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Tékhne - Revista de Estudos Politécnicos

 ISSN 1645-9911

     

 

Principles of Deterministic Spatial Interpolators

João Negreiros[1], Marco Painho1, Fernando Aguilar[2]

c8057@isegi.unl.pt, painho@isegi.unl.pt, faguilar@aul.es

(recebido em 8 de Abril de 2008; aceite em 2 de Maio de 2008)

 

 

Resumo: A interpolação espacial é o processo de prever o valor de atributos em locais não amostrados a partir de medições realizadas em localizações diversas de uma determinada região [Burrough, McDonnell, 1998]. Rever e comparar os interpoladores determinísticos espaciais usados em Sistemas de Informação Geográficos (GIS) como a B-Spline, Fourier, TIN, IDW e as superfícies de tendência polinomiais é o objectivo principal deste artigo curto. Alguns aspectos técnicos computacionais são igualmente examinados.

Palavras-chave: Interpolação espacial, TIN, Superfícies de tendência global, Fourier, B-Spline.

 

 

Abstract: Spatial interpolation is the process of predicting the value of attributes at unsampling sites from measurements made at point locations within the same area or region [Burrough, McDonnell, 1998]. To review and to compare spatial deterministic interpolators used in Geographical Information Systems (GIS) such as B-Splines, Fourier, TIN, IDW and polynomial trend surfaces is the main goal of this short article. Some computational technical aspects are examined, as well.

Keywords: Spatial interpolation, TIN, Global polynomial trend, Fourier, B-Splines.

 

 

Texto completo disponível apenas em PDF.

Full text only available in PDF format.

 

 

Bibliography

Anselin, L. & O'Loughlin, J. (1992). Geography of International Conflict and Cooperation: Spatial Dependence and Regional Context in Africa. In Ficher (Ed.), The New Geopolitics (pp. 39-75). New York: Gordon and Breach Science Publication.        [ Links ]

Anselin, L. (Ed.). (1992). SpaceStat Tutorial. A Workbook for Using SpaceStat in the Analysis of Spatial Data. Morgantown: Regional Research Institute, West Virginia University.  

Billingsley, F.C. (1983). Data Processing and Reprocessing. Manual of Remote Sensing, Volume 1, 719-722.

Burrough, P. & McDonnell, R. (Ed.). (1998). Principles of Geographical Information Systems. Oxford University Press.

Clark, I. & Harper, W. (Ed.). (2000). Practical Geostatistics. Ecosse North America.

ESRI (Ed.). (2001). Using ArcGIS Geostatistical Analyst. ESRI Press.

Goovaerts, P. (1999). Using Elevation to Aid the Geostatistical Mapping. Catena Research, 34 (3), 227-242. Retrieved April 9, 2008, from http://www.sciencedirect.com/science.

Goovaerts, P. (2002). Geostatistical Analysis of Environmental Data. Lisboa, Portugal: ISEGI-UNL.

Griffith, D. & Layne, L. (Ed.). (1999). A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets. Oxford University Press.

Isaaks, E. & Srivastava, R. (Ed.). (1989). An Introduction to Applied Geostatistics. Oxford University Press.

Kravchenko, A. & Bullock, D. (1999). A Comparative Study of Interpolation Methods for Mapping Soil Properties. Agronomy Journal, 91, 393-400.

Levine, N. (1997). Spatial Statistics and GIS Software Tools to Quantify Spatial Patterns. APA Journal, 34, 456-471.

Matos, J. (Ed.). (2001). Fundamentos de Informação Geográfica. FCA-Lidel.

Nalder, I. & Wein, R. (1998). Spatial Interpolation of Climatic Normals: Test of a New Method in the Canadian Boreal Forest. Agriculture and Forest Meteorology, 92, 211-225.

Sarkozy, F. (1999). GIS Function Interpolation. Periodical Civil Engineer, 23, 63-86.

Shamsi, S. (2008). GIS Applications in Floodplain Management. Retrieved April 9, 2008, from http://gis.esri.com/library/userconf/proc02/pap0490/p0490.htm.

Walter, N. (Ed.). (2001). Spatial Interpolation. University of Calgary Press.

Wang, X. & Zhang, Z. (1999). A Comparison of Conditional Simulation, Kriging and Trend Surface Analysis for Soil Heavy Metal Pollution Pattern Analysis. Journal of Environmental Sciences and Health, 34, 73-89.

Yao, T. (1999). Nonparametric Cross-covariance Modeling as Exemplified by Soil Heavy Metal Concentrations from The Swiss Jura. Geoderma, 88, 13-38.

Zimmerman, D., Pavlik, C., Ruggles, A. & Armstrong, M. (1999). An Experimental Comparison of Ordinary and UK and Inverse Distance Weighting. Mathematical Geology, 31, 370-395.

 

[1] ISEGI-UNL, Campus de Campolide, Lisboa, Portugal; http://www.isegi.unl.pt

[2] Universidad de Almeria, La Canãda de San Urbano, Almeria, Spain; http://www.ual.es