Veranderingen
On juni 9, 2021, 16:32:42 (SAST),
wasim:
-
Added resource zaf_men_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": "2021-06-09T14:32:27.187135", | n | 15 | "metadata_modified": "2021-06-09T14:32:41.973039", |
| 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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| 227 | "revision_id": "d057e563-772d-44e4-be3d-1031a26dc0ad", | 250 | "revision_id": "d057e563-772d-44e4-be3d-1031a26dc0ad", | ||
| 228 | "state": "draft", | 251 | "state": "draft", | ||
| 229 | "tags": [ | 252 | "tags": [ | ||
| 230 | { | 253 | { | ||
| 231 | "display_name": "children", | 254 | "display_name": "children", | ||
| 232 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 255 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
| 233 | "name": "children", | 256 | "name": "children", | ||
| 234 | "state": "active", | 257 | "state": "active", | ||
| 235 | "vocabulary_id": null | 258 | "vocabulary_id": null | ||
| 236 | }, | 259 | }, | ||
| 237 | { | 260 | { | ||
| 238 | "display_name": "deodata", | 261 | "display_name": "deodata", | ||
| 239 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 262 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
| 240 | "name": "deodata", | 263 | "name": "deodata", | ||
| 241 | "state": "active", | 264 | "state": "active", | ||
| 242 | "vocabulary_id": null | 265 | "vocabulary_id": null | ||
| 243 | }, | 266 | }, | ||
| 244 | { | 267 | { | ||
| 245 | "display_name": "elderly", | 268 | "display_name": "elderly", | ||
| 246 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 269 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
| 247 | "name": "elderly", | 270 | "name": "elderly", | ||
| 248 | "state": "active", | 271 | "state": "active", | ||
| 249 | "vocabulary_id": null | 272 | "vocabulary_id": null | ||
| 250 | }, | 273 | }, | ||
| 251 | { | 274 | { | ||
| 252 | "display_name": "men", | 275 | "display_name": "men", | ||
| 253 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 276 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
| 254 | "name": "men", | 277 | "name": "men", | ||
| 255 | "state": "active", | 278 | "state": "active", | ||
| 256 | "vocabulary_id": null | 279 | "vocabulary_id": null | ||
| 257 | }, | 280 | }, | ||
| 258 | { | 281 | { | ||
| 259 | "display_name": "population density", | 282 | "display_name": "population density", | ||
| 260 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 283 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 261 | "name": "population density", | 284 | "name": "population density", | ||
| 262 | "state": "active", | 285 | "state": "active", | ||
| 263 | "vocabulary_id": null | 286 | "vocabulary_id": null | ||
| 264 | }, | 287 | }, | ||
| 265 | { | 288 | { | ||
| 266 | "display_name": "south africa", | 289 | "display_name": "south africa", | ||
| 267 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 290 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
| 268 | "name": "south africa", | 291 | "name": "south africa", | ||
| 269 | "state": "active", | 292 | "state": "active", | ||
| 270 | "vocabulary_id": null | 293 | "vocabulary_id": null | ||
| 271 | }, | 294 | }, | ||
| 272 | { | 295 | { | ||
| 273 | "display_name": "women", | 296 | "display_name": "women", | ||
| 274 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 297 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 275 | "name": "women", | 298 | "name": "women", | ||
| 276 | "state": "active", | 299 | "state": "active", | ||
| 277 | "vocabulary_id": null | 300 | "vocabulary_id": null | ||
| 278 | }, | 301 | }, | ||
| 279 | { | 302 | { | ||
| 280 | "display_name": "youth", | 303 | "display_name": "youth", | ||
| 281 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 304 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 282 | "name": "youth", | 305 | "name": "youth", | ||
| 283 | "state": "active", | 306 | "state": "active", | ||
| 284 | "vocabulary_id": null | 307 | "vocabulary_id": null | ||
| 285 | } | 308 | } | ||
| 286 | ], | 309 | ], | ||
| 287 | "title": "South Africa: High Resolution Population Density Maps + | 310 | "title": "South Africa: High Resolution Population Density Maps + | ||
| 288 | Demographic Estimates", | 311 | Demographic Estimates", | ||
| 289 | "type": "dataset", | 312 | "type": "dataset", | ||
| 290 | "url": | 313 | "url": | ||
| 291 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 314 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 292 | "version": "" | 315 | "version": "" | ||
| 293 | } | 316 | } |