1)지오코딩을 통한 공간데이터로의 변환, 2)공간 관계 정의를 통한 위치정보 변환, 3)데이터 척도를 고려한 속성정보 변환을 수행하는 방법론을 정리하였다. 제시한 방법론은 서울시 A지역의 인구 밀도 통계정보를 격자로 변환하기 위해 적용하였다. 특히, 동일한 통계정보를 표현하는 참조데이터가 서로 다르더라도 유사한 격자 표현이 가능해야 함을 검증하기 위해 공간 자기상관성을 통해 분석하였다. 그 결과, 집계구와 건물을 통해 표현되는 인구 밀도를 각각 격자로 변환하였을 때, 두 데이터 모두 유사한 격자 분포를 표현함을 파악할 수 있었다. 이러한 결과를 통해 본 연구에서 제안하는 방법론은 일관된 결과를 표현할 수 있음을 확인하였다.

The purpose of this paper is to propose a data transformation method for visualizing the statistical information based on the grid system which has regular shape and size. Grid is better solution than administrator boundary or census block to check the distribution of the statistical information and be able to use as a spatial unit on the map flexibly. On the other hand, we need the additional process to convert the various statistical information to grid if we use the current method which is areal interpolation. Therefore, this paper proposes the 3 steps to convert the various statistical information to grid. 1)Geocoding the statistical information, 2)Converting the spatial information through the defining the spatial relationship, 3)Attribute transformation considering the data scale measurement. This method applies to the population density of Seoul to convert to the grid. Especially, spatial autocorrelation is performed to check the consistency of grid display if the reference data is different for same statistic information. As a result, both distribution of grid are similar to each other when the population density data which is represented by census block and building is converted to grid. Through the result of implementation, it is demonstrated to be able to perform the consistent data conversion based on the proposed method.

키워드

참고문헌

  1. Kim, K. 2011, Effects of the Modifiable Areal Unit Problem (MAUP) on a Spatial Interaction Model, Journal of the Association of Korean Geographers, 46(2):197-211.
  2. Sudsom, N; Techato, K; Thammapalo, S; Monprapussorn, S. 2012, Grid and Census: a Geographic Sampling Strategy for Studying Dengue Vector Breeding Sites in Urban Area, Paper presented at the 33 rd Asian Conference on Remote Sensing, Pattaya, November 26-30.
  3. Shin, M. Y; Yun, J. I; Suh, A. S. 1999, Estimation of Daily Maximum/Minimum Temperature Distribution over the Korean Peninsula by Using Spatial Statistical Technique, Journal of the Korean Society of Remote Sensing, 15(1):9-20.
  4. Song, M. Y; Jung, K. S; Lee, G. H; Kim, Y. S; Shin, Y. A. 2014, DAD Analysis of Yongdam Dam Watershed Using the Cell-Based Automatic Rainfall Field Tracking Method, Journal of the Korean Association of Geographic Information Studies, 17(3):68-81. https://doi.org/10.11108/kagis.2014.17.3.068
  5. Kim, J. Y; Park, I. S; Park, C. Y; Park, S. K. 2010, Effects of Grid Size on Noise Prediction Results of Road Traffic Noise Map, Korean Society for Noise and Vibration Engineering, 20(2):199-204. https://doi.org/10.5050/KSNVE.2010.20.2.199
  6. Um, D. Y; Lee, B. S. 2012, Analysis of the Effect on the Location Evaluation of Golf Course according to the Unit Grid Size applied in Slope Analysis (In flank of Environment), Korea Society of Surveying, Geodesy, Photogrammetry, and Cartography, 30(5):467-475. https://doi.org/10.7848/ksgpc.2012.30.5.467
  7. Lee, S; Kim K. 2007, Representing the Population Density Distribution of Seoul Using Dasymetric Mapping Techniques in a GIS Environment, Journal of the Korean Cartographic Association, 7(2):53-67.
  8. Reibel, M; Agrawal, A. 2007, Areal interpolation of population counts using pre-classified land cover data, Population Research and Policy Review, 26(5-6):619-633. https://doi.org/10.1007/s11113-007-9050-9
  9. Sridharan, H; Qiu, F. 2013, A spatially disaggregated areal interpolation model using light detection and ranging-derived building volumes, Geographical Analysis, 45(3):238-258. https://doi.org/10.1111/gean.12010
  10. Lee, J. 2009, GIS-based geocoding methods for area-based addresses and 3D addresses in urban areas, Environment and Planning B: Planning and Design, 36:86-106. https://doi.org/10.1068/b31169
  11. Clementini, E; Sharma, J; Egenhofer, M. J. 1994, Modeling Topological Spatial Relations : Strategies for Query Processing, Computers and Graphics, 18(6):815-822. https://doi.org/10.1016/0097-8493(94)90007-8
  12. Lee, H. Y; Noh, S. C. 2013, Advanced Statistical Analysis : Theory and Practice 2nd Edition, Moon Woo (in Korean).
  13. National Geographic Information Institute. 2014, Development Plan of National Territorial Statistic Monitoring and Construction of Platform Project, Korea.
  14. Lee, G. 2011, A review of Object and Field Perspective on Modifiable Areal Unit Problem, Journal of the Korean Cartographic Association, 11(1):25-32.
  15. Swift, A; Liu, L; Uber, J. 2008, Reducing MAUP bias of correlation statistics between water quality and GI illness, Computer, Environment and Urban Systems, 32:134-148. https://doi.org/10.1016/j.compenvurbsys.2008.01.002
  16. Zhang, N; Zhang, H. 2011, Scale Variance Analysis Coupled with Moran's I Scalogram to Identify Hierarchy and Characteristic Scale, International Journal of Geographical Information Science, 25(9):1525-1543. https://doi.org/10.1080/13658816.2010.532134

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