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On Július 4, 2022, 15:03:08 (SAST),
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Moved Senegal: High Resolution Population Density Maps + Demographic Estimates from organization HDX - The Humanitarian Data Exchange to organization Open Cities Lab
f | 1 | { | f | 1 | { |
2 | "author": "Data for Good at Meta", | 2 | "author": "Data for Good at Meta", | ||
3 | "author_email": "", | 3 | "author_email": "", | ||
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5 | "extras": [], | 5 | "extras": [], | ||
6 | "groups": [ | 6 | "groups": [ | ||
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8 | "description": "These are backlog datasets that have been | 8 | "description": "These are backlog datasets that have been | ||
9 | sorted.", | 9 | sorted.", | ||
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19 | July 2014, the goal of HDX is to make humanitarian data easy to find | ||||
20 | and use for analysis. Our growing collection of datasets has been | ||||
21 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | ||||
22 | managed by OCHA's Centre for Humanitarian Data, which is located in | ||||
23 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
24 | responsible for bringing together humanitarian actors to ensure a | ||||
25 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
26 | a number of consultants who are based in North America, Europe and | ||||
27 | Africa.", | ||||
28 | "display_name": "HDX: Humanitarian Data Exchange", | ||||
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33 | "title": "HDX: Humanitarian Data Exchange" | ||||
15 | }, | 34 | }, | ||
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17 | "description": "", | 36 | "description": "", | ||
18 | "display_name": "Population ", | 37 | "display_name": "Population ", | ||
19 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | 38 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | ||
20 | "image_display_url": "", | 39 | "image_display_url": "", | ||
21 | "name": "population", | 40 | "name": "population", | ||
22 | "title": "Population " | 41 | "title": "Population " | ||
23 | } | 42 | } | ||
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27 | "license_id": "cc-by", | 46 | "license_id": "cc-by", | ||
28 | "license_title": "Creative Commons Attribution", | 47 | "license_title": "Creative Commons Attribution", | ||
29 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 48 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
30 | "maintainer": "Heiko Heilgendorff (OCL)", | 49 | "maintainer": "Heiko Heilgendorff (OCL)", | ||
31 | "maintainer_email": "[email protected]", | 50 | "maintainer_email": "[email protected]", | ||
32 | "metadata_created": "2021-06-04T11:21:16.687039", | 51 | "metadata_created": "2021-06-04T11:21:16.687039", | ||
n | 33 | "metadata_modified": "2022-06-15T10:42:04.152695", | n | 52 | "metadata_modified": "2022-07-04T13:03:08.577993", |
34 | "name": | 53 | "name": | ||
35 | enegal-high-resolution-population-density-maps-demographic-estimates", | 54 | enegal-high-resolution-population-density-maps-demographic-estimates", | ||
36 | "notes": "VERSION 1.5. The world's most accurate population | 55 | "notes": "VERSION 1.5. The world's most accurate population | ||
37 | datasets. Seven maps/datasets for the distribution of various | 56 | datasets. Seven maps/datasets for the distribution of various | ||
38 | populations in Nigeria: (1) Overall population density (2) Women (3) | 57 | populations in Nigeria: (1) Overall population density (2) Women (3) | ||
39 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | 58 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | ||
40 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 59 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
41 | Methodology\r\n\r\nThese high-resolution maps are created using | 60 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
42 | machine learning techniques to identify buildings from commercially | 61 | machine learning techniques to identify buildings from commercially | ||
43 | available satellite images. This is then overlayed with general | 62 | available satellite images. This is then overlayed with general | ||
44 | population estimates based on publicly available census data and other | 63 | population estimates based on publicly available census data and other | ||
45 | population statistics at Columbia University. The resulting maps are | 64 | population statistics at Columbia University. The resulting maps are | ||
46 | the most detailed and actionable tools available for aid and research | 65 | the most detailed and actionable tools available for aid and research | ||
47 | organizations. For more information about the methodology used to | 66 | organizations. For more information about the methodology used to | ||
48 | create our high resolution population density maps and the demographic | 67 | create our high resolution population density maps and the demographic | ||
49 | distributions, click | 68 | distributions, click | ||
50 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | 69 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | ||
51 | information about how to use HDX to access these datasets, please | 70 | information about how to use HDX to access these datasets, please | ||
52 | visit: | 71 | visit: | ||
53 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 72 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
54 | to match the census population with the UN estimates are applied at | 73 | to match the census population with the UN estimates are applied at | ||
55 | the national level. The UN estimate for a given country (or | 74 | the national level. The UN estimate for a given country (or | ||
56 | state/territory) is divided by the total census estimate of population | 75 | state/territory) is divided by the total census estimate of population | ||
57 | for the given country. The resulting adjustment factor is multiplied | 76 | for the given country. The resulting adjustment factor is multiplied | ||
58 | by each administrative unit census value for the target year. This | 77 | by each administrative unit census value for the target year. This | ||
59 | preserves the relative population totals across administrative units | 78 | preserves the relative population totals across administrative units | ||
60 | while matching the UN total. More information can be found | 79 | while matching the UN total. More information can be found | ||
61 | ocs/census-information-for-high-resolution-population-density-maps/)", | 80 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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70 | and use for analysis. Our growing collection of datasets has been | 89 | government. You can find our website | ||
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