12 1 2010 2 JOURNAL OF GEO2INFORMATION SC IENCE Vol112, No11 Feb1, 2010, 3, (, 100101) :,,,,,, CBERS IRS - P5, ;,, 20m 1km ;,, CBERS, 200m, 76% ; P5, 100m, 84% : ; ; ; 1 [ 4-5 ], GIS, [ 6-13 ], ( 1995 5km, GPW ) ( [ 1 ] 2000 1km ),, 1km 5km [ 14-19 ],,, ( ),,, [ 2 ],,,,, CBERS P5,,,, [ 3 ], ; 20 90,, : 2009-08 - 18; : 2009-11 - 04. : 863 (2006AA120105-2) : (1984 - ),,, GIS E2mail: yej@ lreis1ac1cn 3 : (1965 - ),,,, GIS E2mail: yangxh@ lreis1ac1cn
1 : 41,, 2 (1) P5, 7,,, 6 2007, 1105km 2, 73km 2 72, 421,,,,, (2) 3 : : CBERS - 02 CCD 1, 1915m ; IRS - P5 6, 215m 2007 : 2007 (3) 1915m CBERS 215m,, 2007 ( 1), 1 10, 6, 1 CBERS( a) P5 ( b) 2007 Fig11 Landuse of Yiwu in 2007 extracted by CBERS ( a) and P5 ( b), (4) CBERS P5,, 2007,,, : P i n = a j j =1 s ij, P i i ; a j
42 2010 j ( /km 2 ) ; j ( km 2 ) ; s ij i n = 3, 3,,, a j,,,,,,,, a j, a ij :, a ij = P i a P j i a ij i j ( P /km 2 ), P i i, P i i a ij,,,, ( 2), 3 311,,,,,,,, :, CBERS EP = ( pop j - pop t ) 1km 500m 200m 100m pop t 100% ; P5 200m 100m 50m 20m EP, pop j, pop t 2 CBERS - 200m ( a) P5-100m ( b) Fig12 Population distribution of Yiwu based on CBERS - 200m ( a) and P5-100m ( b)
1 : 43 312 CBERS CBERS 1km 500m 200m 100m,, ( 1), 1km 14131% 100m 1124%, 1km 500m 56% ; 500m 200m 76% ; 200m 100m 19%, 200m, 1 ( CBERS) Tab11 The error between popula tion by gr id and sta tistica l popula tion ( CBERS) 1km ( % ) 500m ( % ) 200m ( % ) 100m ( % ) 116 591 96 429-17129 124 744 6199 118 640 1176 117 373 0167 66 953 51 191-23154 66 993 0106 67 620 1100 66 394-0184 46 702 49 310 5158 42 668-8164 48 328 3148 47 289 1126 44 424 45 662 2179 39 537-11100 45 487 2139 44 188-0153 51 001 74 626 46132 50 924-0115 52 136 2123 51 411 0180 79 386 75 374-5105 72 892-8118 79 216-0121 79 113-0134 39 165 39 027-0135 41 225 5126 39 743 1147 41 527 6103 52 832 48 906-7143 51 048-3138 52 694-0126 52 863 0106 39 753 53 684 35104 47 996 20173 39 778 0106 39 553-0150 49 833 53 983 8133 48 317-3104 48 092-3149 49 781-0111 48 305 51 564 6175 50 453 4145 48 174-0127 48 890 1121 38 492 47 940 24155 40 577 5142 38 773 0173 38 340-0139 42 848 40 263-6103 40 595-5126 41 751-2156 41 423-3133 ( 3), 0, 200m 1km 50% 100m 5%, 1km, 0 ; 500m, 0 ; 200m 100m 313 P5 P5 200m 100m 50m 20m,, 0,, 200m, 0 ( 4), ( 2), 200m 2178% 20m 1163%, 1km 10% 200m 100m 40%; 100m 50m 100m 5%, 1km 500m 1%; 50m 20m 1%, 1km, 100m, 500m ; 200m 100m, < 5%, 200m,, CBERS ( 5), 0 200m 8% 20m 4%, 200m, 0 ; 100m 50m
44 2010 20m, 0 100m, 0 ( 6), 200m 5% 20m 5%, 200m, ; 100m 50m 20m, < 5%,, 100m,, P5, 100m 314,, 30, CBERS 200m P5 100m,, CBERS - 200m P5-100m ( 3) : CBERS - 200m 24%, 76% ; P5-100m 16%, 84% 2 ( P5) Tab12 The error between popula tion by gr id and sta tistica l popula tion ( P5) 200m ( % ) 100m ( % ) 50m ( % ) 20m ( % ) 116 591 124 445 6174 118 463 1161 118 375 1153 118 623 1172 66 953 67 880 1138 67 975 1153 67 903 1142 67 842 1129 46 702 48 251 3132 48 033 2185 47 775 2130 47 893 2142 44 424 45 779 3105 44 784 0181 45 098 1152 45 148 1163 51 001 52 691 3131 52 384 2171 52 220 2139 52 357 2152 79 386 79 044-0143 77 966-1179 78 144-1156 78 242-1144 39 165 41 164 5110 39 551 0199 39 591 1109 39 570 1103 52 832 51 813-1193 53 179 0166 52 471-0168 52 627-0136 39 753 38 984-1194 39 064-1173 38 607-2188 38 794-2131 49 833 47 899-3188 51 077 2150 50 175 0169 50 236 0169 48 305 49 324 2111 48 663 0174 48 750 0192 48 834 1105 38 492 39 605 2189 38 932 1114 39 441 2147 39 409 2135 42 848 42 842-0101 41 764-2153 42 020-1193 41 830-2138
1 : 45 3 CBERS - 200m P5-100m Tab13 The accuracy of CBERS - 200m and P5-100m data CBERS - 200 ( % ) P5-100 ( % ) 2 570 2 139-16178 3 317 29105 1 222 1 195-2118 1 243 1174 1 216 1 685 38158 1 500 23134 1 132 807-28171 817-27183 1 039 626-39175 892-14116 926 569-38154 759-17198 894 743-16189 696-22117 832 487-41148 785-5162 813 752-7145 793-2146 789 560-29102 605-23137 784 955 21183 844 7167 783 585-25130 743-5117 687 402-41153 849 23163 683 752 10107 531-22132 655 817 24173 759 15188 571 385-32166 481-15177 566 493-12186 497-12117 557 403-27165 584 4184 541 357-33196 421-22111 4 CBERS IRS - P5,, 20m 1km ;, : (1) : CBERS, 200m; IRS - P5, 100m (2) : CBERS, 76% ; IRS - P5, 84% 523 410-21161 489-6151 444 474 6181 379-14158 432 594 37148 341-21101 401 259-35135 498 24119 300 199-33180 217-27171 247 212-14124 194-21142 237 209-11164 215-9124 225 260 15156 202-10108 221 215-2172 245 10166 200 159-20150 234 16185 200 174-13106 216 7178, : [ 1 ]. [M ]. :, 1983. [ 2 ],,,. RS GIS [ J ]., 2002, 17 (5) : 734-738.
46 2010 [ 3 ],,. [ J ]., 2006, 8 (2). [ 4 ] Tobler W, Deichmann U, Gottsegen J,Maloy K. The Global Demography Project[ R ]. Technical Report TR - 95-6. National Center for Geographic Information and Analysis, Department of Geography, University of California: Santa Barabara, 1995. [ 5 ] ToblerW, Deichmann U, Gottsegen J,Maloy K. World Popu2 lation in a Grid of Spherical Quadrilaterals[ J ]. Int. J. Pop2 ul. Geogr. 1997, 3: 203-225. [ 6 ]Balk D, B rickman M, Anderson B, Pozzi F, Yetman G. Esti2 mates of Future Global Population D istribution to 2015 [ EB ]. Palisades, NY: C IESIN, Columbia University, 2005. Available online: http: / / sedac. ciesin. columbia. edu / gpw / documentation. jsp#. [ 7 ]Balk D, Pozzi F, Yetman G, Deichmann U, Nelson A. The D istribution of Peop le and the D imension of Place: Method2 ologies to Imp rove the Global Estimation ofuurban Extents [ EB ]. D raft version. Palisades, NY: C IESIN, Columbia U2 niversity, 2004. Available online: http: / / beta. sedac. cie2 sin. columbia. edu / gpw / documentation. jsp. [ 8 ]Balk D, Yetman G. The Global D istribution of Population: Evaluating the Gains in Resolution Refinement, Documenta2 tion for GPW v3 [ EB ]. Palisades, NY: C IESIN, Columbia University, Available online, 2004. http: / / beta. sedac. cie2 sin. columbia. edu / gpw / documentation. jsp. [ 9 ]C IESIN (Center for International Earth Science Information Network), Columbia University, Centro Internacional de Ag2 ricultura Trop ical (C IAT). Gridded Population of the World Version 3 ( GPW v3 ) : Population Grids [ EB ]. Palisades, NY: Socioeconom ic Data and App lications Center ( SE2 DAC), Columbia University, 2005. Available online: ht2 tp: / / sedac. ciesin. columbia. edu /gpw. [ 10 ] Salvatore M, Pozzi F, A taman E, Huddleston B, B loise M. Mapp ing Global U rban and Rural Population D istributions [M ]. Rome: FAO, 2005. [ 11 ]Balk D L, Deichmann U, Yetman G, Pozzi F, Hay S I. De2 term ining Global Population D istribution: Methods, App li2 cations and Data[ J ]. Adv. Parasit. 2006, 62: 119-156. [ 12 ]Dobson J E, B right E A, Coleman, P R, Durfee RC,Worley B A. LandScan: A Global Population Database for Estima2 ting Populations at R isk[ J ]. Photogramm. Eng. Rem. Sens. 2000, 66: 849-857. [ 13 ]Bhaduri B, B right E, Coleman P, Dobson J. LandScan: Lo2 cating Peop le Is W hat Matters [ J ]. Geoinformatics 2002, 5: 34-37. [ 14 ],,,. [ J ]., 2002, 57 ( ) : 70-75. [ 15 ],. GIS [ J ]., 2003, 58 (1) : 25-33. [ 16 ],. [ J ]., 2004, 30 (3) : 91-93. [ 17 ],. RS GIS [ J ]., 2006, 25 (1). [ 18 ],. [ J ]., 2007, 16 (2) : 265-268. [ 19 ] Yang Xiaohuan, Huang Yaohuan, Dong Pinliang, J iang Dong, L iu Honghui. An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies [ J ]. Sensors, 2009, 9 (3) : 1128-1140. The Gr id Sca le Effect Ana lysis on Town leveled Popula tion Sta tistica l Da ta Spa tia liza tion YE J ing, YANG Xiaohuan, J IANG Dong ( Institute of Geographical Sciences & N atural Resources Research, CAS, B eijing 100101, China) Abstract: The gird scale effect is one of the basic issues on population data spatialization. For the demand of all kinds of spatial population data in the fields of resources and environment and global change models, a lot of resear2 ches have been done based on remote sensing and GIS technology both at home and abroad. But the models used are mostly on global ( such as GPW, 1995, 5km ), national ( such as national population database, 2000, 1km ) or p rovincial scale, and their resolution ranges from 1km to several kilometers. In recent years, there are studies on lo2 cal distribution of population by using of high2resolution im ages. For all the researches, both the method of data
1 : 47 source selection according to specific app lication and the analysis on p roduction suitability are deficient. So, many uncertainties exit in population data app lication, especially in county level and secondary or tertiary rivers. To solve the p roblem s mentioned above, in this article we mainly p ropose the method of scale effect analysis on population data spatialization. Taking Yiwu City, Zhejiang Province as the study area, using CBERS and IRS2P5 images we ex2 tract land use inform ation and build a spatialization model to the statistical population data of rural towns, then get a set of population data gird ranging from 20m to 1km. Moreover, by comparing population data by grid and statisti2 cal population data in rural towns, the grid scale effect analysis is m ade; by comparing population data by grid and statistical population data in villages, the remote sensing data source scale effect analysis is made. The result of scale effect analysis shows: by using CBERS as data source, the suitable grid scale of p roduction is 200m and its p recision is 76% ; by using P5 as a data source, the suitable grid scale of p roduction is 100m and its p recision is 84%. The method of scale effect analysis in spatial distribution of statistical population is argued in this paper and it can p rovide basic technical solutions and examp les to op timum scale selection in the p rocess of humanistic factors ( such as population) spatialization. Key words: population; spatialization; grid; scale effect