한국지역지리학회지 (Journal of the Korean association of regional geographers)

  • 제22권1호
  • /
  • Pages.251-266
  • /
  • 2016
  • /
  • 1226-7392(pISSN)
  • /
  • 2586-1468(eISSN)
한국지역지리학회 (The Korean Association of Regional Geographers)
권호 표지

지표피복 데이터와 지리가중회귀모형을 이용한 인구분포 추정에 관한 연구

Locally adaptive intelligent interpolation for population distribution modeling using pre-classified land cover data and geographically weighted regression

  • Kim, Hwahwan (Department of Geography, Chonnam National University)
  • 투고 : 2016.01.29
  • 심사 : 2016.02.25
  • 발행 : 2016.02.29
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

데시메트릭 매핑은 행정구역 단위로 집계된 인구자료를 행정구역 내부의 공간적 변이에 따라 재집계하여 고해상도의 인구분포 자료를 작성하는 가장 보편적인 기법이다. 본 연구에서는 데시메트릭 매핑을 이용한 인구분포 추정의 장단점을 검토하고, 그 개선방안으로서 지리가중회귀모형을 이용한 다변량 데시메트릭 매핑 기법을 제안하였다. 기존의 지표피복 데이터와 인구센서스 자료를 기반으로 지리가중회귀모형을 적용하여 각 집계단위별로 지표피복 유형과 인구밀도의 상관관계를 분석하고, 모형에서 산출된 회귀계수를 이용해 하위 공간구획의 인구 총수를 산정하였다. 그 결과 지리가중회귀모형 기반 다변량 데시메트릭 매핑 기법을 이용했을 때, 면적가중 보간법, 이진 데시메트릭 매핑, 피크노필렉틱 보간법, 최소자승회귀모형 기반 데시메트릭 매핑 기법 등 다른 지능형 보간법에 비해 정확한 인구분포 추정이 가능하다는 것을 확인하였다. 이는 지리가중회귀모형을 통해서 인구센서스 집계 단위별로 상이한 구역 내 공간적 이질성이 인구분포 추정에 적절히 반영되었기 때문인 것으로 평가할 수 있다.

Intelligent interpolation methods such as dasymetric mapping are considered to be the best way to disaggregate zone-based population data by observing and utilizing the internal variation within each source zone. This research reviews the advantages and problems of the dasymetric mapping method, and presents a geographically weighted regression (GWR) based method to take into consideration the spatial heterogeneity of population density - land cover relationship. The locally adaptive intelligent interpolation method is able to make use of readily available ancillary information in the public domain without the need for additional data processing. In the case study, we use the preclassified National Land Cover Dataset 2011 to test the performance of the proposed method (i.e. the GWR-based multi-class dasymetric method) compared to four other popular population estimation methods (i.e. areal weighting interpolation, pycnophylactic interpolation, binary dasymetric method, and globally fitted ordinary least squares (OLS) based multi-class dasymetric method). The GWR-based multi-class dasymetric method outperforms all other methods. It is attributed to the fact that spatial heterogeneity is accounted for in the process of determining density parameters for land cover classes.

키워드

참고문헌

  1. Cockings, S., P. F. Fisher, and M. Langford. 1997. Parameterization and Visualization of the Errors in Areal Interpolation. Geographical Analysis 29 (4):314-328. https://doi.org/10.1111/j.1538-4632.1997.tb00967.x
  2. Dobson, J. E., E. A. Bright, P. R. Coleman, and B. L. Bhaduri. 2003. LandScan: A global population database for estimating population at risk. In Remotely Sensed Cities, edited by V. Mesev. London: Taylor & Francis.
  3. Eicher, C., and C. Brewer. 2001. Dasymetric mapping and areal interpolation: implementation and evaluation. Cartography and Geographic Information Science 28:125-138. https://doi.org/10.1559/152304001782173727
  4. Fisher, P. F., and Mitchel Langford. 1996. Modeling sensitivity to accuracy in classified imagery: A study of areal interpolation. Professional Geographer 48 (3):299-309. https://doi.org/10.1111/j.0033-0124.1996.00299.x
  5. Fisher, Peter F., and M. Langford. 1995. Modelling the errors in areal interpolation between zonal systems by Monte Carlo Simulation. Environment & Planning A 27: 211-224. https://doi.org/10.1068/a270211
  6. Flowerdew, R., and M. Green. 1989. Statistical methods for inference between incompatible zonal systems. In The Accuracy of Spatial Databases, edited by M. F. Goodchild and S. Gopal. London: Taylor and Francis.
  7. Flowerdew, R., and M. Green. 1994. Areal interpolation and types of data. In Spatial Analysis and GIS, edited by A. S. Fotheringham and P. Rogerson. London: Talyor & Francis.
  8. Flowerdew, Robin, and Mick Green. 1992. Developments in areal interpolation methods and GIS. Annals of Regional Science 26 (1):67. https://doi.org/10.1007/BF01581481
  9. Fotheringham, A. Stewart, Chris Brunsdon, and Martin Charlton. 2002. Geographically weighted regression: the analysis of spatially varying relationships. Chichester: Wiley.
  10. Garb, Jane L., Robert G. Cromley, and Richard B. Wait. 2007. Estimating Populations at Risk for Disaster Preparedness and Response. Journal of Homeland Security and Emergency Management 4 (1):1-17.
  11. Goodchild, M. F., L. Anselin, and U. Deichmann. 1993. A framework for the areal interpolation of socioeconomic data. Environment and Planning A 25 (3):383-397. https://doi.org/10.1068/a250383
  12. Goodchild, M. F., and N. S. Lam. 1980. Areal Interpolation: A variant of the traditional spatial problem. Geo-Processing 1:297-312.
  13. Gregory, I. N. 2002. The accuracy of areal interpolation techniques: standardising 19th and 20th century census data to allow long-term comparisons. Computers, Environment and Urban Systems 26 (4):293-314. https://doi.org/10.1016/S0198-9715(01)00013-8
  14. Hay, S. I., A. M. Noor, A. Nelson, and A. J. Tatem. 2005. The accuracy of human population maps for public health application. Tropical Medicine and International Health 10 (20): 1073-1086. https://doi.org/10.1111/j.1365-3156.2005.01487.x
  15. Holt, J. B., C. P. Lo, and Thomas W. Hodler. 2004. Dasymetric estimation of population density and areal interpolation of census data. Cartography and Geographic Information Science 31 (2):103-121. https://doi.org/10.1559/1523040041649407
  16. Homer, Collin, Chengquan Huang, Limin Yang, Bruce Wylie, and Michael Coan. 2004. Development of a 2001 National Landcover Database for the United States. Photogrammetric Engineering and Remote Sensing 70 (7):829-840. https://doi.org/10.14358/PERS.70.7.829
  17. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81 (5):345-354
  18. Huang, Y., and Y. Leung. 2002. Analysing Regional Industrialization in Jiangsu Province Using Geographically Weighted Regression. Journal of Geographical Systems 4:233-249. https://doi.org/10.1007/s101090200081
  19. Langford, M., D. J. Maguire, and D. J. Unwin. 1991. The areal interpolation problem: estimating population using remote sensing within a GIS framework. In Handling Geographical Information: Methodology and Potential Applications, edited by I. Masser and M. Blackmore. London: Longman.
  20. Langford, M., and D. J. Unwin. 1994. Generating and mapping population density surfaces within a geographical information system. The Cartographic Journal 31 (June):21-25. https://doi.org/10.1179/caj.1994.31.1.21
  21. Langford, Mitchel. 2006. Obtaining population estimates in non-census reporting zones: An evaluation of the 3-class dasymetric method. Computers, Environment and Urban Systems 30 (2):161-180. https://doi.org/10.1016/j.compenvurbsys.2004.07.001
  22. Langford, Mitchel. 2007. Rapid facilitation of dasymetric-based population interpolation by means of raster pixel maps. Computers, Environment and Urban Systems 31 (1):19-32. https://doi.org/10.1016/j.compenvurbsys.2005.07.005
  23. Langford, Mitchel, and Gary Higgs. 2006. Measuring Potential Access to Primary Healthcare Services: The Influence of Alternative Spatial Representations of Population. Professional Geographer 58 (3):294-306. https://doi.org/10.1111/j.1467-9272.2006.00569.x
  24. Lo, C. 2008. Population Estimation Using Geographically Weighted Regression. GIScience & Remote Sensing 45 (2):131-148. https://doi.org/10.2747/1548-1603.45.2.131
  25. Martin, David. 1989. Mapping Population Data from Zone Centroid Locations. Transactions of the Institute of British Geographers 14 (1): 90-97. https://doi.org/10.2307/622344
  26. Martin, David, Nicholas J. Tate, and Mitchel Langford. 2000. Refining Population Surface Models: Experiments with Northern Ireland Census Data. Transactions in GIS 4 (4):343. https://doi.org/10.1111/1467-9671.00060
  27. McCleary, G. F., Jr. 1984. Cartography, geography and the dasymetric method. Paper read at 12th conference of international cartographic association, at Perth, Australia.
  28. Mennis, J., and Torrin Hultgren. 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
  29. Mrozinski, R. D., and R. G. Cromley. 1999. Singly - and Doubly - Constrained Methods of Areal Interpolation for Vector-based GIS. Transactions in GIS 3 (3):285-301. https://doi.org/10.1111/1467-9671.00022
  30. Reibel, M., and A Agrawal. 2007. Areal Interpolation of Population Counts Using Pre-classified Land Cover Data. Population Research and Policy Review 26:619-633. https://doi.org/10.1007/s11113-007-9050-9
  31. Sadahiro, Y. 2000. Accuracy of Areal Interpolation: A Comparison of Alternative Methods. Journal of Geographical Systems 1 (4):323-346. https://doi.org/10.1007/s101090050017
  32. Sleeter, R. 2004. Dasymetric mapping techniques for the San Francisco bay region, California. Paper read at Urban and Regional Information Systems Association Annual Conference, November 7-10, 2004., at Reno, NV.
  33. Tobler, W. 1979. Smooth pycnophylactic interpolation for geographic regions. Journal of the American Statistical Association 74 (367): 519-536. https://doi.org/10.1080/01621459.1979.10481647
  34. Wu, S., X. Qiu, and L. Wang. 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
  35. Yang, Xiaojun, and C.P. Lo. 2002. Using a Time Series of Satellite Imagery to Detect Land Use and Land Cover Changes in the Atlanta, Georgia Metropolitan Area. International Journal of Remote Sensing 23 (9):1775-1798. https://doi.org/10.1080/01431160110075802
  36. Yuan, Yew, Richard M. Smith, and W. Fredrick Limp. 1997. Remodeling census population with spatial information from LandSat TM imagery. Computers, Environment and Urban Systems 21 (3-4):245-258. https://doi.org/10.1016/S0198-9715(97)01003-X