diff --git a/.gitattributes b/.gitattributes index 5f5cb361e1a50ee211b726b1c6e52d6635b6170d..684ea9e631623162b5031c709fc3d8c0680789c4 100644 --- a/.gitattributes +++ b/.gitattributes @@ -65,3 +65,23 @@ Estimates/T.nc filter=lfs diff=lfs merge=lfs -text Estimates/flows.nc filter=lfs diff=lfs merge=lfs -text Estimates/stocks.nc filter=lfs diff=lfs merge=lfs -text Estimates/test_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Flow_data/Facebook/facebook_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Flow_data/National_Statistics/FIN_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Flow_data/National_Statistics/NZL_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Flow_data/National_Statistics/SWE_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Flow_data/QuantMig_data/Quantmig_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Flow_data/flows.nc filter=lfs diff=lfs merge=lfs -text +Data/GDP_data/GDP_data.nc filter=lfs diff=lfs merge=lfs -text +Data/Gravity_datasets/col.nc filter=lfs diff=lfs merge=lfs -text +Data/Gravity_datasets/dist.nc filter=lfs diff=lfs merge=lfs -text +Data/Gravity_datasets/lang.nc filter=lfs diff=lfs merge=lfs -text +Data/Gravity_datasets/relig.nc filter=lfs diff=lfs merge=lfs -text +Data/Gravity_datasets/trade_flows.csv filter=lfs diff=lfs merge=lfs -text +Data/Gravity_datasets/trade_flows.nc filter=lfs diff=lfs merge=lfs -text +Data/Net_migration/UN_WPP_2024_net_migration.nc filter=lfs diff=lfs merge=lfs -text +Data/UNHCR_data/refugee_data.csv filter=lfs diff=lfs merge=lfs -text +Data/UNHCR_data/refugee_data_all.nc filter=lfs diff=lfs merge=lfs -text +Data/UN_WPP_data/UN_WPP_data.nc filter=lfs diff=lfs merge=lfs -text +Data/UN_stock_data/stock_data.nc filter=lfs diff=lfs merge=lfs -text +Data/world_shapefile/ne_50m_admin_0_countries.dbf filter=lfs diff=lfs merge=lfs -text +Data/world_shapefile/ne_50m_admin_0_countries.shp filter=lfs diff=lfs merge=lfs -text diff --git a/Data/.DS_Store b/Data/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..53769640e7c19f319a02ab0f51371e0fb204f3b1 Binary files /dev/null and b/Data/.DS_Store differ diff --git a/Data/Coordinates.csv b/Data/Coordinates.csv new file mode 100644 index 0000000000000000000000000000000000000000..150afbe8196f7ee523c65f8d9ac386009c7d9e6c --- /dev/null +++ b/Data/Coordinates.csv @@ -0,0 +1,263 @@ +Country,Alpha-2 code,Alpha-3 code,Numeric code,Latitude,Longitude +Afghanistan,AF,AFG,4,33,65 +Åland Islands,AX,ALA,248,60.116667,19.9 +Albania,AL,ALB,8,41,20 +Algeria,DZ,DZA,12,28,3 +American Samoa,AS,ASM,16,-14.3333,-170 +Andorra,AD,AND,20,42.5,1.6 +Angola,AO,AGO,24,-12.5,18.5 +Anguilla,AI,AIA,660,18.25,-63.1667 +Antarctica,AQ,ATA,10,-90,0 +Antigua and Barbuda,AG,ATG,28,17.05,-61.8 +Argentina,AR,ARG,32,-34,-64 +Armenia,AM,ARM,51,40,45 +Aruba,AW,ABW,533,12.5,-69.9667 +Australia,AU,AUS,36,-27,133 +Austria,AT,AUT,40,47.3333,13.3333 +Azerbaijan,AZ,AZE,31,40.5,47.5 +Bahamas,BS,BHS,44,24.25,-76 +Bahrain,BH,BHR,48,26,50.55 +Bangladesh,BD,BGD,50,24,90 +Barbados,BB,BRB,52,13.1667,-59.5333 +Belarus,BY,BLR,112,53,28 +Belgium,BE,BEL,56,50.8333,4 +Belize,BZ,BLZ,84,17.25,-88.75 +Benin,BJ,BEN,204,9.5,2.25 +Bermuda,BM,BMU,60,32.3333,-64.75 +Bhutan,BT,BTN,64,27.5,90.5 +"Bolivia, Plurinational State of",BO,BOL,68,-17,-65 +Bolivia,BO,BOL,68,-17,-65 +"Bonaire, Sint Eustatius and Saba",BQ,BES,535,12.183333,-68.233333 +Bosnia and Herzegovina,BA,BIH,70,44,18 +Botswana,BW,BWA,72,-22,24 +Bouvet Island,BV,BVT,74,-54.4333,3.4 +Brazil,BR,BRA,76,-10,-55 +British Indian Ocean Territory,IO,IOT,86,-6,71.5 +Brunei Darussalam,BN,BRN,96,4.5,114.6667 +Brunei,BN,BRN,96,4.5,114.6667 +Bulgaria,BG,BGR,100,43,25 +Burkina Faso,BF,BFA,854,13,-2 +Burma,MM,MMR,104,22,98 +Burundi,BI,BDI,108,-3.5,30 +Cambodia,KH,KHM,116,13,105 +Cameroon,CM,CMR,120,6,12 +Canada,CA,CAN,124,60,-95 +Cape Verde,CV,CPV,132,16,-24 +Cayman Islands,KY,CYM,136,19.5,-80.5 +Central African Republic,CF,CAF,140,7,21 +Chad,TD,TCD,148,15,19 +Chile,CL,CHL,152,-30,-71 +China,CN,CHN,156,35,105 +Christmas Island,CX,CXR,162,-10.5,105.6667 +Cocos (Keeling) Islands,CC,CCK,166,-12.5,96.8333 +Colombia,CO,COL,170,4,-72 +Comoros,KM,COM,174,-12.1667,44.25 +"Congo, the Democratic Republic of the",CD,COD,180,0,25 +Congo,CG,COG,178,-1,15 +Cook Islands,CK,COK,184,-21.2333,-159.7667 +Costa Rica,CR,CRI,188,10,-84 +Côte d'Ivoire,CI,CIV,384,8,-5 +Croatia,HR,HRV,191,45.1667,15.5 +Cuba,CU,CUB,192,21.5,-80 +Curaçao,CW,CUW,531,12.166667,-68.966667 +Cyprus,CY,CYP,196,35,33 +Czech Republic,CZ,CZE,203,49.75,15.5 +Denmark,DK,DNK,208,56,10 +Djibouti,DJ,DJI,262,11.5,43 +Dominica,DM,DMA,212,15.4167,-61.3333 +Dominican Republic,DO,DOM,214,19,-70.6667 +Ecuador,EC,ECU,218,-2,-77.5 +Egypt,EG,EGY,818,27,30 +El Salvador,SV,SLV,222,13.8333,-88.9167 +Equatorial Guinea,GQ,GNQ,226,2,10 +Eritrea,ER,ERI,232,15,39 +Estonia,EE,EST,233,59,26 +Ethiopia,ET,ETH,231,8,38 +Falkland Islands (Malvinas),FK,FLK,238,-51.75,-59 +Faroe Islands,FO,FRO,234,62,-7 +Fiji,FJ,FJI,242,-18,175 +Finland,FI,FIN,246,64,26 +France,FR,FRA,250,46,2 +French Guiana,GF,GUF,254,4,-53 +French Polynesia,PF,PYF,258,-15,-140 +French Southern Territories,TF,ATF,260,-43,67 +Gabon,GA,GAB,266,-1,11.75 +Gambia,GM,GMB,270,13.4667,-16.5667 +Georgia,GE,GEO,268,42,43.5 +Germany,DE,DEU,276,51,9 +Ghana,GH,GHA,288,8,-2 +Gibraltar,GI,GIB,292,36.1833,-5.3667 +Greece,GR,GRC,300,39,22 +Greenland,GL,GRL,304,72,-40 +Grenada,GD,GRD,308,12.1167,-61.6667 +Guadeloupe,GP,GLP,312,16.25,-61.5833 +Guam,GU,GUM,316,13.4667,144.7833 +Guatemala,GT,GTM,320,15.5,-90.25 +Guernsey,GG,GGY,831,49.5,-2.56 +Guinea-Bissau,GW,GNB,624,12,-15 +Guinea,GN,GIN,324,11,-10 +Guyana,GY,GUY,328,5,-59 +Haiti,HT,HTI,332,19,-72.4167 +Heard Island and McDonald Islands,HM,HMD,334,-53.1,72.5167 +Holy See (Vatican City State),VA,VAT,336,41.9,12.45 +Honduras,HN,HND,340,15,-86.5 +Hong Kong,HK,HKG,344,22.25,114.1667 +Hungary,HU,HUN,348,47,20 +Iceland,IS,ISL,352,65,-18 +India,IN,IND,356,20,77 +Indonesia,ID,IDN,360,-5,120 +"Iran, Islamic Republic of",IR,IRN,364,32,53 +Iraq,IQ,IRQ,368,33,44 +Ireland,IE,IRL,372,53,-8 +Isle of Man,IM,IMN,833,54.23,-4.55 +Israel,IL,ISR,376,31.5,34.75 +Italy,IT,ITA,380,42.8333,12.8333 +Ivory Coast,CI,CIV,384,8,-5 +Jamaica,JM,JAM,388,18.25,-77.5 +Japan,JP,JPN,392,36,138 +Jersey,JE,JEY,832,49.21,-2.13 +Jordan,JO,JOR,400,31,36 +Kazakhstan,KZ,KAZ,398,48,68 +Kenya,KE,KEN,404,1,38 +Kiribati,KI,KIR,296,1.4167,173 +"Korea, Democratic People's Republic of",KP,PRK,408,40,127 +"Korea, Republic of",KR,KOR,410,37,127.5 +Kosovo,XK,XKX,95,42.583333,21 +Kuwait,KW,KWT,414,29.3375,47.6581 +Kyrgyzstan,KG,KGZ,417,41,75 +Lao People's Democratic Republic,LA,LAO,418,18,105 +Latvia,LV,LVA,428,57,25 +Lebanon,LB,LBN,422,33.8333,35.8333 +Lesotho,LS,LSO,426,-29.5,28.5 +Liberia,LR,LBR,430,6.5,-9.5 +Libya,LY,LBY,434,25,17 +Libyan Arab Jamahiriya,LY,LBY,434,25,17 +Liechtenstein,LI,LIE,438,47.1667,9.5333 +Lithuania,LT,LTU,440,56,24 +Luxembourg,LU,LUX,442,49.75,6.1667 +Macao,MO,MAC,446,22.1667,113.55 +"Macedonia, the former Yugoslav Republic of",MK,MKD,807,41.8333,22 +Madagascar,MG,MDG,450,-20,47 +Malawi,MW,MWI,454,-13.5,34 +Malaysia,MY,MYS,458,2.5,112.5 +Maldives,MV,MDV,462,3.25,73 +Mali,ML,MLI,466,17,-4 +Malta,MT,MLT,470,35.8333,14.5833 +Marshall Islands,MH,MHL,584,9,168 +Martinique,MQ,MTQ,474,14.6667,-61 +Mauritania,MR,MRT,478,20,-12 +Mauritius,MU,MUS,480,-20.2833,57.55 +Mayotte,YT,MYT,175,-12.8333,45.1667 +Mexico,MX,MEX,484,23,-102 +"Micronesia, Federated States of",FM,FSM,583,6.9167,158.25 +"Moldova, Republic of",MD,MDA,498,47,29 +Monaco,MC,MCO,492,43.7333,7.4 +Mongolia,MN,MNG,496,46,105 +Montenegro,ME,MNE,499,42,19 +Montserrat,MS,MSR,500,16.75,-62.2 +Morocco,MA,MAR,504,32,-5 +Mozambique,MZ,MOZ,508,-18.25,35 +Myanmar,MM,MMR,104,22,98 +Namibia,NA,NAM,516,-22,17 +Nauru,NR,NRU,520,-0.5333,166.9167 +Nepal,NP,NPL,524,28,84 +Netherlands Antilles,AN,ANT,530,12.25,-68.75 +Netherlands,NL,NLD,528,52.5,5.75 +New Caledonia,NC,NCL,540,-21.5,165.5 +New Zealand,NZ,NZL,554,-41,174 +Nicaragua,NI,NIC,558,13,-85 +Niger,NE,NER,562,16,8 +Nigeria,NG,NGA,566,10,8 +Niue,NU,NIU,570,-19.0333,-169.8667 +Norfolk Island,NF,NFK,574,-29.0333,167.95 +Northern Mariana Islands,MP,MNP,580,15.2,145.75 +Norway,NO,NOR,578,62,10 +Oman,OM,OMN,512,21,57 +Pakistan,PK,PAK,586,30,70 +Palau,PW,PLW,585,7.5,134.5 +"Palestinian Territory, Occupied",PS,PSE,275,32,35.25 +Panama,PA,PAN,591,9,-80 +Papua New Guinea,PG,PNG,598,-6,147 +Paraguay,PY,PRY,600,-23,-58 +Peru,PE,PER,604,-10,-76 +Philippines,PH,PHL,608,13,122 +Pitcairn,PN,PCN,612,-24.7,-127.4 +Poland,PL,POL,616,52,20 +Portugal,PT,PRT,620,39.5,-8 +Puerto Rico,PR,PRI,630,18.25,-66.5 +Qatar,QA,QAT,634,25.5,51.25 +Réunion,RE,REU,638,-21.1,55.6 +Romania,RO,ROU,642,46,25 +Russia,RU,RUS,643,60,100 +Russian Federation,RU,RUS,643,60,100 +Rwanda,RW,RWA,646,-2,30 +Saint Barthélemy,BL,BLM,652,17.897728,-62.834244 +"Saint Helena, Ascension and Tristan da Cunha",SH,SHN,654,-15.9333,-5.7 +Saint Kitts and Nevis,KN,KNA,659,17.3333,-62.75 +Saint Lucia,LC,LCA,662,13.8833,-61.1333 +Saint Martin (French part),MF,MAF,663,18.075278,-63.06 +Saint Pierre and Miquelon,PM,SPM,666,46.8333,-56.3333 +Saint Vincent & the Grenadines,VC,VCT,670,13.25,-61.2 +Saint Vincent and the Grenadines,VC,VCT,670,13.25,-61.2 +Samoa,WS,WSM,882,-13.5833,-172.3333 +San Marino,SM,SMR,674,43.7667,12.4167 +Sao Tome and Principe,ST,STP,678,1,7 +Saudi Arabia,SA,SAU,682,25,45 +Senegal,SN,SEN,686,14,-14 +Serbia,RS,SRB,688,44,21 +Seychelles,SC,SYC,690,-4.5833,55.6667 +Sierra Leone,SL,SLE,694,8.5,-11.5 +Singapore,SG,SGP,702,1.3667,103.8 +Sint Maarten (Dutch part),SX,SXM,534,18.033333,-63.05 +Slovakia,SK,SVK,703,48.6667,19.5 +Slovenia,SI,SVN,705,46,15 +Solomon Islands,SB,SLB,90,-8,159 +Somalia,SO,SOM,706,10,49 +South Africa,ZA,ZAF,710,-29,24 +South Georgia and the South Sandwich Islands,GS,SGS,239,-54.5,-37 +South Korea,KR,KOR,410,37,127.5 +South Sudan,SS,SSD,728,8,30 +Spain,ES,ESP,724,40,-4 +Sri Lanka,LK,LKA,144,7,81 +St. Vincent and the Grenadines,VC,VCT,670,13.25,-61.2 +Sudan,SD,SDN,736,15,30 +Suriname,SR,SUR,740,4,-56 +Svalbard and Jan Mayen,SJ,SJM,744,78,20 +Swaziland,SZ,SWZ,748,-26.5,31.5 +Sweden,SE,SWE,752,62,15 +Switzerland,CH,CHE,756,47,8 +Syrian Arab Republic,SY,SYR,760,35,38 +Taiwan,TW,TWN,158,23.5,121 +Tajikistan,TJ,TJK,762,39,71 +"Tanzania, United Republic of",TZ,TZA,834,-6,35 +Thailand,TH,THA,764,15,100 +Timor-Leste,TL,TLS,626,-8.55,125.5167 +Togo,TG,TGO,768,8,1.1667 +Tokelau,TK,TKL,772,-9,-172 +Tonga,TO,TON,776,-20,-175 +Trinidad and Tobago,TT,TTO,780,11,-61 +Tunisia,TN,TUN,788,34,9 +Turkey,TR,TUR,792,39,35 +Turkmenistan,TM,TKM,795,40,60 +Turks and Caicos Islands,TC,TCA,796,21.75,-71.5833 +Tuvalu,TV,TUV,798,-8,178 +Uganda,UG,UGA,800,1,32 +Ukraine,UA,UKR,804,49,32 +United Arab Emirates,AE,ARE,784,24,54 +United Kingdom,GB,GBR,826,54,-2 +United States Minor Outlying Islands,UM,UMI,581,19.2833,166.6 +United States,US,USA,840,38,-97 +Uruguay,UY,URY,858,-33,-56 +Uzbekistan,UZ,UZB,860,41,64 +Vanuatu,VU,VUT,548,-16,167 +"Venezuela, Bolivarian Republic of",VE,VEN,862,8,-66 +Venezuela,VE,VEN,862,8,-66 +Viet Nam,VN,VNM,704,16,106 +Vietnam,VN,VNM,704,16,106 +"Virgin Islands, British",VG,VGB,92,18.5,-64.5 +"Virgin Islands, U.S.",VI,VIR,850,18.3333,-64.8333 +Wallis and Futuna,WF,WLF,876,-13.3,-176.2 +Western Sahara,EH,ESH,732,24.5,-13 +Yemen,YE,YEM,887,15,48 +Zambia,ZM,ZMB,894,-15,30 +Zimbabwe,ZW,ZWE,716,-20,30 \ No newline at end of file diff --git a/Data/Flow_data/.DS_Store b/Data/Flow_data/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..60d37e8de5b2db49a3769167be4a9019b333629d Binary files /dev/null and b/Data/Flow_data/.DS_Store differ diff --git a/Data/Flow_data/Facebook/facebook_flows.nc b/Data/Flow_data/Facebook/facebook_flows.nc new file mode 100644 index 0000000000000000000000000000000000000000..b657e34b3ebeff31525ef31c1eba2bc331f77a1e --- /dev/null +++ b/Data/Flow_data/Facebook/facebook_flows.nc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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+13661,2019,BWA,16725908148.5481,16211987795.536,7172.08956480028,6951.71989827806,3.0322025744566,18106.0168508915,97.9002218652352,10.7558666666667 +13662,2019,CAF,2221301351.43918,1986909345.61394,449.228467602439,401.82582161435,3.09999999999999,1145.09563657752,170.437019697284,585.911013180369 +13663,2019,CAN,1743725183672.52,1696605089927.38,46352.8693445211,45100.2914903262,1.9084319253883,57491.9820463203,106.6356995625,1.32679336266012 +13664,2019,CHE,721369112726.724,747109782866.389,84121.9310304414,87123.6604363226,1.14198232505277,79465.7241719733,99.7043252183795,0.993709166666667 +13665,2019,CHL,278285058719.466,261676645409.828,14495.716721687,13630.5935431699,0.634367533828438,28255.4410129059,102.567787213795,702.897422561529 +13666,2019,CHN,14279968506271.7,14296369668065.2,10143.8602206165,10155.5108830542,5.95050075367413,18465.298945245,99.5099196976254,6.90838500992901 +13667,2019,CIV,60382894697.3557,59010785540.9857,2141.76836170115,2093.09994335779,6.72137167584978,5894.87874674153,101.407756696452,585.911013180369 +13668,2019,CMR,39667757527.6578,37502066832.7403,1555.22660476477,1470.31785276187,3.47505998194981,4882.35560028896,104.145761213872,585.911013180369 +13669,2019,COD,51775829877.2088,44487659679.0188,557.044161335917,478.632426258904,4.38452887429834,1296.94757256244,673.855504173913,1647.76012727639 +13670,2019,COG,13976637779.9763,10590275024.738,2488.42466724916,1885.51080877731,1.12447391976529,6939.36913397466,174.454145494735,585.911013180369 +13671,2019,COK,,,,23830.0,1.466862532200719,,, +13672,2019,COL,323031701192.84,321405606655.842,6472.54544924705,6439.96359812647,3.18685539245533,17305.8529037485,120.294953383022,3281.62219381788 +13673,2019,COM,1195019531.9011,1092843848.73706,1519.26386436068,1389.36488019886,1.76078023923176,3516.12668473385,125.871099458733,439.463110317875 +13674,2019,CPV,2252177133.74705,2116002110.92756,4380.7811916403,4115.90283469406,6.94782634051859,9111.62993173269,105.481803212106,98.4951785878976 +13675,2019,CRI,64417670521.3545,64382494195.8,12884.6859951989,12877.650101079,2.41751181762513,23374.0295195047,104.815546908869,587.294595686124 +13676,2019,CUB,103427600000.0,91020354637.1474,9232.26115935183,8124.75282059108,-0.163087223606624,,181.669614643511, +13677,2019,CUW,3026124134.07821,2830872598.16815,19220.6866958303,17980.5298376417,-3.20613219619177,28659.8000348261,112.904914484912,1.79 +13678,2019,CYM,5941896607.58643,5434269977.07991,88254.2903677044,80714.5718222987,3.91563534249416,77440.552064534,109.341211103746,0.83333 +13679,2019,CYP,26196667099.5303,25246562673.7657,29703.052734375,28625.779296875,5.87559123003267,46157.0234375,103.068147493417,0.893276257067409 +13680,2019,CZE,256794209028.77,217279912678.49,24062.7190013343,20360.0599218778,3.56577657204843,47720.4922288837,110.179114228271,22.93225 +13681,2019,DEU,3957208055215.68,3673343090202.78,47623.8656074829,44207.6320519514,0.987893349167578,62913.9179414209,106.76908828018,0.893276257067409 +13682,2019,DJI,3088853638.56832,3028730597.33671,2837.17103336917,2781.94687224659,5.5451305999467,5784.44800821916,105.138690068451,177.721 +13683,2019,DMA,611537037.037037,566874380.554411,9009.09011545429,8351.12522914571,5.5024834367041,17318.9628927132,132.424650722615,2.7 +13684,2019,DNK,345401473012.615,332088518914.51,59404.2663247723,57114.6227285377,1.7114009439059,66520.0708667803,103.105101953382,6.669446615 +13685,2019,DOM,88941372558.2397,89287393475.0793,8164.22080932117,8195.9832061503,5.05216802016253,21363.8610383998,173.692985902322,51.2948583333333 +13686,2019,DZA,193459662090.677,202300870249.462,4468.45341883656,4672.66408682198,0.899999999999949,15199.1989965648,281.401940363062,119.353558333333 +13687,2019,ECU,107595829000.0,103544017315.435,6205.05759479761,5971.38938386723,0.165403532646692,13853.4207787933,99.9434248127307,1.0 +13688,2019,EGY,318678815489.749,398080548973.052,2962.98892023003,3701.2446345188,5.55209307156956,15026.5682438097,81.3472078467025,16.7705818428763 +13689,2019,ERI,,,,705.0,3.969958704217058,,,15.075 +13690,2019,ESH,,,,,,,, +13691,2019,ESP,1403496387686.27,1333521400772.79,29776.2011500384,28291.6306843873,1.9611785315135,46837.8205371836,104.310842353138,0.893276257067409 +13692,2019,EST,31873748770.0313,27307395615.8101,24021.2501413306,20579.875480866,3.72665318178397,42613.9987801371,115.683389824614,0.893276257067409 +13693,2019,ETH,95912607722.3032,89640011587.0442,828.70897229725,774.512169391667,8.3640856990789,2437.97033495522,135.303661045075,29.06975 +13694,2019,FIN,267014821129.411,253433824963.672,48358.1807773701,45898.5709888883,1.34973843826123,56838.8120639167,104.421260928382,0.893276257067409 +13695,2019,FJI,5444407214.31758,5215934725.85137,5950.82868635508,5701.10441245577,-0.581946371012094,13567.4738284912,110.224235168419,2.16039311531464 +13696,2019,FLK,,,,,,,, +13697,2019,FRA,2722793515171.76,2608036639287.32,40408.2848574751,38705.2072997191,2.02744647639635,53400.2330030167,103.471122936494,0.893276257067409 +13698,2019,FRO,3266432734.22819,2901879820.04399,63064.6343127366,56026.2538863596,4.53851028676189,67207.832475691,117.915811921863,6.669446615 +13699,2019,FSM,394000000.0,333726825.078682,3572.65918282222,3026.12235068898,4.08163265306123,3938.55708825637,154.509803921569,1.0 +13700,2019,GAB,16874405465.391,15460241661.1259,7441.1786472281,6817.56879468764,3.92080879452831,19491.4067631457,172.021206846481,585.911013180369 +13701,2019,GBR,2851407164907.81,3156963551827.52,42662.5353740311,47234.2466070513,1.62447515947532,52490.7177986978,108.108144992106,0.783445110011929 +13702,2019,GEO,17638337116.9126,18510819818.4957,4741.2832715876,4975.81148194814,5.38071926589951,18186.8816465807,100.0,2.818145 +13703,2019,GHA,68337969247.7067,62465003607.231,2186.18924111824,1998.3081197152,6.5077747939152,6508.24652305901,215.685354366544,5.21736666666667 +13704,2019,GIB,,,,,,,,0.783445110011929 +13705,2019,GIN,13442861496.4454,12075307751.6835,1031.34146480732,926.42221954853,5.61691446889876,3557.46233883145,183.020595942045,9183.87586390984 +13706,2019,GLP,,,,,,,, +13707,2019,GMB,1813609692.49994,1677527292.27233,738.186751987483,682.797642940426,6.22205315994859,2750.82132179634,146.989202087314,50.0624613684222 +13708,2019,GNB,1588638853.59328,1392722780.6646,807.359903965493,707.793673750722,5.55045564999175,2379.60416810935,113.044423447341,585.911013180369 +13709,2019,GNQ,11364133549.7478,10051326714.9325,6804.14923073888,6018.12066321865,-5.48182537893445,18446.5916579681,130.880930150275,585.911013180369 +13710,2019,GRC,207305649887.019,206033167541.336,19335.3602002969,19216.6760037219,2.27718063644569,33472.5437804923,99.7222668543782,0.893276257067409 +13711,2019,GRD,1213485185.18519,1134939081.03144,10462.7929159533,9785.56040240592,0.676511738873756,17047.3549261258,131.598057605103,2.7 +13712,2019,GRL,2997309971.87623,2708402650.63237,53309.2035905065,48170.7896955512,2.83217756798683,67604.0507009986,128.878860163755,6.669446615 +13713,2019,GTM,77172317258.6478,70794725855.0224,4511.99755157656,4139.12191663003,4.0178979343828,11470.3642213488,115.255980154376,7.69669833333333 +13714,2019,GUF,,,,,,,, +13715,2019,GUM,6355000000.0,5959409099.18107,39274.9431424899,36830.1264410973,2.52360203340596,,112.537630600319,1.0 +13716,2019,GUY,5173760191.84652,5071030135.73162,6405.82443444562,6278.63054079553,5.35284003535665,13402.4215131048,103.317329011879,208.5 +13717,2019,HKG,363074545072.389,331813812733.896,48359.0011950597,44195.2893264289,-1.67239123872136,65388.3300696863,97.0519959296469,7.83591666666667 +13718,2019,HND,24882225741.8119,24338200129.3672,2502.32744328073,2447.61649282181,2.55963434592314,6264.01884276715,276.53204804679,24.5085389148926 +13719,2019,HRV,61467582193.6824,57823926882.8566,15563.8167397199,14641.2298817935,3.10043911443758,35093.7433630856,97.1806001228084,6.62254516666667 +13720,2019,HTI,15016090929.5125,15485274058.243,1352.17204191532,1394.42114071112,-1.72309699839232,3381.584787365,195.28564065202,88.8149661769193 +13721,2019,HUN,164020460331.659,147383974295.866,16786.2136399075,15083.5991718742,4.86422573511891,37209.6180516892,115.800801153602,290.66 +13722,2019,IDN,1119099871385.79,1049330234035.85,4106.94856173421,3850.90321753072,5.01928768046282,12757.7850854842,144.601630186014,14147.6713605454 +13723,2019,IMN,7314967866.28289,7985161928.93659,87127.5517977404,95110.1388679513,0.254878786117672,,109.647238595238,0.783445110011929 +13724,2019,IND,2835606256558.19,2689205295885.31,2041.42863698585,1936.03067741067,3.87143694070356,7930.0911303899,138.315030740154,70.4203405359551 +13725,2019,IRL,407124824064.995,380522434249.483,82508.4659883582,77117.1897861686,5.040550288018,97104.4968281656,106.038960255681,0.893276257067409 +13726,2019,IRN,333519146339.191,435344072056.544,3831.27894763337,5000.98599002449,-3.0705879849703,14040.6646532072,194.237249763401,42000.0 +13727,2019,IRQ,233636097800.338,201765390224.362,5671.8568632942,4898.14897652181,5.51379057540518,14910.2250318827,124.316349550327,1182.0 +13728,2019,ISL,24681343649.2952,20729999827.2907,68452.23622306,57493.4195335925,1.86117794293874,65065.9056817227,110.656664173755,122.606773601907 +13729,2019,ISR,399651065197.871,355846209596.501,44140.8289372511,39302.6518220125,3.78514145224655,44350.2472861814,102.997020835382,3.56452734661093 +13730,2019,ITA,2019606796583.53,1922107755102.45,33812.788724868,32180.4341021496,0.429162534371912,49645.6963150395,104.137521157959,0.893276257067409 +13731,2019,JAM,15830766570.7266,14934129346.6919,5607.24300668501,5289.65492391338,0.892291653822227,10216.0655361472,234.48083151398,133.31211833796 +13732,2019,JOR,44503006338.0282,41823826702.478,4170.1144003465,3919.06426916073,1.75124077866225,9288.5272415511,104.976087916437,0.71 +13733,2019,JPN,5117993853016.51,4564332550094.45,40415.9567649547,36043.7844013365,-0.402169200910933,44772.8952471381,100.982037837271,109.009665900863 +13734,2019,KAZ,181667184854.501,211106940610.612,9457.12614657136,10989.6840718388,4.50000000091883,33298.2123695035,470.292743709789,382.747310606061 +13735,2019,KEN,100378436207.371,84264984851.3549,1960.40808854893,1645.70961777862,5.11415885767801,5171.71264279257,116.909787955755,101.99129838935 +13736,2019,KGZ,9371275264.36735,7920623120.2686,1421.99927504102,1201.87701429721,4.60062565707598,6087.60193826927,100.0,69.7893491807476 +13737,2019,KHM,36685356407.7206,33099747942.811,2225.87705485686,2008.32094006706,7.93695013588524,6448.88455952805,114.049214543649,4061.14896313364 +13738,2019,KIR,216985362.338171,227422251.262382,1750.99750920482,1835.21962590991,3.26425930432951,2927.48363377396,100.0,1.43850654421382 +13739,2019,KNA,1107855555.55556,1045836615.95477,23595.4923231291,22274.591411543,2.83126737870639,31496.5931147241,125.986530369846,2.7 +13740,2019,KOR,1651422932447.77,1638146960195.06,31902.4169048194,31645.9498343307,2.24397786011012,46368.9140025247,103.877206387507,1165.3575 +13741,2019,KWT,140856394870.02,118077764579.78,31707.8737464917,26580.226300826,2.26381690553512,51122.3312398328,104.957891562362,0.303611163035755 +13742,2019,LAO,18740561512.5555,18491844275.7106,2589.32081035237,2554.95638019245,5.4577350924848,8075.27502377003,124.692020514079,8679.40909309671 +13743,2019,LBN,51605959131.2741,46727888410.6848,8905.88005497437,8064.04873416236,-6.91492472851026,15659.5661040012,133.69751120119,1507.5 +13744,2019,LBR,3319596500.0,3211334057.92828,658.164180770507,636.699384824869,-2.46729763532686,1575.76665001238,96.6330777388255,186.429744551072 +13745,2019,LBY,69254496471.6938,60949510655.8708,9963.19489084483,8768.4104874586,-11.1960828853386,14333.4224412442,105.85367453351,1.39826289736928 +13746,2019,LCA,2095370370.37037,1974662583.82062,11794.2056521711,11114.7780538251,-0.700859001545481,22738.0109584032,102.387071087303,2.7 +13747,2019,LIE,6436467007.11932,,166907.839305016,174421.0,-0.4417934301778059,,, +13748,2019,LKA,89014978344.1404,97207245817.5168,4082.69404871533,4458.43442725849,-0.220483886072614,14637.4442911872,120.480408108813,178.744925045848 +13749,2019,LSO,2390702295.82777,2299976266.27346,1082.05706777516,1040.99351014117,-1.41799383085205,2773.28639887236,163.131585395991,14.4484270548333 +13750,2019,LTU,55122066225.796,49002127219.1508,19727.7607453736,17537.4819556631,4.67735155796807,43284.5236558711,111.48814722606,0.893276257067409 +13751,2019,LUX,69890505323.5842,66557495896.7378,112726.439672814,107350.626687276,2.9158479150005,131609.09961567,104.073305754166,0.893276257067409 +13752,2019,LVA,33099503950.847,29335802644.3992,17294.9751600969,15328.3861531528,0.675375256812075,35176.5509290232,111.825707120665,0.893276257067409 +13753,2019,MAC,55082293844.1008,50961633173.3488,81967.699172769,75835.7636508166,-2.56346757368024,133549.18803809,99.9867296147765,8.07047033333333 +13754,2019,MAR,128920266409.458,123651944968.713,3508.09765625,3364.73950195313,2.89097476137077,8768.17578125,105.946308180961,9.61707609950743 +13755,2019,MCO,7383496254.23721,7072373236.60927,193746.785647412,185582.7556905,7.12088951975396,,97.106890459364,0.893276257067409 +13756,2019,MDA,11736797054.6965,9161406662.06896,4405.33418162155,3438.67732670712,3.5523140853476,14621.2401362286,119.635620817469,17.5734688664875 +13757,2019,MDG,14104664678.5063,13186360203.1442,499.818837622534,467.27741349648,4.41123212909589,1713.796122026,233.240571633661,3618.32185816072 +13758,2019,MDV,5726094799.37709,5493408290.20633,11740.2724029785,11263.1928054734,7.29833574515027,21855.501224579,100.0,15.3820419226831 +13759,2019,MEX,1304106203694.81,1277682077988.06,10369.5553568761,10159.4448355881,-0.392690521579212,21697.9251453896,104.319010220043,19.2636333333333 +13760,2019,MHL,232900000.0,227200000.0,5292.33985502306,5162.81500670348,10.4521147301896,6405.075649183,102.508802816901,1.0 +13761,2019,MKD,12606338448.547,11185494933.6944,6718.85826635458,5961.58475399191,3.91041953840173,21747.2740903169,147.257169582687,54.9472 +13762,2019,MLI,17280250810.0321,16028741130.4821,820.197390833912,760.795187413671,4.75616080770808,2451.1221609713,186.765502563912,585.911013180369 +13763,2019,MLT,16337793530.7788,14876818854.4189,32412.2697818499,29513.8670529001,4.08470602847339,53256.4429769188,108.843403045852,0.893276257067409 +13764,2019,MMR,75065106227.6892,75854734833.5625,1425.98954211903,1440.98988236657,6.57911006765414,6562.98898478581,121.728385733394,1518.25511666667 +13765,2019,MNE,5542054181.12616,4779777587.71771,8909.6538759126,7684.18397197185,4.06294499167197,25002.8344753992,122.814485445862,0.893215581479226 +13766,2019,MNG,14206359017.5143,14174566537.4555,4347.54610314873,4337.8167085432,5.60224422064255,15790.6961126441,135.487138998204,2663.54139820163 +13767,2019,MNP,1179000000.0,1032436363.63636,24496.6652122421,21451.4401636511,-11.4474929044465,,125.961538461538,1.0 +13768,2019,MOZ,15512759046.672,18442825951.8799,519.09255091362,617.13932000195,2.31781394821475,1528.88988354373,100.000006750509,62.5483333333333 +13769,2019,MRT,7894764885.82312,7171275534.84383,1767.072710621,1605.1352359742,3.14100489306969,6162.21045071341,87.9615087017752,36.6908333333334 +13770,2019,MSR,,,,14517.0,6.5859030837004395,,, +13771,2019,MTQ,,,,,,,, +13772,2019,MUS,14436346979.6355,13871328044.4569,11403.2527870674,10956.9450226163,2.89128499346516,25739.354897588,99.5342120041577,35.4735166666667 +13773,2019,MWI,11051865579.8121,10842133933.6548,580.889816066776,569.866249368475,5.71682519194337,1687.28944917892,114.356391017723,745.540667877372 +13774,2019,MYS,365177721021.516,364602265936.14,10920.191763972,10902.9834855856,4.41318742129586,31300.5086554849,106.235167643949,4.14246973569731 +13775,2019,MYT,,,,,,,, +13776,2019,NAM,12541928303.48,11246291861.588,4731.9210318831,4243.09274481966,-0.839152163419357,10813.3044681944,125.081789884835,14.4486904166667 +13777,2019,NCL,9475655324.59495,,33411.3358835672,30915.0,-1.40000000198451,,101.640799103589,106.588933804405 +13778,2019,NER,12889555561.2299,12212475570.6373,561.691304349698,532.186024570389,5.93985257123522,1612.63734693259,104.59787885306,585.911013180369 +13779,2019,NGA,474517490844.461,509371727735.743,2265.15520863056,2431.53528472982,2.20842927714881,5628.17765244652,202.012580098559,306.920951495223 +13780,2019,NIC,12699028889.666,13096501920.7484,1958.62132892996,2019.92516272041,-2.89702791954369,6701.19426432436,240.814597252996,33.121745265284 +13781,2019,NIU,,,,,,,, +13782,2019,NLD,928903005576.469,854304699361.253,53554.9007491475,49254.0158758866,2.30009159476197,67786.606978674,107.76449941146,0.893276257067409 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+13789,2019,PAN,69778991200.0,66072084974.6328,16477.9066285687,15602.5420867199,3.10306788630359,33163.7269089339,100.53835074119,1.0 +13790,2019,PER,228346006003.648,215017545590.315,7037.00803692604,6626.26083494969,2.24063176875049,15466.2951511415,139.40304241637,3.3372655465368 +13791,2019,PHL,376823402244.928,396224439244.72,3400.78949772302,3575.88170930212,6.11852566118505,9452.29397140303,100.697076301045,51.7957826517332 +13792,2019,PLW,281941562.5,269381349.37935,15841.1935329812,15135.4842892095,0.199999999999847,19312.791184717,100.0,1.0 +13793,2019,PNG,24750626040.5917,24719771866.935,2575.77274251891,2572.56177971872,4.4805145710407,4301.61139203983,127.588043082972,3.38753768145423 +13794,2019,POL,602683770144.88,577884228677.544,15874.5220531254,15221.3090624454,4.58045817256199,38147.1493549039,106.224681919848,3.839375 +13795,2019,PRI,105126400000.0,96389357184.3707,32916.866800639,30181.1498485361,1.67546379498209,43078.0104179099,104.508337690896,1.0 +13796,2019,PRK,,,,603.0,0.30476269688659574,,, +13797,2019,PRT,240115970063.019,221866334776.915,23343.3628970034,21569.1874470753,2.74568471619554,40152.0329276017,107.262466678683,0.893276257067409 +13798,2019,PRY,37925338329.1494,40677000526.59,5821.18199548637,6243.53620897773,-0.40185510068001,15321.3245528933,113.582778811232,6240.72207844257 +13799,2019,PSE,17133500000.0,15829000000.0,3656.85827137011,3378.4346209191,1.36268746558062,6269.89134527985,108.241202855518, +13800,2019,PYF,6022276196.13871,5951258396.41142,21582.5892046801,21328.0761355656,2.69993449863051,,110.949828625356,106.588933804405 +13801,2019,QAT,176371267689.082,167371229308.421,66841.3013472693,63430.4607641011,0.688240959581748,113160.973780742,95.5440857882136,3.64 +13802,2019,REU,,,,,,,, +13803,2019,ROU,251677082534.495,218327690029.17,12992.0326104674,11270.4758020159,3.92326384963494,36602.2803632673,229.756968985063,4.237925 +13804,2019,RUS,1693115002708.32,1461551317106.21,11447.701171875,9882.0244140625,2.19807571335009,37698.953125,119.664070340812,64.7376583333333 +13805,2019,RWA,10349300380.33,11174485687.4325,810.051420243718,874.639605475352,9.44738518613097,2665.18683971413,101.852931988365,899.350509 +13806,2019,SAU,838564800000.0,718133267432.91,27892.8421521179,23886.9767401289,1.09773374954092,56365.5085533134,97.977252902887,3.75 +13807,2019,SDN,32338079165.2893,51312429477.3951,709.975280761719,1126.55297851563,-2.17825104530091,3625.52319335938,9394.98741404506,45.7670454545455 +13808,2019,SEN,23403995992.1354,22560634320.127,1431.18128932159,1379.60875249861,4.61362809749708,4070.61227058594,103.907929657529,585.911013180369 +13809,2019,SGP,376901649222.451,349820740821.559,66081.7199235165,61333.6563161696,1.34505874082728,119572.277084107,106.905422958222,1.36415833333333 +13810,2019,SHN,,,,,,,, +13811,2019,SLB,1619155017.3924,1487659506.79743,2223.66502559565,2043.07578936317,1.74856655451832,2661.59973429284,123.267511177347,8.17339929777839 +13812,2019,SLE,6523577590.38319,8012098354.41336,843.71251730417,1036.22706679474,5.50270937378153,2851.97923261248,109.91558334837,9.01022114400915 +13813,2019,SLV,26881140000.0,25779775126.5993,4320.11671534627,4143.11437097867,2.43747159065784,10543.9383291317,105.635687012894,1.0 +13814,2019,SMR,1616232125.12587,1508721722.60406,46627.0122356944,43525.4225717352,2.06568514732739,60171.0471344217,117.404085490811,0.893215581479226 +13815,2019,SOM,8655023960.002,7928267011.33294,539.893931565468,494.559375806552,2.82431336541845,1491.1076062356,81.8961605182228, +13816,2019,SPM,,,,,,,, +13817,2019,SRB,53864693664.5568,47719025212.1981,7755.632986437,6870.75746352687,4.75020793899014,21521.7017523971,92.1794921219884,105.249558333333 +13818,2019,SSD,,,,623.0,1.0256762943935405,,,157.999166666667 +13819,2019,STP,412976082.620705,303157496.465873,1935.2931816596,1420.66008316091,2.01431672129407,5702.08347071236,229.383805309422,21.8847418164689 +13820,2019,SUR,4016040575.08796,5256433828.6406,6629.66775248603,8677.30522021731,1.16758977233877,22966.7405999124,176.688500627301,7.458 +13821,2019,SVK,105843498304.094,99543529380.5199,19406.0589683582,18250.9802872053,2.27589918160336,37160.5732017056,105.382693307125,0.893276257067409 +13822,2019,SVN,53909922735.545,50002577211.6531,25814.1687167572,23943.1796396034,3.50525166840163,44540.5851554924,106.854905205442,0.893276257067409 +13823,2019,SWE,532169294165.94,546393370671.191,51773.0464558993,53156.8613091273,2.54964785253048,61177.2012483349,109.215138700455,9.45834916666667 +13824,2019,SWZ,4594818143.27624,4351364120.03953,3894.32951592818,3687.99051427485,4.96212142479391,10027.6527487324,100.0,14.4517892288824 +13825,2019,SXM,1407880446.92737,1368162086.82719,34861.469528968,33877.976645467,10.9650009352392,45996.7912346922,100.759240923004,1.79 +13826,2019,SYC,1868690096.73454,1901895701.03715,19141.5118743615,19481.6461053742,5.5142920737342,34346.0708923483,102.523626408918,14.03325 +13827,2019,SYR,22583045060.0821,15703131995.8402,1109.53926036049,771.518695271305,1.22070522070523,4825.40602053761,1699.14785728803,436.5 +13828,2019,TCA,1177285211.72521,1095647325.71769,27273.4376992357,25382.1833321986,5.31092524837173,27826.0149071376,113.817685065111,1.0 +13829,2019,TCD,11314951091.7283,10526057301.7412,678.142035723965,630.861050028594,3.24718191743429,1868.01948907901,122.643911032552,585.911013180369 +13830,2019,TGO,6992700101.37188,6859436220.97836,826.260654099894,810.514132815471,4.92292591685973,2501.03087536243,99.5771823126909,585.911013180369 +13831,2019,THA,543976691793.886,460212749509.626,7605.69657797759,6434.53770518034,2.11455779628278,21277.08251178,154.672411347264,31.0476057805499 +13832,2019,TJK,8300813599.3084,10943705646.9105,871.022372934846,1148.34676712493,7.40000016310556,3649.88375518646,1092.19981804079,9.53036666666667 +13833,2019,TKL,,,,,,,, +13834,2019,TKM,45232857142.8571,45710025794.8484,6648.0348960628,6718.16608056274,6.30000004226711,15231.3056176565,256.936198508464, +13835,2019,TLS,2032550400.0,1960846076.69825,1562.36458881813,1507.24748294374,24.2128840085001,5103.28342241868,103.656798784117,1.0 +13836,2019,TON,494197841.113077,483631249.208267,4676.84790348236,4576.85081914532,-0.208437487640992,6875.02547778858,109.898783346916,2.28948563032632 +13837,2019,TTO,23775659262.5735,23734663451.5336,17431.0269266696,17400.9710162015,0.3612724057636,34349.1287560803,97.5264996935728,6.7543266828632 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+13845,2019,URY,62222325807.0578,60329073569.2244,18315.7352857194,17758.4384253485,0.928351033363754,30108.0750507886,123.015132456699,35.255375 +13846,2019,USA,21539982000000.0,20159639089698.1,65604.6815198734,61400.5481543687,2.58382533018855,69511.7634409067,106.847061617325,1.0 +13847,2019,UZB,67293639798.3996,107444145839.555,2041.38480729431,3259.36964632427,6.77992399653822,8755.12593985156,426.83320363371,8836.7875 +13848,2019,VCT,910766666.666667,863098154.725852,8743.33202133753,8285.71577107771,0.661727443679865,16186.3452529407,102.312470615646,2.7 +13849,2019,VEN,,,,4827.0,-27.799528019874497,,, +13850,2019,VGB,,,,41540.0,11.257675078843231,,, +13851,2019,VIR,4121000000.0,3873124066.16862,38633.5298915336,36309.7438446842,2.90833549727343,45632.6364715693,103.986878627303,1.0 +13852,2019,VNM,334365270496.667,314947640805.601,3440.90025375434,3241.07648966529,7.35926270105006,11628.6139147451,158.37855419817,23050.2416666667 +13853,2019,VUT,936526267.622513,864199385.188828,3207.44650452083,2959.73897696398,3.24118765540442,3512.07197244964,152.225653812371,114.7325 +13854,2019,WLF,,,,,,,, +13855,2019,WSM,912950466.064906,936894420.746212,4351.94234943706,4466.08075482035,4.45185891688691,7033.52113371126,109.101094247884,2.64882632186605 +13856,2019,YEM,,,623.376164898164,1069.78648981973,2.192383407245413,,,486.730922939068 +13857,2019,ZAF,389330032224.269,359483563936.676,6533.71121032856,6032.82972598668,0.25993557687633,14370.2380390151,122.722843148459,14.4484270548333 +13858,2019,ZMB,23308667781.2258,24089861649.0155,1258.98619844462,1301.18132976178,1.44130602603785,3591.56418883108,212.029336888599,12.89 +13859,2019,ZWE,25717409842.4479,20621078646.8294,1684.02790388182,1350.30985088104,-6.33244640703352,3294.80608355087,100.0, +13860,2020,ABW,2481857122.8401,2383266968.91162,22855.9323200761,21947.9953301189,-26.2118207828803,28521.6888517757,115.654945180086,1.79 +13861,2020,AFG,19955929052.1496,20621957125.2077,510.787063366811,527.834554499306,-2.35110067203466,2769.68574507111,110.457895899583,76.8135364354897 +13862,2020,AGO,48501561203.5686,81399194250.7301,1449.92286669308,2433.37637275564,-5.63821471714934,7556.96792771976,2170.22881322863,578.25878028011 +13863,2020,AIA,,,,16294.0,-29.966474684088375,,, +13864,2020,ALB,15241458744.8688,12534895529.6924,5370.77862312929,4417.04105105395,-3.31371614712083,14650.3959291802,111.048999392741,108.65 +13865,2020,AND,2891001149.3611,2672445970.88657,37361.0900666982,34536.6499209948,-11.1839399355384,55488.4902991095,106.556507462058, +13866,2020,ARE,349473015336.939,383342656303.629,37173.8754100281,40776.6308681689,-4.95705243652739,65784.6769388646,88.9718744148615,3.6725 +13867,2020,ARG,385740508436.965,514874343768.066,8535.59938004389,11393.0505957877,-9.9004848136464,23877.0931404855,4356.4191087013,70.5391666666667 +13868,2020,ARM,12641698583.2152,11941035821.9185,4268.68093304583,4032.09043454955,-7.19999999920631,15052.9359791136,115.94779169987,489.008858590915 +13869,2020,ASM,721000000.0,656560305.343511,14489.2586563775,13194.274740128,4.41176470588236,,112.83255086072,1.0 +13870,2020,ATG,1410796296.2963,1371776497.48049,15360.4544160475,14935.6150238496,-18.8803992026325,23915.1619842858,101.524269981556,2.7 +13871,2020,AUS,1328414058378.38,1491062566266.52,51791.540179984,58132.798523626,-0.119590735829973,57023.6678163314,84.9829126135317,1.45308511847016 +13872,2020,AUT,434397601557.696,387247796564.086,48716.4098900349,43428.6983141255,-6.31825528670208,60295.4951497108,108.965781866192,0.875506396987998 +13873,2020,AZE,42693000000.0,51308988759.0274,4229.91064904503,5083.56025445721,-4.30000010230737,19126.094903135,249.48255592103,1.7 +13874,2020,BDI,2649680261.44299,3218331064.79216,210.008139902508,255.078218433565,0.327156892638385,833.920257458019,331.024205972539,1915.04617583333 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sha256:8a4bff0e3f1200b490a23a26af2227b97aba1de0622daca090259c6aa3da611c +size 948064 diff --git a/Data/GDP_data/README.md b/Data/GDP_data/README.md new file mode 100644 index 0000000000000000000000000000000000000000..4e71270225947c297f9090989c350498963502b8 --- /dev/null +++ b/Data/GDP_data/README.md @@ -0,0 +1,143 @@ +GDP data for 231 nations and territories +--- +This folder contains economic data for 231 nations and territories for the period 1960–2023. Real GDP per capita and +real GDP growth rates are complete for the period 1990–2023, and extend even further back in many cases. The dataset +contains the following variables: + +- Nominal GDP +- Real GDP, in 2015 USD +- Nominal GDP/capita +- Real GDP/capita, in 2015 USD +- GDP real annual growth (in %) +- GDP deflator +- Exchange rate + +# Main sources +Data is mostly taken from four sources: + +- The [World Bank](https://data.worldbank.org) +- [UNCTAD](https://unctadstat.unctad.org/datacentre/dataviewer/US.GDPTotal) +- The [IMF World Economic Outlook Database](https://www.imf.org/en/Publications/WEO/weo-database/2024/October) +- The [Maddison Project](https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2023) + +Since we need complete data on real GDP/capita and real growth rates for the years 1990–2023, we fill gaps by +extrapolating available data back using the real growth rate, where available. Where this is impossible, we use national +statistics and estimates from reliable sources to infer gaps; where no data is available at all, we extrapolate using +neighbouring or similar countries. + +### World Bank +The World Bank provides data on nominal and real GDP and GDP per capita, as well as +growth rates, the GDP deflator, and the exchange rate. + +### IMF +The IMF provides GDP/capita in current USD, current PPP, and 2021 PPP, as well as GDP at current prices +and GDP % change in their World Economic Outlook dataset. + +### Maddison Project +The Maddison Project provides [historical GDP per capita data](https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2023.) +in const. international 2011 USD. + +### UNCTAD +UNCTAD provides real GDP per capita in 2015 USD. + +### UN DESA +UN DESA provides some nominal GDP per capita figures [here](https://data.un.org/Data.aspx?d=SNAAMA&f=grID:101;currID:USD;pcFlag:1&c=2,3,5,6&s=_crEngNameOrderBy:asc,yr:desc&v=1) + +# Additional Sources +The above datasets allow us to calculate real growth rates for many missing countries and +territories, and extrapolate values backwards or forwards to estimate GDP/capita. However, many territories +still lack individual values, which we estimate from national statistics (where available), or infer by +extrapolating back based on data (e.g. deflators) from neighbouring countries. + +## Caribbean Netherlands + +![NL_Caribbean.png](../../Images/Github/NL_Caribbean.png) + +UN DESA supplies data for the Former Antilles in nominal terms from 1970–2009. These broadly correspond with the WB +figures for Curacao, the largest of the islands (Aruba became an independent constituent in 1986). +[Statistics Netherlands](https://www.cbs.nl/en-gb/figures/detail/85251ENG) gives data for the BES +islands from 2012 on, also in nominal terms. We convert and fill the gaps using the deflator for +neighbouring Aruba (which uses base year 2013, and is also a constituent part of the Netherlands). + +## French overseas territories +![FR_overseas.png](../../Images/Github/FR_overseas.png) + +Data from [Statista](https://www.statista.com/statistics/1075036/gdp-french-overseas-regions/). +We extrapolate back to 1989 and forward to 2023 using average growth rates. + +## Small Pacific Islands + +![PacificIslands.png](../../Images/Github/Pacific_Islands.png) + +Source: [Pacific Community](https://sdd.spc.int/dataset/df_national_accounts). This gives nominal GDP from 2025–2023, +as well as country-specific inflation rates until 2022, which we use as a proxy for the GDP deflator. Additionally, we +infer missing values using average growth rates from neighbouring countries: +- Niue, Wallis-Futuna, Tokelau: deflated using the inflation rate as a proxy +- American Samoa, Niue, Wallis-Futuna: extrapolated back using the average growth rates of New Caledonia, +Cook Islands, and Fiji +- Tokelau: extrpolated back using the growth rate of Fiji + +## Guam, Northern Marianas, U.S. Virgin Islands. + +![USoverseas.png](../../Images/Github/US_overseas_territories.png) + +These are all U.S. overseas territories. +We extrapolate Guam and the Marianas back from Puerto Rico, and the U.S. Virgin Islands +using an average of Puerto Rico, Antigua, and St. Kitts and Nevis. + +## Serbia and Montenegro + +![SRBMNE.png](../../Images/Github/SRB_MNE.png) + +We can extrapolate back using the growth rates given by the Maddison Project. + +## South Sudan + +![SRBMNE.png](../../Images/Github/SSD.png) + +The World Bank data for South Sudan is clearly erroneous. +Since we have stocks from South Sudan from before its founding (2011), +we can project the GDP of South Sudan back using GDP growth of Sudan +and adjusting to match the 2009 value. + +## East Timor +The growth rate for 1990 is missing; +[this](https://www.aph.gov.au/~/media/wopapub/senate/committee/fadt_ctte/completed_inquiries/1999_02/east_timor/report/c02_pdf.ashx#:~:text=In%20spite%20of%20the%20economic%20crisis%2C%20the,compared%20to%20the%20national%20figure%20of%206.8%.) +report by the Australian government suggests an average 10% economic growth from 1987-1997, +due to heavy investment in construction. Given the data we already have, this means GDP +growth from 1987–1990 must have averaged 11.7%. + +## Western Sahara + +![ESH.png](../../Images/Github/ESH.png) + +We use GDP/capita from Morocco, since ESH is under occupation. + +## Saint Pierre-Miquelon +We use GDP/capita figures from Canada. + +## Eritrea +![ERI.png](../../Images/Github/ERI.png) +We extrapolate past 1990 using data from Ethiopia. + +## Falklands, Isle of Man, Gibraltar, St Helena + +![UKoverseas.png](../../Images/Github/UK_overseas.png) + +- **Isle of Man**: [Government figures](https://www.gov.im/media/1386794/moodys-isle-of-man-credit-rating-report-december-2024.pdf) +estimate real GDP growth at 2.9% in 2022, and 1.5% in 2023. +- **Falklands**: Some data is available from the [Falklands government](https://www.falklands.gov.fk/policy/statistics/national-accounts) +and [historical archives](https://www.nationalarchives.gov.fk/jdownloads/Trade%20and%20Industry/R-TRA-ECO-2-2.%20Economy%20of%20the%20Falkland%20Islands.pdf). +These datapoints match the population-scaled GDP of the Isle of Man very well, so we will the gaps using data from the Isle of Man. +- **Gibraltar** [Government figures](https://www.gibraltar.gov.gi/uploads/statistics/2024/National%20Income/2024.01.02%20Revised%202024.07.01%20National%20Income%20for%20Website%20BB24.png) +give GDP in nominal terms. We use the deflator for GBR. Missing values (pre-2009) are extrapolated back using Isle of Man data. +- **St Helena**: Nominal St Helena data is available from the [government website](https://www.sainthelena.gov.sh/st-helena/statistics/st-helena-in-figures/economy/#gdp). +We extrapolate back using data from Brazil. + +## Faroe Islands + +![FRO.png](../../Images/Github/FRO.png) + +We use the Danish deflator to extrapolate back. + + diff --git a/Data/Gravity_datasets/README.md b/Data/Gravity_datasets/README.md new file mode 100644 index 0000000000000000000000000000000000000000..652890cf94d0303e912b88a92e6b9b7926a3ea24 --- /dev/null +++ b/Data/Gravity_datasets/README.md @@ -0,0 +1,51 @@ +Gravity covariates +--- +This folder contains unilateral and bilateral covariates used to train the model. +GDP data, which is stored in the `Data/GDP_data` folder. Demographic data, such as total population, and +birth and death rates, is given in the `Data/UN_WPP_data` folder. Conflict data is stored in the +`Data/UNCDP_data` folder, and refugee data is given in `Data/UNHCR_data`. + +## Bilateral trade (`trade_flows`) +Total trade flow between origin and destination is taken from the [CEPII BACI dataset](https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37), +which contains data from 1995–2023. We deflate to const. 2015 USD using the World Bank GDP deflator (provided in the +`GDP_data` folder) and fill missing values using the growth rates from UN Comtrade and IMF data, which are also provided +by CEPII. Remaining NaN values are backwards-filled using the last known value. Only the `Value (2015 USD)` variable is +complete for the period from 1990, since this is the only covariate we are using as a training input. + +![Trade.png](../../Images/Github/tradeflows_extrapolation.png) + +## Linguistic similarity (`lang`) +Linguistic similarity is taken from the USITC [Domestic and International Common Language Database (DICL)](https://www.usitc.gov/data/gravity/dicl.htm) +language database. We use the ``csl`` (common spoken language index). +As detailed in the description, data for 46 countries is missing: ``AFG, ALA, ANT, ARE, BHR, BIH, BLM, BMU, CCK, CXR, CYM, ERI, FLK, FRO, GGY, GRD, GRL, IOT, ISL, JEY, KNA, KWT, LBY, LIE, MAF, MDV, MSR, NFK, OMN, PCN, PRK, PSE, SDN, SHN, SMR, SOM, +SPM, SXM, SYR, TCA, TJK, TKL, VAT, VCT, VGB, VNM`` +In these cases, we use the average of linguistic proximity (``lpn``) and common native language (``cnl``) instead, as +recommended by the authors. For the remaining missing datapoint (BES islands), we use Curacao as a proxy. + +![Ling.png](../../Images/Github/ling.png) + +## Religious similarity (`relig`) +This is calculated from the ['Correlates of War'](https://correlatesofwar.org/data-sets/world-religion-data/) database, +which gives the shares of religious adherence for each country. +Missing values are taken from the [CIA World Factbook](https://www.cia.gov/the-world-factbook/). +The religious similarity between two countries is then simply $\langle \alpha_i, \alpha_j \rangle$, +where $\alpha_i$ is the religious makeup of country $i$. The 'other' category is excluded (``othrgenpct``). + +![Relig.png](../../Images/Github/relig_sim.png) + +## EU membership (`eu`) +Information on EU membership is taken from the CEPII dataset. +Since EU membership structure has not changed since 2021 (Brexit), we can extrapolate those values forward. +EU membership is marked with a '1', non-membership is marked with a 0. + +## Colonial ties (`col`) +Colonial ties are taken from the USITC DGD gravity database (``colony_of_destination_ever``). + +## Geographic distance (`dist.nc`) +Distance is taken from the CEPII dataset (``distw_harmonic``). +This is the population-weighted distance between countries. +Though the covariates in CEPII is time-dependent, we take the time-averaged value to facilitate predictions on +new (future/past) data. Missing datapoints are the British Virgin Islands (VGB) and the Isle of Man (IMN). For the former, +we use the data for the U.S. Virgin Islands and set the distance (VGB-VIR) to 80 km. 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+12541,DMA,FLK,0.0 +12542,DMA,FRA,0.0 +12543,DMA,FRO,0.0 +12544,DMA,FSM,0.0 +12545,DMA,GAB,0.0 +12546,DMA,GBR,1.0 +12547,DMA,GEO,0.0 +12548,DMA,GHA,0.0 +12549,DMA,GIB,0.0 +12550,DMA,GIN,0.0 +12551,DMA,GLP,0.0 +12552,DMA,GMB,0.0 +12553,DMA,GNB,0.0 +12554,DMA,GNQ,0.0 +12555,DMA,GRC,0.0 +12556,DMA,GRD,0.0 +12557,DMA,GRL,0.0 +12558,DMA,GTM,0.0 +12559,DMA,GUF,0.0 +12560,DMA,GUM,0.0 +12561,DMA,GUY,0.0 +12562,DMA,HKG,0.0 +12563,DMA,HND,0.0 +12564,DMA,HRV,0.0 +12565,DMA,HTI,0.0 +12566,DMA,HUN,0.0 +12567,DMA,IDN,0.0 +12568,DMA,IMN,0.0 +12569,DMA,IND,0.0 +12570,DMA,IRL,0.0 +12571,DMA,IRN,0.0 +12572,DMA,IRQ,0.0 +12573,DMA,ISL,0.0 +12574,DMA,ISR,0.0 +12575,DMA,ITA,0.0 +12576,DMA,JAM,0.0 +12577,DMA,JOR,0.0 +12578,DMA,JPN,0.0 +12579,DMA,KAZ,0.0 +12580,DMA,KEN,0.0 +12581,DMA,KGZ,0.0 +12582,DMA,KHM,0.0 +12583,DMA,KIR,0.0 +12584,DMA,KNA,0.0 +12585,DMA,KOR,0.0 +12586,DMA,KWT,0.0 +12587,DMA,LAO,0.0 +12588,DMA,LBN,0.0 +12589,DMA,LBR,0.0 +12590,DMA,LBY,0.0 +12591,DMA,LCA,0.0 +12592,DMA,LIE,0.0 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+22577,IRN,PSE,0.0 +22578,IRN,PYF,0.0 +22579,IRN,QAT,0.0 +22580,IRN,REU,0.0 +22581,IRN,ROU,0.0 +22582,IRN,RUS,0.0 +22583,IRN,RWA,0.0 +22584,IRN,SAU,0.0 +22585,IRN,SDN,0.0 +22586,IRN,SEN,0.0 +22587,IRN,SGP,0.0 +22588,IRN,SHN,0.0 +22589,IRN,SLB,0.0 +22590,IRN,SLE,0.0 +22591,IRN,SLV,0.0 +22592,IRN,SMR,0.0 +22593,IRN,SOM,0.0 +22594,IRN,SPM,0.0 +22595,IRN,SRB,0.0 +22596,IRN,SSD,0.0 +22597,IRN,STP,0.0 +22598,IRN,SUR,0.0 +22599,IRN,SVK,0.0 +22600,IRN,SVN,0.0 +22601,IRN,SWE,0.0 +22602,IRN,SWZ,0.0 +22603,IRN,SXM,0.0 +22604,IRN,SYC,0.0 +22605,IRN,SYR,0.0 +22606,IRN,TCA,0.0 +22607,IRN,TCD,0.0 +22608,IRN,TGO,0.0 +22609,IRN,THA,0.0 +22610,IRN,TJK,0.0 +22611,IRN,TKL,0.0 +22612,IRN,TKM,0.0 +22613,IRN,TLS,0.0 +22614,IRN,TON,0.0 +22615,IRN,TTO,0.0 +22616,IRN,TUN,0.0 +22617,IRN,TUR,0.0 +22618,IRN,TUV,0.0 +22619,IRN,TWN,0.0 +22620,IRN,TZA,0.0 +22621,IRN,UGA,0.0 +22622,IRN,UKR,0.0 +22623,IRN,URY,0.0 +22624,IRN,USA,0.0 +22625,IRN,UZB,0.0 +22626,IRN,VCT,0.0 +22627,IRN,VEN,0.0 +22628,IRN,VGB,0.0 +22629,IRN,VIR,0.0 +22630,IRN,VNM,0.0 +22631,IRN,VUT,0.0 +22632,IRN,WLF,0.0 +22633,IRN,WSM,0.0 +22634,IRN,YEM,0.0 +22635,IRN,ZAF,0.0 +22636,IRN,ZMB,0.0 +22637,IRN,ZWE,0.0 +22638,IRQ,ABW,0.0 +22639,IRQ,AFG,0.0 +22640,IRQ,AGO,0.0 +22641,IRQ,AIA,0.0 +22642,IRQ,ALB,0.0 +22643,IRQ,AND,0.0 +22644,IRQ,ARE,0.0 +22645,IRQ,ARG,0.0 +22646,IRQ,ARM,0.0 +22647,IRQ,ASM,0.0 +22648,IRQ,ATG,0.0 +22649,IRQ,AUS,0.0 +22650,IRQ,AUT,0.0 +22651,IRQ,AZE,0.0 +22652,IRQ,BDI,0.0 +22653,IRQ,BEL,0.0 +22654,IRQ,BEN,0.0 +22655,IRQ,BES,0.0 +22656,IRQ,BFA,0.0 +22657,IRQ,BGD,0.0 +22658,IRQ,BGR,0.0 +22659,IRQ,BHR,0.0 +22660,IRQ,BHS,0.0 +22661,IRQ,BIH,0.0 +22662,IRQ,BLR,0.0 +22663,IRQ,BLZ,0.0 +22664,IRQ,BMU,0.0 +22665,IRQ,BOL,0.0 +22666,IRQ,BRA,0.0 +22667,IRQ,BRB,0.0 +22668,IRQ,BRN,0.0 +22669,IRQ,BTN,0.0 +22670,IRQ,BWA,0.0 +22671,IRQ,CAF,0.0 +22672,IRQ,CAN,0.0 +22673,IRQ,CHE,0.0 +22674,IRQ,CHL,0.0 +22675,IRQ,CHN,0.0 +22676,IRQ,CIV,0.0 +22677,IRQ,CMR,0.0 +22678,IRQ,COD,0.0 +22679,IRQ,COG,0.0 +22680,IRQ,COK,0.0 +22681,IRQ,COL,0.0 +22682,IRQ,COM,0.0 +22683,IRQ,CPV,0.0 +22684,IRQ,CRI,0.0 +22685,IRQ,CUB,0.0 +22686,IRQ,CUW,0.0 +22687,IRQ,CYM,0.0 +22688,IRQ,CYP,0.0 +22689,IRQ,CZE,0.0 +22690,IRQ,DEU,0.0 +22691,IRQ,DJI,0.0 +22692,IRQ,DMA,0.0 +22693,IRQ,DNK,0.0 +22694,IRQ,DOM,0.0 +22695,IRQ,DZA,0.0 +22696,IRQ,ECU,0.0 +22697,IRQ,EGY,0.0 +22698,IRQ,ERI,0.0 +22699,IRQ,ESH,0.0 +22700,IRQ,ESP,0.0 +22701,IRQ,EST,0.0 +22702,IRQ,ETH,0.0 +22703,IRQ,FIN,0.0 +22704,IRQ,FJI,0.0 +22705,IRQ,FLK,0.0 +22706,IRQ,FRA,0.0 +22707,IRQ,FRO,0.0 +22708,IRQ,FSM,0.0 +22709,IRQ,GAB,0.0 +22710,IRQ,GBR,1.0 +22711,IRQ,GEO,0.0 +22712,IRQ,GHA,0.0 +22713,IRQ,GIB,0.0 +22714,IRQ,GIN,0.0 +22715,IRQ,GLP,0.0 +22716,IRQ,GMB,0.0 +22717,IRQ,GNB,0.0 +22718,IRQ,GNQ,0.0 +22719,IRQ,GRC,0.0 +22720,IRQ,GRD,0.0 +22721,IRQ,GRL,0.0 +22722,IRQ,GTM,0.0 +22723,IRQ,GUF,0.0 +22724,IRQ,GUM,0.0 +22725,IRQ,GUY,0.0 +22726,IRQ,HKG,0.0 +22727,IRQ,HND,0.0 +22728,IRQ,HRV,0.0 +22729,IRQ,HTI,0.0 +22730,IRQ,HUN,0.0 +22731,IRQ,IDN,0.0 +22732,IRQ,IMN,0.0 +22733,IRQ,IND,0.0 +22734,IRQ,IRL,0.0 +22735,IRQ,IRN,0.0 +22736,IRQ,IRQ,0.0 +22737,IRQ,ISL,0.0 +22738,IRQ,ISR,0.0 +22739,IRQ,ITA,0.0 +22740,IRQ,JAM,0.0 +22741,IRQ,JOR,0.0 +22742,IRQ,JPN,0.0 +22743,IRQ,KAZ,0.0 +22744,IRQ,KEN,0.0 +22745,IRQ,KGZ,0.0 +22746,IRQ,KHM,0.0 +22747,IRQ,KIR,0.0 +22748,IRQ,KNA,0.0 +22749,IRQ,KOR,0.0 +22750,IRQ,KWT,0.0 +22751,IRQ,LAO,0.0 +22752,IRQ,LBN,0.0 +22753,IRQ,LBR,0.0 +22754,IRQ,LBY,0.0 +22755,IRQ,LCA,0.0 +22756,IRQ,LIE,0.0 +22757,IRQ,LKA,0.0 +22758,IRQ,LSO,0.0 +22759,IRQ,LTU,0.0 +22760,IRQ,LUX,0.0 +22761,IRQ,LVA,0.0 +22762,IRQ,MAC,0.0 +22763,IRQ,MAR,0.0 +22764,IRQ,MCO,0.0 +22765,IRQ,MDA,0.0 +22766,IRQ,MDG,0.0 +22767,IRQ,MDV,0.0 +22768,IRQ,MEX,0.0 +22769,IRQ,MHL,0.0 +22770,IRQ,MKD,0.0 +22771,IRQ,MLI,0.0 +22772,IRQ,MLT,0.0 +22773,IRQ,MMR,0.0 +22774,IRQ,MNE,0.0 +22775,IRQ,MNG,0.0 +22776,IRQ,MNP,0.0 +22777,IRQ,MOZ,0.0 +22778,IRQ,MRT,0.0 +22779,IRQ,MSR,0.0 +22780,IRQ,MTQ,0.0 +22781,IRQ,MUS,0.0 +22782,IRQ,MWI,0.0 +22783,IRQ,MYS,0.0 +22784,IRQ,MYT,0.0 +22785,IRQ,NAM,0.0 +22786,IRQ,NCL,0.0 +22787,IRQ,NER,0.0 +22788,IRQ,NGA,0.0 +22789,IRQ,NIC,0.0 +22790,IRQ,NIU,0.0 +22791,IRQ,NLD,0.0 +22792,IRQ,NOR,0.0 +22793,IRQ,NPL,0.0 +22794,IRQ,NRU,0.0 +22795,IRQ,NZL,0.0 +22796,IRQ,OMN,0.0 +22797,IRQ,PAK,0.0 +22798,IRQ,PAN,0.0 +22799,IRQ,PER,0.0 +22800,IRQ,PHL,0.0 +22801,IRQ,PLW,0.0 +22802,IRQ,PNG,0.0 +22803,IRQ,POL,0.0 +22804,IRQ,PRI,0.0 +22805,IRQ,PRK,0.0 +22806,IRQ,PRT,0.0 +22807,IRQ,PRY,0.0 +22808,IRQ,PSE,0.0 +22809,IRQ,PYF,0.0 +22810,IRQ,QAT,0.0 +22811,IRQ,REU,0.0 +22812,IRQ,ROU,0.0 +22813,IRQ,RUS,0.0 +22814,IRQ,RWA,0.0 +22815,IRQ,SAU,1.0 +22816,IRQ,SDN,0.0 +22817,IRQ,SEN,0.0 +22818,IRQ,SGP,0.0 +22819,IRQ,SHN,0.0 +22820,IRQ,SLB,0.0 +22821,IRQ,SLE,0.0 +22822,IRQ,SLV,0.0 +22823,IRQ,SMR,0.0 +22824,IRQ,SOM,0.0 +22825,IRQ,SPM,0.0 +22826,IRQ,SRB,0.0 +22827,IRQ,SSD,0.0 +22828,IRQ,STP,0.0 +22829,IRQ,SUR,0.0 +22830,IRQ,SVK,0.0 +22831,IRQ,SVN,0.0 +22832,IRQ,SWE,0.0 +22833,IRQ,SWZ,0.0 +22834,IRQ,SXM,0.0 +22835,IRQ,SYC,0.0 +22836,IRQ,SYR,0.0 +22837,IRQ,TCA,0.0 +22838,IRQ,TCD,0.0 +22839,IRQ,TGO,0.0 +22840,IRQ,THA,0.0 +22841,IRQ,TJK,0.0 +22842,IRQ,TKL,0.0 +22843,IRQ,TKM,0.0 +22844,IRQ,TLS,0.0 +22845,IRQ,TON,0.0 +22846,IRQ,TTO,0.0 +22847,IRQ,TUN,0.0 +22848,IRQ,TUR,1.0 +22849,IRQ,TUV,0.0 +22850,IRQ,TWN,0.0 +22851,IRQ,TZA,0.0 +22852,IRQ,UGA,0.0 +22853,IRQ,UKR,0.0 +22854,IRQ,URY,0.0 +22855,IRQ,USA,0.0 +22856,IRQ,UZB,0.0 +22857,IRQ,VCT,0.0 +22858,IRQ,VEN,0.0 +22859,IRQ,VGB,0.0 +22860,IRQ,VIR,0.0 +22861,IRQ,VNM,0.0 +22862,IRQ,VUT,0.0 +22863,IRQ,WLF,0.0 +22864,IRQ,WSM,0.0 +22865,IRQ,YEM,0.0 +22866,IRQ,ZAF,0.0 +22867,IRQ,ZMB,0.0 +22868,IRQ,ZWE,0.0 +22869,ISL,ABW,0.0 +22870,ISL,AFG,0.0 +22871,ISL,AGO,0.0 +22872,ISL,AIA,0.0 +22873,ISL,ALB,0.0 +22874,ISL,AND,0.0 +22875,ISL,ARE,0.0 +22876,ISL,ARG,0.0 +22877,ISL,ARM,0.0 +22878,ISL,ASM,0.0 +22879,ISL,ATG,0.0 +22880,ISL,AUS,0.0 +22881,ISL,AUT,0.0 +22882,ISL,AZE,0.0 +22883,ISL,BDI,0.0 +22884,ISL,BEL,0.0 +22885,ISL,BEN,0.0 +22886,ISL,BES,0.0 +22887,ISL,BFA,0.0 +22888,ISL,BGD,0.0 +22889,ISL,BGR,0.0 +22890,ISL,BHR,0.0 +22891,ISL,BHS,0.0 +22892,ISL,BIH,0.0 +22893,ISL,BLR,0.0 +22894,ISL,BLZ,0.0 +22895,ISL,BMU,0.0 +22896,ISL,BOL,0.0 +22897,ISL,BRA,0.0 +22898,ISL,BRB,0.0 +22899,ISL,BRN,0.0 +22900,ISL,BTN,0.0 +22901,ISL,BWA,0.0 +22902,ISL,CAF,0.0 +22903,ISL,CAN,0.0 +22904,ISL,CHE,0.0 +22905,ISL,CHL,0.0 +22906,ISL,CHN,0.0 +22907,ISL,CIV,0.0 +22908,ISL,CMR,0.0 +22909,ISL,COD,0.0 +22910,ISL,COG,0.0 +22911,ISL,COK,0.0 +22912,ISL,COL,0.0 +22913,ISL,COM,0.0 +22914,ISL,CPV,0.0 +22915,ISL,CRI,0.0 +22916,ISL,CUB,0.0 +22917,ISL,CUW,0.0 +22918,ISL,CYM,0.0 +22919,ISL,CYP,0.0 +22920,ISL,CZE,0.0 +22921,ISL,DEU,0.0 +22922,ISL,DJI,0.0 +22923,ISL,DMA,0.0 +22924,ISL,DNK,1.0 +22925,ISL,DOM,0.0 +22926,ISL,DZA,0.0 +22927,ISL,ECU,0.0 +22928,ISL,EGY,0.0 +22929,ISL,ERI,0.0 +22930,ISL,ESH,0.0 +22931,ISL,ESP,0.0 +22932,ISL,EST,0.0 +22933,ISL,ETH,0.0 +22934,ISL,FIN,0.0 +22935,ISL,FJI,0.0 +22936,ISL,FLK,0.0 +22937,ISL,FRA,0.0 +22938,ISL,FRO,0.0 +22939,ISL,FSM,0.0 +22940,ISL,GAB,0.0 +22941,ISL,GBR,0.0 +22942,ISL,GEO,0.0 +22943,ISL,GHA,0.0 +22944,ISL,GIB,0.0 +22945,ISL,GIN,0.0 +22946,ISL,GLP,0.0 +22947,ISL,GMB,0.0 +22948,ISL,GNB,0.0 +22949,ISL,GNQ,0.0 +22950,ISL,GRC,0.0 +22951,ISL,GRD,0.0 +22952,ISL,GRL,0.0 +22953,ISL,GTM,0.0 +22954,ISL,GUF,0.0 +22955,ISL,GUM,0.0 +22956,ISL,GUY,0.0 +22957,ISL,HKG,0.0 +22958,ISL,HND,0.0 +22959,ISL,HRV,0.0 +22960,ISL,HTI,0.0 +22961,ISL,HUN,0.0 +22962,ISL,IDN,0.0 +22963,ISL,IMN,0.0 +22964,ISL,IND,0.0 +22965,ISL,IRL,0.0 +22966,ISL,IRN,0.0 +22967,ISL,IRQ,0.0 +22968,ISL,ISL,0.0 +22969,ISL,ISR,0.0 +22970,ISL,ITA,0.0 +22971,ISL,JAM,0.0 +22972,ISL,JOR,0.0 +22973,ISL,JPN,0.0 +22974,ISL,KAZ,0.0 +22975,ISL,KEN,0.0 +22976,ISL,KGZ,0.0 +22977,ISL,KHM,0.0 +22978,ISL,KIR,0.0 +22979,ISL,KNA,0.0 +22980,ISL,KOR,0.0 +22981,ISL,KWT,0.0 +22982,ISL,LAO,0.0 +22983,ISL,LBN,0.0 +22984,ISL,LBR,0.0 +22985,ISL,LBY,0.0 +22986,ISL,LCA,0.0 +22987,ISL,LIE,0.0 +22988,ISL,LKA,0.0 +22989,ISL,LSO,0.0 +22990,ISL,LTU,0.0 +22991,ISL,LUX,0.0 +22992,ISL,LVA,0.0 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+12464,DJI,VGB,0.00085944843228515 +12465,DJI,VIR,0.0 +12466,DJI,VNM,0.0 +12467,DJI,VUT,0.2314628814549066 +12468,DJI,WLF,0.4513967870608245 +12469,DJI,WSM,0.0 +12470,DJI,YEM,0.0699800605060357 +12471,DJI,ZAF,5.113982993808071e-06 +12472,DJI,ZMB,0.0 +12473,DJI,ZWE,0.0 +12474,DMA,ABW,0.488428887079893 +12475,DMA,AFG,0.0052562208443326 +12476,DMA,AGO,0.0 +12477,DMA,AIA,0.987969344627418 +12478,DMA,ALB,0.0160003991465244 +12479,DMA,AND,0.3556379407658259 +12480,DMA,ARE,0.0015157628468791002 +12481,DMA,ARG,0.0627115828293485 +12482,DMA,ARM,0.0099619227527435 +12483,DMA,ASM,1.0 +12484,DMA,ATG,0.5805759195007411 +12485,DMA,AUS,1.0004128553967413 +12486,DMA,AUT,0.7088677444771213 +12487,DMA,AZE,0.0003016952038004 +12488,DMA,BDI,0.0141937822800302 +12489,DMA,BEL,0.4180102214476864 +12490,DMA,BEN,0.0617449689082291 +12491,DMA,BES,0.0322510350838962 +12492,DMA,BFA,0.0521772745818169 +12493,DMA,BGD,0.0050201211604954 +12494,DMA,BGR,0.2338060446137998 +12495,DMA,BHR,0.00182252920280475 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+12528,DMA,DMA,1.8890593238115212 +12529,DMA,DNK,0.8712276188461135 +12530,DMA,DOM,0.0032077449524284 +12531,DMA,DZA,0.1462090821378519 +12532,DMA,ECU,0.0 +12533,DMA,EGY,0.3020738127431808 +12534,DMA,ERI,0.0 +12535,DMA,ESH,0.0 +12536,DMA,ESP,0.2148478337301267 +12537,DMA,EST,0.430876925055921 +12538,DMA,ETH,0.0025741866606596 +12539,DMA,FIN,0.6883155414876047 +12540,DMA,FJI,0.2093144950287807 +12541,DMA,FLK,0.030506772580393297 +12542,DMA,FRA,0.5033856515120138 +12543,DMA,FRO,0.01637554585152835 +12544,DMA,FSM,0.5523826682280876 +12545,DMA,GAB,0.1545851528384279 +12546,DMA,GBR,0.9755751881848592 +12547,DMA,GEO,0.0 +12548,DMA,GHA,0.3552759620116491 +12549,DMA,GIB,0.6423982869379015 +12550,DMA,GIN,0.0409389073133549 +12551,DMA,GLP,0.1356619758829115 +12552,DMA,GMB,0.0311829756164341 +12553,DMA,GNB,0.0495869803045145 +12554,DMA,GNQ,0.0296279918191365 +12555,DMA,GRC,0.4838698039832197 +12556,DMA,GRD,0.13497164886062069 +12557,DMA,GRL,0.00131385809484925 +12558,DMA,GTM,0.0 +12559,DMA,GUF,0.1545851528384279 +12560,DMA,GUM,0.8996692392502756 +12561,DMA,GUY,0.5157015599575538 +12562,DMA,HKG,0.541145062953209 +12563,DMA,HND,0.003868429196693 +12564,DMA,HRV,0.4794229974279018 +12565,DMA,HTI,0.0972869753360367 +12566,DMA,HUN,0.2013528153021417 +12567,DMA,IDN,0.0 +12568,DMA,IMN,1.0 +12569,DMA,IND,0.1861756028055167 +12570,DMA,IRL,1.0148999994636032 +12571,DMA,IRN,0.0 +12572,DMA,IRQ,0.3290095744750222 +12573,DMA,ISL,0.0163591866648635 +12574,DMA,ISR,0.0177582755737294 +12575,DMA,ITA,0.2753651174135325 +12576,DMA,JAM,0.0185698262607055 +12577,DMA,JOR,0.3921931121909994 +12578,DMA,JPN,0.0005718044920389 +12579,DMA,KAZ,0.1221518052991674 +12580,DMA,KEN,0.0727430106898817 +12581,DMA,KGZ,0.0 +12582,DMA,KHM,0.2185050340443726 +12583,DMA,KIR,0.240566037735849 +12584,DMA,KNA,0.13298949941572846 +12585,DMA,KOR,0.1029103033795743 +12586,DMA,KWT,0.0 +12587,DMA,LAO,0.0044142647600046 +12588,DMA,LBN,0.3361537395127909 +12589,DMA,LBR,0.5826514369349738 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+22666,IRQ,BRA,0.0 +22667,IRQ,BRB,0.1745695131695211 +22668,IRQ,BRN,0.1030648786112298 +22669,IRQ,BTN,0.0 +22670,IRQ,BWA,0.1014973565990227 +22671,IRQ,CAF,0.0 +22672,IRQ,CAN,0.2605042256746404 +22673,IRQ,CHE,0.1780254889957982 +22674,IRQ,CHL,0.0332501527810695 +22675,IRQ,CHN,0.0 +22676,IRQ,CIV,0.0 +22677,IRQ,CMR,0.2418943781718774 +22678,IRQ,COD,0.0 +22679,IRQ,COG,0.0 +22680,IRQ,COK,0.3290095744750222 +22681,IRQ,COL,0.0134351154747761 +22682,IRQ,COM,0.0 +22683,IRQ,CPV,0.0 +22684,IRQ,CRI,0.0 +22685,IRQ,CUB,0.0 +22686,IRQ,CUW,0.0106108993293317 +22687,IRQ,CYM,0.01658281835981785 +22688,IRQ,CYP,0.1897573457913237 +22689,IRQ,CZE,0.0873872963906796 +22690,IRQ,DEU,0.1715390923484173 +22691,IRQ,DJI,0.0389324284260484 +22692,IRQ,DMA,0.3290095744750222 +22693,IRQ,DNK,0.2831281664657 +22694,IRQ,DOM,0.0002790137908305 +22695,IRQ,DZA,0.6218209735163843 +22696,IRQ,ECU,0.0 +22697,IRQ,EGY,0.6638084369232309 +22698,IRQ,ERI,0.1180933016009418 +22699,IRQ,ESH,0.3962082472821507 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+22733,IRQ,IND,0.0612532856859809 +22734,IRQ,IRL,0.3275352434351401 +22735,IRQ,IRN,0.0386336138725693 +22736,IRQ,IRQ,0.996829813991716 +22737,IRQ,ISL,0.02409438226191155 +22738,IRQ,ISR,0.1855803105260148 +22739,IRQ,ITA,0.0844063017849073 +22740,IRQ,JAM,0.0061096506361098 +22741,IRQ,JOR,0.7926422549839369 +22742,IRQ,JPN,0.0001881291526086 +22743,IRQ,KAZ,0.0404207783485348 +22744,IRQ,KEN,0.02393314699311 +22745,IRQ,KGZ,0.0002250552350772 +22746,IRQ,KHM,0.0705791614277373 +22747,IRQ,KIR,0.0791485297086138 +22748,IRQ,KNA,0.0004634603505798 +22749,IRQ,KOR,0.0338584751240092 +22750,IRQ,KWT,0.00735739576925375 +22751,IRQ,LAO,0.0 +22752,IRQ,LBN,0.7838673188569397 +22753,IRQ,LBR,0.191697901333236 +22754,IRQ,LBY,0.00584190322160365 +22755,IRQ,LCA,0.0857568815674243 +22756,IRQ,LIE,0.0185609585581685 +22757,IRQ,LKA,0.0351723148599509 +22758,IRQ,LSO,0.0743020719229951 +22759,IRQ,LTU,0.10132658525194 +22760,IRQ,LUX,0.2220804713399644 +22761,IRQ,LVA,0.1304545735252113 +22762,IRQ,MAC,0.0078173092835312 +22763,IRQ,MAR,0.3359648492953429 +22764,IRQ,MCO,0.0313944143442931 +22765,IRQ,MDA,0.0 +22766,IRQ,MDG,0.0 +22767,IRQ,MDV,0.039925756470077 +22768,IRQ,MEX,0.0177255038005009 +22769,IRQ,MHL,0.3290095744750222 +22770,IRQ,MKD,2.796279168638468e-06 +22771,IRQ,MLI,0.0026160909766731 +22772,IRQ,MLT,0.2708267695277273 +22773,IRQ,MMR,0.0 +22774,IRQ,MNE,0.0738788989387449 +22775,IRQ,MNG,0.0 +22776,IRQ,MNP,0.3290095744750222 +22777,IRQ,MOZ,0.0 +22778,IRQ,MRT,0.384472282384845 +22779,IRQ,MSR,0.00061526726133095 +22780,IRQ,MTQ,0.0 +22781,IRQ,MUS,0.0548677946838863 +22782,IRQ,MWI,0.0136497518605187 +22783,IRQ,MYS,0.1092058320224999 +22784,IRQ,MYT,0.0 +22785,IRQ,NAM,0.0432330681674975 +22786,IRQ,NCL,0.0 +22787,IRQ,NER,0.0003977438378168 +22788,IRQ,NGA,0.1208995052690092 +22789,IRQ,NIC,0.0 +22790,IRQ,NIU,0.1825472477732381 +22791,IRQ,NLD,0.2433493543291466 +22792,IRQ,NOR,0.2537081599682469 +22793,IRQ,NPL,0.0866297278758198 +22794,IRQ,NRU,0.2827045232526117 +22795,IRQ,NZL,0.2874540068191866 +22796,IRQ,OMN,0.015585260079741651 +22797,IRQ,PAK,0.0280928784355747 +22798,IRQ,PAN,0.0 +22799,IRQ,PER,0.0 +22800,IRQ,PHL,0.1860758928958451 +22801,IRQ,PLW,0.3290095744750222 +22802,IRQ,PNG,0.2491475738649481 +22803,IRQ,POL,0.112236794046049 +22804,IRQ,PRI,0.1796869404337384 +22805,IRQ,PRK,0.0 +22806,IRQ,PRT,0.0916497106292407 +22807,IRQ,PRY,0.0 +22808,IRQ,PSE,0.0460247932872666 +22809,IRQ,PYF,0.0 +22810,IRQ,QAT,0.4399156791959343 +22811,IRQ,REU,0.0 +22812,IRQ,ROU,0.1002921328719104 +22813,IRQ,RUS,0.0179932688709495 +22814,IRQ,RWA,0.0451165268170038 +22815,IRQ,SAU,0.8100512354321435 +22816,IRQ,SDN,0.0069722837922863 +22817,IRQ,SEN,0.0 +22818,IRQ,SGP,0.205266144563914 +22819,IRQ,SHN,0.02411847664417345 +22820,IRQ,SLB,0.1515299288445344 +22821,IRQ,SLE,0.2310609510943019 +22822,IRQ,SLV,0.0 +22823,IRQ,SMR,0.0219104901948564 +22824,IRQ,SOM,0.0744830934872453 +22825,IRQ,SPM,0.0150369972748522 +22826,IRQ,SRB,4.217562375154057e-07 +22827,IRQ,SSD,0.5626913573458834 +22828,IRQ,STP,0.0170147431654628 +22829,IRQ,SUR,0.0883386264190587 +22830,IRQ,SVK,0.0839455941127749 +22831,IRQ,SVN,0.1907156884530171 +22832,IRQ,SWE,0.2710887461265467 +22833,IRQ,SWZ,0.0178112250219111 +22834,IRQ,SXM,0.0083666009330001 +22835,IRQ,SYC,0.1332292341499768 +22836,IRQ,SYR,0.013321128626688949 +22837,IRQ,TCA,0.00179042645191705 +22838,IRQ,TCD,0.0559272484548938 +22839,IRQ,TGO,0.0 +22840,IRQ,THA,0.0020312765899614 +22841,IRQ,TJK,0.0669268019540759 +22842,IRQ,TKL,0.00667467135547135 +22843,IRQ,TKM,0.0086807371565534 +22844,IRQ,TLS,0.0002560469432875 +22845,IRQ,TON,0.0972412986184683 +22846,IRQ,TTO,0.1627520726094098 +22847,IRQ,TUN,0.6455362491955886 +22848,IRQ,TUR,0.017774118582939 +22849,IRQ,TUV,0.0260601643148532 +22850,IRQ,TWN,0.0 +22851,IRQ,TZA,0.025801915244353 +22852,IRQ,UGA,0.0242805401543446 +22853,IRQ,UKR,5.465770161606395e-06 +22854,IRQ,URY,0.0 +22855,IRQ,USA,0.3114385879020245 +22856,IRQ,UZB,7.27669585347998e-05 +22857,IRQ,VCT,6.965625023585e-05 +22858,IRQ,VEN,0.0 +22859,IRQ,VGB,0.01215036606759365 +22860,IRQ,VIR,0.3274382998619208 +22861,IRQ,VNM,0.0 +22862,IRQ,VUT,0.2101580122004492 +22863,IRQ,WLF,0.0 +22864,IRQ,WSM,0.1949425714001736 +22865,IRQ,YEM,0.5596339261471933 +22866,IRQ,ZAF,0.1024992296505226 +22867,IRQ,ZMB,0.0533079771645699 +22868,IRQ,ZWE,0.1531054646932675 +22869,ISL,ABW,0.0462312627412326 +22870,ISL,AFG,0.08016480675006125 +22871,ISL,AGO,0.0360687221751671 +22872,ISL,AIA,0.0190572479729106 +22873,ISL,ALB,0.1423699579264284 +22874,ISL,AND,0.07914638997841505 +22875,ISL,ARE,0.02256411104037835 +22876,ISL,ARG,0.0768782328120953 +22877,ISL,ARM,0.164115355823728 +22878,ISL,ASM,0.00970682018997225 +22879,ISL,ATG,0.14030183080898295 +22880,ISL,AUS,0.22169661393296405 +22881,ISL,AUT,0.1730696108208026 +22882,ISL,AZE,0.00550044899239855 +22883,ISL,BDI,1.0665081189061046e-05 +22884,ISL,BEL,0.1579436235619412 +22885,ISL,BEN,0.00012405958169975 +22886,ISL,BES,0.0302502607345291 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(the),PH,PHL,608 +Pitcairn,PN,PCN,612 +Poland,PL,POL,616 +Portugal,PT,PRT,620 +Puerto Rico,PR,PRI,630 +Qatar,QA,QAT,634 +Republic of North Macedonia,MK,MKD,807 +Romania,RO,ROU,642 +Russian Federation (the),RU,RUS,643 +Rwanda,RW,RWA,646 +Réunion,RE,REU,638 +Saint Barthélemy,BL,BLM,652 +"Saint Helena, Ascension and Tristan da Cunha",SH,SHN,654 +Saint Kitts and Nevis,KN,KNA,659 +Saint Lucia,LC,LCA,662 +Saint Martin (French part),MF,MAF,663 +Saint Pierre and Miquelon,PM,SPM,666 +Saint Vincent and the Grenadines,VC,VCT,670 +Samoa,WS,WSM,882 +San Marino,SM,SMR,674 +Sao Tome and Principe,ST,STP,678 +Saudi Arabia,SA,SAU,682 +Senegal,SN,SEN,686 +Serbia,RS,SRB,688 +Seychelles,SC,SYC,690 +Sierra Leone,SL,SLE,694 +Singapore,SG,SGP,702 +Sint Maarten (Dutch part),SX,SXM,534 +Slovakia,SK,SVK,703 +Slovenia,SI,SVN,705 +Solomon Islands,SB,SLB,90 +Somalia,SO,SOM,706 +South Africa,ZA,ZAF,710 +South Georgia and the South Sandwich Islands,GS,SGS,239 +South Sudan,SS,SSD,728 +Spain,ES,ESP,724 +Sri 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+999,2023.0,FIN,57914.0 +1000,2023.0,FRA,193000.0 +1001,2023.0,ISL,6790.0 +1002,2023.0,IRL,77700.0 +1003,2023.0,ITA,273809.0 +1004,2023.0,JPN,377937.0 +1005,2023.0,LVA,2415.0 +1006,2023.0,LTU,44994.0 +1007,2023.0,NLD,137366.0 +1008,2023.0,NZL,103056.0 +1009,2023.0,NOR,52578.0 +1010,2023.0,POL,-8000.0 +1011,2023.0,PRT,155701.0 +1012,2023.0,SVN,11528.0 +1013,2023.0,KOR,121000.0 +1014,2023.0,ESP,642296.0 +1015,2023.0,SWE,21080.0 +1016,2023.0,CHE,139118.0 +1017,2023.0,TWN,225599.0 +1018,2023.0,GBR,724750.0 +1019,2023.0,USA,2294299.0 diff --git a/Data/Net_migration/Net_migration_data.nc b/Data/Net_migration/Net_migration_data.nc new file mode 100644 index 0000000000000000000000000000000000000000..e61f5d444d60ddde8445194b5d8c5235b796350f Binary files /dev/null and b/Data/Net_migration/Net_migration_data.nc differ diff --git a/Data/Net_migration/README.md b/Data/Net_migration/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6d4bf97c2dd25a642648668e4d30369f0322c7cd --- /dev/null +++ b/Data/Net_migration/README.md @@ -0,0 +1,50 @@ + Net migration data from national statistical offices +--- +This folder contains net migration figures from around 30 countries from 1990 to 2023. The data is provided both in `.csv` +and `.nc` (netcdf) format, indexed by `Year` and `Country ISO` (the ISO3 code of country, following the UN norm). +For some countries, we include the UN WPP figures for missing years -- this is specified below: +All UN WPP figures relate to the 2024 report. All figures are given in units of 1. + +**Sources** +- Australia: [Australian Bureau of Statistics](https://www.abs.gov.au/statistics/people/population/overseas-migration/latest-release#data-downloads). WPP data is included. +Austria: [Statistik Austria](http://statcube.at/statcube/opendatabase?id=debevwan010). WPP data is included. +- Belgium: [StatBEL](https://statbel.fgov.be/en/themes/population/population-movement/migration). WPP 2024 data is included. +- Bulgaria: [National Statistics Institute of Bulgaria](https://www.nsi.bg/en/content/3072/international-migration-age-and-sex), data given from 2010. +- Canada: [Statistics Canada](https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1710000801). + These figures are mid-year to mid-year, so we take midpoint averages. WPP data is included. +- Czech Republic: [Czech Statistical Database](https://vdb.czso.cz/vdbvo2/faces/en/index.jsf?page=vystup-objekt&z=T&f=TABULKA&skupId=4791&katalog=33157&pvo=DEMDCR1&pvo=DEMDCR1#w=). +- Denmark: [Statistics Denmark](https://www.dst.dk/en/Statistik/emner/borgere/flytninger/ind-og-udvandring) +- Germany: [Statistisches Bundesamt](https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Wanderungen/Tabellen/wanderungen-zwischen-deutschland-und-dem-ausland-jahr-02.html). WPP data is included. +- Estonia: [Statistics Estonia](https://andmed.stat.ee/en/stat/rahvastik__rahvastikusundmused__ranne/RVR03). WPP 2024 data is included. +- Finland: [Statistics Finland](https://pxdata.stat.fi/PxWeb/pxweb/en/StatFin/StatFin__muutl/statfin_muutl_pxt_11ab.px/). +- France: [INSEE](https://www.insee.fr/fr/statistiques/2381468) +- Iceland: [Statistics Iceland](https://px.hagstofa.is/pxen/pxweb/en/Ibuar/Ibuar__buferlaflutningar__buferlaflmillilanda/MAN01400.px/) +- Ireland: [CSO Ireland](https://data.cso.ie), PEA03 - Estimated Migration (Persons in April). + Data is given April-April needs to be shifted back by 4 months. Figures from 2017 onwards do not agree with UN WPP + estimates due to a revision of the Irish census. +- Italy: Istat [historical](https://seriestoriche.istat.it/index.php?id=1&no_cache=1&L=1&tx_usercento_centofe%5Bcategoria%5D=32&tx_usercento_centofe%5Baction%5D=show&tx_usercento_centofe%5Bcontroller%5D=Categoria&cHash=04e5a2e51acfa92f173aac082f0d8872) and [current](https://www.istat.it/it/files/2024/05/Migrazioni-interne-e-internazionali-della-popolazione-residente.pdf) datasets. +- Japan: Data is provided by the Japanese [Immigration Services Agency](https://www.moj.go.jp/isa/policies/statistics/toukei_ichiran_nyukan.html?hl=en) +- Latvia: [Official statistics of Lativa](https://data.stat.gov.lv/pxweb/en/OSP_PUB/START__POP__IB__IBE/IBE010/) +- Lithuania: [Statistics Lithuania](https://osp.stat.gov.lt/statistiniu-rodikliu-analize?indicator=S3R363#/) +- Netherlands: [Statistics Netherlands](https://www.cbs.nl/nl-nl/dossier/dossier-asiel-migratie-en-integratie/hoeveel-immigranten-komen-naar-nederland). WPP 2024 data is included. +- New Zealand: [StatNZ](https://www.stats.govt.nz/information-releases/international-migration-september-2024/#annual). WPP data is included. +- Norway: [Statistics Norway](https://www.ssb.no/en/statbank/table/05426/). Pre-1999 we use UN WPP data. +- Poland*: [Statistics Poland](https://stat.gov.pl/en/topics/population/internationa-migration/main-directions-of-emigration-and-immigration-in-the-years-1966-2020-migration-for-permanent-residence,2,2.html). WPP data is included. + This data is currently not included in to train the model, since it is based on registrations and thus presumably + significantly underestimates the real flows. +- Portugal: Data from Statistics Portgual ([from 2002](https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_indicadores&indOcorrCod=0006200&contexto=bd&selTab=tab2) and [from 2013](https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_indicadores&indOcorrCod=0008269&contexto=bd&selTab=tab2)) +- Slovenia: [Statistical Office Slovenia](https://pxweb.stat.si/SiStatData/pxweb/en/Data/Data/05N1002S.px/) +- South Korea: [Statistics Korea](https://kostat.go.kr/board.es?mid=a20108050000&bid=11745) International Migration Statistics. WPP data is not included. +- Spain: [INE](https://www.ine.es/dyngs/INEbase/en/operacion.htm?c=Estadistica_C&cid=1254736177000&menu=ultiDatos&idp=1254735573002). WPP data is included. +- Sweden: [Statistics Sweden](https://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__BE__BE0101__BE0101J/ImmiEmiFlyttN/) +- Switzerland: Source: [Bundesamt für Statistik](https://www.bfs.admin.ch/bfs/de/home/statistiken/bevoelkerung/migration-integration/internationale-wanderung.assetdetail.32229097.html). WPP data is included. +- Taiwan: [Statistics Taiwan](https://eng.stat.gov.tw/News_Content.aspx?n=4302&s=232173). WPP data is included. +- United Kingdom: [ONS](https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/internationalmigration/datasets/longterminternationalimmigrationemigrationandnetmigrationflowsprovisional). WPP data is included. +- United States: We combine various estimates: + - https://www.census.gov/library/stories/2022/12/net-international-migration-returns-to-pre-pandemic-levels.html + - https://www.cbo.gov/publication/59683 + - https://www.census.gov/newsroom/blogs/random-samplings/2024/12/international-migration-population-estimates.html + - https://www.brookings.edu/articles/immigration-is-driving-the-nations-modest-post-pandemic-population-growth-new-census-data-shows/ + - https://www.census.gov/data-tools/demo/idb/#/dashboard?dashboard_page=country&COUNTRY_YR_ANIM=2025 + +![Netmigrationoverview.png](../../Images/Github/net_migration_overview.png) \ No newline at end of file diff --git a/Data/Net_migration/UN_WPP_2024_net_migration.csv b/Data/Net_migration/UN_WPP_2024_net_migration.csv new file mode 100644 index 0000000000000000000000000000000000000000..b678eaee072b2876b50f81ff9a3fcb2b513b6c0f --- /dev/null +++ b/Data/Net_migration/UN_WPP_2024_net_migration.csv @@ -0,0 +1,17776 @@ +,Year,Country ISO,Net Number of Migrants +0,1950.0,ABW,0.0 +1,1950.0,AFG,6000.0 +2,1950.0,AGO,-25000.0 +3,1950.0,AIA,0.0 +4,1950.0,ALB,7000.0 +5,1950.0,AND,0.0 +6,1950.0,ARE,3000.0 +7,1950.0,ARG,60000.0 +8,1950.0,ARM,9000.0 +9,1950.0,ASM,-0.0 +10,1950.0,ATG,-0.0 +11,1950.0,AUS,145000.0 +12,1950.0,AUT,-24000.0 +13,1950.0,AZE,-0.0 +14,1950.0,BDI,-13000.0 +15,1950.0,BEL,5000.0 +16,1950.0,BEN,-5000.0 +17,1950.0,BES,-0.0 +18,1950.0,BFA,-18000.0 +19,1950.0,BGD,8000.0 +20,1950.0,BGR,-1000.0 +21,1950.0,BHR,1000.0 +22,1950.0,BHS,-1000.0 +23,1950.0,BIH,-11000.0 +24,1950.0,BLM,0.0 +25,1950.0,BLR,1000.0 +26,1950.0,BLZ,-1000.0 +27,1950.0,BMU,-0.0 +28,1950.0,BOL,-7000.0 +29,1950.0,BRA,10000.0 +30,1950.0,BRB,-0.0 +31,1950.0,BRN,1000.0 +32,1950.0,BTN,1000.0 +33,1950.0,BWA,-1000.0 +34,1950.0,CAF,0.0 +35,1950.0,CAN,60000.0 +36,1950.0,CHE,12000.0 +37,1950.0,CHL,-25000.0 +38,1950.0,CHN,-21000.0 +39,1950.0,CIV,17000.0 +40,1950.0,CMR,0.0 +41,1950.0,COD,4000.0 +42,1950.0,COG,0.0 +43,1950.0,COK,0.0 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0000000000000000000000000000000000000000..1abf4b2d354158376cea774e6ad744f7c0a38465 --- /dev/null +++ b/Data/README.md @@ -0,0 +1,41 @@ +Data used as input, training targets, and validation +--- +This folder contains all the data used to train, evaluate, and test the neural network. +It is stored thematically in different folders, and most folders again contains its own `README` file to further +explain the specific sources and imputation methods. All data is given *both* as a `.csv` file and a `.nc` file, and +follows the ISO3-naming convention outlined in the main README. + +> [!NOTE] +> This is a reminder that all data is stored in this repository using git LFS (large file +> storage); if you wish to clone the repository with the data, you should follow the instructions +> from the main README. You can still download the files manually from the webpage. + +## Training_data +This folder contains all the tensors used to train the neural network. All data is given as a PyTorch +tensor (`.pt`) and can be loaded using `torch.load()`. The folder contains targets, weights, masks, input covariates (scaled +and unscaled), and the edge indices of each input. See the folder README for further details. + +## Net migration (`Net_migration`) +This folder contains net migration data, sourced from national statistical offices, together with a list of sources +and the UN WPP net migration figures. + +## GDP indicators (`GDP_data`) +This folder contains data on GDP/capita, GDP growth, nominal GDP, and other GDP-related indicators for all countries and +years included in the training period. + +## Gravity covariates (`Gravity_datasets`) + +## Demographic indicators (`UN_WPP_data`) + +## Migrant stocks (`UN_stock_data`) + +## Refugee figures (`UNHCR_data`) +Total number of refugees, asylum-seekers, and other people in need of international protection, taken from the +[UNHCR dataset](https://www.unhcr.org/refugee-statistics/download). + +## Conflict deaths (`UCDP_data`) +This folder contains data on deaths in conflict provided by +[UCDP Georeferenced Event](https://ucdp.uu.se/downloads/index.html#ged_global) dataset. +NaN values are filled with 0. + 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+UN DESA stock data and associated estimates +--- +The `stock_data.nc` file contains the various UN stock data estimates. +Stock data is provided by the [UN DESA](https://www.un.org/development/desa/pd/content/international-migrant-stock), +and give the foreign-born stocks at midyear. We have two revisions of the stock data, one from 2020, one from 2024. +We use the 2024 values where available, and fill NaNs with the 2020 value. +Some obvious mistakes are manually corrected. We interpolate missing values using neighbourhood correlation, +and then scale the estimates at mid-year to give a value at the start of each year. + +The data contains the following data variables: + +- `2020 Revision`: original data from the 2020 Revision, mid-year estimates +- `2025 Revision`: original data from the 2025 Revision, mid-year estimates +- `Combined`: Union of the two, with mistakes corrected +- `Mask`: Mask for `NaN` values +- `Interpolated`: interpolated stock values +- `Start of year estimate`: start of year estimates, latest version +- `Error`: errors on stock data +- `Weight`: weights on the stocks + +Details on each are given below: + +## `2020 Revision`/`2025 Revision` +These are all the original estimates. + +## `Combined` +The union of the 2025 and 2020 estimates, with the following mistakes corrected: +```python +# Canada +stock_data["Combined"].loc[{"Origin ISO": "SYC", "Destination ISO": "CAN", "Year": [1990, 1995]}] = [2248., 1160.] +stock_data["Combined"].loc[{"Origin ISO": "CUW", "Destination ISO": "CAN", "Year": 2010}] = 399 +stock_data["Combined"].loc[{"Origin ISO": "BES", "Destination ISO": "CAN", "Year": [2010, 2015, 2020]}] = [40.0, 40.0, 44.0] +stock_data["Combined"].loc[{"Origin ISO": "SXM", "Destination ISO": "CAN", "Year": 2010}] = 105.0 + +# Germany in US +stock_data['Combined'].loc[{"Origin ISO": "DEU", "Destination ISO": "USA", "Year": 1995}] = 1335539 +# France +stock_data["Combined"].loc[{"Origin ISO": "AIA", "Destination ISO": "FRA", "Year": 2005}] = 12 + +# Vietnamese in Malaysia +stock_data["Combined"].loc[{"Origin ISO": "VNM", "Destination ISO": "MYS", "Year": range(1990, 2023, 5)}] = stock_data["2020 Revision"].sel({"Origin ISO": "VNM", "Destination ISO": "MYS", "Year": range(1990, 2023, 5)}).copy() +stock_data["Combined"].loc[{"Origin ISO": "VNM", "Destination ISO": "MYS", "Year": 2024}] = 1.09e+5 + +# Turkmenistan in Greece +stock_data["Combined"].loc[{"Origin ISO": "TKM", "Destination ISO": "GRC", "Year": [1990, 1995]}] = [96., 82.] + +# Bangladeshis in Egypt +stock_data["Combined"].loc[{"Origin ISO": "BGD", "Destination ISO": "EGY", "Year": [2010, 2015, 2020, 2024]}] = [557., 1015., 1173., 762.] + +# Russians in Serbia +stock_data["Combined"].loc[{"Origin ISO": "RUS", "Destination ISO": "SRB", "Year": 1995}] = 810 + +# Russians in Egypt +stock_data["Combined"].loc[{"Origin ISO": "RUS", "Destination ISO": "EGY", "Year": 2015}] = 23462 + +# Ukrainians in Egypt: mask 2015 value +stock_data["Combined"].loc[{"Origin ISO": "UKR", "Destination ISO": "EGY", "Year": 2015}] = 2349 + +# Lebanese in Egypt +stock_data["Combined"].loc[{"Origin ISO": "LBN", "Destination ISO": "EGY", "Year": 2015}] = 2045 + +# North Macedonia in Hungary +stock_data["Combined"].loc[{"Origin ISO": "MKD", "Destination ISO": "HUN", "Year": [1990, 1995]}] = [460, 230] + +# Comoros in South Africa +stock_data["Combined"].loc[{"Origin ISO": "COM", "Destination ISO": "ZAF", "Year": 2005}] = 71 + +# Aruba in France +stock_data["Combined"].loc[{"Origin ISO": "ABW", "Destination ISO": "FRA", "Year": 2005}] = 7 +``` +We also require the stocks to be at least as large as the UNHCR refugee data. + +## `Mask` +We calcualte a mask of missing (NaN) values. The mask excludes these points from the loss function +used to train the neural network. + +## `Interpolated` +We extrapolate and interpolate missing values to obtain initial values for stocks, where missing. +This is necessary because the migrant stocks are a training covariate, and though they are recursively +estimated through the neural network, the initial value (at 1990) is needed to start the process. +The interpolation is performed using a weighted average growth rate: +let $S_{ij}(t)$ be a stock time series with missing data. We then consider the correlation +$$C_{jk} = \mathrm{corr}(S_{ij}, S_{ik})$$ and the distance coefficient +$$\rho_{jk} = \exp(-d_{jk})$$ and calculate the weighted growth rate using the weight +$$w_{jk} = C_{jk} \times \rho_{jk} / \sum_k \rho_{jk}.$$ Then, given a matrix of stock growth values, +$$g_{ij}(t) = S_{ij}(t) / S_{ij}(t-1) - 1,$$ the average growth rate is simply +$$\bar{g}_{ij}(t) = \dfrac{\sum_k g_{ik} w_{jk}}{\sum_k w_{jk}}.$$ + +![interp.png](../../Images/Github/stock_extrapolation.png) + +The diagonal is filled with estimates of the native-born population, given by $$S_{ii} = P_i - \sum_{j \neq i} S_{ji}.$$ + + +## `Start of year estimate` +Since DESA provides mid-year estimates, we need to scale our estimates to the start of the year. We do this using iterative +proportional fitting (IPF). We do so by subtracting half the total births from that year, and then dividing by the half-annual +death rate $1-\gamma_{1/2} = \sqrt{1-\gamma}$. + +![stofyear.png](../../Images/Github/start_of_year_estimates.png) + +## `Error` and `Weights` +Weights on the stock targets are calculated by scaling successive stock matrices to the midpoints of their marginals, +after accounting for demographic change (births and deaths). +We can scale two matrices to their midpoints or their endpoints. +This gives between 2 and 4 new estimates for each value $S_{ij}(t)$, from which we can calculate a weight. + +The weights and errors are based on the start-of-year estimates. +Weights are given by the negative exponential of the normalised relative error $$\dfrac{\langle \hat{S}_k - S \rangle_k}{S},$$ +where $k$ indexes the different stock matrix estimates we obtain from IPF. + +The weight is clipped to $[0.5, 2]$. + +![weights.png](../../Images/Github/stock_weights.png) + +## Stock differences +We are actually training the neural network to match stock *differences*, not absolute values. +We actually want to learn the stock differences, not the absolute values. +As before, we then set the weights on the differences to the negative exponential of the normalised relative error +$$\dfrac{\sqrt{\sigma_1^2 + \sigma_2^2}}{\max(\vert \Delta S\vert, 1)}.$$ + +![weights_2.png](../../Images/Github/stock_diff_weights.png) +![weights_3.png](../../Images/Github/stock_weight_comparison.png) + diff --git a/Data/UN_stock_data/stock_data.nc b/Data/UN_stock_data/stock_data.nc new file mode 100644 index 0000000000000000000000000000000000000000..13f0e274c7edbd1a1408c68353a12cc93e23c808 --- /dev/null +++ b/Data/UN_stock_data/stock_data.nc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7295828a6f9899e41db44697e9105d886a9085f4499d0af14929ba5d5002220c +size 24335052 diff --git a/Data/__init__.py b/Data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ce5259d431b0f2693b627644ae58581a30c06cbe --- /dev/null +++ b/Data/__init__.py @@ -0,0 +1,73 @@ +import pandas as pd +import os + +# We make relevant datasets contained in this folder available across the project, for easier integration and plotting. +# The base directory points to the folder containing this file: +base_dir = os.path.dirname(__file__) + +# Make the ISO code lookup table available across the project. Also group countries by region. +lookup_table = pd.read_csv(os.path.join(base_dir, "Iso_code_lookup.csv")) +countries_by_region = pd.read_csv(os.path.join(base_dir, "countries_by_region.csv")) +countries_by_region = countries_by_region.groupby('Region').agg(list).to_dict()['Country ISO'] +countries_by_region['Eastern Asia'].append('TWN') +countries_by_region['Middle East'] = countries_by_region.pop('Western Asia') +countries_by_region['South Asia'] = countries_by_region.pop('Southern Asia') + +# Make the world shapefile available for plotting +import geopandas as gpd +world = gpd.read_file(os.path.join(base_dir, "world_shapefile/ne_50m_admin_0_countries.shp")) +world.to_crs("EPSG:3857", inplace=True) # Mercator projection by default + +# Make the coordinates available. These are regular latitude/longitude coordinates +coordinates = pd.read_csv(os.path.join(base_dir, "Coordinates.csv"))[['Alpha-3 code', 'Latitude', 'Longitude']].set_index('Alpha-3 code').to_dict() +coordinates = dict((k, (coordinates['Latitude'][k], coordinates['Longitude'][k])) for k in coordinates['Latitude'].keys()) + +# Load the various comparison datasets into a dictionary for easy plotting, assigning consistent colours +# and markers for each. +import sys +from os.path import dirname as up +from dantro._import_tools import import_module_from_path +sys.path.append(up(__file__)) +Plots = import_module_from_path(mod_path=up(__file__), mod_str="Plots") +from Plots import colors +import xarray as xr +flow_dsets = { + "Quantmig": dict( + data=xr.open_dataset(os.path.join(base_dir, "Flow_data/QuantMig_data/Quantmig_flows.nc")), + central='flow_50%', lower='flow_2.5%', upper='flow_97.5%', + primary_color=colors['c_orange'], secondary_color=colors['c_yellow'] + ), + "Statistics Sweden": dict( + data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/National_Statistics/SWE_flows.nc")), + primary_color=colors['c_red'], secondary_color=colors['c_pink'], marker='v', s=10, + ), + "Statistics Finland": dict( + data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/National_Statistics/FIN_flows.nc")), + primary_color=colors['c_lightblue'], secondary_color=colors['c_darkblue'], marker='s', s=10 + ), + "StatNZ": dict( + data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/National_Statistics/NZL_flows.nc")), + primary_color=colors['c_darkgreen'], secondary_color=colors['c_lightgreen'], marker='^', s=10 + ), + "Facebook data": dict( + data=xr.open_dataarray(os.path.join(base_dir, "Flow_data/Facebook/facebook_flows.nc")).where(lambda x: x>=25), + primary_color=colors['c_purple'], secondary_color='#E1C6EC', ec=colors['c_darkgrey'], marker='o', s=10, lw=0.5 + ) +} + +# UN WPP data +population = 1e3 * xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))['Total Population, as of 1 January (thousands)'] +death_rate = 1e-3 * xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))['Crude Death Rate (deaths per 1,000 population)'] +WPP_net_migration = 1e3 * xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc"))['Net Number of Migrants (thousands)'] +NatStat_net_migration = xr.open_dataarray(os.path.join(base_dir, "Net_migration/National_statistics.nc")) +WPP_data = xr.open_dataset(os.path.join(base_dir, "UN_WPP_data/UN_WPP_data.nc")) + +# Calculate the fraction of population alive at start of 1990 in each destination country still alive at start of year +import numpy as np +gamma = (1-death_rate.sel({"Year": range(1989, 2024)})).assign_coords({"Year": np.arange(1990, 2025, 1)}).rename({ + "Country ISO": "Destination ISO"}) +gamma.loc[{"Year": 1990}] = 1.0 +gamma = gamma.cumprod('Year') + +# Stock data +stock_data = xr.load_dataset(os.path.join(base_dir, "UN_stock_data/stock_data.nc")) \ No newline at end of file diff --git a/Data/__pycache__/__init__.cpython-311.pyc b/Data/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..417ce2c0d3f79c97adc981952c4f912f68eca0c4 Binary files /dev/null and b/Data/__pycache__/__init__.cpython-311.pyc differ diff --git a/Data/countries_by_region.csv b/Data/countries_by_region.csv new file mode 100644 index 0000000000000000000000000000000000000000..e2e4a8dc7531a80187052c52e619da7a344ecd5b --- /dev/null +++ b/Data/countries_by_region.csv @@ -0,0 +1,231 @@ +Country ISO,Region +ABW,Latin America and the Caribbean +AFG,Southern Asia +AGO,Sub-Saharan Africa +AIA,Latin America and the Caribbean +ALB,Europe +AND,Europe +ARE,Western Asia +ARG,Latin America and the Caribbean +ARM,Western Asia +ASM,Oceania +ATG,Latin America and the Caribbean +AUS,Oceania +AUT,Europe +AZE,Western Asia +BDI,Sub-Saharan Africa +BEL,Europe +BEN,Sub-Saharan Africa +BES,Latin America and the Caribbean +BFA,Sub-Saharan Africa +BGD,Southern Asia +BGR,Europe +BHR,Western Asia +BHS,Latin America and the Caribbean +BIH,Europe +BLR,Europe +BLZ,Latin America and the Caribbean +BMU,Northern America +BOL,Latin America and the Caribbean +BRA,Latin America and the Caribbean +BRB,Latin America and the Caribbean +BRN,South-eastern Asia +BTN,Southern Asia +BWA,Sub-Saharan Africa +CAF,Sub-Saharan Africa +CAN,Northern America +CHE,Europe +CHL,Latin America and the Caribbean +CHN,Eastern Asia +CIV,Sub-Saharan Africa +CMR,Sub-Saharan Africa +COD,Sub-Saharan Africa +COG,Sub-Saharan Africa +COK,Oceania +COL,Latin America and the Caribbean +COM,Sub-Saharan Africa +CPV,Sub-Saharan Africa +CRI,Latin America and the Caribbean +CUB,Latin America and the Caribbean +CUW,Latin America and the Caribbean +CYM,Latin America and the Caribbean +CYP,Western Asia +CZE,Europe +DEU,Europe +DJI,Sub-Saharan Africa +DMA,Latin America and the Caribbean +DNK,Europe +DOM,Latin America and the Caribbean +DZA,Northern Africa +ECU,Latin America and the Caribbean +EGY,Northern Africa +ERI,Sub-Saharan Africa +ESH,Northern Africa +ESP,Europe +EST,Europe +ETH,Sub-Saharan Africa +FIN,Europe +FJI,Oceania +FLK,Latin America and the Caribbean +FRA,Europe +FRO,Europe +FSM,Oceania +GAB,Sub-Saharan Africa +GBR,Europe +GEO,Western Asia +GHA,Sub-Saharan Africa +GIB,Europe +GIN,Sub-Saharan Africa +GLP,Latin America and the Caribbean +GMB,Sub-Saharan Africa +GNB,Sub-Saharan Africa +GNQ,Sub-Saharan Africa +GRC,Europe +GRD,Latin America and the Caribbean +GRL,Northern America +GTM,Latin America and the Caribbean +GUF,Latin America and the Caribbean +GUM,Oceania +GUY,Latin America and the Caribbean +HKG,Eastern Asia +HND,Latin America and the Caribbean +HRV,Europe +HTI,Latin America and the Caribbean +HUN,Europe +IDN,South-eastern Asia +IMN,Europe +IND,Southern Asia +IRL,Europe +IRN,Southern Asia +IRQ,Western Asia +ISL,Europe +ISR,Western Asia +ITA,Europe +JAM,Latin America and the Caribbean +JOR,Western Asia +JPN,Eastern Asia +KAZ,Central Asia +KEN,Sub-Saharan Africa +KGZ,Central Asia +KHM,South-eastern Asia +KIR,Oceania +KNA,Latin America and the Caribbean +KOR,Eastern Asia +KWT,Western Asia +LAO,South-eastern Asia +LBN,Western Asia +LBR,Sub-Saharan Africa +LBY,Northern Africa +LCA,Latin America and the Caribbean +LIE,Europe +LKA,Southern Asia +LSO,Sub-Saharan Africa +LTU,Europe +LUX,Europe +LVA,Europe +MAC,Eastern Asia +MAR,Northern Africa +MCO,Europe +MDA,Europe +MDG,Sub-Saharan Africa +MDV,Southern Asia +MEX,Latin America and the Caribbean +MHL,Oceania +MKD,Europe +MLI,Sub-Saharan Africa +MLT,Europe +MMR,South-eastern Asia +MNE,Europe +MNG,Eastern Asia +MNP,Oceania +MOZ,Sub-Saharan Africa +MRT,Sub-Saharan Africa +MSR,Latin America and the Caribbean +MTQ,Latin America and the Caribbean +MUS,Sub-Saharan Africa +MWI,Sub-Saharan Africa +MYS,South-eastern Asia +MYT,Sub-Saharan Africa +NAM,Sub-Saharan Africa +NCL,Oceania +NER,Sub-Saharan Africa +NGA,Sub-Saharan Africa +NIC,Latin America and the Caribbean +NIU,Oceania +NLD,Europe +NOR,Europe +NPL,Southern Asia +NRU,Oceania +NZL,Oceania +OMN,Western Asia +PAK,Southern Asia +PAN,Latin America and the Caribbean +PER,Latin America and the Caribbean +PHL,South-eastern Asia +PLW,Oceania +PNG,Oceania +POL,Europe +PRI,Latin America and the Caribbean +PRK,Eastern Asia +PRT,Europe +PRY,Latin America and the Caribbean +PSE,Western Asia +PYF,Oceania +QAT,Western Asia +REU,Sub-Saharan Africa +ROU,Europe +RUS,Europe +RWA,Sub-Saharan Africa +SAU,Western Asia +SDN,Northern Africa +SEN,Sub-Saharan Africa +SGP,South-eastern Asia +SHN,Sub-Saharan Africa +SLB,Oceania +SLE,Sub-Saharan Africa +SLV,Latin America and the Caribbean +SMR,Europe +SOM,Sub-Saharan Africa +SPM,Northern America +SRB,Europe +SSD,Sub-Saharan Africa +STP,Sub-Saharan Africa +SUR,Latin America and the Caribbean +SVK,Europe +SVN,Europe +SWE,Europe +SWZ,Sub-Saharan Africa +SXM,Latin America and the Caribbean +SYC,Sub-Saharan Africa +SYR,Western Asia +TCA,Latin America and the Caribbean +TCD,Sub-Saharan Africa +TGO,Sub-Saharan Africa +THA,South-eastern Asia +TJK,Central Asia +TKL,Oceania +TKM,Central Asia +TLS,South-eastern Asia +TON,Oceania +TTO,Latin America and the Caribbean +TUN,Northern Africa +TUR,Western Asia +TUV,Oceania +TZA,Sub-Saharan Africa +UGA,Sub-Saharan Africa +UKR,Europe +URY,Latin America and the Caribbean +USA,Northern America +UZB,Central Asia +VCT,Latin America and the Caribbean +VEN,Latin America and the Caribbean +VGB,Latin America and the Caribbean +VIR,Latin America and the Caribbean +VNM,South-eastern Asia +VUT,Oceania +WLF,Oceania +WSM,Oceania +YEM,Western Asia +ZAF,Sub-Saharan Africa +ZMB,Sub-Saharan Africa +ZWE,Sub-Saharan Africa diff --git a/Data/world_shapefile/ne_50m_admin_0_countries.VERSION.txt b/Data/world_shapefile/ne_50m_admin_0_countries.VERSION.txt new file mode 100644 index 0000000000000000000000000000000000000000..ac14c3dfaa865ea332c62348d1bca867bdbbd1cf --- /dev/null +++ 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