PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2004 | nr 3 | 15--24
Tytuł artykułu

Modelowanie czasowo-przestrzenne metodami geostatycznymi

Warianty tytułu
Modelling by Geostatistical Methods in Respect of Time and Space
Języki publikacji
PL
Abstrakty
Geostatystyka jest działem statystyki, który dostarcza metod opisu ciągłości przestrzennej oraz adaptuje metody regresji klasycznej w celu wykorzystania tej ciągłości. Do podstawowych zagadnień, którymi zajmuje się geostatystyka należy interpretacja oraz przewidywanie (estymacja, symulacja) rozkładów przestrzennych badanych zjawisk. Celem artykułu jest przedstawienie sposobu wykorzystania metod geostatystycznych do opisu zjawisk czasowo-przestrzennych i omówienie najważniejszych trudności.
EN
There are many random phenomena which are intrinsically spatio-temporal in which relevant role plays both temporal variability and spatial one. These variabilities should be taken into consideration together. Spatio-temporal phenomena are frequent in technology, economy, agriculture, geography, the environmental studies etc. There is urgent need to develop statistical methods for description of such phenomena. The most appropriate for this purpose are geostatistical methods. Geostatistical methods of modelling the spatio-temporal phenomena have been intensively developed and studied for over a dozen years. The main trend in these studies consists in the extension of geostatistical methods designed for spatial analysis into the space-time domain, by adding the temporal dimension. Despite the straightforward simplicity of this extension, the application of these methods for analysing real spatio-temporal phenomena is very difficult. The difficulties arise from different properties of spatial and temporal data. Geostatistical methods of modelling the spatio-temporal phenomena take advantage of kriging methods in which both spatio-temporal variable and the elements of covariance matrix used in cok-riging equations are the functions of time and spatial co-ordinates. The most difficult is modelling of two-dimensional empirical covariances (or equivalent empirical semivari-ances). These models are necessary for calculation elements of the covariance matrix used in kriging equations. The appropriate separation and subsequent independent modelling of temporal and spatial parts of the covariance matrix makes that the modelling of the spatio-temporal changes much easier. The intensive studies are also carried on the development of non-separable models of the spatio-temporal covariance. (original abstract)
Rocznik
Numer
Strony
15--24
Opis fizyczny
Twórcy
Bibliografia
  • [1] Anderson, T.W., (1971), The statistical Analysis of Time Series, Wiley, New York
  • [2] Bilonick, E.A., (1983), Risk qualified maps of hydrogen ion concentration for the New York State area for 1966—1978. Atmospheric Environment 17, 2513—2524
  • [3] Bilonick, E.A., (1985), The space-time distribution of sulfate deposition in the Northeastern United States. Atmos. Environ 19, s. 1829 — 1845
  • [4] Box, G.E.P., Jenkins, G.M., (1983), Time Series Analysis. Forecasting and Control, Holden Day, Inc., 500
  • [5] Cressie, N.A.C., The Origins of Kriging, Mathematical Geology, Vol. 22, No. 3, 1990
  • [6] Isaaks, E.H. and Srivastava, R.M., An Introduction to Applied Geostatistics, Oxford University Press, New York 1989
  • [7] Cressie, N., Huang, H., (1999), Classes of Nonseparable Spatiotemporal Stationary Covariance Functions. J. Amer. Statist. Assoc. 94, s. 1330—1340
  • [8] Czeranka M., Odermatt and M. Frehner, Regulation of Intercommunal Financial Flows with Geostatistics and GIS, proceedings of ERSA 2002, Konferencja, 27 — 31 sierpnia, Niemcy
  • [9] Deutsch, C.V.&Journel, A. G., GSL1B Geostatistical Software Library and User's Guide, Oxford University Press, New York 1992
  • [10] Greyer, J.D., (1986), Time Series Analysis, PWS Publishers, Boston
  • [11] De Cesare, L., Myers, D., Posa, D., (1997), Spatial-Temporal Modeling of SO2 in Milan District, 5th International Geostatistical Congress, Wollongong, Australia, 22 — 27 września
  • [12] De Iaco S., Myers, D.E, and Posa. D., (2002), Space-time variograms and a functional form for total air pollution measurements. Computational Statistics & Data Analysis, 42, 2, s. 311— 328
  • [13] Dimitrakopoulos, R., Luo, X., (1994), Spatiotemporal Modeling: Covariances and Ordinary Kriging Systems. Geostatistics for the Next Century. Kluwer Academic Publishers, Dordrecht, s. 88 — 93
  • [14] Egbert, G.D. and Lettenmaier, D.P., (1986), Stochastic modeling of the space-time structure of atmospheric chemical deposition. Water Resources Research 22, s. 165 — 179
  • [15] Elhorst, J. Paul, (2001), Dynamic Models in Space and Time. Geographical Analysis 33, s. 119—140[16] Handcook, M.S., and J.R. Wallis, (1994), An approach to statistical spatial-temporal modelling of mete-orological fields. Journal of the American Statistical Associacion, 89 (426), s. 368 — 390.
  • [17] Hannan, E.J., (1970), Multiple Time Series. Wiley, New York
  • [18] Hansen, J. and Lebedeff, S., (1987), Global trends of measured surface air temperatures. J. Geophys. Research D92, s. 13345—13372
  • [19] Haslett, J. and Raftery, A.E., (1989), Space-time modelling with long-memory dependence: assessing Ireland's power resource. Applied Statistics 38, s. l — 50
  • [20] Journel A.G., Rossi M.E., (1989), When Do We Need a Trend Model in Kriging? Mathematical Geology, 21,8.715—739
  • [21] Krige, D.G., A statistical approach to some mine valuations and allied problems at the Witwatersrand, Masrer's thesis, University of Witwatersrand, RPA, 1951
  • [22] Luc, A., (2000), Spatial Econometrics. In B. Baltagi (Ed.), Companion to Econometrics. Oxford: Basil Blackwell
  • [23] Myers, D.E., Journel, A.G., (1990), Variograms with zonal anisotropies and non-invertible kriging systems. Math. Geol. 22, s. 779—785
  • [24] Pace, R.K., R. Barry, J. Clapp and M. Rodriguez, (1998), Spatiotemporal Autoregressive Models of Neighborhood Effects. Journal of Real Estate Finance and Economics 17, s. 15 — 33
  • [25] Posa, D., (1993), A simple description of spatial-tempotal processes. Comput. Statist. Data Anal. 15, s. 425—437
  • [26] Ranneby, B., (1982), Stochastic models of variation in time and space. In Statistics in Theory and Practice. Essays in honour of Bertil Matern, B. Ranneby, ed. Swedish University of Agricultural Sciences, Umea, s. 227—245
  • [27] Rodriguez-Iturbe, I. Meija, J.M., (1974), The design of rainfall networks in time and space. Water Resources Res. 10, s. 713—728
  • [28] Rouhani, S., Hall, T.J., (1989), Space-time kriging of groundwater data. In: Armstrong, M. (Ed.), Geoststistics. Kluwer Academic Publishers, Dordrecht, Vol. 2, s. 639 — 651
  • [29] Rouhani, S., Myers, D.E., (1990), Problems in space-time kriging of hydrogeological data. Math. Geology 22, s. 611—623
  • [30] Smith, R.L., Kolenikov, S. and Cox, L.H. (2003), Spatio-temporal modeling of PM 2.5 data with missing values. Journal of Geophysical Research-Atmospheres
  • [31] Stein, M.L., (1985), A simple model for spatial-temporal processes with an application to estimation of acid deposition. Technical Report No. 82, Department of Statistics, Stanford University
  • [32] Stein, M.L., (1986), A simple model for spatial-temporal processes. Water Resources Research 22, s. 2107— 2110
  • [33] Switzer, P. and K. Srlna, (1966), Time-trend estimation for geographic region. Journal of the American Statistical Association, 91 (434), s. 577 — 589
  • [34] Zawadzki, J. Wykorzystanie metod geostatystycznych do analizy danych przestrzennych, „Wiadomości Statystyczne" 12, 2002
  • [35] Zawadzki, J. Wstęp do integracji danych przestrzennych metodami kokrigingu, „Wiadomości Statystyczne" 5, 2003
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000000120250

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.