=====================================================================
===========

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.

=====================================================================
===========