ለውጦች
On ሰኔ 15, 2022, 19:30:22 (SAST),
wasim:
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Updated description of Ghana: High Resolution Population Density Maps + Demographic Estimates from
VERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Nigeria: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). ### Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click [here](https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/ For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found [here](https://dataforgood.fb.com/docs/census-information-for-high-resolution-population-density-maps/)
toVERSION 1.5. The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in Ghana: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49). ### Methodology These high-resolution maps are created using machine learning techniques to identify buildings from commercially available satellite images. This is then overlayed with general population estimates based on publicly available census data and other population statistics at Columbia University. The resulting maps are the most detailed and actionable tools available for aid and research organizations. For more information about the methodology used to create our high resolution population density maps and the demographic distributions, click [here](https://dataforgood.fb.com/docs/methodology-high-resolution-population-density-maps-demographic-estimates/). For information about how to use HDX to access these datasets, please visit: https://dataforgood.fb.com/docs/high-resolution-population-density-maps-demographic-estimates-documentation/ Adjustments to match the census population with the UN estimates are applied at the national level. The UN estimate for a given country (or state/territory) is divided by the total census estimate of population for the given country. The resulting adjustment factor is multiplied by each administrative unit census value for the target year. This preserves the relative population totals across administrative units while matching the UN total. More information can be found [here](https://dataforgood.fb.com/docs/census-information-for-high-resolution-population-density-maps/)
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| 2 | "author": "Data for Good at Meta", | 2 | "author": "Data for Good at Meta", | ||
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| 8 | "description": "These are backlog datasets that have been | 8 | "description": "These are backlog datasets that have been | ||
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| 18 | "display_name": "Population ", | 18 | "display_name": "Population ", | ||
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| 27 | "license_id": "cc-by", | 27 | "license_id": "cc-by", | ||
| 28 | "license_title": "Creative Commons Attribution", | 28 | "license_title": "Creative Commons Attribution", | ||
| 29 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 29 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| 30 | "maintainer": "Heiko Heilgendorff (OCL)", | 30 | "maintainer": "Heiko Heilgendorff (OCL)", | ||
| 31 | "maintainer_email": "[email protected]", | 31 | "maintainer_email": "[email protected]", | ||
| 32 | "metadata_created": "2021-06-09T14:01:13.877901", | 32 | "metadata_created": "2021-06-09T14:01:13.877901", | ||
| n | 33 | "metadata_modified": "2022-06-15T17:27:30.701174", | n | 33 | "metadata_modified": "2022-06-15T17:30:22.688466", |
| 34 | "name": | 34 | "name": | ||
| 35 | "ghana-high-resolution-population-density-maps-demographic-estimates", | 35 | "ghana-high-resolution-population-density-maps-demographic-estimates", | ||
| 36 | "notes": "VERSION 1.5. The world's most accurate population | 36 | "notes": "VERSION 1.5. The world's most accurate population | ||
| 37 | datasets. Seven maps/datasets for the distribution of various | 37 | datasets. Seven maps/datasets for the distribution of various | ||
| n | 38 | populations in Nigeria: (1) Overall population density (2) Women (3) | n | 38 | populations in Ghana: (1) Overall population density (2) Women (3) Men |
| 39 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | 39 | (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) | ||
| 40 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 40 | (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
| 41 | Methodology\r\n\r\nThese high-resolution maps are created using | 41 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
| 42 | machine learning techniques to identify buildings from commercially | 42 | machine learning techniques to identify buildings from commercially | ||
| 43 | available satellite images. This is then overlayed with general | 43 | available satellite images. This is then overlayed with general | ||
| 44 | population estimates based on publicly available census data and other | 44 | population estimates based on publicly available census data and other | ||
| 45 | population statistics at Columbia University. The resulting maps are | 45 | population statistics at Columbia University. The resulting maps are | ||
| 46 | the most detailed and actionable tools available for aid and research | 46 | the most detailed and actionable tools available for aid and research | ||
| 47 | organizations. For more information about the methodology used to | 47 | organizations. For more information about the methodology used to | ||
| 48 | create our high resolution population density maps and the demographic | 48 | create our high resolution population density maps and the demographic | ||
| 49 | distributions, click | 49 | distributions, click | ||
| n | 50 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | n | 50 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor |
| 51 | information about how to use HDX to access these datasets, please | 51 | information about how to use HDX to access these datasets, please | ||
| 52 | visit: | 52 | visit: | ||
| 53 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 53 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
| 54 | to match the census population with the UN estimates are applied at | 54 | to match the census population with the UN estimates are applied at | ||
| 55 | the national level. The UN estimate for a given country (or | 55 | the national level. The UN estimate for a given country (or | ||
| 56 | state/territory) is divided by the total census estimate of population | 56 | state/territory) is divided by the total census estimate of population | ||
| 57 | for the given country. The resulting adjustment factor is multiplied | 57 | for the given country. The resulting adjustment factor is multiplied | ||
| 58 | by each administrative unit census value for the target year. This | 58 | by each administrative unit census value for the target year. This | ||
| 59 | preserves the relative population totals across administrative units | 59 | preserves the relative population totals across administrative units | ||
| 60 | while matching the UN total. More information can be found | 60 | while matching the UN total. More information can be found | ||
| 61 | ocs/census-information-for-high-resolution-population-density-maps/)", | 61 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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| 64 | "organization": { | 64 | "organization": { | ||
| 65 | "approval_status": "approved", | 65 | "approval_status": "approved", | ||
| 66 | "created": "2022-06-15T09:44:22.871262", | 66 | "created": "2022-06-15T09:44:22.871262", | ||
| 67 | "description": "The Humanitarian Data Exchange (HDX) is an open | 67 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||
| 68 | platform for sharing data across crises and organisations. Launched in | 68 | platform for sharing data across crises and organisations. Launched in | ||
| 69 | July 2014, the goal of HDX is to make humanitarian data easy to find | 69 | July 2014, the goal of HDX is to make humanitarian data easy to find | ||
| 70 | and use for analysis. Our growing collection of datasets has been | 70 | and use for analysis. Our growing collection of datasets has been | ||
| 71 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | 71 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | ||
| 72 | managed by OCHA's Centre for Humanitarian Data, which is located in | 72 | managed by OCHA's Centre for Humanitarian Data, which is located in | ||
| 73 | The Hague. OCHA is part of the United Nations Secretariat and is | 73 | The Hague. OCHA is part of the United Nations Secretariat and is | ||
| 74 | responsible for bringing together humanitarian actors to ensure a | 74 | responsible for bringing together humanitarian actors to ensure a | ||
| 75 | coherent response to emergencies. The HDX team includes OCHA staff and | 75 | coherent response to emergencies. The HDX team includes OCHA staff and | ||
| 76 | a number of consultants who are based in North America, Europe and | 76 | a number of consultants who are based in North America, Europe and | ||
| 77 | Africa.", | 77 | Africa.", | ||
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| 469 | "name": "population density", | 469 | "name": "population density", | ||
| 470 | "state": "active", | 470 | "state": "active", | ||
| 471 | "vocabulary_id": null | 471 | "vocabulary_id": null | ||
| 472 | }, | 472 | }, | ||
| 473 | { | 473 | { | ||
| 474 | "display_name": "reproductive", | 474 | "display_name": "reproductive", | ||
| 475 | "id": "6fb38736-99e6-40c9-b3b3-da34da5e6cf5", | 475 | "id": "6fb38736-99e6-40c9-b3b3-da34da5e6cf5", | ||
| 476 | "name": "reproductive", | 476 | "name": "reproductive", | ||
| 477 | "state": "active", | 477 | "state": "active", | ||
| 478 | "vocabulary_id": null | 478 | "vocabulary_id": null | ||
| 479 | }, | 479 | }, | ||
| 480 | { | 480 | { | ||
| 481 | "display_name": "women", | 481 | "display_name": "women", | ||
| 482 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 482 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 483 | "name": "women", | 483 | "name": "women", | ||
| 484 | "state": "active", | 484 | "state": "active", | ||
| 485 | "vocabulary_id": null | 485 | "vocabulary_id": null | ||
| 486 | }, | 486 | }, | ||
| 487 | { | 487 | { | ||
| 488 | "display_name": "youth", | 488 | "display_name": "youth", | ||
| 489 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 489 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 490 | "name": "youth", | 490 | "name": "youth", | ||
| 491 | "state": "active", | 491 | "state": "active", | ||
| 492 | "vocabulary_id": null | 492 | "vocabulary_id": null | ||
| 493 | } | 493 | } | ||
| 494 | ], | 494 | ], | ||
| 495 | "title": "Ghana: High Resolution Population Density Maps + | 495 | "title": "Ghana: High Resolution Population Density Maps + | ||
| 496 | Demographic Estimates", | 496 | Demographic Estimates", | ||
| 497 | "type": "dataset", | 497 | "type": "dataset", | ||
| 498 | "url": | 498 | "url": | ||
| 499 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-gha", | 499 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-gha", | ||
| 500 | "version": "" | 500 | "version": "" | ||
| 501 | } | 501 | } |