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Hi,
With the attached raw data, i would like to make a map like below.
How should I set up to get that region associated with the prevalence value?
Country | Population | Region | Estimated prevalence of modern slavery per 1,000 population |
Afghanistan | 38928000 | Asia and the Pacific | 12.95997238 |
Albania | 2878000 | Europe and Central Asia | 11.81394482 |
Algeria | 43851000 | Africa | 1.922730684 |
Angola | 32866000 | Africa | 4.136548519 |
Antigua and Barbuda | 98000 | Americas | |
Argentina | 45196000 | Americas | 4.178747654 |
Armenia | 2963000 | Europe and Central Asia | 8.938274384 |
Australia | 25500000 | Asia and the Pacific | 1.609539032 |
Austria | 9006000 | Europe and Central Asia | 1.855276346 |
Azerbaijan | 10139000 | Europe and Central Asia | 10.56178951 |
Bahamas | 393000 | Americas | |
Bahrain | 1702000 | Arab States | 6.736337385 |
Bangladesh | 1.65E+08 | Asia and the Pacific | 7.05629921 |
Barbados | 287000 | Americas | |
Belarus | 9449000 | Europe and Central Asia | 11.34423828 |
Belgium | 11590000 | Europe and Central Asia | 0.971031845 |
Belize | 398000 | Americas | |
Benin | 12123000 | Africa | 3.042785406 |
Bolivia | 11673000 | Americas | 7.15239954 |
Bosnia and Herzegovina | 3281000 | Europe and Central Asia | 10.08794212 |
Botswana | 2352000 | Africa | 1.842855215 |
Brazil | 2.13E+08 | Americas | 4.954716682 |
Brunei Darussalam | 437000 | Asia and the Pacific | |
Bulgaria | 6948000 | Europe and Central Asia | 8.484215736 |
Burkina Faso | 20903000 | Africa | 3.701391697 |
Burundi | 11891000 | Africa | 7.506071568 |
Cambodia | 16719000 | Asia and the Pacific | 4.982629299 |
Cameroon | 26546000 | Africa | 5.84619236 |
Canada | 37742000 | Americas | 1.826060414 |
Cape Verde | 0 | Africa | |
Central African Republic | 4830000 | Africa | 5.247385025 |
Chad | 16426000 | Africa | 5.881432056 |
Chile | 19116000 | Americas | 3.173375845 |
China | 1.44E+09 | Asia and the Pacific | 4.009806633 |
Colombia | 50883000 | Americas | 7.808035374 |
Costa Rica | 5094000 | Americas | 3.193997383 |
Côte d'Ivoire | 26378000 | Africa | 7.312020302 |
Croatia | 4105000 | Europe and Central Asia | 5.2431674 |
Cuba | 11327000 | Americas | 5.406132221 |
Cyprus | 1207000 | Europe and Central Asia | 8.044442177 |
Czechia | 10709000 | Europe and Central Asia | 4.240109444 |
Democratic Republic of the Congo | 89561000 | Africa | 4.54722929 |
Denmark | 5792000 | Europe and Central Asia | 0.642040074 |
Djibouti | 988000 | Africa | 7.135610104 |
Dominican Republic | 10848000 | Americas | 6.632640839 |
Ecuador | 17643000 | Americas | 7.634540081 |
Egypt | 1.02E+08 | Africa | 4.31552124 |
El Salvador | 6486000 | Americas | 8.082976341 |
Equatorial Guinea | 1403000 | Africa | 7.812037945 |
Eritrea | 3546000 | Africa | 90.28486633 |
Estonia | 1327000 | Europe and Central Asia | 4.113351345 |
Eswatini | 1160000 | Africa | 3.60433197 |
Ethiopia | 1.15E+08 | Africa | 6.319561481 |
Fiji | 896000 | Asia and the Pacific | |
Finland | 5541000 | Europe and Central Asia | 1.412570953 |
France | 65274000 | Europe and Central Asia | 2.061271667 |
Gabon | 2226000 | Africa | 7.609169483 |
Gambia | 2417000 | Africa | 6.546003342 |
Georgia | 3989000 | Europe and Central Asia | 7.793662071 |
Germany | 83784000 | Europe and Central Asia | 0.564997911 |
Ghana | 31073000 | Africa | 2.93255806 |
Greece | 10423000 | Europe and Central Asia | 6.36806488 |
Guatemala | 17916000 | Americas | 7.820735931 |
Guinea | 13133000 | Africa | 4.023453236 |
Guinea-Bissau | 1968000 | Africa | 4.495571136 |
Guyana | 787000 | Americas | 4.188868523 |
Haiti | 11403000 | Americas | 8.217990875 |
Honduras | 9905000 | Americas | 6.956237793 |
Hong Kong | 7497000 | Asia and the Pacific | 2.762860775 |
Hungary | 9660000 | Europe and Central Asia | 6.565171719 |
Iceland | 341000 | Europe and Central Asia | |
India | 1.38E+09 | Asia and the Pacific | 8.007246017 |
Indonesia | 2.74E+08 | Asia and the Pacific | 6.702832222 |
Iran | 83993000 | Asia and the Pacific | 7.104307175 |
Iraq | 40223000 | Arab States | 5.492262267 |
Ireland | 4938000 | Europe and Central Asia | 1.101991296 |
Israel | 8656000 | Europe and Central Asia | 3.792859554 |
Italy | 60462000 | Europe and Central Asia | 3.262979269 |
Jamaica | 2961000 | Americas | 7.287474155 |
Japan | 1.26E+08 | Asia and the Pacific | 1.140809536 |
Jordan | 10203000 | Arab States | 10.00585597 |
Kazakhstan | 18777000 | Europe and Central Asia | 11.06179714 |
Kenya | 53771000 | Africa | 5.003462791 |
Kosovo | 0 | Europe and Central Asia | 8.021943092 |
Kuwait | 4271000 | Arab States | 12.95925319 |
Kyrgyzstan | 6524000 | Europe and Central Asia | 8.725506783 |
Lao PDR | 7276000 | Asia and the Pacific | 5.161807537 |
Latvia | 1886000 | Europe and Central Asia | 3.378555059 |
Lebanon | 6825000 | Arab States | 7.56728143 |
Lesotho | 2142000 | Africa | 1.646948338 |
Liberia | 5058000 | Africa | 3.145802498 |
Libya | 6871000 | Africa | 6.821667194 |
Liechtenstein | 0 | Europe and Central Asia | |
Lithuania | 2722000 | Europe and Central Asia | 6.08598423 |
Luxembourg | 626000 | Europe and Central Asia | |
Madagascar | 27691000 | Africa | 4.572755337 |
Malawi | 19130000 | Africa | 4.865484238 |
Malaysia | 32366000 | Asia and the Pacific | 6.255043507 |
Maldives | 541000 | Asia and the Pacific | |
Mali | 20251000 | Africa | 5.233090401 |
Malta | 442000 | Europe and Central Asia | |
Mauritania | 4650000 | Africa | 32.01548386 |
Mauritius | 1272000 | Africa | 1.507123351 |
Mexico | 1.29E+08 | Americas | 6.589330673 |
Moldova | 4034000 | Europe and Central Asia | 9.474068642 |
Mongolia | 3278000 | Asia and the Pacific | 4.044466496 |
Montenegro | 628000 | Europe and Central Asia | |
Morocco | 36911000 | Africa | 2.292283058 |
Mozambique | 31255000 | Africa | 2.974953413 |
Myanmar | 54410000 | Asia and the Pacific | 12.07966709 |
Namibia | 2541000 | Africa | 2.351321459 |
Nepal | 29137000 | Asia and the Pacific | 3.322043419 |
Netherlands | 17135000 | Europe and Central Asia | 0.568413556 |
New Zealand | 4822000 | Asia and the Pacific | 1.611743212 |
Nicaragua | 6625000 | Americas | 7.343417168 |
Niger | 24207000 | Africa | 4.645125389 |
Nigeria | 2.06E+08 | Africa | 7.814763546 |
North Korea | 25779000 | Asia and the Pacific | 104.6 |
North Macedonia | 2083000 | Europe and Central Asia | 12.60249519 |
Norway | 5421000 | Europe and Central Asia | 0.515135705 |
Oman | 5107000 | Arab States | 6.500495536 |
Pakistan | 2.21E+08 | Asia and the Pacific | 10.63333225 |
Palau | 0 | Asia and the Pacific | |
Panama | 4315000 | Americas | 4.661962509 |
Papua New Guinea | 8947000 | Asia and the Pacific | 10.34617519 |
Paraguay | 7133000 | Americas | 6.41713953 |
Peru | 32972000 | Americas | 7.098518372 |
Philippines | 1.1E+08 | Asia and the Pacific | 7.842455864 |
Poland | 37847000 | Europe and Central Asia | 5.510272026 |
Portugal | 10197000 | Europe and Central Asia | 3.813815355 |
Qatar | 2881000 | Arab States | 6.804503563 |
Republic of the Congo | 5518000 | Africa | 7.990757942 |
Romania | 19238000 | Europe and Central Asia | 7.549961567 |
Russia | 1.46E+08 | Europe and Central Asia | 13.01579189 |
Rwanda | 12952000 | Africa | 4.2523036 |
Saint Lucia | 184000 | Americas | |
Saint Vincent and the Grenadines | 111000 | Americas | |
Saudi Arabia | 34814000 | Arab States | 21.26152056 |
Senegal | 16744000 | Africa | 2.941902161 |
Serbia | 8737000 | Europe and Central Asia | 7.015529633 |
Seychelles | 98000 | Africa | |
Sierra Leone | 7977000 | Africa | 3.402239561 |
Singapore | 5850000 | Asia and the Pacific | 2.13350296 |
Slovakia | 5460000 | Europe and Central Asia | 7.651405811 |
Slovenia | 2079000 | Europe and Central Asia | 4.448514462 |
Solomon Islands | 687000 | Asia and the Pacific | |
Somalia | 15893000 | Africa | 6.173291206 |
South Africa | 59309000 | Africa | 2.662498236 |
South Korea | 51269000 | Asia and the Pacific | 3.50293541 |
South Sudan | 11194000 | Africa | 10.29315186 |
Spain | 46755000 | Europe and Central Asia | 2.312372208 |
Sri Lanka | 21413000 | Asia and the Pacific | 6.470595837 |
Sudan | 43849000 | Africa | 3.974432945 |
Suriname | 587000 | Americas | |
Sweden | 10099000 | Europe and Central Asia | 0.569348752 |
Switzerland | 8655000 | Europe and Central Asia | 0.498128146 |
Syria | 17501000 | Arab States | 8.733797199 |
Taiwan | 23817000 | Asia and the Pacific | 1.685270905 |
Tajikistan | 9538000 | Europe and Central Asia | 13.95576954 |
Tanzania | 59734000 | Africa | 2.859690666 |
Thailand | 69800000 | Asia and the Pacific | 5.741637707 |
Timor-Leste | 1318000 | Asia and the Pacific | 6.050777912 |
Togo | 8279000 | Africa | 3.349859715 |
Trinidad and Tobago | 1399000 | Americas | 4.731824875 |
Tunisia | 11819000 | Africa | 2.316662788 |
Türkiye | 84339000 | Europe and Central Asia | 15.64948082 |
Turkmenistan | 6031000 | Europe and Central Asia | 11.8753624 |
Uganda | 45741000 | Africa | 4.151514053 |
Ukraine | 43734000 | Europe and Central Asia | 12.79318714 |
United Arab Emirates | 9890000 | Arab States | 13.36672792 |
United Kingdom | 67886000 | Europe and Central Asia | 1.80371964 |
United States of America | 3.31E+08 | Americas | 3.295266867 |
Uruguay | 3474000 | Americas | 1.888077855 |
Uzbekistan | 33469000 | Europe and Central Asia | 7.44015789 |
Vanuatu | 307000 | Asia and the Pacific | |
Venezuela | 28436000 | Americas | 9.482327461 |
Viet Nam | 97339000 | Asia and the Pacific | 4.067520142 |
Yemen | 29826000 | Arab States | 6.049450596 |
Zambia | 18384000 | Africa | 5.092990398 |
Zimbabwe | 14863000 | Africa | 4.981103897 |
Solved! Go to Solution.
Hi @dogburalHK82 ,
Thanks @PhilipTreacy for the quick reply. I have some other ideas:
(1)We can create a Filled Map->Changing the fill color.
(2) We can create a rank column.
rank = IF([Estimated prevalence of modern slavery per 1,000 population]<> BLANK(), RANKX('Table',[Estimated prevalence of modern slavery per 1,000 population],,DESC,Dense) ,BLANK())
(3)Then the result is as follows.
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
Hi @dogburalHK82 ,
Thanks @PhilipTreacy for the quick reply. I have some other ideas:
(1)We can create a Filled Map->Changing the fill color.
(2) We can create a rank column.
rank = IF([Estimated prevalence of modern slavery per 1,000 population]<> BLANK(), RANKX('Table',[Estimated prevalence of modern slavery per 1,000 population],,DESC,Dense) ,BLANK())
(3)Then the result is as follows.
Best Regards,
Neeko Tang
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.
What you've shown in the image and what you are asking for are different. The map shows values plotted against countries, not regions.
If you import that data into PBI (via Power Query) and create a filled map, you get this
Do you want the data plotted aginst countries or regions? As a heatmap (varying shades of the same/similar colour) ?
Regards
Phil
Proud to be a Super User!
@PhilipTreacy my bad - I typed wrong... supposed to be legend (not region).
Like you said, the plot againt country with the legend varying shades of the same/similar colour.
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