Pakeitimai
On Birželis 9, 2021, 16:33:29 (SAST),
wasim:
-
Added resource zaf_women_geotiff.zip to South Africa: 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": [], | ||
| 7 | "id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | 7 | "id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | ||
| 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": "2021-06-09T14:30:20.667813", | 14 | "metadata_created": "2021-06-09T14:30:20.667813", | ||
| n | 15 | "metadata_modified": "2021-06-09T14:33:08.937910", | n | 15 | "metadata_modified": "2021-06-09T14:33:29.707940", |
| 16 | "name": | 16 | "name": | ||
| 17 | africa-high-resolution-population-density-maps-demographic-estimates", | 17 | africa-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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| 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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| 278 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 301 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
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| 281 | "vocabulary_id": null | 304 | "vocabulary_id": null | ||
| 282 | }, | 305 | }, | ||
| 283 | { | 306 | { | ||
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| 285 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 308 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
| 286 | "name": "deodata", | 309 | "name": "deodata", | ||
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| 292 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 315 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
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| 294 | "state": "active", | 317 | "state": "active", | ||
| 295 | "vocabulary_id": null | 318 | "vocabulary_id": null | ||
| 296 | }, | 319 | }, | ||
| 297 | { | 320 | { | ||
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| 299 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 322 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
| 300 | "name": "men", | 323 | "name": "men", | ||
| 301 | "state": "active", | 324 | "state": "active", | ||
| 302 | "vocabulary_id": null | 325 | "vocabulary_id": null | ||
| 303 | }, | 326 | }, | ||
| 304 | { | 327 | { | ||
| 305 | "display_name": "population density", | 328 | "display_name": "population density", | ||
| 306 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 329 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 307 | "name": "population density", | 330 | "name": "population density", | ||
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| 309 | "vocabulary_id": null | 332 | "vocabulary_id": null | ||
| 310 | }, | 333 | }, | ||
| 311 | { | 334 | { | ||
| 312 | "display_name": "south africa", | 335 | "display_name": "south africa", | ||
| 313 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 336 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
| 314 | "name": "south africa", | 337 | "name": "south africa", | ||
| 315 | "state": "active", | 338 | "state": "active", | ||
| 316 | "vocabulary_id": null | 339 | "vocabulary_id": null | ||
| 317 | }, | 340 | }, | ||
| 318 | { | 341 | { | ||
| 319 | "display_name": "women", | 342 | "display_name": "women", | ||
| 320 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 343 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 321 | "name": "women", | 344 | "name": "women", | ||
| 322 | "state": "active", | 345 | "state": "active", | ||
| 323 | "vocabulary_id": null | 346 | "vocabulary_id": null | ||
| 324 | }, | 347 | }, | ||
| 325 | { | 348 | { | ||
| 326 | "display_name": "youth", | 349 | "display_name": "youth", | ||
| 327 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 350 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 328 | "name": "youth", | 351 | "name": "youth", | ||
| 329 | "state": "active", | 352 | "state": "active", | ||
| 330 | "vocabulary_id": null | 353 | "vocabulary_id": null | ||
| 331 | } | 354 | } | ||
| 332 | ], | 355 | ], | ||
| 333 | "title": "South Africa: High Resolution Population Density Maps + | 356 | "title": "South Africa: High Resolution Population Density Maps + | ||
| 334 | Demographic Estimates", | 357 | Demographic Estimates", | ||
| 335 | "type": "dataset", | 358 | "type": "dataset", | ||
| 336 | "url": | 359 | "url": | ||
| 337 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 360 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 338 | "version": "" | 361 | "version": "" | ||
| 339 | } | 362 | } |