한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제19권3호
  • /
  • Pages.127-143
  • /
  • 2016
  • /
  • 1226-9719(pISSN)
  • /
  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
권호 표지
DOI QR코드

DOI QR Code

격자크기가 밀도구분적 인구추정의 정확성에 미치는 영향

Effect of Grid Cell Size on the Accuracy of Dasymetric Population Estimation

  • 투고 : 2016.09.04
  • 심사 : 2016.09.27
  • 발행 : 2016.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 상이한 셀 크기에 따라 밀도구분적 인구추정의 정확성이 어떻게 변화하는지를 탐색하였다. 미국 조지아주 풀턴 카운티를 사례로 한 밀도구분적 인구 지도가 지능적인 밀도구분적 지도제작기법, 인구자료, 원본 및 모의된 토지이용 및 피복 자료를 이용하여 30m에서 420m의 해상도까지 매 30m 간격으로 생성되었다. 밀도구분적 인구 지도의 정확성은 RMSE 및 수정 RMSE 통계치를 이용하여 평가되었다. 프랙털 차원 값은 TPSA 방법을 사용하면서 30m에서 420m의 해상도까지 생성된 밀도구분적 인구 지도에 대해 각각 계산되었다. 연구결과에 따르면, 속성의 정확성 측면에서 인구를 보다 정확하게 추정하기 위해서 210m 이하의 격자 셀 크기가 적절하였나, 사례지역에서 밀도구분적 인구추정의 허용가능한 공간적 정확성을 충족시키기 위해 30m의 격자 셀 크기가 적절하였다. 또한, 프랙털 분석은 120m의 격자 셀 크기가 사례지역에서 밀도구분적 인구추정을 위한 최적의 해상도 이다는 것을 보여준다.

This study explored the variability in the accuracy of dasymetric population estimation with different grid cell sizes. Dasymetric population maps for Fulton County, Georgia in the US were generated from 30m to 420m at intervals of 30m using an automated intelligent dasymetric mapping technique, population data, and original and simulated land use and cover data. The accuracies of dasymetric population maps were evaluated using RMSE and adjusted RMSE statistics. Lumped fractal dimension values were calculated for the dasymetric population maps generated from resolutions of 30m to 420m using the triangular prism surface area (TPSA) method. The results show that a grid cell size of 210m or smaller is required to estimate population more accurately in terms of thematic accuracy, but a grid cell size of 30m is required to meet an acceptable spatial accuracy of dasymetric population estimation in the study area. The fractal analysis also indicates that a grid cell size of 120m is the optimal resolution for dasymetric population estimation in the study area.

키워드

참고문헌

  1. Bozheva, A.M., A.N. Petrov and R. Sugumaran. 2005. The effect of spatial resolution of remotely sensed data in dasymetric mapping of residential areas. GIScience & Remote Sensing 42(2): 113-130. https://doi.org/10.2747/1548-1603.42.2.113
  2. Cao, C. and N.S.N. Lam. 1997. Understanding the scale and resolution effects in remote sensing and GIS. In Quattrochi, D. and M. Goodchil. (eds.). Scale in Remote Sensing and GIS. CRC/Lewis Publishers, Boca Raton, FL, USA, pp.57-71.
  3. Choei, N.Y. 2010. An application of the genetic algorithm on population estimation using urban environmental factors. Journal of the Korean Association of Geographic Information Studies 13(3):119-130 (최내영. 2010. 도시환경변수를 이용한 격자 인구추정에 있어서의 유전적 알고리즘 기법 활용 연구. 한국지리정보학회지 13(3):119-130).
  4. Cockx, K. and F. Canters. 2015. Incorporating spatial non-stationarity to improve dasymetric mapping of population. Applied Geography 63:220-230. https://doi.org/10.1016/j.apgeog.2015.07.002
  5. Dobson, J., E.A. Bright, P.R. Coleman, R.C. Durfree and B.A. Worley. 2000. LandScan: a global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing 66(7):849‐857.
  6. Eicher, C.L. and C.A. Brewer. 2001. Dasymetric mapping and areal interpolation: implementation and evaluation. Cartography and Geographic Information Science 28(2):125‐138. https://doi.org/10.1559/152304001782173727
  7. Fisher, P.F. and M. Langford. 1995. Modeling the errors in areal interpolation between zonal systems by Monte Carlo simulation. Environment and Planning A 27:211‐224. https://doi.org/10.1068/a270211
  8. Fisher, P.F. and M. Langford. 1996. Modeling sensitivity to accuracy in classified imagery: a study of areal interpolation by dasymetric mapping. Professional Geographer 48(3):299‐309. https://doi.org/10.1111/j.0033-0124.1996.00299.x
  9. Gallego, F.J. F. Batista, C. Rocha and S. Mubareka. 2011. Disaggregating population density of the European Union with CORINE land cover. International Journal of Geographical Information Science 25(12):2051-2069. https://doi.org/10.1080/13658816.2011.583653
  10. Goerlich, F.J. and I. Cantarino. 2013. A population density grid for Spain. International Journal of Geographical Information Science 27(12):2247-2263. https://doi.org/10.1080/13658816.2013.799283
  11. Gregory, I.N. 2002. The accuracy of areal interpolation techniques: standardizing 19th and 20th 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
  12. Hawley, K. and H. Moellering. 2005. A comparative analysis of areal interpolation methods. Cartography and Geographic Information Science 32(4): 411-423. https://doi.org/10.1559/152304005775194818
  13. Higgs, G. and M. Landford. 2009. GIScience, environmental justice & estimating populations at risk: The case of landfills in Wales. Applied Geography 29:63-76. https://doi.org/10.1016/j.apgeog.2008.07.006
  14. Hultgren, T. 2005. The role of resolution in dasymetric population mapping. M.A. Thesis, Univ. of Colorado, Denver, USA, p.85.
  15. Jensen, J.R. abd D.C. Cowen, 1999. Remote sensing of urban suburban infrastructure and economic attributes. Photogrammetric Engineering and Remote Sensing 65(5):611‐622.
  16. Jun, B.W. 2006. An evaluation of a dasymetric surface model for spatial disaggregation of zonal population data. Journal of the Korean Association of Regional Geographers 12(5):614-630 (전병운. 2006. 구역단위 인구자료의 공간적 세분화를 위한 밀도구분적 표면모델에 대한 평가. 한국지역지리학회지 12(5):614- 630).
  17. Lam, N.S‐N. 1992. On the issues of scale, resolution, and fractal analysis in the mapping sciences. Professional Geographer 44(1):88-98. https://doi.org/10.1111/j.0033-0124.1992.00088.x
  18. Lam, N.S‐N., H-L. Qui, D.A. Quattrochi and C.W. Emerson. 2002. An evaluation of fractal methods for characterizing image complexity. Cartography and Geographic Information Science 29(1): 25-35. https://doi.org/10.1559/152304002782064600
  19. 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(2): 161-180. https://doi.org/10.1016/j.compenvurbsys.2004.07.001
  20. Liang, B., Q. Weng, and X. Tong. 2013. An evaluation of fractal characteristics of urban landscape in Indianapolis, USA, using multi-sensor satellite images. International Journal of Remote Sensing 34(3):804-823. https://doi.org/10.1080/01431161.2012.714506
  21. Liu, X. 2003. Estimation of the spatial distribution of urban population using high spatial resolution satellite imagery. Ph.D. Dissertation, Univ. of California, Santa Barbara, USA. p.175.
  22. Liu, X. and M. Herold. 2007. Population estimation and interpolation using remote sensing. In Q. Weng and D. Quattrochi. (eds.). Urban Remote Sensing. CRC Press, Boca Raton, FL, USA, pp.269-290.
  23. Maantay, J.A., A.R. Maroko and C. Herrmann. 2007. Mapping population distribution in the urban environment: the cadastral-based expert dasymetric systems (CEDS). Cartography and Geographic Information Science 34(2): 77-102. https://doi.org/10.1559/152304007781002190
  24. Martin, D., N.J. Tate, and M. Langford. 2000. Refining population surface models: experiments with Northern Ireland census data. Transactions in GIS 4(4):343-360. https://doi.org/10.1111/1467-9671.00060
  25. Mennis, J. 2003. Generating surface models of population using dasymetric mapping. The Professional Geographer 55(1):31‐42.
  26. Mennis, J. and T. 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
  27. Moon, T.H. 2005. Fractal analysis urban morphology considering distributed situation of buildings. Journal of the Korean Association of Geographic Information Studies 8(3):1-10 (문태헌, 2005. 건물분포를 고려한 도시형태의 프랙털(Fractal) 해석. 한국지리정보학회지 8(3): 1-10).
  28. Openshaw, S. 1983, The Modifiable Areal Unit Problem. Concepts and Techniques in Modern Geography 38. Geo Books, Norwich, England.
  29. Patino, J.E. and J.C. Duque. 2013. A review of regional science applications of satellite remote sensing in urban settings. Computers, Environment and Urban Systems 37:1-17. https://doi.org/10.1016/j.compenvurbsys.2012.06.003
  30. Quattrochi, D.A., N.S.-N. Lam, H.L. Qiu and W. Zhao. 1997. Image characterization and modeling system(ICAMS): a geographic information system for the characterization and modeling of multiscale remote sensing data. In Quattrochi, D. and M. Goodchil. (eds.). Scale in Remote Sensing and GIS. CRC/Lewis Publishers, Boca Raton, FL, USA, pp.295-307.
  31. Sleeter, R. and M. Gould. 2007. Geographic information system software to remodel population data using dasymetric mapping methods. U.S. Geological Survey Techniques and Methods 11-C2. p.15.
  32. Usery, E.L., M.P. Finn, D.J. Scheidt, S. Ruhl, T. Beard and M. Bearden. 2004. Geospatial data resampling and resolution effects on watershed modeling: A case study using the agricultural non-point source pollution model. Journal of Geographical System 6(3):289-306. https://doi.org/10.1007/s10109-004-0138-z
  33. Wong, D.W.S. 1996. Aggregation effects in georeferenced data, In: S.L. Arlinghaus (ed.), Practical Handbook of Spatial Statistics, CRC Press, Boca Raton, pp.83-106.
  34. Wong, D.W.S., H. Lasus, and R.F. Falk. 1999. Exploring the variability of segregation index D with scale and zonal systems: an analysis of thirty US cities. Environment and Planning A 31:507-522. https://doi.org/10.1068/a310507
  35. Wright, J.K. 1936. A method of mapping densities of population with Cape Cod as an example. Geographical Review 26(1):103‐110. https://doi.org/10.2307/209467
  36. Wu, S.S., E.L. Usery, M.P. Finn, and D.D. Bosch. 2008. An assessment of the effects of cell size on AGNPS modeling of watershed runoff. Cartography and Geographic Information Systems 35(4): 265-278. https://doi.org/10.1559/152304008786140542
  37. Wu, S.S., X. Qiu and L. Wang. 2005. Population estimation methods in GIS and remote sensing: A review. GIScience and Remote Sensing 42(1):80-96. https://doi.org/10.2747/1548-1603.42.1.80
  38. Zhou, G. and N.S‐N. Lam. 2005. A comparison of fractal dimension estimators based on multiple surface generation algorithms. Computers & Geosciences 31: 1260-1269. https://doi.org/10.1016/j.cageo.2005.03.016

피인용 문헌

  1. 화소 기반 공간메트릭스를 이용한 도시 녹지의 공간적 변화 분석: 대구시를 사례로 vol.23, pp.1, 2016, https://doi.org/10.26863/jkarg.2017.02.23.1.136