Veranderingen
On juni 9, 2021, 16:31:55 (SAST),
wasim:
-
Added resource zaf_elderly_60_plus_csv.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", | ||
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| 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": "2021-06-09T14:30:20.667813", | 14 | "metadata_created": "2021-06-09T14:30:20.667813", | ||
| n | 15 | "metadata_modified": "2021-06-09T14:31:39.984412", | n | 15 | "metadata_modified": "2021-06-09T14:31:55.430968", |
| 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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| 46 | "organization": { | 46 | "organization": { | ||
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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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| 54 | "name": "humanitarian-emergency-response-africa", | 54 | "name": "humanitarian-emergency-response-africa", | ||
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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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| 159 | "state": "draft", | 182 | "state": "draft", | ||
| 160 | "tags": [ | 183 | "tags": [ | ||
| 161 | { | 184 | { | ||
| 162 | "display_name": "children", | 185 | "display_name": "children", | ||
| 163 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 186 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
| 164 | "name": "children", | 187 | "name": "children", | ||
| 165 | "state": "active", | 188 | "state": "active", | ||
| 166 | "vocabulary_id": null | 189 | "vocabulary_id": null | ||
| 167 | }, | 190 | }, | ||
| 168 | { | 191 | { | ||
| 169 | "display_name": "deodata", | 192 | "display_name": "deodata", | ||
| 170 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 193 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
| 171 | "name": "deodata", | 194 | "name": "deodata", | ||
| 172 | "state": "active", | 195 | "state": "active", | ||
| 173 | "vocabulary_id": null | 196 | "vocabulary_id": null | ||
| 174 | }, | 197 | }, | ||
| 175 | { | 198 | { | ||
| 176 | "display_name": "elderly", | 199 | "display_name": "elderly", | ||
| 177 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 200 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
| 178 | "name": "elderly", | 201 | "name": "elderly", | ||
| 179 | "state": "active", | 202 | "state": "active", | ||
| 180 | "vocabulary_id": null | 203 | "vocabulary_id": null | ||
| 181 | }, | 204 | }, | ||
| 182 | { | 205 | { | ||
| 183 | "display_name": "men", | 206 | "display_name": "men", | ||
| 184 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 207 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
| 185 | "name": "men", | 208 | "name": "men", | ||
| 186 | "state": "active", | 209 | "state": "active", | ||
| 187 | "vocabulary_id": null | 210 | "vocabulary_id": null | ||
| 188 | }, | 211 | }, | ||
| 189 | { | 212 | { | ||
| 190 | "display_name": "population density", | 213 | "display_name": "population density", | ||
| 191 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 214 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 192 | "name": "population density", | 215 | "name": "population density", | ||
| 193 | "state": "active", | 216 | "state": "active", | ||
| 194 | "vocabulary_id": null | 217 | "vocabulary_id": null | ||
| 195 | }, | 218 | }, | ||
| 196 | { | 219 | { | ||
| 197 | "display_name": "south africa", | 220 | "display_name": "south africa", | ||
| 198 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 221 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
| 199 | "name": "south africa", | 222 | "name": "south africa", | ||
| 200 | "state": "active", | 223 | "state": "active", | ||
| 201 | "vocabulary_id": null | 224 | "vocabulary_id": null | ||
| 202 | }, | 225 | }, | ||
| 203 | { | 226 | { | ||
| 204 | "display_name": "women", | 227 | "display_name": "women", | ||
| 205 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 228 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 206 | "name": "women", | 229 | "name": "women", | ||
| 207 | "state": "active", | 230 | "state": "active", | ||
| 208 | "vocabulary_id": null | 231 | "vocabulary_id": null | ||
| 209 | }, | 232 | }, | ||
| 210 | { | 233 | { | ||
| 211 | "display_name": "youth", | 234 | "display_name": "youth", | ||
| 212 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 235 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 213 | "name": "youth", | 236 | "name": "youth", | ||
| 214 | "state": "active", | 237 | "state": "active", | ||
| 215 | "vocabulary_id": null | 238 | "vocabulary_id": null | ||
| 216 | } | 239 | } | ||
| 217 | ], | 240 | ], | ||
| 218 | "title": "South Africa: High Resolution Population Density Maps + | 241 | "title": "South Africa: High Resolution Population Density Maps + | ||
| 219 | Demographic Estimates", | 242 | Demographic Estimates", | ||
| 220 | "type": "dataset", | 243 | "type": "dataset", | ||
| 221 | "url": | 244 | "url": | ||
| 222 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 245 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 223 | "version": "" | 246 | "version": "" | ||
| 224 | } | 247 | } |