Changes
On June 3, 2021, 16:13:25 (SAST),
wasim:
-
Added resource nga_elderly_60_plus_2020_geotiff.zip to Nigeria: High Resolution Population Density Maps + Demographic Estimates
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| 2 | "author": "Facebook Data for Good", | 2 | "author": "Facebook Data for Good", | ||
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| 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", | ||
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| 14 | "metadata_created": "2021-06-03T14:01:21.364366", | 14 | "metadata_created": "2021-06-03T14:01:21.364366", | ||
| n | 15 | "metadata_modified": "2021-06-03T14:05:04.271953", | n | 15 | "metadata_modified": "2021-06-03T14:13:25.401528", |
| 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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| 182 | "state": "draft", | 205 | "state": "draft", | ||
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| 184 | { | 207 | { | ||
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| 186 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 209 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
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| 194 | "name": "gender", | 217 | "name": "gender", | ||
| 195 | "state": "active", | 218 | "state": "active", | ||
| 196 | "vocabulary_id": null | 219 | "vocabulary_id": null | ||
| 197 | }, | 220 | }, | ||
| 198 | { | 221 | { | ||
| 199 | "display_name": "nigeria", | 222 | "display_name": "nigeria", | ||
| 200 | "id": "86671c58-04e8-4323-8904-b2b506740271", | 223 | "id": "86671c58-04e8-4323-8904-b2b506740271", | ||
| 201 | "name": "nigeria", | 224 | "name": "nigeria", | ||
| 202 | "state": "active", | 225 | "state": "active", | ||
| 203 | "vocabulary_id": null | 226 | "vocabulary_id": null | ||
| 204 | }, | 227 | }, | ||
| 205 | { | 228 | { | ||
| 206 | "display_name": "population", | 229 | "display_name": "population", | ||
| 207 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | 230 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | ||
| 208 | "name": "population", | 231 | "name": "population", | ||
| 209 | "state": "active", | 232 | "state": "active", | ||
| 210 | "vocabulary_id": null | 233 | "vocabulary_id": null | ||
| 211 | }, | 234 | }, | ||
| 212 | { | 235 | { | ||
| 213 | "display_name": "population density", | 236 | "display_name": "population density", | ||
| 214 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 237 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 215 | "name": "population density", | 238 | "name": "population density", | ||
| 216 | "state": "active", | 239 | "state": "active", | ||
| 217 | "vocabulary_id": null | 240 | "vocabulary_id": null | ||
| 218 | }, | 241 | }, | ||
| 219 | { | 242 | { | ||
| 220 | "display_name": "women", | 243 | "display_name": "women", | ||
| 221 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 244 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 222 | "name": "women", | 245 | "name": "women", | ||
| 223 | "state": "active", | 246 | "state": "active", | ||
| 224 | "vocabulary_id": null | 247 | "vocabulary_id": null | ||
| 225 | }, | 248 | }, | ||
| 226 | { | 249 | { | ||
| 227 | "display_name": "youth", | 250 | "display_name": "youth", | ||
| 228 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 251 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 229 | "name": "youth", | 252 | "name": "youth", | ||
| 230 | "state": "active", | 253 | "state": "active", | ||
| 231 | "vocabulary_id": null | 254 | "vocabulary_id": null | ||
| 232 | } | 255 | } | ||
| 233 | ], | 256 | ], | ||
| 234 | "title": "Nigeria: High Resolution Population Density Maps + | 257 | "title": "Nigeria: High Resolution Population Density Maps + | ||
| 235 | Demographic Estimates", | 258 | Demographic Estimates", | ||
| 236 | "type": "dataset", | 259 | "type": "dataset", | ||
| 237 | "url": | 260 | "url": | ||
| 238 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | 261 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | ||
| 239 | "version": "" | 262 | "version": "" | ||
| 240 | } | 263 | } |