15 3 2000 6 ADVAN CE IN EA R TH SC IEN CES V o l. 15 N o. 3 Jun., 2000 Ξ,, (, 730000),,, ;, ; ; P208 A 1001 8166 (2000) 0320260206 1 ;,,,,,,,,, (2),, ( ), GCM ( ), G IS, ;, ;,,,,, 1 ( ) ;,,,, ; ;,, ;, (1), Ξ ( KJ2B222102),, 1969 10,, 1999208219; 1999211203
3 261,, 2. 2. 1 2 ; ;, 2 4 8, y = A Η+ e (2) 2. 1, y n 1, n ; A n ; Η A Η 5,, e,,, ;,, ( ),,, (, ), 2. 1. 1 ( ) 2. 2. 2,,,,,,,,,,, A,, 2. 1. 2,,,,,, ;,, 1 i= 1 (D i) p Z,,, i Z = i= 1 (1) 1 (D i) p, Z, Z i i (i= 1,, n), D i, ;,, ( ) 2. 3 (Geo statistics), p,,, 20 50 H u sar 6, 60 M atheron 9,,, 10 2. 2,,, (in trin sic hypo thesis) 1, 11 15,,,,,,,,, 1, 7
, Χ(h) = 1, ; 2n 2n z (x i + h) 2 (9) i= 1,, (variogram ) K riging, ; K riging nugget (Cok riging ),, 2. 3. 1 K riging (1) K riging 1, 17, K riging K rige, N ugget M atheron 16 K riging K riging, > Η, z (x 0) = Κiz (x ) (3) i= 1 i, Η,, z (x i), x i 95% ; x 0 ; Κi, 1, i= 1 Κi = 1 (4) Κi, z (x 0), Ρ 2e Ρ 2e = i= 1 ΚiΧ(x i, x 0 ) + Υ (5) V ar Z (x ) - Z (x + h) = 2 62 15,, Η, ΚiΧ(x i, x ) i= 1 j + Υ= Χ(x j, x 0) Π j (6), Χ(x i, x j) z x i x j,, ( sem i2variance), Χ(x j, x 0) z x i x 0,,,, Υ,, 2. 3. 2 Cok riging (ESDA ) (2) (ESDA ) Cok riging ( k riging) K riging, K riging, ; Χ(h ) h,,, Cok riging ; (cro ss2variogram ), h ; 0 (7) (8), Cok riging E Z (x ) - Z (x + h) = 0 (7), h V ar Z (x ) - Z k (x ) = 2Χ k (h) (10), Z k (x ) Z (x ) k Cok riging, E{ Ε (x ) - Ε (x + h) }= 2Χ(h) (8), Χ(h),, ;
3 263, ;,, ; 2. 4. 4, Χ k (h) = 1 2n i= 2n z (x ) 1 i - z (x i + h) z k (x i) - z k (x i + h) (11) 16, 20,,, Cok riging, Cok riging ( ) K riging, 2. 4 ( ),,, 2. 5,,, 21,, ( ) 22,,,,,, 2. 4. 1,,, 2. 4. 2,,,, 2. 6 ;, ;,,,,,, ;,, (T IN ), GCM,, 23,, B 24, 26, 18 19 2. 4. 3 ( ), 2. 7 4,,, ( ) 27,, = + + + (12)
(2),,,,,,,,, 28 3 3. 1 (1),,,? ( 2) G IS?,,, (3) 1 H aining R.,,,, (4),,,,,,,,, 5, 3. 2 (1), G IS 7 G IS, in geography A A RCgIN FO K riging,,, ;,, 9,, ;, G IS 2 64 15,, (3) Spatial D ata A nalysis in the Social and Environ2 m ental Sciences M. Great B ritain Cam bridge U niversity P ress, 1990. 291 312. 2 Co llins F C. A comparison of spatial interpo lation techniques ODBC ( ) 11 D eutsch C V, Journel A G., G IS L ibrary and U ser s Guide M ( ) U niversity P ress, 1998. in temperature estim ation EBgOL. h ttp ggwww. ncgia. ucsb. edugconfgsan TA FE CD 2ROM gsf papersgco llins fredg co llins. htm l, 1999201213g1999210225. 3 W aters N M. U nit 402spatial interpo lation 1 EBgOL. h ttp ggwww. gisca. adelaide. edu. augkea gisrsgncgiagu40. htm l, 1999203212g1999210225. 4 W aters N M. U nit 412spatial interpo lation 2 EBgOL. h ttp ggwww. gisca. adelaide. edu. augkea gisrsgncgiagu41. htm l, 1999203212g1999210225. BungeW. T heo retical Geography M. L und L und Studies in Geography, 1966. 6 H usar R B, Falke S R. U ncertainty in the spatial interpo lation of PM 10 monito ring data in Southern Califo rnia EBgOL. http ggcap ita. w ustl. edugca P ITA gcap itarepo rtsgca Interpg Ca IN TERP. H TM L, 1997203203g1999210225. M ark D M. Som e p roblem s w ith the use of regression analysis. In Gaile G L. Spatial Statistics and M odels C. N etherlands D Reidel Publishing Company, 1984. 191 199. 8 A gterberg F P. T rend surface analysis A. In Gaile G L. Spatial Statistics and M odels C. N etherlands D Reidel Publish ing Company, 1984. JournelA G, H uijbregts Ch. 147 171. M.,,., 1982. 10. M., 1999. GSL IB, Geo statistical Softw are. N ew Yo rk O xfo rd
3 265 12 Goovaerts P. Geo statistics fo r N atural Resource Evaluation M. N ew Yo rk O xfo rd U niversity P ress, 1997. 13 O liver M A. Geo statistics, rare disease and the environm ent A. In F ischer M, Scho lten H J, U nw in D. Spatial A nalytical Perspectives on G IS C. London Taylo r and F rancis L td, 1996. 67 85. 14 Gunnink J L, Burrough P A. Interactive spatial analysis of so il attribute patterns using exp lo rato ry data analysis (EDA ) and G IS A. In F ischer M, Scho lten H J, U nw in D. Spatial A nalytical Perspectives on G IS C. London Taylo r and F rancis L td, 1996. 87 100. 15 R ip ley B D. Spatial Statistics M. N ew Yo rk W iley, 1981. 16 M atheron G. P rincip les of geo statistics J. Econom ic Geo logy, 1963, 58 1 246 1 266. 17 Bogaert P, M ahau P, Beckers F. The spatial interpo lation of agro2clim atic data, Cokriging softw are and source code user s m anual R. A grom eteo ro logy Series W o rking Paper, N um ber 12. FAO, Rom e, Italy, 1995. 18,. M. 28 C sillag F, Kert sz M, Kumm ert A., 1990. 19 H utchinson M F. smoo th ing Interpo lating m ean rainfall thin p late sp lines J. IN T J Geographical Info rm ation System s, 1995, 9 (4) 385 403. 20 ERDA S. ERDA S F ield Guild Z. ERDA S Inc, 1994. 21 Fo theringham A S, Charlton M, B runsdon C. The geography of param eter space an investigation of spatial non2stationarity J. IN T J Geographical Info rm ation System s, 1996, 10 (5) 605 627. 22 Journel A G. M odeling uncertainty and spatial dependence Stochastic im aging J. IN T J Geographical Info rm ation System s, 1996, 10 (5) 517 522. 23 T renberth I, Kevin E. C lim ate System M odelling M. Cam bridge Cam bridge U niversity P ress, 1992. 24. J., 1988, 46 (3) 319 326. 25,,. M., 1996. 26. J., 1985, 40 (4) 323 332. 27,, F ischerm M. J., 1996, 12 ( ) 78 88. Samp ling and m app ing of heterogeneous surfaces m ulti2reso lution tiling adjusted to spatial variability J. IN T J Geographical Info rm ation System s, 1996, 10 (7) 851 875. COM PAR ISON OF SPATIAL INTERPOLATION M ETHOD S L I X in, CH EN G Guodong, LU L ing (Cold and A rid R eg ions E nv ironm en t and E ng ineering R esea rch Institu te, Ch inese A cad em y of S ciences, L anz hou 730000, Ch ina) Abstract Sp a t ia l in terpo la t ion can be cla ssified in acco rdance w ith their ba sic hypo theses and m athem atical natu res as geom etric m ethod, statistical m ethod, geo statistical m ethod, stochastic sim u lation m ethod, physical m odel sim u lation m ethod and com b ined m ethod. T he app lication areas, special algo rithm, advan tages and disadvan tages of each in terpo lation m ethod are in troduced and com pared in the papeṙ T he com parison show s that there is no ab so lu tely op tim al spatial in terpo lation m ethod; there is on ly relatively op tim al in terpo lation m ethod in special situation. H ence, the best spatial in terpo lation m ethod shou ld be selected in acco rdance w ith the qualitative analysis of the data, exp lo rato ry spatial data analysis and repeated experim en tṡ In addition, the resu lt of spatial in terpo lation shou ld be strictly exam ined fo r its validity. D evelopm en t of general softw are fo r spatial in terpo lation and strengthen ing the basic theo ry research are key issues in the fu tu re. Key words Spatial in terpo lation; Exp lo rato ry spatial data analysis; Geograph ic Info rm ation System 1