ⓘ Global Data Lab publishes its own subnational regions, which match national admin units in some countries and group several together in others. The level shown is the nearest administrative tier, not a claim that these are that tier.
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
region_code |
Admin code | string | SEL | 100% | - | - |
region_name |
Admin name | string | SEL | 0% | 11 | Total, Central Equatoria, Eastern Equatoria, Jonglei, Lakes |
human_development_index |
Human development index | float | SEL | 0% | 89 | 0.396, 0.475, 0.383, 0.344, 0.356 |
health_index |
Health index | float | SEL | 0% | 93 | 0.577, 0.538, 0.53, 0.676, 0.636 |
education_index |
Education index | float | SEL | 0% | 56 | 0.277, 0.474, 0.287, 0.162, 0.174 |
income_index |
Income index | float | SEL | 0% | 59 | 0.389, 0.42, 0.368, 0.372, 0.406 |
life_expectancy |
Life expectancy | float | SEL | 0% | 99 | 57.49, 54.99, 54.47, 63.95, 61.36 |
mean_years_schooling |
Mean years schooling | float | SEL | 0% | 66 | 4.153, 6.705, 4.294, 2.604, 2.737 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 10 | 2010, 2010, 2010, 2010, 2010 |
level |
level | string | 0% | 2 | national, subnational, subnational, subnational, subnational |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SS, SS, SS, SS, SS |
population_count |
Population count | float | SEL | 2% | 65 | 2931559.0, 2976724.0, 3024308.0, 3072669.0, 3129918.0 |
life_expectancy |
Life expectancy | float | SEL | 3% | 64 | 28.054, 28.278, 28.506, 28.249, 32.458 |
gdp_per_capita_usd |
Gdp per capita usd | float | SEL | 88% | 8 | 1654.21153546555, 1323.22068747862, 1498.27034985194,... |
literacy_rate_pct |
Literacy rate percent | float | SEL | 99% | 1 | 26.8299999237061 |
under5_mortality_per_1000 |
Under5 mortality per 1000 | float | SEL | 3% | 60 | 354.9, 352.3, 349.4, 345.8, 342.7 |
poverty_headcount_pct |
Poverty headcount percent | float | SEL | 99% | 1 | 82.3 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | South Sudan, South Sudan, South Sudan, South Sudan, South Sudan |
year |
year | integer | 0% | 66 | 1960, 1961, 1962, 1963, 1964 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SS, SS, SS, SS, SS |
life_expectancy |
Life expectancy | float | SEL | 3% | 64 | 28.054, 28.278, 28.506, 28.249, 32.458 |
infant_mortality_per_1000 |
Infant mortality per 1000 | float | SEL | 47% | 32 | 65.8, 65.2, 64.6, 64.8, 64.1 |
under5_mortality_per_1000 |
Under5 mortality per 1000 | float | SEL | 3% | 60 | 354.9, 352.3, 349.4, 345.8, 342.7 |
maternal_mortality_per_100k |
Maternal mortality per 100k | float | SEL | 41% | 39 | 3845.0, 3720.0, 8045.0, 5535.0, 5103.0 |
fertility_rate |
Fertility rate | float | SEL | 3% | 61 | 7.161, 7.178, 7.195, 7.196, 7.22 |
birth_rate_per_1000 |
Birth rate per 1000 | float | SEL | 3% | 64 | 52.027, 52.287, 52.507, 52.683, 52.976 |
death_rate_per_1000 |
Death rate per 1000 | float | SEL | 3% | 63 | 37.378, 37.107, 36.832, 37.604, 32.277 |
physicians_per_1000 |
Physicians per 1000 | float | SEL | 94% | 3 | 0.041, 0.033, 0.043, 0.041 |
hospital_beds_per_1000 |
Hospital beds per 1000 | string | SEL | 100% | - | - |
vaccination_rate_pct |
Vaccination rate percent | float | SEL | 79% | 11 | 61.0, 63.0, 53.0, 50.0, 46.0 |
health_expenditure_pct_gdp |
Health expenditure percent gdp | float | SEL | 89% | 7 | 22.11322403, 11.69539356, 9.06570053, 9.35116673, 6.30117083 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | South Sudan, South Sudan, South Sudan, South Sudan, South Sudan |
year |
year | integer | 0% | 66 | 1960, 1961, 1962, 1963, 1964 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
pcode |
Admin code | string | SEL | 100% | - | - |
region_name |
Region name | string | SEL | 0% | 1 | South Sudan |
F_TL |
Female population | integer | SEL | 0% | 1 | 6243943 |
M_TL |
Male population | integer | SEL | 0% | 1 | 6151029 |
T_TL |
Total population | integer | SEL | 0% | 1 | 12394970 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
admin_level |
Admin level | string | 0% | 1 | admin_0 |
year |
Reference year | string | 100% | - | - |
ADM0_PCODE |
ADM0_PCODE | string | 0% | 1 | SS |
F_00_04 |
Female population age 0-4 | integer | 0% | 1 | 1113239 |
F_05_17 |
Female population age 5-17 | integer | 0% | 1 | 2156956 |
F_18_60 |
Female population age 18-60 | integer | 0% | 1 | 2504834 |
F_61plus |
F_61plus | integer | 0% | 1 | 468914 |
M_00_04 |
Male population age 0-4 | integer | 0% | 1 | 1192983 |
M_05_17 |
Male population age 5-17 | integer | 0% | 1 | 2165580 |
M_18_60 |
Male population age 18-60 | integer | 0% | 1 | 2369296 |
M_61plus |
M_61plus | integer | 0% | 1 | 423170 |
T_00_04 |
Total population age 0-4 | integer | 0% | 1 | 2306221 |
T_05_17 |
Total population age 5-17 | integer | 0% | 1 | 4322537 |
T_18_60 |
Total population age 18-60 | integer | 0% | 1 | 4874126 |
T_61plus |
T_61plus | integer | 0% | 1 | 892086 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
pcode |
Admin code | string | SEL | 0% | 10 | SS01, SS02, SS03, SS04, SS05 |
region_name |
Region name | string | SEL | 0% | 10 | Central Equatoria, Eastern Equatoria, Jonglei, Lakes,... |
F_TL |
Female population | integer | SEL | 0% | 10 | 785160, 567603, 1031609, 602038, 487231 |
M_TL |
Male population | integer | SEL | 0% | 10 | 760518, 557746, 1000167, 607714, 447923 |
T_TL |
Total population | integer | SEL | 0% | 10 | 1545676, 1125347, 2031777, 1209753, 935155 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
admin_level |
Admin level | string | 0% | 1 | admin_1, admin_1, admin_1, admin_1, admin_1 |
year |
Reference year | string | 100% | - | - |
ADM0_PCODE |
ADM0_PCODE | string | 0% | 1 | SS, SS, SS, SS, SS |
F_00_04 |
Female population age 0-4 | integer | 0% | 10 | 139922, 116343, 195791, 84378, 90136 |
F_05_17 |
Female population age 5-17 | integer | 0% | 10 | 226855, 190487, 330744, 249695, 189500 |
F_18_60 |
Female population age 18-60 | integer | 0% | 10 | 368594, 225308, 422456, 224594, 175937 |
F_61plus |
F_61plus | integer | 0% | 10 | 49789, 35465, 82618, 43371, 31658 |
M_00_04 |
Male population age 0-4 | integer | 0% | 10 | 115080, 132441, 227165, 103358, 92719 |
M_05_17 |
Male population age 5-17 | integer | 0% | 10 | 217924, 196996, 345604, 241246, 176137 |
M_18_60 |
Male population age 18-60 | integer | 0% | 10 | 357949, 202965, 364209, 232025, 154079 |
M_61plus |
M_61plus | integer | 0% | 10 | 69565, 25344, 63189, 31085, 24988 |
T_00_04 |
Total population age 0-4 | integer | 0% | 10 | 255002, 248782, 422957, 187735, 182856 |
T_05_17 |
Total population age 5-17 | integer | 0% | 10 | 444777, 387483, 676348, 490942, 365639 |
T_18_60 |
Total population age 18-60 | integer | 0% | 10 | 726542, 428272, 786667, 456618, 330016 |
T_61plus |
T_61plus | integer | 0% | 10 | 119355, 60810, 145805, 74458, 56644 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
pcode |
Admin code | string | SEL | 0% | 78 | SS0101, SS0102, SS0103, SS0104, SS0105 |
region_name |
Region name | string | SEL | 0% | 78 | Juba, Kajo-keji, Lainya, Morobo, Terekeka |
F_TL |
Female population | integer | SEL | 0% | 78 | 269978, 109627, 53673, 56837, 133979 |
M_TL |
Male population | integer | SEL | 0% | 78 | 252729, 123029, 60040, 59180, 124418 |
T_TL |
Total population | integer | SEL | 0% | 78 | 522706, 232657, 113712, 116016, 258397 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
admin_level |
Admin level | string | 0% | 1 | admin_2, admin_2, admin_2, admin_2, admin_2 |
year |
Reference year | string | 100% | - | - |
ADM0_PCODE |
ADM0_PCODE | string | 0% | 1 | SS, SS, SS, SS, SS |
F_00_04 |
Female population age 0-4 | integer | 0% | 78 | 50912, 7003, 11485, 12182, 21473 |
F_05_17 |
Female population age 5-17 | integer | 0% | 78 | 83110, 13517, 11826, 18911, 42377 |
F_18_60 |
Female population age 18-60 | integer | 0% | 78 | 125972, 74450, 26609, 23551, 59690 |
F_61plus |
F_61plus | integer | 0% | 78 | 9984, 14657, 3753, 2193, 10439 |
M_00_04 |
Male population age 0-4 | integer | 0% | 77 | 38680, 3792, 11940, 9455, 22868 |
M_05_17 |
Male population age 5-17 | integer | 0% | 78 | 80497, 13378, 12508, 20651, 46770 |
M_18_60 |
Male population age 18-60 | integer | 0% | 77 | 121268, 76312, 32522, 25292, 49974 |
M_61plus |
M_61plus | integer | 0% | 78 | 12284, 29547, 3070, 3782, 4806 |
T_00_04 |
Total population age 0-4 | integer | 0% | 78 | 89592, 10795, 23425, 21637, 44341 |
T_05_17 |
Total population age 5-17 | integer | 0% | 78 | 163607, 26895, 24334, 39561, 89147 |
T_18_60 |
Total population age 18-60 | integer | 0% | 78 | 247240, 150762, 59130, 48843, 109664 |
T_61plus |
T_61plus | integer | 0% | 78 | 22267, 44205, 6823, 5975, 15245 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
glottocode |
Glottocode | string | CCL | 0% | 87 | acol1236, ajas1235, anua1242, arin1244, avok1242 |
name |
Name | string | CCL | 0% | 87 | Acoli, Aja (South Sudan), Anuak, Aringa, Avokaya |
iso639_3 |
Iso639 3 | string | CCL | 6% | 82 | ach, aja, anu, luc, avu |
level |
Level | string | CCL | 0% | 1 | language, language, language, language, language |
family_name |
Family name | string | CCL | 100% | - | - |
family_glottocode |
Family glottocode | string | CCL | 0% | 10 | nilo1247, kres1240, nilo1247, cent2225, cent2225 |
parent_name |
Parent name | string | CCL | 100% | - | - |
parent_glottocode |
Parent glottocode | string | CCL | 0% | 53 | sout2831, kres1240, nort2814, lugb1241, avok1245 |
endangerment_status |
Endangerment status | string | CCL | 100% | - | - |
country_codes |
Country codes | string | SEL+ | 0% | 15 | ['SS', 'UG'], ['SD', 'SS'], ['ET', 'SS'], ['SS', 'UG'],... |
child_family_count |
Child family count | integer | CCL | 0% | 1 | 0, 0, 0, 0, 0 |
child_language_count |
Child language count | integer | CCL | 0% | 1 | 0, 0, 0, 0, 0 |
child_dialect_count |
Child dialect count | integer | CCL | 0% | 13 | 3, 0, 4, 0, 4 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
latitude |
latitude | float | 0% | 87 | 3.57738, 8.58003, 7.57714, 3.56143, 4.38639 |
longitude |
longitude | float | 0% | 87 | 32.5147, 25.6122, 34.0267, 31.3071, 29.9379 |
classification |
classification | string | 0% | 1 | [], [], [], [], [] |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_of_origin_iso |
Country of origin iso | string | CCL | 0% | 1 | -, -, -, -, - |
refugees |
Refugees | integer | CCL | 0% | 75 | 2116011, 1952928, 1847304, 1749628, 1717966 |
idps |
Idps | integer | CCL | 0% | 34 | 0, 0, 0, 0, 0 |
stateless |
Stateless | integer | CCL | 0% | 23 | 0, 0, 0, 0, 0 |
others_of_concern |
Others of concern | integer | CCL | 0% | 30 | 0, 0, 0, 0, 0 |
total_population |
Total population | string | CCL | 100% | - | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 75 | 1951, 1952, 1953, 1954, 1955 |
country_of_origin |
country_of_origin | string | 100% | - | - |
country_of_asylum |
country_of_asylum | string | 100% | - | - |
country_of_asylum_iso |
country_of_asylum_iso | string | 0% | 1 | -, -, -, -, - |
population_type |
population_type | string | 100% | - | - |
asylum_seekers |
asylum_seekers | integer | 0% | 34 | 0, 0, 0, 0, 0 |
female_total |
female_total | string | 100% | - | - |
male_total |
male_total | string | 100% | - | - |
female_0_4 |
female_0_4 | string | 100% | - | - |
female_5_11 |
female_5_11 | string | 100% | - | - |
female_12_17 |
female_12_17 | string | 100% | - | - |
female_18_59 |
female_18_59 | string | 100% | - | - |
female_60_plus |
female_60_plus | string | 100% | - | - |
male_0_4 |
male_0_4 | string | 100% | - | - |
male_5_11 |
male_5_11 | string | 100% | - | - |
male_12_17 |
male_12_17 | string | 100% | - | - |
male_18_59 |
male_18_59 | string | 100% | - | - |
male_60_plus |
male_60_plus | string | 100% | - | - |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_of_origin_iso |
Country of origin iso | string | CCL | 0% | 1 | -, -, -, -, - |
refugees |
Refugees | integer | CCL | 0% | 75 | 2116011, 1952928, 1847304, 1749628, 1717966 |
idps |
Idps | integer | CCL | 0% | 34 | 0, 0, 0, 0, 0 |
stateless |
Stateless | integer | CCL | 0% | 23 | 0, 0, 0, 0, 0 |
others_of_concern |
Others of concern | integer | CCL | 0% | 30 | 0, 0, 0, 0, 0 |
total_population |
Total population | string | CCL | 100% | - | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 75 | 1951, 1952, 1953, 1954, 1955 |
country_of_origin |
country_of_origin | string | 100% | - | - |
country_of_asylum |
country_of_asylum | string | 100% | - | - |
country_of_asylum_iso |
country_of_asylum_iso | string | 0% | 1 | -, -, -, -, - |
population_type |
population_type | string | 100% | - | - |
asylum_seekers |
asylum_seekers | integer | 0% | 34 | 0, 0, 0, 0, 0 |
female_total |
female_total | string | 100% | - | - |
male_total |
male_total | string | 100% | - | - |
female_0_4 |
female_0_4 | string | 100% | - | - |
female_5_11 |
female_5_11 | string | 100% | - | - |
female_12_17 |
female_12_17 | string | 100% | - | - |
female_18_59 |
female_18_59 | string | 100% | - | - |
female_60_plus |
female_60_plus | string | 100% | - | - |
male_0_4 |
male_0_4 | string | 100% | - | - |
male_5_11 |
male_5_11 | string | 100% | - | - |
male_12_17 |
male_12_17 | string | 100% | - | - |
male_18_59 |
male_18_59 | string | 100% | - | - |
male_60_plus |
male_60_plus | string | 100% | - | - |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
admin_level |
Admin level | string | SEL | 0% | 1 | admin_0 |
admin_name |
Admin name | string | SEL | 0% | 1 | South Sudan |
admin_code |
Admin code | string | SEL | 0% | 1 | 54869323B67570698233756 |
area_sqkm |
Area sqkm | float | SEL | 0% | 1 | 632498.1008 |
pop_2024 |
Population count | integer | SEL | 0% | 1 | 12695657 |
pop_density_2024 |
Population density | float | SEL | 0% | 1 | 20.07 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
admin_level |
Admin level | string | SEL | 0% | 1 | admin_1, admin_1, admin_1, admin_1, admin_1 |
admin_name |
Admin name | string | SEL | 0% | 10 | Warrap, Northern Bahr el Ghazal, Central Equatoria, Lakes, Unity |
admin_code |
Admin code | string | SEL | 0% | 10 | 48771326B11845073429118, 48771326B49579010016854,... |
area_sqkm |
Area sqkm | float | SEL | 0% | 10 | 35854.8026, 30852.0094, 43078.53, 43211.8768, 37084.8019 |
pop_2024 |
Population count | integer | SEL | 0% | 10 | 2712872, 1977735, 1360242, 1304806, 1209784 |
pop_density_2024 |
Population density | float | SEL | 0% | 10 | 75.66, 64.1, 31.58, 30.2, 32.62 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
admin_level |
Admin level | string | SEL | 0% | 1 | locality, locality, locality, locality, locality |
admin_name |
Admin name | string | SEL | 0% | 17 | Juba, Wau, Yei, Nimule, Bor |
admin_code |
Admin code | integer | SEL | 0% | 17 | 986, 118, 598, 1104, 866 |
area_sqkm |
Area sqkm | float | SEL | 0% | 17 | 78.4788, 30.8, 23.8408, 13.9072, 18.8761 |
pop_2024 |
Population count | integer | SEL | 0% | 17 | 480828, 169561, 87669, 66126, 65369 |
pop_density_2024 |
Population density | float | SEL | 0% | 17 | 6126.85, 5505.23, 3677.27, 4754.8, 3463.06 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
ghs_pop_2025 |
ghs_pop_2025 | integer | 0% | 17 | 296087, 115385, 166143, 665398, 139876 |
landscan_vs_ghs_ratio |
landscan_vs_ghs_ratio | float | 0% | 16 | 1.624, 1.47, 0.528, 0.099, 0.467 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
society_id |
Society id | string | CCL | 0% | 18 | Ai33, Ai34, Ai35, Ai36, Ai38 |
society_name |
Society name | string | CCL | 0% | 18 | Madi, Moru, Bongo, Jur, Korongo |
language_glottocode |
Language glottocode | string | CCL | 0% | 18 | madi1260, moru1253, bong1285, luwo1239, kron1241 |
language_name |
Language name | string | CCL | 0% | 1 | , , , , |
kinship_system |
Kinship system | string | CCL | 0% | 15 | EA001:0; EA002:1; EA003:2; EA004:3; EA005:4, EA001:0;... |
marriage_pattern |
Marriage pattern | string | CCL | 0% | 11 | EA006:1; EA007:2; EA008:8; EA009:5; EA010:8, EA006:1;... |
subsistence_pattern |
Subsistence pattern | string | CCL | 0% | 14 | EA028:3; EA029:6; EA030:5; EA031:NA; EA032:3, EA028:3;... |
political_complexity |
Political complexity | string | CCL | 0% | 7 | EA033:2; EA034:2; EA035:NA, EA033:2; EA034:NA; EA035:NA,... |
religion_importance |
Religion importance | string | CCL | 0% | 9 | EA034:2; EA112:NA, EA034:NA; EA112:NA, EA034:2; EA112:3,... |
residence_pattern |
Residence pattern | string | CCL | 0% | 5 | EA011:1; EA012:8; EA013:9, EA011:1; EA012:8; EA013:9,... |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name |
country_name | string | 0% | 1 | South Sudan, South Sudan, South Sudan, South Sudan, South Sudan |
dataset |
dataset | string | 0% | 1 | EA, EA, EA, EA, EA |
region |
region | string | 0% | 1 | , , , , |
latitude |
latitude | float | 0% | 7 | 4.0, 5.0, 7.0, 8.0, 10.0 |
longitude |
longitude | float | 0% | 8 | 32.0, 30.0, 29.0, 28.0, 30.0 |
assignment_method |
assignment_method | string | 0% | 1 | point_in_polygon, point_in_polygon, point_in_polygon,... |
assignment_confidence |
assignment_confidence | string | 0% | 1 | approximate, approximate, approximate, approximate, approximate |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
city_name |
Admin name | string | SEL | 0% | 17 | Nimule, Torit, Riwoto, Kapoeta, Juba |
country_code |
Country code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
population |
Population count | integer | SEL | 0% | 17 | 665398, 526257, 432310, 345377, 296087 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
city_id |
city_id | integer | 0% | 17 | 1104, 1526, 1975, 2214, 986 |
name_alternates |
name_alternates | string | 100% | - | - |
country_name |
country_name | string | 0% | 1 | South Sudan, South Sudan, South Sudan, South Sudan, South Sudan |
population_year |
population_year | integer | 0% | 1 | 2025, 2025, 2025, 2025, 2025 |
latitude |
latitude | string | 100% | - | - |
longitude |
longitude | string | 100% | - | - |
region |
region | string | 100% | - | - |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
telephones_fixed_lines_subscriptions_per_100_inhabitants_numeric |
Fixed line subscriptions per 100 | float | SEL | 0% | 1 | 2023.0 |
telephones_mobile_cellular_subscriptions_per_100_inhabitants_numeric |
Mobile subscriptions per 100 | float | SEL | 0% | 1 | 30.0 |
internet_country_code_text |
Internet country code text | string | SEL+ | 0% | 1 | .ss |
internet_users_percent_of_population_numeric |
Internet penetration percent | float | SEL | 0% | 1 | 9.0 |
broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_numeric |
Broadband subscriptions per 100 | float | SEL | 0% | 1 | 2023.0 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
telephones_fixed_lines_total_subscriptions_text |
telephones_fixed_lines_total_subscriptions_text | string | 0% | 1 | 0 (2023 est.) |
telephones_fixed_lines_total_subscriptions_numeric |
telephones_fixed_lines_total_subscriptions_numeric | float | 0% | 1 | 0.0 |
telephones_fixed_lines_subscriptions_per_100_inhabitants_text |
telephones_fixed_lines_subscriptions_per_100_inhabitants_text | string | 0% | 1 | (2023 est.) less than 1 |
telephones_mobile_cellular_total_subscriptions_text |
telephones_mobile_cellular_total_subscriptions_text | string | 0% | 1 | 6.17 million (2023 est.) |
telephones_mobile_cellular_total_subscriptions_numeric |
telephones_mobile_cellular_total_subscriptions_numeric | float | 0% | 1 | 6.17 |
telephones_mobile_cellular_subscriptions_per_100_inhabitants_text |
telephones_mobile_cellular_subscriptions_per_100_inhabitants_text | string | 0% | 1 | 30 (2022 est.) |
broadcast_media_text |
broadcast_media_text | string | 0% | 1 | 1 state-controlled TV channel and radio station; several... |
broadcast_media_numeric |
broadcast_media_numeric | float | 0% | 1 | 1.0 |
internet_users_percent_of_population_text |
internet_users_percent_of_population_text | string | 0% | 1 | 9% (2022 est.) |
broadband_fixed_subscriptions_total_text |
broadband_fixed_subscriptions_total_text | string | 0% | 1 | 0 (2023 est.) |
broadband_fixed_subscriptions_total_numeric |
broadband_fixed_subscriptions_total_numeric | float | 0% | 1 | 0.0 |
broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text |
broadband_fixed_subscriptions_subscriptions_per_100_inhabitants_text | string | 0% | 1 | (2023 est.) less than 1 |
source_section |
source_section | string | 0% | 1 | Communications |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
gdp_official_exchange_rate_numeric |
Gdp total usd | float | SEL | 0% | 1 | 4.629 |
population_below_poverty_line_numeric |
Poverty headcount percent | float | SEL | 0% | 1 | 82.3 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
economic_overview_text |
economic_overview_text | string | 0% | 1 | low-income, oil-based Sahelian economy; extreme poverty... |
economic_overview_numeric |
economic_overview_numeric | float | 0% | 1 | -19.0 |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_text |
Real gdp purchasing power parity 2023 (text) | string | 0% | 1 | $6.752 billion (2023 est.) |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2023_numeric |
Real gdp purchasing power parity 2023 (numeric) | float | 0% | 1 | 6.752 |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_text |
Real gdp purchasing power parity 2022 (text) | string | 0% | 1 | $6.585 billion (2022 est.) |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2022_numeric |
Real gdp purchasing power parity 2022 (numeric) | float | 0% | 1 | 6.585 |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2021_text |
Real gdp purchasing power parity 2021 (text) | string | 0% | 1 | $6.945 billion (2021 est.) |
real_gdp_purchasing_power_parity_real_gdp_purchasing_power_parity_2021_numeric |
Real gdp purchasing power parity 2021 (numeric) | float | 0% | 1 | 6.945 |
real_gdp_purchasing_power_parity_note |
real_gdp_purchasing_power_parity_note | string | 0% | 1 | note: data in 2015 dollars |
real_gdp_growth_rate_real_gdp_growth_rate_2017_text |
Real gdp growth rate 2017 (text) | string | 0% | 1 | -5.2% (2017 est.) |
real_gdp_growth_rate_real_gdp_growth_rate_2017_numeric |
Real gdp growth rate 2017 (numeric) | float | 0% | 1 | -5.2 |
real_gdp_growth_rate_real_gdp_growth_rate_2016_text |
Real gdp growth rate 2016 (text) | string | 0% | 1 | -13.9% (2016 est.) |
real_gdp_growth_rate_real_gdp_growth_rate_2016_numeric |
Real gdp growth rate 2016 (numeric) | float | 0% | 1 | -13.9 |
real_gdp_growth_rate_real_gdp_growth_rate_2015_text |
Real gdp growth rate 2015 (text) | string | 0% | 1 | -10.8% (2015 est.) |
real_gdp_growth_rate_real_gdp_growth_rate_2015_numeric |
Real gdp growth rate 2015 (numeric) | float | 0% | 1 | -10.8 |
real_gdp_growth_rate_note |
real_gdp_growth_rate_note | string | 0% | 1 | note: annual GDP % growth based on constant local currency |
real_gdp_per_capita_real_gdp_per_capita_2023_text |
Real gdp per capita 2023 (text) | string | 0% | 1 | $400 (2023 est.) |
real_gdp_per_capita_real_gdp_per_capita_2023_numeric |
Real gdp per capita 2023 (numeric) | float | 0% | 1 | 400.0 |
real_gdp_per_capita_real_gdp_per_capita_2022_text |
Real gdp per capita 2022 (text) | string | 0% | 1 | $400 (2022 est.) |
real_gdp_per_capita_real_gdp_per_capita_2022_numeric |
Real gdp per capita 2022 (numeric) | float | 0% | 1 | 400.0 |
real_gdp_per_capita_real_gdp_per_capita_2021_text |
Real gdp per capita 2021 (text) | string | 0% | 1 | $400 (2021 est.) |
real_gdp_per_capita_real_gdp_per_capita_2021_numeric |
Real gdp per capita 2021 (numeric) | float | 0% | 1 | 400.0 |
real_gdp_per_capita_note |
real_gdp_per_capita_note | string | 0% | 1 | note: data in 2015 dollars |
gdp_official_exchange_rate_text |
gdp_official_exchange_rate_text | string | 0% | 1 | $4.629 billion (2023 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_text |
Inflation rate consumer prices 2024 (text) | string | 0% | 1 | 91.4% (2024 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2024_numeric |
Inflation rate consumer prices 2024 (numeric) | float | 0% | 1 | 91.4 |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_text |
Inflation rate consumer prices 2023 (text) | string | 0% | 1 | 2.4% (2023 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2023_numeric |
Inflation rate consumer prices 2023 (numeric) | float | 0% | 1 | 2.4 |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_text |
Inflation rate consumer prices 2022 (text) | string | 0% | 1 | -6.7% (2022 est.) |
inflation_rate_consumer_prices_inflation_rate_consumer_prices_2022_numeric |
Inflation rate consumer prices 2022 (numeric) | float | 0% | 1 | -6.7 |
| +100 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
electricity_access_electrification_total_population_numeric |
Electricity access percent | float | SEL | 0% | 1 | 8.4 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
electricity_access_electrification_total_population_text |
electricity_access_electrification_total_population_text | string | 0% | 1 | 8.4% (2022 est.) |
electricity_access_electrification_urban_areas_text |
electricity_access_electrification_urban_areas_text | string | 0% | 1 | 15% |
electricity_access_electrification_urban_areas_numeric |
electricity_access_electrification_urban_areas_numeric | float | 0% | 1 | 15.0 |
electricity_access_electrification_rural_areas_text |
electricity_access_electrification_rural_areas_text | string | 0% | 1 | 1.7% |
electricity_access_electrification_rural_areas_numeric |
electricity_access_electrification_rural_areas_numeric | float | 0% | 1 | 1.7 |
electricity_installed_generating_capacity_text |
electricity_installed_generating_capacity_text | string | 0% | 1 | 136,000 kW (2023 est.) |
electricity_installed_generating_capacity_numeric |
electricity_installed_generating_capacity_numeric | float | 0% | 1 | 136000.0 |
electricity_consumption_text |
electricity_consumption_text | string | 0% | 1 | 566.034 million kWh (2023 est.) |
electricity_consumption_numeric |
electricity_consumption_numeric | float | 0% | 1 | 566.034 |
electricity_transmission_distribution_losses_text |
electricity_transmission_distribution_losses_text | string | 0% | 1 | 23.966 million kWh (2023 est.) |
electricity_transmission_distribution_losses_numeric |
electricity_transmission_distribution_losses_numeric | float | 0% | 1 | 23.966 |
electricity_generation_sources_fossil_fuels_text |
electricity_generation_sources_fossil_fuels_text | string | 0% | 1 | 93.2% of total installed capacity (2023 est.) |
electricity_generation_sources_fossil_fuels_numeric |
electricity_generation_sources_fossil_fuels_numeric | float | 0% | 1 | 93.2 |
electricity_generation_sources_solar_text |
electricity_generation_sources_solar_text | string | 0% | 1 | 6.8% of total installed capacity (2023 est.) |
electricity_generation_sources_solar_numeric |
electricity_generation_sources_solar_numeric | float | 0% | 1 | 6.8 |
coal_imports_text |
coal_imports_text | string | 0% | 1 | 100 metric tons (2022 est.) |
coal_imports_numeric |
coal_imports_numeric | float | 0% | 1 | 100.0 |
petroleum_total_petroleum_production_text |
petroleum_total_petroleum_production_text | string | 0% | 1 | 146,000 bbl/day (2023 est.) |
petroleum_total_petroleum_production_numeric |
petroleum_total_petroleum_production_numeric | float | 0% | 1 | 146000.0 |
petroleum_refined_petroleum_consumption_text |
petroleum_refined_petroleum_consumption_text | string | 0% | 1 | 11,000 bbl/day (2023 est.) |
petroleum_refined_petroleum_consumption_numeric |
petroleum_refined_petroleum_consumption_numeric | float | 0% | 1 | 11000.0 |
petroleum_crude_oil_estimated_reserves_text |
petroleum_crude_oil_estimated_reserves_text | string | 0% | 1 | 3.75 billion barrels (2021 est.) |
petroleum_crude_oil_estimated_reserves_numeric |
petroleum_crude_oil_estimated_reserves_numeric | float | 0% | 1 | 3.75 |
energy_consumption_per_capita_total_energy_consumption_per_capita_2023_text |
energy_consumption_per_capita_total_energy_consumption_per_capita_2023_text | string | 0% | 1 | 2.092 million Btu/person (2023 est.) |
energy_consumption_per_capita_total_energy_consumption_per_capita_2023_numeric |
energy_consumption_per_capita_total_energy_consumption_per_capita_2023_numeric | float | 0% | 1 | 2.092 |
source_section |
source_section | string | 0% | 1 | Energy |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
land_use_agricultural_land_numeric |
Agricultural land percent | float | SEL | 0% | 1 | 44.9 |
land_use_forest_numeric |
Forest area percent | float | SEL | 0% | 1 | 11.3 |
urbanization_urban_population_numeric |
Urban population percent | float | SEL | 0% | 1 | 21.2 |
urbanization_rate_of_urbanization_numeric |
Urban growth rate percent | float | SEL | 0% | 1 | 4.12 |
waste_and_recycling_municipal_solid_waste_generated_annually_numeric |
Municipal waste kg per capita | float | SEL | 0% | 1 | 2.681 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
environmental_issues_text |
environmental_issues_text | string | 0% | 1 | water pollution; inadequate supplies of potable water;... |
international_environmental_agreements_party_to_text |
international_environmental_agreements_party_to_text | string | 0% | 1 | Biodiversity, Climate Change, Climate Change-Paris... |
international_environmental_agreements_signed_but_not_ratified_text |
international_environmental_agreements_signed_but_not_ratified_text | string | 0% | 1 | none of the selected agreements |
climate_text |
climate_text | string | 0% | 1 | hot with seasonal rainfall influenced by the annual... |
land_use_agricultural_land_text |
land_use_agricultural_land_text | string | 0% | 1 | 44.9% (2023 est.) |
land_use_agricultural_land_arable_land_text |
land_use_agricultural_land_arable_land_text | string | 0% | 1 | arable land: 3.9% (2023 est.) |
land_use_agricultural_land_arable_land_numeric |
land_use_agricultural_land_arable_land_numeric | float | 0% | 1 | 3.9 |
land_use_agricultural_land_permanent_crops_text |
land_use_agricultural_land_permanent_crops_text | string | 0% | 1 | permanent crops: 0.1% (2023 est.) |
land_use_agricultural_land_permanent_crops_numeric |
land_use_agricultural_land_permanent_crops_numeric | float | 0% | 1 | 0.1 |
land_use_agricultural_land_permanent_pasture_text |
land_use_agricultural_land_permanent_pasture_text | string | 0% | 1 | permanent pasture: 40.8% (2023 est.) |
land_use_agricultural_land_permanent_pasture_numeric |
land_use_agricultural_land_permanent_pasture_numeric | float | 0% | 1 | 40.8 |
land_use_forest_text |
land_use_forest_text | string | 0% | 1 | 11.3% (2023 est.) |
land_use_other_text |
land_use_other_text | string | 0% | 1 | 43.8% (2023 est.) |
land_use_other_numeric |
land_use_other_numeric | float | 0% | 1 | 43.8 |
urbanization_urban_population_text |
urbanization_urban_population_text | string | 0% | 1 | 21.2% of total population (2023) |
urbanization_rate_of_urbanization_text |
urbanization_rate_of_urbanization_text | string | 0% | 1 | 4.12% annual rate of change (2020-25 est.) |
carbon_dioxide_emissions_total_emissions_text |
carbon_dioxide_emissions_total_emissions_text | string | 0% | 1 | 1.725 million metric tonnes of CO2 (2023 est.) |
carbon_dioxide_emissions_total_emissions_numeric |
carbon_dioxide_emissions_total_emissions_numeric | float | 0% | 1 | 1.725 |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_text |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_text | string | 0% | 1 | 1.725 million metric tonnes of CO2 (2023 est.) |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric |
carbon_dioxide_emissions_from_petroleum_and_other_liquids_numeric | float | 0% | 1 | 1.725 |
particulate_matter_emissions_text |
particulate_matter_emissions_text | string | 0% | 1 | 20.6 micrograms per cubic meter (2019 est.) |
particulate_matter_emissions_numeric |
particulate_matter_emissions_numeric | float | 0% | 1 | 20.6 |
methane_emissions_energy_text |
methane_emissions_energy_text | string | 0% | 1 | 59.4 kt (2022-2024 est.) |
methane_emissions_energy_numeric |
methane_emissions_energy_numeric | float | 0% | 1 | 59.4 |
methane_emissions_agriculture_text |
methane_emissions_agriculture_text | string | 0% | 1 | 696 kt (2019-2021 est.) |
methane_emissions_agriculture_numeric |
methane_emissions_agriculture_numeric | float | 0% | 1 | 696.0 |
methane_emissions_waste_text |
methane_emissions_waste_text | string | 0% | 1 | 120.2 kt (2019-2021 est.) |
methane_emissions_waste_numeric |
methane_emissions_waste_numeric | float | 0% | 1 | 120.2 |
methane_emissions_other_text |
methane_emissions_other_text | string | 0% | 1 | 12.7 kt (2019-2021 est.) |
methane_emissions_other_numeric |
methane_emissions_other_numeric | float | 0% | 1 | 12.7 |
| +11 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
nationality_noun_text |
nationality_noun_text | string | 0% | 1 | South Sudanese (singular and plural) |
nationality_adjective_text |
nationality_adjective_text | string | 0% | 1 | South Sudanese |
ethnic_groups_text |
ethnic_groups_text | string | 0% | 1 | Dinka (Jieng) approximately 35-40%, Nuer (Naath)... |
ethnic_groups_numeric |
ethnic_groups_numeric | float | 0% | 1 | 35.0 |
source_section |
source_section | string | 0% | 1 | People and Society:ethnic_groups |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
area_total_numeric |
Area sqkm | float | SEL | 0% | 1 | 644329.0 |
land_boundaries_total_numeric |
Land boundary km | float | SEL | 0% | 1 | 6018.0 |
coastline_numeric |
Coastline km | float | SEL | 0% | 1 | 0.0 |
elevation_highest_point_numeric |
Elevation max m | float | SEL | 0% | 1 | 3187.0 |
elevation_lowest_point_numeric |
Elevation min m | float | SEL | 0% | 1 | 381.0 |
land_use_agricultural_land_numeric |
Agricultural land percent | float | SEL | 0% | 1 | 44.9 |
land_use_forest_numeric |
Forest area percent | float | SEL | 0% | 1 | 11.3 |
irrigated_land_numeric |
Irrigated land sqkm | float | SEL | 0% | 1 | 1000.0 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
location_text |
location_text | string | 0% | 1 | East-Central Africa; south of Sudan, north of Uganda and... |
geographic_coordinates_text |
geographic_coordinates_text | string | 0% | 1 | 8 00 N, 30 00 E |
geographic_coordinates_numeric |
geographic_coordinates_numeric | float | 0% | 1 | 8.0 |
map_references_text |
map_references_text | string | 0% | 1 | Africa |
area_total_text |
area_total_text | string | 0% | 1 | 644,329 sq km |
area_land_text |
area_land_text | string | 0% | 1 | NA |
area_water_text |
area_water_text | string | 0% | 1 | NA |
area_comparative_text |
area_comparative_text | string | 0% | 1 | more than four times the size of Georgia; slightly... |
land_boundaries_total_text |
land_boundaries_total_text | string | 0% | 1 | 6,018 km |
land_boundaries_border_countries_text |
land_boundaries_border_countries_text | string | 0% | 1 | Central African Republic 1,055 km; Democratic Republic... |
land_boundaries_border_countries_numeric |
land_boundaries_border_countries_numeric | float | 0% | 1 | 1055.0 |
land_boundaries_note |
land_boundaries_note | string | 0% | 1 | note: South Sudan-Sudan boundary represents 1 January... |
coastline_text |
coastline_text | string | 0% | 1 | 0 km (landlocked) |
maritime_claims_text |
maritime_claims_text | string | 0% | 1 | none (landlocked) |
climate_text |
climate_text | string | 0% | 1 | hot with seasonal rainfall influenced by the annual... |
terrain_text |
terrain_text | string | 0% | 1 | plains in the north and center rise to southern... |
terrain_numeric |
terrain_numeric | float | 0% | 1 | 100000.0 |
elevation_highest_point_text |
elevation_highest_point_text | string | 0% | 1 | Kinyeti 3,187 m |
elevation_lowest_point_text |
elevation_lowest_point_text | string | 0% | 1 | White Nile 381 m |
natural_resources_text |
natural_resources_text | string | 0% | 1 | hydropower, fertile agricultural land, gold, diamonds,... |
land_use_agricultural_land_text |
land_use_agricultural_land_text | string | 0% | 1 | 44.9% (2023 est.) |
land_use_agricultural_land_arable_land_text |
land_use_agricultural_land_arable_land_text | string | 0% | 1 | arable land: 3.9% (2023 est.) |
land_use_agricultural_land_arable_land_numeric |
land_use_agricultural_land_arable_land_numeric | float | 0% | 1 | 3.9 |
land_use_agricultural_land_permanent_crops_text |
land_use_agricultural_land_permanent_crops_text | string | 0% | 1 | permanent crops: 0.1% (2023 est.) |
land_use_agricultural_land_permanent_crops_numeric |
land_use_agricultural_land_permanent_crops_numeric | float | 0% | 1 | 0.1 |
land_use_agricultural_land_permanent_pasture_text |
land_use_agricultural_land_permanent_pasture_text | string | 0% | 1 | permanent pasture: 40.8% (2023 est.) |
land_use_agricultural_land_permanent_pasture_numeric |
land_use_agricultural_land_permanent_pasture_numeric | float | 0% | 1 | 40.8 |
land_use_forest_text |
land_use_forest_text | string | 0% | 1 | 11.3% (2023 est.) |
land_use_other_text |
land_use_other_text | string | 0% | 1 | 43.8% (2023 est.) |
land_use_other_numeric |
land_use_other_numeric | float | 0% | 1 | 43.8 |
| +10 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
country_name_conventional_long_form_text |
country_name_conventional_long_form_text | string | 0% | 1 | Republic of South Sudan |
country_name_conventional_short_form_text |
country_name_conventional_short_form_text | string | 0% | 1 | South Sudan |
country_name_etymology_text |
country_name_etymology_text | string | 0% | 1 | self-descriptive name from the country's geographic... |
government_type_text |
government_type_text | string | 0% | 1 | presidential republic |
capital_name_text |
capital_name_text | string | 0% | 1 | Juba |
capital_geographic_coordinates_text |
capital_geographic_coordinates_text | string | 0% | 1 | 04 51 N, 31 37 E |
capital_geographic_coordinates_numeric |
capital_geographic_coordinates_numeric | float | 0% | 1 | 4.0 |
capital_time_difference_text |
capital_time_difference_text | string | 0% | 1 | UTC+2 (8 hours ahead of Washington, DC, during Standard Time) |
capital_time_difference_numeric |
capital_time_difference_numeric | float | 0% | 1 | 2.0 |
capital_etymology_text |
capital_etymology_text | string | 0% | 1 | the name comes from the name of a small Bari village... |
administrative_divisions_text |
administrative_divisions_text | string | 0% | 1 | 10 states; Central Equatoria, Eastern Equatoria,... |
administrative_divisions_numeric |
administrative_divisions_numeric | float | 0% | 1 | 10.0 |
constitution_history_text |
constitution_history_text | string | 0% | 1 | previous 2005 (pre-independence); latest signed 7 July... |
constitution_history_numeric |
constitution_history_numeric | float | 0% | 1 | 2005.0 |
constitution_amendment_process_text |
constitution_amendment_process_text | string | 0% | 1 | proposed by the National Legislature or by the president... |
citizenship_citizenship_by_birth_text |
Citizenship by birth (text) | string | 0% | 1 | no |
citizenship_citizenship_by_descent_only_text |
Citizenship by descent only (text) | string | 0% | 1 | at least one parent must be a citizen of South Sudan |
citizenship_dual_citizenship_recognized_text |
citizenship_dual_citizenship_recognized_text | string | 0% | 1 | yes |
citizenship_residency_requirement_for_naturalization_text |
citizenship_residency_requirement_for_naturalization_text | string | 0% | 1 | 10 years |
citizenship_residency_requirement_for_naturalization_numeric |
citizenship_residency_requirement_for_naturalization_numeric | float | 0% | 1 | 10.0 |
suffrage_text |
suffrage_text | string | 0% | 1 | 18 years of age; universal |
suffrage_numeric |
suffrage_numeric | float | 0% | 1 | 18.0 |
executive_branch_chief_of_state_text |
executive_branch_chief_of_state_text | string | 0% | 1 | President Salva KIIR Mayardit (since 9 July 2011) |
executive_branch_chief_of_state_numeric |
executive_branch_chief_of_state_numeric | float | 0% | 1 | 9.0 |
executive_branch_head_of_government_text |
executive_branch_head_of_government_text | string | 0% | 1 | President Salva KIIR Mayardit (since 9 July 2011) |
executive_branch_head_of_government_numeric |
executive_branch_head_of_government_numeric | float | 0% | 1 | 9.0 |
executive_branch_cabinet_text |
executive_branch_cabinet_text | string | 0% | 1 | National Council of Ministers appointed by the... |
executive_branch_election_appointment_process_text |
executive_branch_election_appointment_process_text | string | 0% | 1 | president directly elected by simple-majority popular... |
executive_branch_election_appointment_process_numeric |
executive_branch_election_appointment_process_numeric | float | 0% | 1 | 4.0 |
executive_branch_most_recent_election_date_text |
executive_branch_most_recent_election_date_text | string | 0% | 1 | 11-15 April 2010 |
| +68 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
background_text |
background_text | string | 0% | 1 | South Sudan, which gained independence from Sudan in... |
background_numeric |
background_numeric | float | 0% | 1 | 2011.0 |
source_section |
source_section | string | 0% | 1 | Introduction |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
languages_languages_text |
Languages (text) | string | 0% | 1 | English (official), Arabic (includes Juba and Sudanese... |
languages_major_language_sample_s_text |
languages_major_language_sample_s_text | string | 0% | 1 | The World Factbook, the indispensable source for basic... |
source_section |
source_section | string | 0% | 1 | People and Society:languages |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
refugees_and_internally_displaced_persons_refugees_text |
refugees_and_internally_displaced_persons_refugees_text | string | 0% | 1 | 517,471 (2024 est.) |
refugees_and_internally_displaced_persons_refugees_numeric |
refugees_and_internally_displaced_persons_refugees_numeric | float | 0% | 1 | 517471.0 |
refugees_and_internally_displaced_persons_idps_text |
refugees_and_internally_displaced_persons_idps_text | string | 0% | 1 | 1,359,795 (2024 est.) |
refugees_and_internally_displaced_persons_idps_numeric |
refugees_and_internally_displaced_persons_idps_numeric | float | 0% | 1 | 1359795.0 |
refugees_and_internally_displaced_persons_stateless_persons_text |
refugees_and_internally_displaced_persons_stateless_persons_text | string | 0% | 1 | 18,000 (2024 est.) |
refugees_and_internally_displaced_persons_stateless_persons_numeric |
refugees_and_internally_displaced_persons_stateless_persons_numeric | float | 0% | 1 | 18000.0 |
trafficking_in_persons_tier_rating_text |
trafficking_in_persons_tier_rating_text | string | 0% | 1 | Tier 3 — South Sudan does not fully meet the minimum... |
trafficking_in_persons_tier_rating_numeric |
trafficking_in_persons_tier_rating_numeric | float | 0% | 1 | 3.0 |
source_section |
source_section | string | 0% | 1 | Transnational Issues:migration |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
military_and_security_forces_text |
military_and_security_forces_text | string | 0% | 1 | South Sudan People’s Defense Force (SSPDF): Land Forces... |
military_and_security_forces_numeric |
military_and_security_forces_numeric | float | 0% | 1 | 2025.0 |
military_expenditures_military_expenditures_2024_text |
Military expenditures 2024 (text) | string | 0% | 1 | 2% of GDP (2024 est.) |
military_expenditures_military_expenditures_2024_numeric |
Military expenditures 2024 (numeric) | float | 0% | 1 | 2.0 |
military_expenditures_military_expenditures_2023_text |
Military expenditures 2023 (text) | string | 0% | 1 | 2% of GDP (2023 est.) |
military_expenditures_military_expenditures_2023_numeric |
Military expenditures 2023 (numeric) | float | 0% | 1 | 2.0 |
military_expenditures_military_expenditures_2022_text |
Military expenditures 2022 (text) | string | 0% | 1 | 2% of GDP (2022 est.) |
military_expenditures_military_expenditures_2022_numeric |
Military expenditures 2022 (numeric) | float | 0% | 1 | 2.0 |
military_expenditures_military_expenditures_2021_text |
Military expenditures 2021 (text) | string | 0% | 1 | 2% of GDP (2021 est.) |
military_expenditures_military_expenditures_2021_numeric |
Military expenditures 2021 (numeric) | float | 0% | 1 | 2.0 |
military_expenditures_military_expenditures_2020_text |
Military expenditures 2020 (text) | string | 0% | 1 | 2% of GDP (2020 est.) |
military_expenditures_military_expenditures_2020_numeric |
Military expenditures 2020 (numeric) | float | 0% | 1 | 2.0 |
military_and_security_service_personnel_strengths_text |
military_and_security_service_personnel_strengths_text | string | 0% | 1 | information varies; estimated 150-200,000 active Defense... |
military_and_security_service_personnel_strengths_numeric |
military_and_security_service_personnel_strengths_numeric | float | 0% | 1 | 150.0 |
military_equipment_inventories_and_acquisitions_text |
military_equipment_inventories_and_acquisitions_text | string | 0% | 1 | the SSPDF inventory is a mix of primarily of Soviet-era... |
military_equipment_inventories_and_acquisitions_numeric |
military_equipment_inventories_and_acquisitions_numeric | float | 0% | 1 | 2025.0 |
military_service_age_and_obligation_text |
military_service_age_and_obligation_text | string | 0% | 1 | 18 (legal minimum age)-35 for voluntary military service... |
military_service_age_and_obligation_numeric |
military_service_age_and_obligation_numeric | float | 0% | 1 | 18.0 |
military_note_text |
military_note_text | string | 0% | 1 | the South Sudan People's Defense Forces (SSPDF) are... |
military_note_numeric |
military_note_numeric | float | 0% | 1 | 2020.0 |
source_section |
source_section | string | 0% | 1 | Military and Security |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
population_total_numeric |
Population count | float | SEL | 0% | 1 | 12703714.0 |
population_male_numeric |
Population male | float | SEL | 0% | 1 | 6476341.0 |
population_female_numeric |
Population female | float | SEL | 0% | 1 | 6227373.0 |
age_structure_0_14_years_numeric |
Population 0 14 percent | float | SEL | 0% | 1 | 42.1 |
age_structure_15_64_years_numeric |
Population 15 64 percent | float | SEL | 0% | 1 | 55.3 |
age_structure_65_years_and_over_numeric |
Population 65 plus percent | float | SEL | 0% | 1 | 2.6 |
dependency_ratios_total_dependency_ratio_numeric |
Total dependency ratio | float | SEL | 0% | 1 | 80.8 |
dependency_ratios_youth_dependency_ratio_numeric |
Youth dependency ratio | float | SEL | 0% | 1 | 76.1 |
dependency_ratios_elderly_dependency_ratio_numeric |
Elderly dependency ratio | float | SEL | 0% | 1 | 4.7 |
median_age_total_numeric |
Median age | float | SEL | 0% | 1 | 18.7 |
population_growth_rate_numeric |
Population growth rate percent | float | SEL | 0% | 1 | 4.52 |
birth_rate_numeric |
Birth rate per 1000 | float | SEL | 0% | 1 | 35.68 |
death_rate_numeric |
Death rate per 1000 | float | SEL | 0% | 1 | 8.65 |
net_migration_rate_numeric |
Net migration per 1000 | float | SEL | 0% | 1 | 18.2 |
urbanization_urban_population_numeric |
Urban population percent | float | SEL | 0% | 1 | 21.2 |
urbanization_rate_of_urbanization_numeric |
Urban growth rate percent | float | SEL | 0% | 1 | 4.12 |
sex_ratio_at_birth_numeric |
Sex ratio at birth | float | SEL | 0% | 1 | 1.05 |
sex_ratio_total_population_numeric |
Sex ratio overall | float | SEL | 0% | 1 | 1.04 |
maternal_mortality_ratio_numeric |
Maternal mortality per 100k | float | SEL | 0% | 1 | 692.0 |
infant_mortality_rate_total_numeric |
Infant mortality per 1000 | float | SEL | 0% | 1 | 58.6 |
life_expectancy_at_birth_total_population_numeric |
Life expectancy | float | SEL | 0% | 1 | 60.3 |
total_fertility_rate_numeric |
Fertility rate | float | SEL | 0% | 1 | 4.98 |
gross_reproduction_rate_numeric |
Gross reproduction rate | float | SEL | 0% | 1 | 2.43 |
physician_density_numeric |
Physicians per 1000 | float | SEL | 0% | 1 | 0.04 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
population_total_text |
population_total_text | string | 0% | 1 | 12,703,714 (2024 est.) |
population_male_text |
population_male_text | string | 0% | 1 | 6,476,341 |
population_female_text |
population_female_text | string | 0% | 1 | 6,227,373 |
age_structure_0_14_years_text |
age_structure_0_14_years_text | string | 0% | 1 | 42.1% (male 2,725,520/female 2,619,035) |
age_structure_15_64_years_text |
age_structure_15_64_years_text | string | 0% | 1 | 55.3% (male 3,568,064/female 3,458,804) |
age_structure_65_years_and_over_text |
age_structure_65_years_and_over_text | string | 0% | 1 | 2.6% (2024 est.) (male 182,757/female 149,534) |
dependency_ratios_total_dependency_ratio_text |
dependency_ratios_total_dependency_ratio_text | string | 0% | 1 | 80.8 (2024 est.) |
dependency_ratios_youth_dependency_ratio_text |
dependency_ratios_youth_dependency_ratio_text | string | 0% | 1 | 76.1 (2024 est.) |
dependency_ratios_elderly_dependency_ratio_text |
dependency_ratios_elderly_dependency_ratio_text | string | 0% | 1 | 4.7 (2024 est.) |
dependency_ratios_potential_support_ratio_text |
dependency_ratios_potential_support_ratio_text | string | 0% | 1 | 21.1 (2024 est.) |
dependency_ratios_potential_support_ratio_numeric |
dependency_ratios_potential_support_ratio_numeric | float | 0% | 1 | 21.1 |
median_age_total_text |
median_age_total_text | string | 0% | 1 | 18.7 years (2025 est.) |
median_age_male_text |
median_age_male_text | string | 0% | 1 | 18.7 years |
median_age_male_numeric |
median_age_male_numeric | float | 0% | 1 | 18.7 |
median_age_female_text |
median_age_female_text | string | 0% | 1 | 18.7 years |
median_age_female_numeric |
median_age_female_numeric | float | 0% | 1 | 18.7 |
population_growth_rate_text |
population_growth_rate_text | string | 0% | 1 | 4.52% (2025 est.) |
birth_rate_text |
birth_rate_text | string | 0% | 1 | 35.68 births/1,000 population (2025 est.) |
death_rate_text |
death_rate_text | string | 0% | 1 | 8.65 deaths/1,000 population (2025 est.) |
net_migration_rate_text |
net_migration_rate_text | string | 0% | 1 | 18.2 migrant(s)/1,000 population (2025 est.) |
population_distribution_text |
population_distribution_text | string | 0% | 1 | clusters found in urban areas, particularly in the... |
urbanization_urban_population_text |
urbanization_urban_population_text | string | 0% | 1 | 21.2% of total population (2023) |
urbanization_rate_of_urbanization_text |
urbanization_rate_of_urbanization_text | string | 0% | 1 | 4.12% annual rate of change (2020-25 est.) |
major_urban_areas_population_text |
major_urban_areas_population_text | string | 0% | 1 | 459,000 JUBA (capital) (2023) |
major_urban_areas_population_numeric |
major_urban_areas_population_numeric | float | 0% | 1 | 459000.0 |
sex_ratio_at_birth_text |
sex_ratio_at_birth_text | string | 0% | 1 | 1.05 male(s)/female |
sex_ratio_0_14_years_text |
sex_ratio_0_14_years_text | string | 0% | 1 | 1.04 male(s)/female |
sex_ratio_0_14_years_numeric |
sex_ratio_0_14_years_numeric | float | 0% | 1 | 1.04 |
sex_ratio_15_64_years_text |
sex_ratio_15_64_years_text | string | 0% | 1 | 1.03 male(s)/female |
sex_ratio_15_64_years_numeric |
sex_ratio_15_64_years_numeric | float | 0% | 1 | 1.03 |
| +53 more extension fields — download the CSV/Parquet to see them all. | |||||
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
composition_religion_christian_pct_synth |
Christian | numeric | CCL | 0% | - | 60.5 |
composition_religion_muslim_pct_synth |
Muslim | numeric | CCL | 0% | - | 6.2 |
composition_religion_other_pct_synth |
other | numeric | CCL | 0% | - | 0.5 |
composition_ethnicity_primary_label_synth |
Dinka (Jieng) approximately | string | CCL | 0% | - | Dinka (Jieng) approximately |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
religions_text |
religions_text | string | 0% | 1 | Christian 60.5%, folk religion 32.9%, Muslim 6.2%, other... |
religions_numeric |
religions_numeric | float | 0% | 1 | 60.5 |
source_section |
source_section | string | 0% | 1 | People and Society:religions |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
composition_religion_folk_religion_pct_synth |
folk religion | numeric | 0% | - | 32.9 |
composition_religion_unaffiliated_pct_synth |
unaffiliated | numeric | 0% | - | 0.5 |
composition_ethnicity_dinka_jieng_approximately_pct_synth |
Dinka (Jieng) approximately | numeric | 0% | - | 37.5 |
composition_ethnicity_nuer_naath_approximately_pct_synth |
Nuer (Naath) approximately | numeric | 0% | - | 15.0 |
composition_ethnicity_shilluk_pct_synth |
Shilluk | numeric | 0% | - | - |
composition_ethnicity_azande_pct_synth |
Azande | numeric | 0% | - | - |
composition_ethnicity_bari_pct_synth |
Bari | numeric | 0% | - | - |
composition_ethnicity_kakwa_pct_synth |
Kakwa | numeric | 0% | - | - |
composition_ethnicity_kuku_pct_synth |
Kuku | numeric | 0% | - | - |
composition_ethnicity_murle_pct_synth |
Murle | numeric | 0% | - | - |
composition_ethnicity_mandari_pct_synth |
Mandari | numeric | 0% | - | - |
composition_ethnicity_didinga_pct_synth |
Didinga | numeric | 0% | - | - |
composition_ethnicity_ndogo_pct_synth |
Ndogo | numeric | 0% | - | - |
composition_ethnicity_bviri_pct_synth |
Bviri | numeric | 0% | - | - |
composition_ethnicity_lndi_pct_synth |
Lndi | numeric | 0% | - | - |
composition_ethnicity_anuak_pct_synth |
Anuak | numeric | 0% | - | - |
composition_ethnicity_bongo_pct_synth |
Bongo | numeric | 0% | - | - |
composition_ethnicity_lango_pct_synth |
Lango | numeric | 0% | - | - |
composition_ethnicity_dungotona_pct_synth |
Dungotona | numeric | 0% | - | - |
composition_ethnicity_acholi_pct_synth |
Acholi | numeric | 0% | - | - |
composition_ethnicity_baka_pct_synth |
Baka | numeric | 0% | - | - |
composition_ethnicity_fertit_pct_synth |
Fertit | numeric | 0% | - | - |
composition_ethnicity_primary_share_pct_synth |
Dinka (Jieng) approximately | numeric | 0% | - | 37.5 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
civil_aircraft_registration_country_code_prefix_text |
Civil aircraft registration country code prefix text | string | SEL+ | 0% | 1 | Z8 |
airports_numeric |
Airports count | float | SEL | 0% | 1 | 89.0 |
country_code |
Country code | string | SEL | 0% | 1 | SSD |
country_name |
Country name | string | SEL | 0% | 1 | South Sudan |
admin_level |
Admin level | string | SEL | 0% | 1 | national |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
civil_aircraft_registration_country_code_prefix_numeric |
civil_aircraft_registration_country_code_prefix_numeric | float | 0% | 1 | 8.0 |
airports_text |
airports_text | string | 0% | 1 | 89 (2025) |
heliports_text |
heliports_text | string | 0% | 1 | 2 (2025) |
heliports_numeric |
heliports_numeric | float | 0% | 1 | 2.0 |
railways_total_text |
railways_total_text | string | 0% | 1 | 248 km (2018) |
railways_total_numeric |
railways_total_numeric | float | 0% | 1 | 248.0 |
railways_note |
railways_note | string | 0% | 1 | note: a narrow gauge, single-track railroad between... |
source_section |
source_section | string | 0% | 1 | Transportation |
source_profile_path |
source_profile_path | string | 0% | 1 | africa/od.json |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
ethnic_group_name |
Ethnic group name | string | CCL | 0% | 10 | Dinka, Nuer, Azande, Toposa, Shilluk |
ethnic_power_status |
Ethnic power status | string | CCL | 0% | 4 | SENIOR PARTNER, JUNIOR PARTNER, POWERLESS, POWERLESS, POWERLESS |
ethnic_population_share |
Ethnic population share | float | CCL | 0% | 8 | 0.4, 0.2, 0.1, 0.08, 0.05 |
ethnic_group_id |
Ethnic group id | float | CCL | 0% | 10 | 62604000, 62608000, 62601000, 62618000, 62613000 |
regional_autonomy |
Regional autonomy | string | CCL | 0% | 1 | false, false, false, false, false |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
year |
year | integer | 0% | 1 | 2021, 2021, 2021, 2021, 2021 |
group_relevance |
group_relevance | string | 0% | 1 | , , , , |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
country_code | string | SEL | 0% | 1 | SSD, SSD, SSD, SSD, SSD |
gns_language_code |
gns_language_code | string | CCL | 0% | 5 | ara, eng, rus, fra, din |
gns_language_name |
gns_language_name | string | CCL | 0% | 5 | Arabic, English, Russian, French, Dinka |
gns_toponym_count |
gns_toponym_count | integer | CCL | 0% | 5 | 395, 170, 63, 2, 1 |
gns_toponym_share_pct |
gns_toponym_share_pct | float | CCL | 0% | 5 | 62.599, 26.9414, 9.9842, 0.317, 0.1585 |
gns_non_roman_toponym_count |
gns_non_roman_toponym_count | integer | CCL | 0% | 3 | 2, 0, 17, 0, 0 |
gns_dominant_script_code |
gns_dominant_script_code | string | CCL | 0% | 3 | Arab, , Cyrl, , |
gns_dominant_script_name |
gns_dominant_script_name | string | CCL | 0% | 3 | Arabic, , Cyrillic, , |
gns_script_count |
gns_script_count | integer | CCL | 0% | 2 | 1, 0, 1, 0, 0 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
country_code | string | SEL | 0% | 1 | SSD |
admin_level |
admin_level | integer | SEL | 0% | 1 | 0 |
gns_country_name |
gns_country_name | string | SEL | 0% | 1 | South Sudan |
gns_language_count |
gns_language_count | integer | CCL | 0% | 1 | 5 |
gns_script_count |
gns_script_count | integer | CCL | 0% | 1 | 2 |
gns_endonym_share_pct |
gns_endonym_share_pct | float | CCL | 0% | 1 | 99.9867 |
gns_non_roman_name_count |
gns_non_roman_name_count | integer | CCL | 0% | 1 | 22 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
gns_name_count |
gns_name_count | integer | 0% | 1 | 15069 |
gns_feature_count |
gns_feature_count | integer | 0% | 1 | 12083 |
gns_endonym_count |
gns_endonym_count | integer | 0% | 1 | 15067 |
gns_exonym_count |
gns_exonym_count | integer | 0% | 1 | 2 |
gns_source_build_date |
gns_source_build_date | string | 0% | 1 | Wed, 05 Aug 2026 |
gns_source_change_date |
gns_source_change_date | string | 0% | 1 | 2026-08-05 |
gns_name_count_administrative_regions |
gns_name_count_administrative_regions | integer | 0% | 1 | 105 |
gns_feature_count_administrative_regions |
gns_feature_count_administrative_regions | integer | 0% | 1 | 20 |
gns_name_count_populated_places |
gns_name_count_populated_places | integer | 0% | 1 | 6043 |
gns_feature_count_populated_places |
gns_feature_count_populated_places | integer | 0% | 1 | 5206 |
gns_name_count_hypsographic |
gns_name_count_hypsographic | integer | 0% | 1 | 2297 |
gns_feature_count_hypsographic |
gns_feature_count_hypsographic | integer | 0% | 1 | 1579 |
gns_name_count_areas_localities |
gns_name_count_areas_localities | integer | 0% | 1 | 614 |
gns_feature_count_areas_localities |
gns_feature_count_areas_localities | integer | 0% | 1 | 552 |
gns_name_count_spot_features |
gns_name_count_spot_features | integer | 0% | 1 | 593 |
gns_feature_count_spot_features |
gns_feature_count_spot_features | integer | 0% | 1 | 525 |
gns_name_count_vegetation |
gns_name_count_vegetation | integer | 0% | 1 | 30 |
gns_feature_count_vegetation |
gns_feature_count_vegetation | integer | 0% | 1 | 27 |
gns_name_count_hydrographic |
gns_name_count_hydrographic | integer | 0% | 1 | 5387 |
gns_feature_count_hydrographic |
gns_feature_count_hydrographic | integer | 0% | 1 | 4174 |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
economic_conditions |
Economic conditions | integer | CCL | 0% | 1 | 1 |
living_conditions |
Living conditions | integer | CCL | 0% | 1 | 1 |
employment_situation |
Employment situation | integer | CCL | 0% | 1 | 1 |
food_insecurity |
Food insecurity | integer | CCL | 0% | 1 | 1 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
round |
round | string | 0% | 1 | |
year |
year | string | 0% | 1 | |
region |
region | string | 0% | 1 | |
source |
source | string | 0% | 1 | Afrobarometer |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
women_equal_rights |
Women equal rights | integer | CCL | 0% | 1 | 1 |
women_political_leaders |
Women political leaders | integer | CCL | 0% | 1 | 1 |
women_land_rights |
Women land rights | integer | CCL | 0% | 1 | 1 |
domestic_violence_justified |
Domestic violence justified | integer | CCL | 0% | 1 | 1 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
round |
round | string | 0% | 1 | |
year |
year | string | 0% | 1 | |
region |
region | string | 0% | 1 | |
source |
source | string | 0% | 1 | Afrobarometer |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
trust_president |
Trust president | integer | CCL | 0% | 1 | 1 |
trust_parliament |
Trust parliament | integer | CCL | 0% | 1 | 1 |
trust_courts |
Trust courts | integer | CCL | 0% | 1 | 1 |
trust_police |
Trust police | integer | CCL | 0% | 1 | 1 |
trust_army |
Trust army | integer | CCL | 0% | 1 | 1 |
corruption_perception |
Corruption perception | integer | CCL | 0% | 1 | 1 |
democracy_satisfaction |
Democracy satisfaction | integer | CCL | 0% | 1 | 1 |
democracy_preference |
Democracy preference | integer | CCL | 0% | 1 | 1 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
round |
round | string | 0% | 1 | |
year |
year | string | 0% | 1 | |
region |
region | string | 0% | 1 | |
source |
source | string | 0% | 1 | Afrobarometer |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
emigration_considered |
Emigration considered | integer | CCL | 0% | 1 | 1 |
immigration_attitude |
Immigration attitude | integer | CCL | 0% | 1 | 1 |
foreign_workers_attitude |
Foreign workers attitude | integer | CCL | 0% | 1 | 1 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
round |
round | string | 0% | 1 | |
year |
year | string | 0% | 1 | |
region |
region | string | 0% | 1 | |
source |
source | string | 0% | 1 | Afrobarometer |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
country_code |
Country code | string | SEL | 0% | 1 | SSD |
trust_relatives |
Trust relatives | integer | CCL | 0% | 1 | 1 |
trust_neighbors |
Trust neighbors | integer | CCL | 0% | 1 | 1 |
trust_other_ethnic |
Trust other ethnic | integer | CCL | 0% | 1 | 1 |
trust_other_religion |
Trust other religion | integer | CCL | 0% | 1 | 1 |
national_identity_vs_ethnic |
National identity vs ethnic | integer | CCL | 0% | 1 | 1 |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
round |
round | string | 0% | 1 | |
year |
year | string | 0% | 1 | |
region |
region | string | 0% | 1 | |
source |
source | string | 0% | 1 | Afrobarometer |
| Field Name | Label | Type | Schema | Nulls | Unique | Samples |
|---|---|---|---|---|---|---|
oc_anti_money_laundering |
oc_anti_money_laundering | numeric | CCL | 0% | 1 | - |
oc_arms_trafficking |
oc_arms_trafficking | numeric | CCL | 0% | 1 | - |
oc_criminal_actors |
oc_criminal_actors | numeric | CCL | 0% | 1 | - |
oc_criminal_markets |
oc_criminal_markets | numeric | CCL | 0% | 1 | - |
oc_criminality |
oc_criminality | numeric | CCL | 0% | 1 | - |
oc_cyber_dependent_crimes |
oc_cyber_dependent_crimes | numeric | CCL | 0% | 1 | - |
oc_financial_crimes |
oc_financial_crimes | numeric | CCL | 0% | 1 | - |
oc_human_smuggling |
oc_human_smuggling | numeric | CCL | 0% | 1 | - |
oc_human_trafficking |
oc_human_trafficking | numeric | CCL | 0% | 1 | - |
oc_judicial_system_and_detention |
oc_judicial_system_and_detention | numeric | CCL | 0% | 1 | - |
oc_law_enforcement |
oc_law_enforcement | numeric | CCL | 0% | 1 | - |
oc_political_leadership_and_governance |
oc_political_leadership_and_governance | numeric | CCL | 0% | 1 | - |
oc_resilience |
oc_resilience | numeric | CCL | 0% | 1 | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
oc_cannabis_trade |
oc_cannabis_trade | numeric | 0% | 1 | - |
oc_cocaine_trade |
oc_cocaine_trade | numeric | 0% | 1 | - |
oc_criminal_networks |
oc_criminal_networks | numeric | 0% | 1 | - |
oc_economic_regulatory_capacity |
oc_economic_regulatory_capacity | numeric | 0% | 1 | - |
oc_extortion_and_protection_racketeering |
oc_extortion_and_protection_racketeering | numeric | 0% | 1 | - |
oc_fauna_crimes |
oc_fauna_crimes | numeric | 0% | 1 | - |
oc_flora_crimes |
oc_flora_crimes | numeric | 0% | 1 | - |
oc_foreign_actors |
oc_foreign_actors | numeric | 0% | 1 | - |
oc_government_transparency_and_accountability |
oc_government_transparency_and_accountability | numeric | 0% | 1 | - |
oc_heroin_trade |
oc_heroin_trade | numeric | 0% | 1 | - |
oc_illicit_trade_in_excisable_goods |
oc_illicit_trade_in_excisable_goods | numeric | 0% | 1 | - |
oc_international_cooperation |
oc_international_cooperation | numeric | 0% | 1 | - |
oc_mafia_style_groups |
oc_mafia_style_groups | numeric | 0% | 1 | - |
oc_national_policies_and_laws |
oc_national_policies_and_laws | numeric | 0% | 1 | - |
oc_non_renewable_resource_crimes |
oc_non_renewable_resource_crimes | numeric | 0% | 1 | - |
oc_non_state_actors |
oc_non_state_actors | numeric | 0% | 1 | - |
oc_prevention |
oc_prevention | numeric | 0% | 1 | - |
oc_private_sector_actors |
oc_private_sector_actors | numeric | 0% | 1 | - |
oc_state_embedded_actors |
oc_state_embedded_actors | numeric | 0% | 1 | - |
oc_synthetic_drug_trade |
oc_synthetic_drug_trade | numeric | 0% | 1 | - |
oc_territorial_integrity |
oc_territorial_integrity | numeric | 0% | 1 | - |
oc_trade_in_counterfeit_goods |
oc_trade_in_counterfeit_goods | numeric | 0% | 1 | - |
| Field Name | Label | Type | Nulls | Unique | Samples |
|---|---|---|---|---|---|
iso3 |
iso3 | string | 0% | 1 | SSD |
source |
source | string | 0% | 1 | Global Organized Crime Index |
source_url |
source_url | string | 0% | 1 | https://ocindex.net/ |
year |
year | integer | 0% | 1 | 2025 |
license |
license | string | 0% | 1 | Creative Commons (GI-TOC / ENACT) |
oc_anti_money_laundering_rank |
oc_anti_money_laundering_rank | integer | 0% | 1 | 187 |
oc_anti_money_laundering_2023 |
oc_anti_money_laundering_2023 | float | 0% | 1 | 1.5 |
oc_anti_money_laundering_2021 |
oc_anti_money_laundering_2021 | float | 0% | 1 | 1.5 |
oc_anti_money_laundering_2019 |
oc_anti_money_laundering_2019 | integer | 0% | 1 | 1 |
oc_arms_trafficking_rank |
oc_arms_trafficking_rank | integer | 0% | 1 | 26 |
oc_arms_trafficking_2023 |
oc_arms_trafficking_2023 | integer | 0% | 1 | 8 |
oc_arms_trafficking_2021 |
oc_arms_trafficking_2021 | integer | 0% | 1 | 8 |
oc_arms_trafficking_2019 |
oc_arms_trafficking_2019 | integer | 0% | 1 | 8 |
oc_cannabis_trade_rank |
oc_cannabis_trade_rank | integer | 0% | 1 | 173 |
oc_cannabis_trade_2023 |
oc_cannabis_trade_2023 | integer | 0% | 1 | 4 |
oc_cannabis_trade_2021 |
oc_cannabis_trade_2021 | integer | 0% | 1 | 4 |
oc_cannabis_trade_2019 |
oc_cannabis_trade_2019 | integer | 0% | 1 | 5 |
oc_cocaine_trade_rank |
oc_cocaine_trade_rank | integer | 0% | 1 | 178 |
oc_cocaine_trade_2023 |
oc_cocaine_trade_2023 | float | 0% | 1 | 1.5 |
oc_cocaine_trade_2021 |
oc_cocaine_trade_2021 | float | 0% | 1 | 1.5 |
oc_cocaine_trade_2019 |
oc_cocaine_trade_2019 | integer | 0% | 1 | 1 |
oc_criminal_actors_rank |
oc_criminal_actors_rank | integer | 0% | 1 | 13 |
oc_criminal_actors_2023 |
oc_criminal_actors_2023 | float | 0% | 1 | 7.5 |
oc_criminal_actors_2021 |
oc_criminal_actors_2021 | float | 0% | 1 | 7.38 |
oc_criminal_actors_2019 |
oc_criminal_actors_2019 | float | 0% | 1 | 7.75 |
oc_criminal_markets_rank |
oc_criminal_markets_rank | integer | 0% | 1 | 74 |
oc_criminal_markets_2023 |
oc_criminal_markets_2023 | float | 0% | 1 | 5.13 |
oc_criminal_markets_2021 |
oc_criminal_markets_2021 | float | 0% | 1 | 5.3 |
oc_criminal_markets_2019 |
oc_criminal_markets_2019 | float | 0% | 1 | 5.05 |
oc_criminal_networks_rank |
oc_criminal_networks_rank | integer | 0% | 1 | 21 |
| +103 more pending fields — download the CSV/Parquet to see them all. | |||||
Which languages name the landscape here, and in which writing systems. A language's toponymic footprint and its speaker population are different measures and often diverge. Counts include variant and foreign-language renderings of the same place, so a language can rank high because outside sources record names in it rather than because it is spoken locally — and a widely spoken language can be almost absent where official naming is in another language.
| Language | Place names | Share | Script |
|---|---|---|---|
| Arabic (ara) | 395 | 62.6% | Arabic |
| English (eng) | 170 | 26.9% | — |
| Russian (rus) | 63 | 10.0% | Cyrillic |
| French (fra) | 2 | 0.3% | — |
| Dinka (din) | 1 | 0.2% | — |
12,083 distinct features ·
5 languages ·
2 scripts ·
22 names in non-Roman script ·
2 conventional English names
Source: NGA GEOnet Names Server
(public domain) · rebuilt Wed, 05 Aug 2026.
Names follow the US/BGN convention.
The data providers this country's datasets are sourced from — each links out to the provider.
| Source | Type | Access |
|---|---|---|
| HDX COD — Population Statistics (OCHA/UNFPA) | international_organization | bulk_download |
| Global Data Lab | academic | api |
| GI-TOC / ENACT (Global Initiative Against Transnational Organized Crime · ENACT) | academic | bulk_download |
| Afrobarometer | academic | api |
| Ethnic Power Relations Dataset | academic | bulk_download |
| D-PLACE (Database of Places, Language, Culture & Environment) | academic | bulk_download |
| NGA GEOnet Names Server (GNS) | official_government | bulk_download |
| World Bank Open Data | international_organization | api |
| UNHCR Refugee Data Finder (UN High Commissioner for Refugees) | international_organization | api |
| Glottolog Language Catalog | academic | bulk_download |
| GHS Urban Centre Database (Global Human Settlement · EU Joint Research Centre) | international_organization | bulk_download |
| LandScan Global (ORNL — Oak Ridge National Laboratory) | research_institution | earth_engine |
| OpenFactBook | community_compilation | bulk_download |
Pick datasets and admin level(s). You'll get a .zip with one CSV per dataset, each filtered to the levels you choose.