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| f | 1 | { | f | 1 | { |
| 2 | "author": "Facebook Data for Good", | 2 | "author": "Facebook Data for Good", | ||
| 3 | "author_email": "", | 3 | "author_email": "", | ||
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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": "2021-06-03T14:19:30.677063", | n | 15 | "metadata_modified": "2021-06-15T08:28:47.146851", |
| 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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| 57 | "title": "Humanitarian Emergency Response Africa", | 57 | "title": "Humanitarian Emergency Response Africa", | ||
| 58 | "type": "organization" | 58 | "type": "organization" | ||
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