Változások
On Augusztus 24, 2022, 12:23:36 (SAST),
Brian O'Leary:
-
Moved Ethiopia: High Resolution Population Density Maps + Demographic Estimates from organization Humanitarian Emergency Response Africa to organization Open Cities Lab
-
Set maintainer of Ethiopia: High Resolution Population Density Maps + Demographic Estimates to Brian O'Leary (previously Facebook Data For Good)
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Set maintainer email of Ethiopia: High Resolution Population Density Maps + Demographic Estimates to [email protected]
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| 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", | ||
| 5 | "extras": [], | 5 | "extras": [], | ||
| n | 6 | "groups": [], | n | 6 | "groups": [ |
| 7 | { | ||||
| 8 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||||
| 9 | platform for sharing data across crises and organisations. Launched in | ||||
| 10 | July 2014, the goal of HDX is to make humanitarian data easy to find | ||||
| 11 | and use for analysis. Our growing collection of datasets has been | ||||
| 12 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | ||||
| 13 | managed by OCHA's Centre for Humanitarian Data, which is located in | ||||
| 14 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
| 15 | responsible for bringing together humanitarian actors to ensure a | ||||
| 16 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
| 17 | a number of consultants who are based in North America, Europe and | ||||
| 18 | Africa.", | ||||
| 19 | "display_name": "HDX: Humanitarian Data Exchange", | ||||
| 20 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | ||||
| 21 | "image_display_url": | ||||
| 22 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | ||||
| 23 | "name": "hdx-humanitarian-data-exchange", | ||||
| 24 | "title": "HDX: Humanitarian Data Exchange" | ||||
| 25 | }, | ||||
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| 27 | "description": "", | ||||
| 28 | "display_name": "Still to be sorted", | ||||
| 29 | "id": "3e71b430-62d6-4b65-8157-527fd8e9c52f", | ||||
| 30 | "image_display_url": "", | ||||
| 31 | "name": "not-useful", | ||||
| 32 | "title": "Still to be sorted" | ||||
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| 8 | "isopen": true, | 36 | "isopen": true, | ||
| 9 | "license_id": "cc-by", | 37 | "license_id": "cc-by", | ||
| 10 | "license_title": "Creative Commons Attribution", | 38 | "license_title": "Creative Commons Attribution", | ||
| 11 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 39 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| n | 12 | "maintainer": "Facebook Data For Good", | n | 40 | "maintainer": "Brian O'Leary", |
| 13 | "maintainer_email": "", | 41 | "maintainer_email": "[email protected]", | ||
| 14 | "metadata_created": "2021-06-04T11:03:10.094864", | 42 | "metadata_created": "2021-06-04T11:03:10.094864", | ||
| n | 15 | "metadata_modified": "2021-06-04T11:12:22.172020", | n | 43 | "metadata_modified": "2022-08-24T10:23:36.486002", |
| 16 | "name": | 44 | "name": | ||
| 17 | hiopia-high-resolution-population-density-maps-demographic-estimates", | 45 | hiopia-high-resolution-population-density-maps-demographic-estimates", | ||
| 18 | "notes": "VERSION 1.5. The world's most accurate population | 46 | "notes": "VERSION 1.5. The world's most accurate population | ||
| 19 | datasets. Seven maps/datasets for the distribution of various | 47 | datasets. Seven maps/datasets for the distribution of various | ||
| 20 | populations in Nigeria: (1) Overall population density (2) Women (3) | 48 | 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 | 49 | 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### | 50 | 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 | 51 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
| 24 | machine learning techniques to identify buildings from commercially | 52 | machine learning techniques to identify buildings from commercially | ||
| 25 | available satellite images. This is then overlayed with general | 53 | available satellite images. This is then overlayed with general | ||
| 26 | population estimates based on publicly available census data and other | 54 | population estimates based on publicly available census data and other | ||
| 27 | population statistics at Columbia University. The resulting maps are | 55 | population statistics at Columbia University. The resulting maps are | ||
| 28 | the most detailed and actionable tools available for aid and research | 56 | the most detailed and actionable tools available for aid and research | ||
| 29 | organizations. For more information about the methodology used to | 57 | organizations. For more information about the methodology used to | ||
| 30 | create our high resolution population density maps and the demographic | 58 | create our high resolution population density maps and the demographic | ||
| 31 | distributions, click | 59 | distributions, click | ||
| 32 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | 60 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | ||
| 33 | information about how to use HDX to access these datasets, please | 61 | information about how to use HDX to access these datasets, please | ||
| 34 | visit: | 62 | visit: | ||
| 35 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 63 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
| 36 | to match the census population with the UN estimates are applied at | 64 | to match the census population with the UN estimates are applied at | ||
| 37 | the national level. The UN estimate for a given country (or | 65 | the national level. The UN estimate for a given country (or | ||
| 38 | state/territory) is divided by the total census estimate of population | 66 | state/territory) is divided by the total census estimate of population | ||
| 39 | for the given country. The resulting adjustment factor is multiplied | 67 | for the given country. The resulting adjustment factor is multiplied | ||
| 40 | by each administrative unit census value for the target year. This | 68 | by each administrative unit census value for the target year. This | ||
| 41 | preserves the relative population totals across administrative units | 69 | preserves the relative population totals across administrative units | ||
| 42 | while matching the UN total. More information can be found | 70 | while matching the UN total. More information can be found | ||
| 43 | ocs/census-information-for-high-resolution-population-density-maps/)", | 71 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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| 46 | "organization": { | 74 | "organization": { | ||
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| n | 48 | "created": "2021-02-15T10:09:07.942828", | n | 76 | "created": "2022-07-04T08:06:45.420882", |
| 49 | "description": "Humanitarian Emergency Response Africa Datasets", | 77 | "description": "We work to build inclusion and participatory | ||
| 50 | "id": "4607d385-0a0b-4fd9-a553-cc650210c096", | 78 | democracy in cities and urban spaces through empowering citizens, | ||
| 79 | building trust and accountability in civic space, and capacitating | ||||
| 80 | government. You can find our website | ||||
| 81 | [here](https://opencitieslab.org/).", | ||||
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| n | 57 | "title": "Humanitarian Emergency Response Africa", | n | 89 | "title": "Open Cities Lab", |
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