Index of /ftp/db1016
================================================================================
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
Email: azt@ornl.gov
DOI: 10.3334/CDIAC/lue.db1016
================================================================================
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 four files: this db1016.doc file, one data file,
and a SAS and FORTRAN data retrieval code, all in ASCII text format.
The file names and sizes are as follows:
db1016.doc (42570 bytes)
merged.dat (3358007 bytes; 24511 records; 12 variables)
merged.sas (3466 bytes)
merged.f (3472 bytes)
================================================================================
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.
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