A GIS-Based Method for Delineating Spatial Clusters: A Modified AMOEBA Technique

공간 클러스터의 범역 설정을 위한 GIS-기반 방법론 연구 -수정 AMOEBA 기법-

  • Lee, Sang-Il (Department of Geography Education, Seoul National University) ;
  • Cho, Dae-Heon (The Graduate School of Education, Ewha Womans University) ;
  • Sohn, Hak-Gi (Korea Research Institute for Human and Settlements) ;
  • Chae, Mi-Ok (Korea Research Institute for Human and Settlements)
  • Received : 2010.07.21
  • Accepted : 2010.08.20
  • Published : 2010.08.31

Abstract

The main objective of the paper is to develop a GIS-based method for delineating spatial clusters. Major tasks are: (i) to devise a sustainable algorithm with reference to various methods developed in the fields of geographic boundary analysis and cluster detection; (ii) to develop a GIS-based program to implement the algorithm. The main results are as follows. First, it is recognized that the AMOEBA technique utilizing LISA is the best candidate. Second, a modified version of the AMOEBA technique is proposed and implemented in a GIS environment. Third, the validity and usefulness of the modified AMOEBA algorithm is assured by its applications to test and real data sets.

이 연구의 주된 목적은 공간 클러스터의 범역을 설정하는 GIS-기반 방법론을 개발하는 것이다. 주요 과제는 지리적 경계 분석과 LISA-기반 클러스터 탐지에 대한 기존 방법론을 비교 검토함으로써 진일보한 방법론을 고안하고, 그것을 실행하는 GIS-기반 프로그램을 개발하는 것이다. 주요 연구 결과는 다음과 같다. 첫째, 기존 방법론을 검토한 결과, LISA를 이용한 AMOEBA 기법이 가장 타당한 것으로 판단되었다. 둘째, 수정 AMOEBA 기법의 알고리즘을 확립했으며 실행 소프트웨어를 상용 GIS 프로그램의 확장 기능형태로 개발하였다. 셋째, 수정 AMOEBA 기법을 실험 데이터와 실 데이터에 적용한 결과 제안된 기법의 유용성이 확인되었다.

Keywords

References

  1. Aldstadt, J. and Getis, A., 2006, Using AMOEBA to create a spatial weights matrix and identify spatial clusters, Geographical Analysis, 38(4), 327-343. https://doi.org/10.1111/j.1538-4632.2006.00689.x
  2. Anselin, L., 1995, Local indicators of spatial association-- LISA, Geographical Analysis, 27(2), 93-115.
  3. Anselin, L., 1996, The Moran scatterplot as an ESDA tool to assess local instability in spatial association, in Fisher, M., Scholter, H., and Unwin, D. (eds.), Spatial Analytical Perspectives on GIS, Taylor & Francis, London, 111-125.
  4. Anselin, L., 1998, Exploratory spatial data analysis in a geocomputational environment, in Longley, P. A., Brooks, S. M., McDonnell, R., and MacMillan, B. (eds.), Geocomputation: A Primer, John Wiley & Sons, Chichester, West Sussex, 77- 94.
  5. Anselin, L., 2003, GeoDa 0.9 User's Guide, Spatial Analysis Laboratory, Department of Agricultural and Consumer Economics, University of Illinois.
  6. Anselin, L. and Bao, S., 1997, Exploratory spatial data analysis linking SpaceStat and ArcView, in Fischer, M. and Getis, G. (eds.), Recent Development in Spatial Analysis, Springer- Verlag, Berlin, 35-59.
  7. Balk, D. L., Deichmann, U., Yetman, G., Pozzi, F., Hay, S. I., and Nelson, A., 2006, Determining global population distribution: Methods, applications and data, Advances in Parasitology, 62, 119-157. https://doi.org/10.1016/S0065-308X(05)62004-0
  8. Boots, B., 2001, Using local statistics for boundary characterization, GeoJournal, 53(4), 339-345. https://doi.org/10.1023/A:1020106528639
  9. Boots, B. and Tiefelsdorf, M., 2000, Global and local spatial autocorrelation in bounded regular tessellations, Journal of Geographical Systems, 2(4), 319-348. https://doi.org/10.1007/PL00011461
  10. Brunsdon, C., 1998, Exploratory spatial data analysis and local indicators of spatial association with XLISP-STAT, Journal of the Royal Statistical Society Series D: The Staistician, 47(3), 471-484. https://doi.org/10.1111/1467-9884.00148
  11. Dykes, J., 1998, Cartographic visualization: Exploratory spatial data analysis with local indicators of spatial association using Tcl/Tk and cdv, Journal of the Royal Statistical Society Series D: The Staistician, 47(3), 485-497. https://doi.org/10.1111/1467-9884.00149
  12. Fortin, M.-J. and Dale, M., 2005, Spatial Analysis: A Guide for Ecologists, Cambridge University Press, Cambridge.
  13. Getis, A. and Ord, J. K., 1992, The analysis of spatial association by use of distance statistics, Geographical Analysis, 24(3), 189-206.
  14. 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.
  15. Goodchild, M. F. and Lam, N. S.-N., 1980, Areal interpolation: A variant of the traditional spatial problem, Geoprocessing, 1, 297-312.
  16. Jacquez, G. M., Maruca, S., and Fortin, M.-J., 2000, From fields to objects: A review of geographic boundary analysis, Journal of Geographical Systems, 2(3), 221-241. https://doi.org/10.1007/PL00011456
  17. Kulldorff, M., 1997, A spatial scan statistic, Communications in Statistics: Theory and Methods, 26(6), 1487-1496.
  18. Kulldorff, M., 2009, SaTScan User Guide (version 8.0), Available at http://www.satscan.org/.
  19. Lawson, A. B. and Kleinman, K., 2005, Spatial and Syndromic Surveillance for Public Health, John Wiley & Sons, Chichester, West Sussex.
  20. Lee, S.-I., 2001, Developing a bivariate spatial association measurer: An integration of Pearson's r and Moran's I, Journal of Geographical Systems, 3(4), 369-385. https://doi.org/10.1007/s101090100064
  21. Lee, S.-I., 2004, 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
  22. Lee, S.-I., 2009, A generalized randomization approach to local measures of spatial association, Geographical Analysis, 41(2), 221-248. https://doi.org/10.1111/j.1538-4632.2009.00749.x
  23. Lee, S.-I. and 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 (in Korean).
  24. Lee, S.-I., Shin, J., Kim, H.-M., Hong, I., Kim, K., Chun, Y., Cho, D., Kim, J.-G., and Lee, G. (translation), 2009, Geographic Information Systems and Science, 2nd Edition, Sigmapress, Seoul (이상일.신정엽.김현미.홍일영.김감영.전용완. 조대헌.김종근.이건학 역, 2009, 지리정보시스템과 지리정보과학, 제2판, 시그마프레스, 서울; Longley, P. A., Goodchild, M., Maguire, D. J., and Rhind, D. W., 2005, Geographic Information Systems and Science, 2nd Edition, John Wiley & Sons, Chichester, West Sussex).
  25. Legendre, P. and Legendre, L., 1998, Numerical Ecology, 2nd English Edition, Elsevier, New York.
  26. Lu, H. and Carlin, B. P., 2005, Bayesian areal wombling for geographical boundary analysis, Geographical Analysis, 37(3), 265-285. https://doi.org/10.1111/j.1538-4632.2005.00624.x
  27. Moreira, G. J. P., Takahashi, R. H. C., and Duczmal, L., 2007, Delineating spatial clusters with artificial neural networks, Advances in Disease Surveillance, 4, 104.
  28. Office of the Deputy Prime Minister, 2002, Producing Boundaries and Statistics for Town Centres: London Pilot Study Summary Report, The Stationery Office, UK.
  29. Ord, J. K. and Getis, A., 1995, Local spatial autocorrelation statistics: Distributional issues and an application, Geographical Analysis, 27(4), 286-306.
  30. Rogerson, P. and Yamada, I., 2009, Statistical Detection and Surveillance of Geographic Clusters, Chapman & Hall/CRC, Boca Raton, FL.
  31. Sohn, H., 2008, Modeling spatial patterns of an overheated speculation area, Journal of the Korean Geographical Society, 43(1), 104-116 (in Korean).
  32. Sohn, H. and Park, K., 2008, A spatial statistical method for exploring hotspots of house price volatility, Journal of the Korean Geographical Society, 43(3), 392-411 (in Korean).
  33. Sutton, P. C., 2003, A scale-adjusted measure of "urban sprawl" using nighttime satellite imagery, Remote Sensing of Environment, 86, 353-369. https://doi.org/10.1016/S0034-4257(03)00078-6
  34. Tango, T., 2010, Statistical Methods for Disease Clustering, Springer, New York.
  35. Thurstain-Goodwin, M. and Unwin, D. J., 2000, Defining and delimiting the central areas of towns for statistical monitoring using continuous surface representations, Transactions in GIS, 4(4), 305-317. https://doi.org/10.1111/1467-9671.00058
  36. Unwin, A., 1996, Exploratory spatial analysis and local statistics, Computational Statistics, 11, 387-400.
  37. Unwin, A. and Unwin, D. J., 1998, Exploratory spatial data analysis with local statistics, Journal of the Royal Statistical Society Series D: The Statistician, 47(3), 415-421. https://doi.org/10.1111/1467-9884.00143
  38. Waller, L. A. and Gotway, C. A., 2004, Applied Spatial Statistics for Public Health Data, John Wiley & Sons, Hoboken, NJ.
  39. Womble, W. H., 1951, Differential systematics, Science, 114, 315-322. https://doi.org/10.1126/science.114.2961.315
  40. Wulder, M. and Boots, B., 1998, Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistics, International Journal of Remote Sensing, 19(11), 2223-2231. https://doi.org/10.1080/014311698214983