Spatial Information Research

Korea Spatial Information Society (대한공간정보학회)
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A Comparative Analysis of Areal Interpolation Methods for Representing Spatial Distribution of Population Subgroups

하위인구집단의 분포 재현을 위한 에어리얼 인터폴레이션의 비교 분석

  • Cho, Daeheon (SNU BK21 Plus for Geography department(4-Zero Land Space Creation group), Seoul National University)
  • Received : 2014.05.09
  • Accepted : 2014.06.23
  • Published : 2014.06.30
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Abstract

Population data are usually provided at administrative spatial units in Korea, so areal interpolation is needed for fine-grained analysis. This study aims to compare various methods of areal interpolation for population subgroups rather than the total population. We estimated the number of elderly people and single-person households for small areal units from Dong data by the different interpolation methods using 2010 census data of Seoul, and compared the estimates to actual values. As a result, the performance of areal interpolation methods varied between the total population and subgroup populations as well as between different population subgroups. It turned out that the method using GWR (geographically weighted regression) and building type data outperformed other methods for the total population and households. However, the OLS regression method using building type data performed better for the elderly population, and the OLS regression method based on land use data was the most effective for single-person households. Based on these results, spatial distribution of the single elderly was represented at small areal units, and we believe that this approach can contribute to effective implementation of urban policies.

공간 분석 연구를 위해 사용되는 대부분의 통계 자료는 행정구역 단위로 구축되어 있어 보다 세밀한 분석을 위해서는 에어리얼 인터폴레이션이 필요하다. 본 연구의 주된 목적은 전체 인구가 아니라 하위 인구집단에 초점을 두어 에어리얼 인터폴레이션을 실행하기 위한 방법들을 비교 검토하는 것이다. 서울의 2010년 센서스 데이터를 이용해 행정동 단위의 고령인구와 일인가구를 사례로 고해상도의 공간 단위로 인터폴레이션 한 후 경험 데이터와 비교함으로써 서로 다른 방법들의 적용 가능성을 평가하였다. 그 결과 전체 집단과 하위 집단 간에, 그리고 하위 집단 간에도 우수한 실행방법이 다소 간의 차이를 나타내었다. 총인구와 총가구는 건물용도 및 GWR(geographically weighted regression)을 이용한 방법이, 고령인구는 건물용도 및 OLS 회귀분석을 이용한 방법이, 일인가구는 토지용도 및 OLS를 이용한 방법이 가장 좋은 결과를 보였다. 이상의 결과를 토대로 대표적인 사회적 약자인 서울의 일인고령가구에 대한 분포를 고해상도의 공간 단위로 재현하였으며, 이러한 접근은 관련 도시 정책의 실행에 큰 기여를 할 것으로 기대된다.

Keywords

References

  1. Bhaduri, B; Bright, E; Coleman, P; Urban, M.L. 2007, LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics, GeoJournal, 69(1-2): 103-117. https://doi.org/10.1007/s10708-007-9105-9
  2. Brunsdon, C; Fotheringham, A.S; Charlton, M.E. 1996, Geographically weighted regression: a method for exploring spatial nonstationarity, Geographical Analysis, 28(4): 281-298.
  3. Cai, Q; Rushton, G; Bhaduri, B; Bright, E; Coleman, P. 2006, Estimating small‐area populations by age and sex using spatial interpolation and statistical inference methods, Transactions in GIS, 10(4): 577-598. https://doi.org/10.1111/j.1467-9671.2006.01013.x
  4. Choei, N.Y. 2010, A comparative study on the genetic algorithm and regression analysis in urban population surface modeling, Journal of Korea Spatial Information Society, 18(5): 107-117.
  5. Eicher, C.L; Brewer, C.A. 2001, Dasymetric mapping and areal interpolation: implementation and evaluation, Cartography and Geographic Information Science, 28(2): 125-138. https://doi.org/10.1559/152304001782173727
  6. Fischer, M.M; Getis, A. 2009, Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Heidelberg: Springer.
  7. Flowerdew, R; Green, M; Kehris, E. 1991, Using areal interpolation methods in geographic information systems, Papers in Regional Science, 70(3): 303-315. https://doi.org/10.1007/BF01434424
  8. Goodchild, M.F; Anselin, L; Deichmann, U. 1993, A framework for the areal interpolation of socioeconomic data, Environment and Planning A, 25(3): 383-397. https://doi.org/10.1068/a250383
  9. Goodchild, M.F; Lam, N. 1980, Areal interpolation: a variant of the traditional spatial problem, Geo- Processing, 1: 297-312.
  10. Harvey, J.T. 2002, Population estimation models based on individual TM pixels, Photogrammetric Engineering & Remote Sensing, 68(11): 1181-1192.
  11. Hwang, S.N; Cho, C.M. 2011, A study on relationships between environmental risk and demographic characteristics using GIS, Journal of Korea Spatial Information Society, 19(4): 1-10.
  12. Langford, M. 2006, Obtaining population estimates in non-census reporting zones: an evaluation of the 3-class dasymetric method, Computers, Environment and Urban Systems, 30: 161-180. https://doi.org/10.1016/j.compenvurbsys.2004.07.001
  13. Lee, S.-I; 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.
  14. Lin, J; Cromley, R; Zhang, C. 2011, Using geographically weighted regression to solve the areal interpolation problem, Annals of GIS, 17(1): 1-14. https://doi.org/10.1080/19475683.2010.540258
  15. Lo, C.P. 2008, Population estimation using geographically weighted regression, GIScience & Remote Sensing, 45(2): 131-148. https://doi.org/10.2747/1548-1603.45.2.131
  16. Lwin, K.K; Murayama, Y. 2010, Development of GIS tool for dasymetric mapping, International Journal of Geoinformatics, 6(1): 11-18.
  17. Maantay, J.A; Maroko, A.R; Herrmann, C. 2007, Mapping population distribution in the urban environment: The cadastral-based expert dasymetric system (CEDS), Cartography and Geographic Information Science, 34(2): 77-102. https://doi.org/10.1559/152304007781002190
  18. Mennis, J; Hultgren, T. 2006, Intelligent dasymetric mapping and its application to areal interpolation, Cartography and Geographic Information Science, 33(3): 179-194. https://doi.org/10.1559/152304006779077309
  19. Mennis, J. 2003, Generating surface models of population using dasymetric mapping, Professional Geographer, 55(1): 31-42.
  20. Mennis, J. 2009, Dasymetric mapping for estimating population in small areas, Geography Compass, 3(2): 727-745. https://doi.org/10.1111/j.1749-8198.2009.00220.x
  21. Murakami, D; Tsutsumi, M. 2012, Practical spatial statistics for areal interpolation, Environment and Planning B, 39(6): 1016-1033. https://doi.org/10.1068/b38034t
  22. Nagle, N.N; Buttenfield, B.P; Leyk, S; Spielman, S. 2013, Dasymetric modeling and uncertainty, Annals of the Association of American Geographers, 104(1): 80-95.
  23. Qiu, F; Sridharan, H; Chun, Y. 2010, Spatial autoregressive model for population estimation at the census block level using LIDAR-derived building volume information, Cartography and Geographic Information Science, 37(3): 239-257. https://doi.org/10.1559/152304010792194949
  24. Reibel, M; Bufalino, M.E. 2005, Streetweighted interpolation techniques for demographic count estimation in incompatible zone systems, Environment and Planning A, 37(1): 127-139. https://doi.org/10.1068/a36202
  25. Schroeder, J.P; Van Riper, D.C. 2013, Because Muncie's densities are not Manhattan's: using geographical weighting in the Expectation- Maximization algorithm for areal interpolation, Geographical Analysis, 45: 216-237. https://doi.org/10.1111/gean.12014
  26. Sridharan, H; Qiu, F. 2013, A spatially disaggregated areal interpolation model using light detection and ranging-derived building volumes, Geographical Analysis, 45: 238-258. https://doi.org/10.1111/gean.12010
  27. Tapp, A.F. 2010, Areal interpolation and dasymetric mapping methods using local ancillary data sources, Cartography and Geographic Information Science, 37(3):215-228. https://doi.org/10.1559/152304010792194976
  28. Tobler, W.R. 1979, Smooth pycnophylactic interpolation for geographical regions, Journal of the American Statistical Association, 74: 519-530. https://doi.org/10.1080/01621459.1979.10481647
  29. Wright, J. 1936, A method of mapping densities of population, The Geographical Review, 26: 103-110. https://doi.org/10.2307/209467
  30. Wu, S.S; Qiu, X; Wang, L. 2005, Population estimation methods in GIS and remote sensing: a review, GIScience & Remote Sensing, 42(1): 80-96. https://doi.org/10.2747/1548-1603.42.1.80
  31. Wu, S.S; Wang, L; Qiu, X. 2008, Incorporating GIS building data and census housing statistics for sub-block-level population estimation, The Professional Geographer, 60(1): 121-135. https://doi.org/10.1080/00330120701724251
  32. Xie, Y.C. 1995, The overlaid network algorithms for areal interpolation problem, Computers, Environment and Urban Systems, 19(4): 287-306. https://doi.org/10.1016/0198-9715(95)00028-3
  33. Xie, Z. 2006, A framework for interpolating the population surface at the residential-housing-unit level, GIScience & Remote Sensing, 43(3): 233-251. https://doi.org/10.2747/1548-1603.43.3.233
  34. Yeo, E; Kim, M; Kwon, M; Choi, O.G; Choi, J.Y. 2012, Old-age Poverty in Korea and How Should the Government Reduce It, Korea Institute for Health and Social Affairs.
  35. Yuan, Y; Smith, R; Limp, W. 1997, Remodeling census population with spatial information from LandSat TM imagery, Computers, Environment and Urban Systems, 21: 245-258. https://doi.org/10.1016/S0198-9715(97)01003-X
  36. Zandbergen, P.A. 2011, Dasymetric Mapping using high resolution address point datasets, Transactions in GIS, 15(s1): 5-27. https://doi.org/10.1111/j.1467-9671.2011.01270.x
  37. Zandbergen, P.A; Ignizio, D.A. 2010, Comparison of dasymetric mapping techniques for small-area population estimates, Cartography and Geographic Information Science, 37(3): 199-214. https://doi.org/10.1559/152304010792194985