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On juni 9, 2021, 16:38:24 (SAST),
-
Added resource uga_women_2020-02-01_csv.zip to Uganda: High Resolution Population Density Maps + Demographic Estimates
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2 | "author": "Facebook Data for Good", | 2 | "author": "Facebook Data for Good", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
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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", | ||
13 | "maintainer_email": "", | 13 | "maintainer_email": "", | ||
14 | "metadata_created": "2021-06-09T14:36:58.143264", | 14 | "metadata_created": "2021-06-09T14:36:58.143264", | ||
n | 15 | "metadata_modified": "2021-06-09T14:38:09.394805", | n | 15 | "metadata_modified": "2021-06-09T14:38:24.623301", |
16 | "name": | 16 | "name": | ||
17 | uganda-high-resolution-population-density-maps-demographic-estimates", | 17 | uganda-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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244 | "state": "active", | 267 | "state": "active", | ||
245 | "vocabulary_id": null | 268 | "vocabulary_id": null | ||
246 | } | 269 | } | ||
247 | ], | 270 | ], | ||
248 | "title": "Uganda: High Resolution Population Density Maps + | 271 | "title": "Uganda: High Resolution Population Density Maps + | ||
249 | Demographic Estimates", | 272 | Demographic Estimates", | ||
250 | "type": "dataset", | 273 | "type": "dataset", | ||
251 | "url": | 274 | "url": | ||
252 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-uga", | 275 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-uga", | ||
253 | "version": "" | 276 | "version": "" | ||
254 | } | 277 | } |