===================================================================== =========== DATA BASE: DB1016 (8-1996) TITLE: Global Population Distribution (1990), Terrestrial Area and Country Name Information on a One by One Degree Grid Cell Basis. CONTRIBUTOR: Yi-Fan Li Canadian Global Emissions Inventory Centre Atmospheric Environment Service Environment Canada 4905 Dufferin Street Downsview, Ontario M3H5T4 Canada Email: yfli@dow.on.doe.CA COMPILED BY: A. L. Brenkert Carbon Dioxide Information Analysis Center (CDIAC) Oak Ridge National Laboratory Oak Ridge, Tennessee 37830-6335 Contact R. M. Cushman (email cushmanrm@ornl.gov) ===================================================================== =========== DOCUMENTATION Introduction: This data base contains gridded (one degree by one degree) information on the world-wide distribution of the population for 1990 and country-specific information on the percentage of the country's population present in each grid cell (Li, 1996a). Secondly, the data base contains the percentage of a country's total area in a grid cell and the country's percentage of the grid cell that is terrestrial (Li, 1996b). Li (1996b) also developed an indicator signifying how many countries are represented in a grid cell and if a grid cell is part of the sea; this indicator is only relevant for the land, countries, and sea-partitioning information of the grid cell. Thirdly, the data base includes the latitude and longitude coordinates of each grid cell; a grid code number, which is a translation of the latitude/longitude value and is used in the Global Emission Inventory Activity (GEIA) data bases; the country or region's name; and the United Nations three-digit country code that represents that name. For the gridded population estimates, Li used FAO and Guinness national population data, and the Rand McNally World Atlas (1991) for approximately 6,000 cities with populations greater than 50,000 inhabitants. These data were updated to 1990 values when necessary, with available census data. For the rural population allocation, global rural population distribution factors were developed, based on national population data, data on approximately 90,000 cities and towns and the assumption that rural population is proportional to the number of cities and towns within each cell for each country. For each grid cell, Li (1996b) generated the percentage of a country's total land area contained in the cell, and the country-specific percentage of the grid cell that is terrestrial. This differs from a previously published Goddard Institute of Space Studies (NASA-GISS) dataset (Lerner, 1988) in that the NASA-GISS dataset took a "one cell, one country" approach, i.e., each grid cell was only assigned to one country, even when this cell was actually occupied by two or more countries. The grid code indicator Li (1996b) developed to indicate how many countries are represented in a grid cell and if sea is present only holds for the values of the percentage of a country's area and the land/sea partitioning of the grid cell. The grid code indicator does not necessarily represent the number of countries with population values. There are instances (i.e., 117 grid cells listed below) where populations are given without companion area and/or land/sea partitioning information (the indicator is then set to "0") and there are instances where the indicator is different from the number of countries in the grid cell with population values. Grid code indicators are as follows: "0": All covered by sea, or no land for the population "1": Entirely contained in one country "2": Shared by two countries "3": Shared by three countries "4": Shared by four countries "10": Shared by one country and sea "20": Shared by two countries and sea "30": Shared by three countries and sea "40": Shared by four countries and sea Li (1996a and 1996b) included a grid code number for each grid cell. This grid code number is also used in the GEIA emission data bases and is a translation of the latitude and longitude center-point around which the grid cell is located. The grid code number equals (j*1000)+i, where j is a row number starting at 1 for the grid cell between 90 and 89 degrees Southern latitude (j equals 180 for the grid cell between 89 and 90 degrees North) and i is a column number starting at 1 for the grid cell between 180 and 179 degrees West (i equals 360 for the grid cell between 179 and 180 degrees East). With other words latitude equals (j-91)+0.5 and longitude equals (i-181)+0.5. Grid cell surface areas were calculated by CDIAC based on the GISS' latitude-dependent grid cell calculations. Where the country-specific terrestrial percentage of the grid cell was known the calculated area was multiplied with this percentage to obtain the actual terrestrial area for each country in that grid cell; where this value was not known, 100% of the grid cell area was listed in a separate column. The files: DB1016 consists of this db1016.doc text documentation file (db1016.txt is identical), one data file (merged.dat), and SAS (merged.sas) and FORTRAN (merged.f) data retrieval codes, all in ASCII text format, plus an image file of the global gridded population distribution map (popmap2.tiff). The file names and sizes are as follows: db1016.doc (42 kbytes) merged.dat (3.2 Mbytes; 24511 records; 12 variables) merged.sas (4 kbytes) merged.f (3 kbytes) popmap2.tiff (28 kbytes) ===================================================================== =========== The merged.dat file contains 12 variables: ---------- 1) geiaid = the GEIA-code-id, which equals (j*1000)+i, where j is a row number starting at 1 for the grid cell between 90 and 89 degrees South (j equals 180 for the grid cell between 89 and 90 degrees North) and i is a column number starting at 1 for the grid cell between 180 and 179 degrees West (i equals 360 for the grid cell between 179 and 180 degrees East). 2) lat(itude) = (jgrid-91)+0.5, which means a range between -89.5 degrees South and +89.5 degrees North 3) long(itude) = (igrid-181)+0.5, which means a range between -179.5 degrees West and +179.5 degrees East 4) pop = 1990 population, represented as the number of people per one degree by one degree grid cell from a specific country NOTE: more then one country can be represented in a gridcell; therefore, multiple entries for the same gridcell will occur. 5) perc = country-specific percentage of a country's population in a grid cell 6) ratec = country-specific percentage of the total area of a country contained in a grid cell. 7) rateg = country-specific percentage of the grid cell that is land 8) uc = cover-id, an indicator signifying how many countries are represented in a grid cell and if part of the grid cell is sea: "0": All covered by sea, or no land for the population "1": Entirely contained in one country "2": Shared by two countries "3": Shared by three countries "4": Shared by four countries "10": Shared by one country and sea "20": Shared by two countries and sea "30": Shared by three countries and sea "40": Shared by four countries and sea 9) unid = United Nations three digit country code in the year 1990 10) name = country/region name in the year 1990 11) acarea (units=m2) = actual country-specific terrestrial area of the grid cell, which was obtained by multiplying the country-specific terrestrial percentage of the grid cell (rateg) with the calculated total surface area of the grid cell 12) area (units=m2) = total grid cell area (only represented when acarea could not be calculated due to a lack of a rateg value) ===================================================================== =========== The following SAS statements from the program merged.sas may be used to read merged.dat: ---------- ---------- *; data new; infile "merged.dat"; input geiaid 6. @9 lat 6.1 @17 long 6.1 @25 pop 10.1 @37 perc 8.4 @46 ratec 8.4 @56 rateg 8.4 @66 uc 4. @72 unid $char3. @77 name $char20. @99 acarea 18.2 @119 area 18.2; * where: geiaid lat long pop perc ratec rateg uc unid name acarea area are described above ; ===================================================================== =========== The FORTRAN code merged.f can be used to read and check merged.dat: -------- ---------- c note double precision to avoid round-off errors real*8 pop,lat,long,area,acarea real*8 spop,sarea character*6 geiaid character*3 unid character*20 name c prepare for summations: spop=0.d0 sperc=0.d0 sratec=0.d0 srateg=0.d0 sarea=0.d0 do 100 i=1,24511 c open and read file: open(10,file= 'merged.dat',status='old') read(10,10,end=911) & geiaid,lat,long,pop,perc,ratec,rateg,uc,unid,name,acarea,area c where: c geiaid,lat,long,pop,perc,ratec,rateg,uc,unid,name,acarea,area c are described above 10 format(a6,2x,f6.1,2x,f6.1,2x,e10.1,2x,f8.4,x,f8.4,2x,f8.4,4x, &i2,2x,a3,2x,a20,2x,f18.2,2x,f18.2) c if grid cell area is zero, use latitude-based grid cell area if (acarea.eq.0.0d0) acarea=area c sum: spop=spop+pop sperc=sperc+perc sratec=sratec+ratec srateg=srateg+rateg sarea=sarea+acarea 100 continue c 911 continue c write global summations to screen: write (*,*) 'spop=',spop write (*,*) 'sperc=',sperc write (*,*) 'sratec=',sratec write (*,*) 'srateg=',srateg write (*,*) 'sarea=',sarea c stop end ===================================================================== ========== The following SAS statements provided the output listed below that can be checked for data transport evaluation: *; * Use next statement (acarea= ) with caution because gridcells may have more than one country, but not necessarily more than one rateg; * Surface areas can therefore be accounted for more than once); if acarea=0.d0 then acarea=area; run; ; proc sort; by name; proc means noprint; by name; var pop perc ratec acarea ; output out=sums sum=spop sperc sratec aream2; data sums; set sums; file 'out'; put @4 unid $char3. @8 name $char20. @29 _freq_ 4. @33 spop 10. @44 sperc 8.4 @53 sratec 8.4 @62 aream2 16.; run; *; ===================================================================== ========== Summation results by country from executing SAS code merged.sas (reading merged.dat): ---------- ---------- UN-id NAME _FREQ_ SPOP SPERC SRATEC AREAM2 (population) (area in m2) 004 Afghanistan 92 16556000 99.9977 100.0070 648326447389 008 Albania 8 3250001 99.9970 99.9969 26353798812 012 Algeria 251 24960006 99.9952 99.9854 2334537881640 020 Andorra 1 55300 100.0000 0.0000 9142732445 024 Angola 131 9194019 99.9985 100.3350 1253498003565 660 Anguilla 2 6900 100.0000 100.0000 84329461 028 Antigua-and-Barbuda 1 65000 100.0000 100.0000 1542515758 032 Argentina 345 32321997 99.9900 99.9811 2786249651030 051 Armenia 15 3373239 100.0000 100.0000 70396956843 533 Aruba 3 61000 100.0000 100.0000 12198815812 036 Australia 790 17065026 99.9687 99.9566 7733403059486 040 Austria 22 7712003 100.0078 100.0076 82544667852 031 Azerbaijan 21 7138480 99.9970 100.0055 127811279908 044 Bahamas 13 255000 100.0038 100.0008 5315676282 048 Bahrain 2 503000 100.0010 100.0000 68255905 050 Bangladesh 27 113684002 99.9960 100.0002 130089498705 052 Barbados 2 257000 100.0000 100.0000 14036310410 112 Belarus 46 10197931 99.9961 99.9994 223882344309 056 Belgium 12 9934999 100.0000 99.9982 30246295989 084 Belize 7 189000 100.0040 99.9894 20512553456 204 Benin 18 4621998 100.0004 99.9992 118501521889 060 Bermuda 1 61000 100.0000 100.0000 523866320 064 Bhutan 13 1539000 99.9920 99.9963 35804122389 068 Bolivia 122 7171003 99.9974 99.9971 1097571180232 072 Botswana 64 1238001 99.9946 99.9975 581102514750 076 Brazil 799 149041985 99.9885 99.9685 8526274538524 096 Brunei 2 257000 100.0000 100.0013 4621101967 100 Bulgaria 22 8991000 100.0080 99.9976 112462382498 854 Burkina 38 8993000 99.9972 100.0034 289269582834 108 Burundi 6 5492001 100.0040 100.0094 27602919574 116 Cambodia 27 8336002 100.0006 100.0008 184100090752 120 Cameroon 61 11523998 99.9955 100.0048 474234716433 124 Canada 2086 26646873 99.9642 99.8811 9621128182462 C04 Canary-Islands 4 0 0.0000 99.9970 1677988531 132 Cape-Verde 2 363000 100.0000 0.0000 17415502497 136 Cayman-Islands 3 25500 100.0000 100.0000 11802774379 140 Central-African-Repu 70 3007998 99.9946 100.0000 622011206695 148 Chad 131 5552996 100.0009 99.9924 1278547680340 152 Chile 135 13173003 99.9953 99.9945 697514783703 156 China 10751130311683 99.9673 99.9378 9365956413209 170 Colombia 130 32299987 99.9952 99.9899 1155346373850 174 Comoros 1 543000 100.0000 100.0000 9268469817 178 Congo 47 2228998 99.9982 99.9992 348111107242 184 Cook-Islands 5 0 0.0000 99.9900 610432395 188 Costa-Rica 11 3034999 100.0000 100.0066 52677707302 192 Cuba 27 10608001 100.0043 99.9991 99548875570 196 Cyprus 5 702000 100.0010 100.0030 8562618658 200 Czechoslovakia 31 15661002 99.9978 99.9989 124111651854 208 Denmark 16 5139999 100.0052 100.0030 32921019150 262 Djibouti 5 440001 100.0000 100.0050 19626735928 212 Dominica 1 72000 100.0000 100.0000 2877665447 214 Dominican-Republic 8 7170000 100.0110 99.9980 48720595910 218 Ecuador 34 10547002 100.0014 100.0002 252611307463 818 Egypt 117 52425996 99.9912 99.9924 997514752197 222 El-Salvador 6 5172000 99.9970 100.0047 18092762273 226 Equatorial-Guinea 7 351999 99.9990 99.9970 39235917092 233 Estonia 18 1580040 99.9964 99.9891 74340561210 230 Ethiopia 130 49830992 99.9930 99.9939 1261396569236 238 Falkland-Islands 7 2000 100.0000 100.0070 8692030890 234 Faroe-Islands 1 48000 100.0000 100.0000 738461575 242 Fiji 7 731000 100.0060 99.9997 12034358585 246 Finland 88 4986000 99.9929 99.9969 320810281049 250 France 90 56735007 99.9952 99.9991 547610473504 254 French-Guiana 14 98001 99.9994 99.9968 81513006725 258 French-Polynesia 2 198000 100.0000 0.0000 23795232411 266 Gabon 36 1159001 100.0023 99.9974 259312286326 270 Gambia 4 861000 99.9980 100.0020 6987444726 B02 Gaza-Strip 1 624000 100.0000 0.0000 10593675506 268 Georgia 20 5463671 100.0002 99.9993 83723000577 276 Germany 64 79364991 100.0020 100.0014 353467995395 288 Ghana 34 15020001 100.0005 99.9996 240219496678 292 Gibraltar 1 31047 100.0000 0.0000 9980030631 300 Greece 39 10089000 100.0061 99.9987 138961312561 304 Greenland 770 55559 99.9999 99.9592 2020349811845 308 Grenada 1 91000 100.0000 99.8900 327312446 312 Guadeloupe 1 390000 100.0000 100.0000 6442052351 320 Guatemala 18 9197001 99.9978 100.0069 112749863957 B01 Guernsey 1 57000 100.0000 0.0000 8038578773 324 Guinea 35 5755003 99.9981 100.0010 248779392432 624 Guinea-Bissau 8 964000 100.0030 99.9875 28152339153 328 Guyana 30 796002 99.9990 99.9989 210794610633 332 Haiti 7 6486000 100.0050 100.0070 26487637053 340 Honduras 18 5137998 100.0092 99.9922 110396270285 344 Hong-Kong 2 5705000 100.0000 100.0000 80430101 348 Hungary 20 10361000 99.9973 99.9942 93699123853 B10 ISRAELI-OCCUPIED-TER 1 1584700 100.0000 0.0000 10593675506 352 Iceland 40 254994 99.9973 99.9973 96167059191 356 India 356 846190994 99.9841 99.9802 3202673395900 360 Indonesia 316 184282991 99.9871 99.9880 1798754583788 364 Iran 195 58266982 99.9868 99.9891 1636269432731 368 Iraq 61 18079998 99.9955 100.0039 440954070328 536 Iraq-Saudi-Arabia-Ne 5 0 0.0000 100.0000 6937835450 372 Ireland 20 3503001 99.9910 100.0057 67432248473 376 Israel 9 4644999 99.9900 99.9894 27889751512 380 Italy 70 57661000 99.9963 100.0003 309621278283 384 Ivory-Coast 40 11980002 99.9955 99.9964 329754059356 388 Jamaica 5 2402999 100.0080 100.0070 8039846936 C07 Jan-Mayen 3 0 0.0000 100.0030 278815069 392 Japan 81 123536998 100.0011 99.9957 393977399739 B03 Jersey 1 84000 100.0000 0.0000 8038578773 400 Jordan 17 3282000 100.0050 99.9975 91005672215 398 Kazakhstan 404 16744198 99.9850 99.9786 2720463273154 404 Kenya 67 23584999 99.9980 99.9983 588192163221 296 Kiribati 2 71000 100.0000 0.0000 24879793263 414 Kuwait 6 2143000 100.0020 99.9979 15175926772 417 Kyrgyzstan 42 4412880 100.0035 99.9964 220934628106 418 Laos 38 4202002 100.0046 99.9999 234935382137 428 Latvia 21 2682306 99.9957 100.0058 61480972019 422 Lebanon 4 2740000 99.9980 100.0000 9591109043 426 Lesotho 8 1747001 100.0000 99.9935 25847408994 430 Liberia 17 2574999 100.0004 100.0023 92611537969 434 Libya 173 4545007 99.9944 99.9918 1626428630547 438 Liechtenstein 1 28452 100.0000 0.0000 8367180718 440 Lithuania 21 3727035 99.9997 99.9962 76755509229 442 Luxembourg 3 414000 100.0000 100.0030 1562562481 446 Macau 1 463000 100.0000 100.0000 574500718 450 Madagascar 72 12009996 100.0030 99.9983 598271502510 454 Malawi 21 9581999 100.0080 100.0041 118012188628 458 Malaysia 52 17891003 100.0033 99.9986 323699875512 462 Maldives 3 0 0.0000 100.0020 267974320 466 Mali 138 9214001 99.9943 99.9929 1262032662122 470 Malta 1 354000 100.0000 100.0000 505453192 584 Marshall-Islands 2 0 0.0000 98.0200 20046849 474 Martinique 2 361000 100.0000 100.0000 90293206 478 Mauritania 114 2024001 100.0000 99.9975 1047853706831 480 Mauritius 1 1075000 100.0000 100.0000 5475969670 175 Mayotte 1 94300 100.0000 0.0000 12149682496 484 Mexico 243 84486010 99.9863 99.9876 1961438991750 583 Micronesia 3 111000 100.0000 100.0000 12191037421 498 Moldova 14 4366792 100.0015 99.9968 51386665136 492 Monaco 1 29800 100.0000 0.0000 8993067492 496 Mongolia 234 2189973 99.9951 99.9863 1570335971731 500 Montserrat 1 12400 100.0000 0.0000 11929726577 504 Morocco 60 25061003 99.9951 100.0009 406484123034 508 Mozambique 101 14200010 99.9999 99.9954 794368287725 104 Myanmar 91 41825003 99.9956 99.9882 663987165831 516 Namibia 94 1438997 99.9968 100.0021 830558527412 524 Nepal 28 19570999 99.9890 99.9971 145385733193 528 Netherlands 14 14944000 99.9920 99.9985 35799793116 530 Netherlands-Antilles 2 175000 100.0000 0.0000 24299364993 540 New-Caledonia 9 168001 100.0030 99.9759 11300912335 554 New-Zealand 60 3329998 99.9998 100.0015 250831264370 558 Nicaragua 20 3675999 100.0033 100.0004 134864669495 562 Niger 130 7730998 99.9930 99.9921 1216352505466 566 Nigeria 100 108541998 99.9937 100.0006 907333031295 570 Niue 2 0 0.0000 99.9970 94712813 408 North-Korea 27 21771002 99.9997 99.9990 124780136255 580 Northern-Mariana-Isl 2 0 0.0000 100.0000 281787475 578 Norway 97 4242011 99.9991 100.0022 275736943275 C16 Ocean 4547 0 0.0000 0.0000 20069020169133 512 Oman 45 1524000 99.9980 99.9974 316360397119 586 Pakistan 115 118121999 99.9943 99.9998 876558870725 585 Palau-Islands 2 15100 100.0000 100.0020 428134489 591 Panama 15 2418001 100.0006 99.9846 68001376619 598 Papua-New-Guinea 70 3875001 99.9945 99.9984 439818161920 600 Paraguay 53 4277001 100.0020 99.9971 394775350271 604 Peru 145 21549997 99.9946 99.9920 1309622714351 608 Philippines 69 62437000 99.9940 99.9993 289005362819 612 Pitcairn 1 61 100.0000 0.0000 11222061901 616 Poland 57 38118994 99.9974 99.9960 316140860098 620 Portugal 16 9868001 99.9990 99.9961 87814705727 630 Puerto-Rico 4 3530000 100.0080 100.0046 5376876525 634 Qatar 5 427001 100.0030 100.0001 8084265647 638 Reunion 2 604000 100.0000 100.0000 1110031097 642 Romania 44 23207000 100.0014 99.9978 235040339907 643 Russia 3307 148546837 99.8969 99.8300 16717030594318 646 Rwanda 5 7027001 100.0000 100.0084 22300274531 674 San-Marino 1 0 0.0000 100.0000 58275077 678 Sao-Tome 2 119000 100.0000 0.0000 24891294473 682 Saudi-Arabia 211 14870010 99.9960 99.9909 1966499479528 686 Senegal 28 7326997 100.0078 99.9981 199009840834 690 Seychelles 1 71000 100.0000 100.0000 5832343134 694 Sierra-Leone 12 4150998 100.0000 100.0050 69926484507 702 Singapore 1 2710000 100.0000 100.0000 210921226 090 Solomon-Islands 16 319999 100.0062 99.9476 20285482949 706 Somalia 80 8677003 100.0006 100.0013 641595390506 710 South-Africa 153 37958996 99.9918 99.9906 1236849449864 410 South-Korea 19 43377000 99.9951 99.9969 98605012926 724 Spain 82 38958995 100.0019 99.9990 526201101454 144 Sri-Lanka 12 17217000 99.9965 99.9970 58093702783 B07 St.-Helena 1 7100 100.0000 0.0000 11990272694 662 St.-Lucia 3 133000 100.0000 99.9990 334734107 C11 St.-Martin 1 0 0.0000 100.0000 19395304 670 St.-Vincent 1 107000 100.0000 100.0000 162385591 736 Sudan 246 25202978 99.9918 99.9863 2531282128561 740 Suriname 17 422001 99.9950 99.9992 140194517339 744 Svalbard 53 0 0.0000 99.9996 51355414255 748 Swaziland 6 751000 100.0000 100.0017 14815506188 752 Sweden 106 8565999 99.9930 99.9912 440854426924 756 Switzerland 12 6712000 99.9980 100.0049 38923108965 760 Syria 33 12355001 100.0028 99.9979 186638265769 762 Tadzhikistan 37 5359952 100.0028 100.0004 192483164156 158 Taiwan 8 20352966 99.9983 99.9890 43537446481 834 Tanzania 97 25993015 99.9954 99.9997 947365126126 764 Thailand 74 54676995 99.9990 99.9995 515850783988 768 Togo 13 3530998 99.9950 100.0097 56801685292 776 Tonga 1 96000 100.0000 0.0000 11572289096 780 Trinidad 2 1236000 99.9980 99.9984 4230204350 788 Tunisia 28 8057002 99.9972 99.9981 151802686189 792 Turkey 109 55991002 99.9974 100.0051 790361883447 795 Turkmenistan 76 3714270 99.9953 99.9978 491481022625 796 Turks-And-Caicos-Isl 2 12350 100.0000 100.0000 125131162 800 Uganda 33 17560001 99.9991 99.9989 241239530394 804 Ukraine 107 51938119 99.9959 99.9943 640260375973 784 United-Arab-Emirates 17 1588998 100.0040 100.0030 80258235573 826 United-Kingdom 58 57620998 99.9948 99.9993 221676513034 840 United-States 1310 248769679 99.9478 99.9346 9481277291224 858 Uruguay 26 3093999 99.9936 100.0029 171341062154 860 Uzbekistan 80 20702528 99.9988 100.0033 533883531606 548 Vanuatu 6 149999 99.9960 100.0020 17156791740 862 Venezuela 106 19320998 99.9931 100.0031 914849446060 704 Vietnam 57 66687999 99.9990 99.9985 317956175424 092 Virgin-Islands-(Brit 1 16600 100.0000 100.0000 589881512 850 Virgin-Islands-(USA) 1 107000 100.0000 100.0000 1186550583 732 Western-Sahara 35 158000 99.9960 100.0036 271765480461 882 Western-Samoa 2 160000 100.0000 0.0000 24200535189 886 Yemen 55 11684005 99.9972 99.9978 422009503100 890 Yugoslavia 46 23791003 100.0009 99.9991 255099599964 180 Zaire 232 37390987 99.9922 99.9904 2352833217696 894 Zambia 90 8137997 99.9912 99.9992 768065789965 716 Zimbabwe 47 9947006 99.9940 99.9953 389164783891 _FREQ_ PERC SPOP SRATEC SRATEG AREAM2 24511 20900.03 5291059610 20097.33 1760973.90 1.5477E14 ===================================================================== =========== Global summation results from executing the FORTRAN code merged.f (reading merged.dat): -------- ---------- spop= 5291059610.00000 sperc= 20899.45 sratec= 20097.38 srateg= 1760972. sarea= 154774743033406. ===================================================================== =========== Data checks performed by CDIAC and data caveats: 1) Populations were summed by country and compared with estimates published by Yi-Fan Li (Li, 1996a). 2) Population percentages were summed by country to determine if the sums approached the expected 100%. 3) Ratec values were summed by country to determine if the sums approached the expected 100%. 4) Rateg values were summed by GEIAid to determine if the sums approached the expected 100%. 5) It was noted that in cases where UN country codes did not exist the the following codes were added by Li: C04 Canary-Islands B02 Gaza-Strip B01 Guernsey B10 ISRAELI-OCCUPIED-TER C07 Jan-Mayen B03 Jersey B07 St.-Helena C11 St.-Martin C16 Ocean 6) Slovakia and the Czech Republic were merged under one name Czechoslovakia and one UN-id ==200 (Slovakia and the Czech Republic came into existence after 1990). 7) The country name Yugoslavia was used in the merged.dat file; not 'Yugoslavia, Socialist Federal Republic of,' as in the original population file (Li, 1996a). 8) It should be noted that not all countries contain information for all variables. The exceptions are listed below: a) Countries with population estimates but no ratec values: Andorra Cape-Verde (has rateg) French-Polynesia Gaza-Strip Gibraltar Guernsey ISRAELI-OCCUPIED_TER Jersey Kiribati Liechtenstein Mayotte Monaco Montserrat Netherlands-Antilles Pitcairn Sao-Tome St.-Helena Tonga Western-Samoa b) Countries with population estimates but no rateg values: Andorra French-Polynesia Gaza-Strip Gibraltar Guernsey ISRAELI-OCCUPIED_TER Jersey Kiribati Liechtenstein Mayotte Monaco Montserrat Netherlands-Antilles Pitcairn Sao-Tome St.-Helena Tonga Western-Samoa c) Countries with ratec values but no population estimates: Canary-Islands Cook-Islands Iraq-Saudi-Arabia-Ne Jan-Mayen Maldives Marshall-Islands Niue San-Marino St.-Martin Svalbard d) Countries with rateg values but no population estimates: Ocean (no ratec) 4560 observations Canary-Islands Cook-Islands Iraq-Saudi-Arabia-Ne Jan-Mayen Maldives Marshall-Islands Niue San-Marino St.-Martin Svalbard e) Countries with rateg values but no ratec values: Cape-Verde f) Countries with ratec values, without rateg values: none 9) It should also be noted that not all grid cells contain information for all variables because: a) Grid cells can have ratec and/or rateg values without being populated. b) Grid cells can have population estimates for more countries than the cover-id value and the ratec and rateg values reflect. c) 117 grid cells have populations outside the map boundaries used for the ratec/rateg compilations and therefore received ratec and rateg values of zero. The following list identifies these grid cells: GEIA-id latitude longitude population percentage (population) UN-id Name 133182 42.5 1.5 55300.0 100.00 020 Andorra 133224 42.5 43.5 27208.0 0.807 051 Armenia 103250 12.5 69.5 61000.0 100.00 533 Aruba 129226 38.5 45.5 32558.0 0.456 031 Azerbaijan 129227 38.5 46.5 32558.0 0.456 031 Azerbaijan 130225 39.5 44.5 32558.0 0.456 031 Azerbaijan 130226 39.5 45.5 220567.0 3.090 031 Azerbaijan 131226 40.5 45.5 91398.0 1.280 031 Azerbaijan 132226 41.5 45.5 135979.0 1.905 031 Azerbaijan 104122 13.5 -58.5 108162.0 42.090 052 Barbados 147209 56.5 28.5 19578.0 0.192 112 Belarus 101181 10.5 0.5 25360.0 0.282 854 Burkina 134121 43.5 -59.5 5.0 0.000 124 Canada 151102 60.5 -78.5 366.0 0.001 124 Canada 152111 61.5 -69.5 230.0 0.001 124 Canada 105157 14.5 -23.5 154475.0 42.560 132 Cape-Verde 110099 19.5 -81.5 25500.0 100.00 136 Cayman-Islands 118302 27.5 121.5 120148.0 0.011 156 China 121303 30.5 122.5 976132.0 0.086 156 China 94189 3.5 8.5 106600.0 30.280 226 Equatorial-Guinea 148203 57.5 22.5 5130.0 0.325 233 Estonia 148204 57.5 23.5 3078.0 0.195 233 Estonia 148205 57.5 24.5 3078.0 0.195 233 Estonia 148206 57.5 25.5 8208.0 0.519 233 Estonia 149203 58.5 22.5 64638.0 4.091 233 Estonia 150209 59.5 28.5 90288.0 5.714 233 Estonia 73031 -17.5 -149.5 154784.0 78.170 258 French-Polynesia 74029 -16.5 -151.5 43216.0 21.830 258 French-Polynesia 122215 31.5 34.5 624000.0 100.00 B02 Gaza-Strip 127175 36.5 -5.5 31047.0 100.00 292 Gibraltar 127209 36.5 28.5 76018.0 0.753 300 Greece 130200 39.5 19.5 79266.0 0.786 300 Greece 140178 49.5 -2.5 57000.0 100.00 B01 Guernsey 122215 31.5 34.5 1584700.0 100.00 B10 ISRAELI-OCCUPIED-TER 113249 22.5 68.5 130579.0 0.015 356 India 119310 28.5 129.5 407738.0 0.330 392 Japan 121312 30.5 131.5 162561.0 0.132 392 Japan 140178 49.5 -2.5 84000.0 100.00 B03 Jersey 131249 40.5 68.5 109516.0 0.654 398 Kazakhstan 137229 46.5 48.5 4821.0 0.029 398 Kazakhstan 138228 47.5 47.5 4821.0 0.029 398 Kazakhstan 89356 -1.5 175.5 47376.0 66.730 296 Kiribati 93354 2.5 173.5 23624.0 33.270 296 Kiribati 130250 39.5 69.5 163254.0 3.699 417 Kyrgyzstan 138190 47.5 9.5 28452.0 100.00 438 Liechtenstein 145206 54.5 25.5 714150.0 19.160 440 Lithuania 79229 -11.5 48.5 161282.0 1.343 450 Madagascar 78226 -12.5 45.5 94300.0 100.00 175 Mayotte 104325 13.5 144.5 111000.0 100.00 583 Micronesia 134188 43.5 7.5 29800.0 100.00 492 Monaco 107118 16.5 -62.5 12400.0 100.00 500 Montserrat 103111 12.5 -69.5 39738.0 22.710 530 Netherlands-Antilles 103112 12.5 -68.5 135262.0 77.290 530 Netherlands-Antilles 103188 12.5 7.5 101936.0 1.319 562 Niger 97301 6.5 120.5 129120.0 0.207 608 Philippines 101302 10.5 121.5 395641.0 0.634 608 Philippines 65050 -25.5 -130.5 61.0 100.00 612 Pitcairn 133224 42.5 43.5 17527.0 0.012 643 Russia 134327 43.5 146.5 4124.0 0.003 643 Russia 138219 47.5 38.5 322703.0 0.217 643 Russia 139220 48.5 39.5 145371.0 0.098 643 Russia 141235 50.5 54.5 12372.0 0.008 643 Russia 141236 50.5 55.5 41240.0 0.028 643 Russia 141237 50.5 56.5 4124.0 0.003 643 Russia 141239 50.5 58.5 4124.0 0.003 643 Russia 141240 50.5 59.5 48457.0 0.033 643 Russia 141262 50.5 81.5 34023.0 0.023 643 Russia 141263 50.5 82.5 7217.0 0.005 643 Russia 142233 51.5 52.5 12372.0 0.008 643 Russia 142242 51.5 61.5 4124.0 0.003 643 Russia 145200 54.5 19.5 40209.0 0.027 643 Russia 145201 54.5 20.5 571174.0 0.385 643 Russia 145202 54.5 21.5 124751.0 0.084 643 Russia 145203 54.5 22.5 67015.0 0.045 643 Russia 145211 54.5 30.5 5155.0 0.003 643 Russia 146201 55.5 20.5 1031.0 0.001 643 Russia 146202 55.5 21.5 54643.0 0.037 643 Russia 146203 55.5 22.5 17527.0 0.012 643 Russia 156216 65.5 35.5 1031.0 0.001 643 Russia 164307 73.5 126.5 722.0 0.000 643 Russia 91187 0.5 6.5 96821.0 81.360 678 Sao-Tome 92188 1.5 7.5 22179.0 18.640 678 Sao-Tome 82338 -8.5 157.5 44189.0 13.810 090 Solomon-Islands 128306 37.5 125.5 199682.0 0.460 410 South-Korea 119164 28.5 -16.5 719956.0 1.848 724 Spain 119165 28.5 -15.5 547362.0 1.405 724 Spain 75175 -15.5 -5.5 7100.0 100.00 B07 St.-Helena 148199 57.5 18.5 57806.0 0.675 752 Sweden 127249 36.5 68.5 16373.0 0.305 762 Tadzhikistan 114300 23.5 119.5 95932.0 0.471 158 Taiwan 69005 -21.5 -175.5 96000.0 100.00 776 Tonga 132220 41.5 39.5 235371.0 0.420 792 Turkey 128247 37.5 66.5 49530.0 1.334 795 Turkmenistan 132242 41.5 61.5 27378.0 0.737 795 Turkmenistan 136209 45.5 28.5 226014.0 0.435 804 Ukraine 136210 45.5 29.5 143679.0 0.277 804 Ukraine 136211 45.5 30.5 40658.0 0.078 804 Ukraine 110024 19.5 -156.5 5082.0 0.002 840 United-States 115099 24.5 -81.5 52193.0 0.021 840 United-States 115100 24.5 -80.5 7770.0 0.003 840 United-States 124103 33.5 -77.5 2345.0 0.001 840 United-States 127058 36.5 -122.5 51442.0 0.021 840 United-States 136108 45.5 -72.5 2573.0 0.001 840 United-States 142004 51.5 -176.5 4830.0 0.002 840 United-States 143006 52.5 -174.5 139.0 0.000 840 United-States 145015 54.5 -165.5 630.0 0.000 840 United-States 147011 56.5 -169.5 204.0 0.000 840 United-States 148010 57.5 -170.5 829.0 0.000 840 United-States 131251 40.5 70.5 649040.0 3.135 860 Uzbekistan 131252 40.5 71.5 2542176.0 12.280 860 Uzbekistan 131253 40.5 72.5 1639792.0 7.921 860 Uzbekistan 131254 40.5 73.5 144480.0 0.698 860 Uzbekistan 132252 41.5 71.5 443968.0 2.145 860 Uzbekistan 132253 41.5 72.5 74256.0 0.359 860 Uzbekistan 73349 -17.5 168.5 42391.0 28.260 548 Vanuatu 77008 -13.5 -172.5 89282.0 55.800 882 Western-Samoa 77009 -13.5 -171.5 70718.0 44.200 882 Western-Samoa 10) The number of observations differ among the population, ratec and rateg files because: a) More than one country per grid cell can have population values and/or ratec and/or rateg values. b) The rateg file contains 4560 ocean observations. 11) Total # countries encountered = 221 Total # lines in merged.dat = 24511 ===================================================================== =========== How to obtain the data: The data file, FORTRAN file, SAS file and this db1016.doc file are available from CDIAC's anonymous FTP (File Transfer Protocol) area: > ftp to cdiac.esd.ornl.gov > enter 'anonymous' as user id > enter your e-mail address as password > cd pub/db1016 > get filenames > quit The same data are available in dBase format from: UNEP Global Resource Information Database, EROS Data Center Gene Fosnight Phone: 605-594-6051 FAX: 605-594-6529 E-mail: fosnight@dgj.cr.usgs.gov or yfli@dow.on.doe.ca in GEIA format through >ftp ncardata.ucar.edu or: GEIA Data Management and Information Exchange Center Debra Hopkins Phone: 303-442-6866 E-mail: hopkins@rmii.com The GISS' grid cell calculations and data bases can be obtained from: GISS@NASAGISS.GISS.NASA.GOV ===================================================================== =========== References: Li, Y. F., 1996a. "Global Population Distribution Database", A Report to the United Nations Environment Programme, under UNEP Sub-Project FP/1205-95-12, March 1996. E-mail: fosnight@dgj.cr.usgs.gov or yfli@dow.on.doe.ca Li, Y. F., 1996b. McMillan, A., and Scholtz, M. T., "Global HCH usage with 1 degrees x 1 degrees longitude/latitude resolution", Environmental Science & Technology, 30, 3525-3533. Lerner, J., E. Matthews and I. Fung, 1988, Methane emission from animals: A global high-resolution database. Global Biogeochemical Cycles, 2, 139-156. Rand McNally World Atlas, Rand McNally, New York, 1991 United Nations FAO Yearbook, Vol 47, Rome, 1993 The Guinness World Data Book, Guinness Pub. Ltd., Middlesex, England, 1993. ===================================================================== ===========