Changes
On June 3, 2021, 16:15:19 (SAST),
wasim:
-
Added resource nga_women_2020_csv.zip to Nigeria: High Resolution Population Density Maps + Demographic Estimates
| f | 1 | { | f | 1 | { |
| 2 | "author": "Facebook Data for Good", | 2 | "author": "Facebook Data for Good", | ||
| 3 | "author_email": "", | 3 | "author_email": "", | ||
| 4 | "creator_user_id": "34f7a594-935e-4ac6-b13c-8bb3b861b487", | 4 | "creator_user_id": "34f7a594-935e-4ac6-b13c-8bb3b861b487", | ||
| 5 | "extras": [], | 5 | "extras": [], | ||
| 6 | "groups": [], | 6 | "groups": [], | ||
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| 8 | "isopen": true, | 8 | "isopen": true, | ||
| 9 | "license_id": "cc-by", | 9 | "license_id": "cc-by", | ||
| 10 | "license_title": "Creative Commons Attribution", | 10 | "license_title": "Creative Commons Attribution", | ||
| 11 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 11 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| 12 | "maintainer": "Facebook Data For Good", | 12 | "maintainer": "Facebook Data For Good", | ||
| 13 | "maintainer_email": "", | 13 | "maintainer_email": "", | ||
| 14 | "metadata_created": "2021-06-03T14:01:21.364366", | 14 | "metadata_created": "2021-06-03T14:01:21.364366", | ||
| n | 15 | "metadata_modified": "2021-06-03T14:14:38.182559", | n | 15 | "metadata_modified": "2021-06-03T14:15:19.591790", |
| 16 | "name": | 16 | "name": | ||
| 17 | igeria-high-resolution-population-density-maps-demographic-estimates", | 17 | igeria-high-resolution-population-density-maps-demographic-estimates", | ||
| 18 | "notes": "VERSION 1.5. The world's most accurate population | 18 | "notes": "VERSION 1.5. The world's most accurate population | ||
| 19 | datasets. Seven maps/datasets for the distribution of various | 19 | datasets. Seven maps/datasets for the distribution of various | ||
| 20 | populations in Nigeria: (1) Overall population density (2) Women (3) | 20 | populations in Nigeria: (1) Overall population density (2) Women (3) | ||
| 21 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | 21 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | ||
| 22 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 22 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
| 23 | Methodology\r\n\r\nThese high-resolution maps are created using | 23 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
| 24 | machine learning techniques to identify buildings from commercially | 24 | machine learning techniques to identify buildings from commercially | ||
| 25 | available satellite images. This is then overlayed with general | 25 | available satellite images. This is then overlayed with general | ||
| 26 | population estimates based on publicly available census data and other | 26 | population estimates based on publicly available census data and other | ||
| 27 | population statistics at Columbia University. The resulting maps are | 27 | population statistics at Columbia University. The resulting maps are | ||
| 28 | the most detailed and actionable tools available for aid and research | 28 | the most detailed and actionable tools available for aid and research | ||
| 29 | organizations. For more information about the methodology used to | 29 | organizations. For more information about the methodology used to | ||
| 30 | create our high resolution population density maps and the demographic | 30 | create our high resolution population density maps and the demographic | ||
| 31 | distributions, click | 31 | distributions, click | ||
| 32 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | 32 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | ||
| 33 | information about how to use HDX to access these datasets, please | 33 | information about how to use HDX to access these datasets, please | ||
| 34 | visit: | 34 | visit: | ||
| 35 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 35 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
| 36 | to match the census population with the UN estimates are applied at | 36 | to match the census population with the UN estimates are applied at | ||
| 37 | the national level. The UN estimate for a given country (or | 37 | the national level. The UN estimate for a given country (or | ||
| 38 | state/territory) is divided by the total census estimate of population | 38 | state/territory) is divided by the total census estimate of population | ||
| 39 | for the given country. The resulting adjustment factor is multiplied | 39 | for the given country. The resulting adjustment factor is multiplied | ||
| 40 | by each administrative unit census value for the target year. This | 40 | by each administrative unit census value for the target year. This | ||
| 41 | preserves the relative population totals across administrative units | 41 | preserves the relative population totals across administrative units | ||
| 42 | while matching the UN total. More information can be found | 42 | while matching the UN total. More information can be found | ||
| 43 | ocs/census-information-for-high-resolution-population-density-maps/)", | 43 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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| 48 | "created": "2021-02-15T10:09:07.942828", | 48 | "created": "2021-02-15T10:09:07.942828", | ||
| 49 | "description": "Humanitarian Emergency Response Africa Datasets", | 49 | "description": "Humanitarian Emergency Response Africa Datasets", | ||
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| 51 | "image_url": | 51 | "image_url": | ||
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| 56 | "state": "active", | 56 | "state": "active", | ||
| 57 | "title": "Humanitarian Emergency Response Africa", | 57 | "title": "Humanitarian Emergency Response Africa", | ||
| 58 | "type": "organization" | 58 | "type": "organization" | ||
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| 251 | "state": "draft", | 274 | "state": "draft", | ||
| 252 | "tags": [ | 275 | "tags": [ | ||
| 253 | { | 276 | { | ||
| 254 | "display_name": "children", | 277 | "display_name": "children", | ||
| 255 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 278 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
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| 258 | "vocabulary_id": null | 281 | "vocabulary_id": null | ||
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| 262 | "id": "9923e71e-0c99-4abb-a0fa-5e7ccbc74956", | 285 | "id": "9923e71e-0c99-4abb-a0fa-5e7ccbc74956", | ||
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| 264 | "state": "active", | 287 | "state": "active", | ||
| 265 | "vocabulary_id": null | 288 | "vocabulary_id": null | ||
| 266 | }, | 289 | }, | ||
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| 268 | "display_name": "nigeria", | 291 | "display_name": "nigeria", | ||
| 269 | "id": "86671c58-04e8-4323-8904-b2b506740271", | 292 | "id": "86671c58-04e8-4323-8904-b2b506740271", | ||
| 270 | "name": "nigeria", | 293 | "name": "nigeria", | ||
| 271 | "state": "active", | 294 | "state": "active", | ||
| 272 | "vocabulary_id": null | 295 | "vocabulary_id": null | ||
| 273 | }, | 296 | }, | ||
| 274 | { | 297 | { | ||
| 275 | "display_name": "population", | 298 | "display_name": "population", | ||
| 276 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | 299 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | ||
| 277 | "name": "population", | 300 | "name": "population", | ||
| 278 | "state": "active", | 301 | "state": "active", | ||
| 279 | "vocabulary_id": null | 302 | "vocabulary_id": null | ||
| 280 | }, | 303 | }, | ||
| 281 | { | 304 | { | ||
| 282 | "display_name": "population density", | 305 | "display_name": "population density", | ||
| 283 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 306 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 284 | "name": "population density", | 307 | "name": "population density", | ||
| 285 | "state": "active", | 308 | "state": "active", | ||
| 286 | "vocabulary_id": null | 309 | "vocabulary_id": null | ||
| 287 | }, | 310 | }, | ||
| 288 | { | 311 | { | ||
| 289 | "display_name": "women", | 312 | "display_name": "women", | ||
| 290 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 313 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 291 | "name": "women", | 314 | "name": "women", | ||
| 292 | "state": "active", | 315 | "state": "active", | ||
| 293 | "vocabulary_id": null | 316 | "vocabulary_id": null | ||
| 294 | }, | 317 | }, | ||
| 295 | { | 318 | { | ||
| 296 | "display_name": "youth", | 319 | "display_name": "youth", | ||
| 297 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 320 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 298 | "name": "youth", | 321 | "name": "youth", | ||
| 299 | "state": "active", | 322 | "state": "active", | ||
| 300 | "vocabulary_id": null | 323 | "vocabulary_id": null | ||
| 301 | } | 324 | } | ||
| 302 | ], | 325 | ], | ||
| 303 | "title": "Nigeria: High Resolution Population Density Maps + | 326 | "title": "Nigeria: High Resolution Population Density Maps + | ||
| 304 | Demographic Estimates", | 327 | Demographic Estimates", | ||
| 305 | "type": "dataset", | 328 | "type": "dataset", | ||
| 306 | "url": | 329 | "url": | ||
| 307 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | 330 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | ||
| 308 | "version": "" | 331 | "version": "" | ||
| 309 | } | 332 | } |