대한지리학회지 (Journal of the Korean Geographical Society)

대한지리학회 (The Korean Geographical Society)
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거주지 분화에 대한 공간통계학적 접근 (II): 국지적 공간 분리성 측도를 이용한 탐색적 공간데이터 분석

A Spatial Statistical Approach to Residential Differentiation (II): Exploratory Spatial Data Analysis Using a Local Spatial Separation Measure

  • Lee, Sang-Il (Department of Geography Education, Seoul National University)
  • 발행 : 2008.03.31
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초록

이 논문의 주된 목적은 국지적 공간 분리성 측도를 이용한 탐색적 공간데이터 분석을 통해 거주지 분화 연구에서 공간통계학적 접근이 가지는 의의를 논증하는 것이다. 탐색적 공간데이터 분석은 공간 데이터를 다양한 과학적 지도학적 시각화 방식을 통해 탐색함으로써 패턴을 발견해 내고, 의미 있는 가설을 수립하며, 더 나아가 공간 데이터에 대한 통계학적 모델을 평가하는 것을 주목적으로 한다. 이 연구는 국지 통계량에 기반한 탐색적 공간데이터 분석이 구체적인 연구 수행에서 실질적인 도움을 줄 수 있다는 믿음에 기반을 두고 진행된 것이다. 중요한 결과는 다음과 같다. 첫째, 이미 개발된 전역적 공간 분리성 측도로부터 국지적 공간 분리성 측도를 도출하였다. 둘째, 두 가지 유의성 검정을 위한 가정, 즉 총체적 랜덤화 가정과 조건적 랜덤화 가정에 기반한 가설검정 방법을 제시하였다 셋째, 측도와 유의성 검정을 바탕으로 한 탐색적 공간데이터 분석 기법으로 '공간 분리성 산포도 지도'와 '공간 분리성 이례치 지도'를 제시하였다. 부가적으로 각 인구 집단 별 집중도에 대한 표준화 지표도 제시되었다. 넷째, 개발된 기법을 우리나라 7대 도시의 고학력 집단과 저학력 집단간 거주지 분화에 적용한 결과, 특히, 이변량 공간적 클러스터와 공간적 특이점을 확인하는 데 유용성이 있는 것으로 드러났다.

The main purpose of the research is to illustrate the value of the spatial statistical approach to residential differentiation by providing a framework for exploratory spatial data analysis (ESDA) using a local spatial separation measure. ESDA aims, by utilizing a variety of statistical and cartographic visualization techniques, at seeking to detect patterns, to formulate hypotheses, and to assess statistical models for spatial data. The research is driven by a realization that ESDA based on local statistics has a great potential for substantive research. The main results are as follows. First, a local spatial separation measure is correspondingly derived from its global counterpart. Second, a set of significance testing methods based on both total and conditional randomization assumptions is provided for the local measure. Third, two mapping techniques, a 'spatial separation scatterplot map' and a 'spatial separation anomaly map', are devised for ESDA utilizing the local measure and the related significance tests. Fourth, a case study of residential differentiation between the highly educated and the least educated in major Korean metropolitan cities shows that the proposed ESDA techniques are beneficial in identifying bivariate spatial clusters and spatial outliers.

키워드

참고문헌

  1. 이상일, 2007, '거주지 분화에 대한 공간통계학적 접근 (I): 공간 분리성 측도의 개발,' 대한지리학회지, 42(4), 616-631
  2. 최은영, 2004, 서울시 거주지 분리 심화와 교육환경의 차별화, 서울대학교 대학원 사회교육과(지리전공) 박사학위논문
  3. Anselin, L., 1995, Local indicators of spatial association: LISA, Geographical analysis, 27(2), 93-115 https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
  4. Anselin, L., 1996, The Moran scatterplot as an ESDA tools to assess local instability in spatial association, in Fischer, M., Scholten, H., and Unwin, D. (eds.), Spatial analytical Perspectives on GIS, Taylor & Francis, London, 111-125
  5. Anselin, L., 1998, Exploratory spatial data analysis in a geocomputational environment, in Longley, P. A., Brooks, S. M., McDonell, R., and MacMillan, B. (eds.), Geocomputation: a Primer, John Wiley & Sons, Chichester, West Sussex, 77-94
  6. Anselin, L., Syabri, I., and Smirnov, O., 2002, Visualizing multivariate spatial correlation with dynamically linked windows, in Anselin, L. and Rey, S. (eds.), New Tools for Spatial Data analysis: Proceedings of the Specialist Meeting, Center for Spatially Integrated Social Science (CSISS), University of California, Santa Barbara
  7. Bailey, T. C., 1994, A review of statistical spatial analysis in geographical information systems, in Fotheringham, A. S. and Rogerson, P. (eds.), Spatial analysis and GIS, Taylor & Francis, London, 13-44
  8. Boots, B. and Tiefelsdorf, M., 2000, Global and local spatial autocorrelation in bounded regular tessellations, Journal of Geographical Systems, 2(3), 319-348 https://doi.org/10.1007/PL00011461
  9. Brown, L. A. and Chung, S.-Y., 2006, Spatial segregation, segregation indices and the geographical perspective, Population, Space and Place, 12(2), 125-143 https://doi.org/10.1002/psp.403
  10. Cliff, A. D. and Ord, J. K., 1981, Spatial Processes: Models & applications, Pion Limited, London
  11. Chung, S.-Y. and Brown, L. A., 2007, Racial/ethnic residential sorting in spatial context: Testing the exploratory frameworks, Urban Geography, 28(4), 312-339 https://doi.org/10.2747/0272-3638.28.4.312
  12. Feitosa, F. F., Camara, G., Monteiro, A. M. V., Koschitzki, T., and Silva, M. P. S., 2007, Global and local spatial indices of urban segregation, International Journal of Geographical Information Science, 21(3), 299-323 https://doi.org/10.1080/13658810600911903
  13. Fischer, M. M. and Nijkamp, P., 1992, Geographic information systems and spatial analysis, Annals of Regional Science, 26(1), 3-17 https://doi.org/10.1007/BF01581477
  14. Fotheringham, A. S., 1997, Trends in quantitative methods I: stressing the local, Progress in Human Geography, 21(1), 88-96 https://doi.org/10.1191/030913297676693207
  15. Fotheringham, A. S., 2000, Context-dependent spatial analysis: a role for GIS-, Journal of Geographical Systems, 2(1), 71-76 https://doi.org/10.1007/s101090050032
  16. Fotheringham, A. S. and Charlton, M., 1994, GIS and exploratory spatial data analysis, an overview of some research issues, Geographical Systems, 1(4), 315-327
  17. Getis, A. and Ord, J. K., 1996, Local spatial statistics: an overview, in Longley, P. and Batty, M. (eds.), Spatial analysis: Modelling in a GIS Environment, GeoInformation International, Cambridge, 261-277
  18. Good, I. J., 1983, The philosophy of exploratory data analysis, Philosophy of Science, 50(2), 283-295 https://doi.org/10.1086/289110
  19. Goodchild, M. F., Haining, R., Wise, S., et al., 1992, Integrating GIS and spatial data analysis: problems and possibilities, International Journal of Geographical Information Systems, 6(5), 407-423 https://doi.org/10.1080/02693799208901923
  20. Hubert, L. J., 1984, Statistical applications of linear assignment, Psychometrika, 49(4), 449-473 https://doi.org/10.1007/BF02302585
  21. Hubert, L. J., 1987, Assignment Methods in Combinatorial Data analysis, Marcel Dekker, New York
  22. Lee, S.-I., 2001a, Developing a bivariate spatial association measure: an integration of Pearson' s r and Moran's I, Journal of Geographical Systems, 3(4), 369-385 https://doi.org/10.1007/s101090100064
  23. Lee, S.-I., 2001b, Spatial association Measures for an ESDa-GIS Framework: Developments, Significance Tests, and applications to Spatiotemporal Income Dynamics of U.S. Labor Market areas, 1969-1999, Ph.D. Dissertation, Department of Geography, The Ohio State University
  24. Lee, S.-I., 2004a, Exploratory spatial data analysis of $\sigma$- convergence in the U.S. regional income distribution, 1969-1999, Journal of the Korean Urban Geographical Society, 7(1), 79-95
  25. Lee, S.-I., 2004b, Spatial data analysis for the U.S. regional income convergence, 1969-1999: A critical appraisal of $\beta$-convergence, Journal of the Korean Geographical Society, 39(2), 212-228
  26. Lee, S.-I., 2004c, A generalized significance testing method for global measures of spatial association: an extension of the Mantel test, Environment and Planning A, 36(9), 1687-1703 https://doi.org/10.1068/a34143
  27. Lee, S.-I., 2005, Between the quantitative and GIS revolutions: Towards an SDA-centered GIScience, Journal of Geography Education, 49, 268-284
  28. Lee, S.-I., 2008, A generalized randomization approach to local measures of spatial association, Geographical analysis, under revision
  29. Leung, Y., Mei, C.-L., and Zhang, W.-X., 2003, Statistical test for local patterns of spatial association, Environment and Planning A, 35(4), 725-744 https://doi.org/10.1068/a3550
  30. Maly, M. T., 2000, The neighborhood diversity index: a complementary measure of racial residential settlement, Journal of Urban affairs, 22(1), 37- 47 https://doi.org/10.1111/0735-2166.00038
  31. Oden, 1995, adjusting Moran's I for population density, Statistics in Medicine, 14(1), 17-26 https://doi.org/10.1002/sim.4780140104
  32. Openshaw, S., 1990, Spatial analysis and geographical information systems: a review of progress and possibilities, in Scholten, H. and Stillwell, J. (eds.), Geographical Information Systems for Urban and Regional Planning, Kluwer, Dordrecht, 153-163
  33. Openshaw, S. and Clarke, G., 1996, Developing spatial analysis functions relevant to GIS environments, in Fischer, M., Scholten, H., and Unwin, D. (eds.), Spatial analytical Perspectives on GIS, Taylor & Francis, London, 21-37
  34. Rogerson, P. A., 1999, The detection of clusters using a spatial version of the chi-square goodness-of-fit statistic, Geographical analysis, 31(1), 130-147 https://doi.org/10.1111/j.1538-4632.1999.tb00973.x
  35. Sokal, R. R., Oden, N. L., and Thomson, B. A., 1998, Local spatial autocorrelation in a biological model, Geographical Analysis, 30(4), 331-354 https://doi.org/10.1111/j.1538-4632.1998.tb00406.x
  36. Tiefelsdorf, M., 1998, Some practical applications of Moran's I's exact conditional distribution, Papers in Regional Science, 77(2), 101-129 https://doi.org/10.1111/j.1435-5597.1998.tb00710.x
  37. Tukey, J. W., 1977, Exploratory Data analysis, addison- Wesley, Reading, MA
  38. Unwin, D. J., 1996, GIS, spatial analysis and spatial statistics, Progress in Human Geography, 20(4), 540-551 https://doi.org/10.1177/030913259602000408
  39. Wise, S., Haining, R., and Signoretta, P., 1999, Scientific visualization and the exploratory analysis of area data, Environment and Planning A, 31(10), 1825-1838 https://doi.org/10.1068/a311825
  40. Wong, D. W. S., 2002, Modeling local segregation: a spatial interaction approach, Geographical & Environmental Modelling, 6(1), 81-97 https://doi.org/10.1080/13615930220127305
  41. Wong, D. W. S., 2003, Spatial decomposition of segregation indices: A framework toward measuring segregation at multiple levels, Geographical analysis, 35(3), 179-194 https://doi.org/10.1353/geo.2003.0011