Modifications
On Juillet 4, 2022, 14:59:04 (SAST),
wasim:
-
Moved South Africa: High Resolution Population Density Maps + Demographic Estimates from organization HDX - The Humanitarian Data Exchange to organization Open Cities Lab
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| 2 | "author": "Data for Good at Meta", | 2 | "author": "Data for Good at Meta", | ||
| 3 | "author_email": "", | 3 | "author_email": "", | ||
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| 6 | "groups": [ | 6 | "groups": [ | ||
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| n | 8 | "description": "", | n | 8 | "description": "These are backlog datasets that have been |
| 9 | sorted.", | ||||
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| 17 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||||
| 18 | platform for sharing data across crises and organisations. Launched in | ||||
| 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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| 32 | "name": "hdx-humanitarian-data-exchange", | ||||
| 33 | "title": "HDX: Humanitarian Data Exchange" | ||||
| 14 | }, | 34 | }, | ||
| 15 | { | 35 | { | ||
| 16 | "description": "", | 36 | "description": "", | ||
| 17 | "display_name": "Population ", | 37 | "display_name": "Population ", | ||
| 18 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | 38 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | ||
| 19 | "image_display_url": "", | 39 | "image_display_url": "", | ||
| 20 | "name": "population", | 40 | "name": "population", | ||
| 21 | "title": "Population " | 41 | "title": "Population " | ||
| 22 | } | 42 | } | ||
| 23 | ], | 43 | ], | ||
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| 25 | "isopen": true, | 45 | "isopen": true, | ||
| 26 | "license_id": "cc-by", | 46 | "license_id": "cc-by", | ||
| 27 | "license_title": "Creative Commons Attribution", | 47 | "license_title": "Creative Commons Attribution", | ||
| 28 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 48 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| 29 | "maintainer": "Heiko Heilgendorff (OCL)", | 49 | "maintainer": "Heiko Heilgendorff (OCL)", | ||
| 30 | "maintainer_email": "[email protected]", | 50 | "maintainer_email": "[email protected]", | ||
| 31 | "metadata_created": "2021-06-09T14:30:20.667813", | 51 | "metadata_created": "2021-06-09T14:30:20.667813", | ||
| n | 32 | "metadata_modified": "2022-06-15T17:34:47.077277", | n | 52 | "metadata_modified": "2022-07-04T12:59:04.030084", |
| 33 | "name": | 53 | "name": | ||
| 34 | africa-high-resolution-population-density-maps-demographic-estimates", | 54 | africa-high-resolution-population-density-maps-demographic-estimates", | ||
| 35 | "notes": "VERSION 1.5. The world's most accurate population | 55 | "notes": "VERSION 1.5. The world's most accurate population | ||
| 36 | datasets. Seven maps/datasets for the distribution of various | 56 | datasets. Seven maps/datasets for the distribution of various | ||
| 37 | populations in South Africa: (1) Overall population density (2) Women | 57 | populations in South Africa: (1) Overall population density (2) Women | ||
| 38 | (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly | 58 | (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly | ||
| 39 | (ages 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 59 | (ages 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
| 40 | Methodology\r\n\r\nThese high-resolution maps are created using | 60 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
| 41 | machine learning techniques to identify buildings from commercially | 61 | machine learning techniques to identify buildings from commercially | ||
| 42 | available satellite images. This is then overlayed with general | 62 | available satellite images. This is then overlayed with general | ||
| 43 | population estimates based on publicly available census data and other | 63 | population estimates based on publicly available census data and other | ||
| 44 | population statistics at Columbia University. The resulting maps are | 64 | population statistics at Columbia University. The resulting maps are | ||
| 45 | the most detailed and actionable tools available for aid and research | 65 | the most detailed and actionable tools available for aid and research | ||
| 46 | organizations. For more information about the methodology used to | 66 | organizations. For more information about the methodology used to | ||
| 47 | create our high resolution population density maps and the demographic | 67 | create our high resolution population density maps and the demographic | ||
| 48 | distributions, click | 68 | distributions, click | ||
| 49 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | 69 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | ||
| 50 | information about how to use HDX to access these datasets, please | 70 | information about how to use HDX to access these datasets, please | ||
| 51 | visit: | 71 | visit: | ||
| 52 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 72 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
| 53 | 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 | ||
| 54 | the national level. The UN estimate for a given country (or | 74 | the national level. The UN estimate for a given country (or | ||
| 55 | state/territory) is divided by the total census estimate of population | 75 | state/territory) is divided by the total census estimate of population | ||
| 56 | for the given country. The resulting adjustment factor is multiplied | 76 | for the given country. The resulting adjustment factor is multiplied | ||
| 57 | by each administrative unit census value for the target year. This | 77 | by each administrative unit census value for the target year. This | ||
| 58 | preserves the relative population totals across administrative units | 78 | preserves the relative population totals across administrative units | ||
| 59 | while matching the UN total. More information can be found | 79 | while matching the UN total. More information can be found | ||
| 60 | ocs/census-information-for-high-resolution-population-density-maps/)", | 80 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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| 63 | "organization": { | 83 | "organization": { | ||
| 64 | "approval_status": "approved", | 84 | "approval_status": "approved", | ||
| n | 65 | "created": "2022-06-15T09:44:22.871262", | n | 85 | "created": "2022-07-04T08:06:45.420882", |
| 66 | "description": "The Humanitarian Data Exchange (HDX) is an open | 86 | "description": "We work to build inclusion and participatory | ||
| 67 | platform for sharing data across crises and organisations. Launched in | 87 | democracy in cities and urban spaces through empowering citizens, | ||
| 68 | July 2014, the goal of HDX is to make humanitarian data easy to find | 88 | building trust and accountability in civic space, and capacitating | ||
| 69 | and use for analysis. Our growing collection of datasets has been | 89 | government. You can find our website | ||
| 70 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | 90 | [here](https://opencitieslab.org/).", | ||
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| 72 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
| 73 | responsible for bringing together humanitarian actors to ensure a | ||||
| 74 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
| 75 | a number of consultants who are based in North America, Europe and | ||||
| 76 | Africa.", | ||||
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| 85 | "type": "organization" | 99 | "type": "organization" | ||
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| 475 | { | 489 | { | ||
| 476 | "display_name": "deodata", | 490 | "display_name": "deodata", | ||
| 477 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 491 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
| 478 | "name": "deodata", | 492 | "name": "deodata", | ||
| 479 | "state": "active", | 493 | "state": "active", | ||
| 480 | "vocabulary_id": null | 494 | "vocabulary_id": null | ||
| 481 | }, | 495 | }, | ||
| 482 | { | 496 | { | ||
| 483 | "display_name": "elderly", | 497 | "display_name": "elderly", | ||
| 484 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 498 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
| 485 | "name": "elderly", | 499 | "name": "elderly", | ||
| 486 | "state": "active", | 500 | "state": "active", | ||
| 487 | "vocabulary_id": null | 501 | "vocabulary_id": null | ||
| 488 | }, | 502 | }, | ||
| 489 | { | 503 | { | ||
| 490 | "display_name": "men", | 504 | "display_name": "men", | ||
| 491 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 505 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
| 492 | "name": "men", | 506 | "name": "men", | ||
| 493 | "state": "active", | 507 | "state": "active", | ||
| 494 | "vocabulary_id": null | 508 | "vocabulary_id": null | ||
| 495 | }, | 509 | }, | ||
| 496 | { | 510 | { | ||
| 497 | "display_name": "population density", | 511 | "display_name": "population density", | ||
| 498 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 512 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 499 | "name": "population density", | 513 | "name": "population density", | ||
| 500 | "state": "active", | 514 | "state": "active", | ||
| 501 | "vocabulary_id": null | 515 | "vocabulary_id": null | ||
| 502 | }, | 516 | }, | ||
| 503 | { | 517 | { | ||
| 504 | "display_name": "south africa", | 518 | "display_name": "south africa", | ||
| 505 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 519 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
| 506 | "name": "south africa", | 520 | "name": "south africa", | ||
| 507 | "state": "active", | 521 | "state": "active", | ||
| 508 | "vocabulary_id": null | 522 | "vocabulary_id": null | ||
| 509 | }, | 523 | }, | ||
| 510 | { | 524 | { | ||
| 511 | "display_name": "women", | 525 | "display_name": "women", | ||
| 512 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 526 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 513 | "name": "women", | 527 | "name": "women", | ||
| 514 | "state": "active", | 528 | "state": "active", | ||
| 515 | "vocabulary_id": null | 529 | "vocabulary_id": null | ||
| 516 | }, | 530 | }, | ||
| 517 | { | 531 | { | ||
| 518 | "display_name": "youth", | 532 | "display_name": "youth", | ||
| 519 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 533 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 520 | "name": "youth", | 534 | "name": "youth", | ||
| 521 | "state": "active", | 535 | "state": "active", | ||
| 522 | "vocabulary_id": null | 536 | "vocabulary_id": null | ||
| 523 | } | 537 | } | ||
| 524 | ], | 538 | ], | ||
| 525 | "title": "South Africa: High Resolution Population Density Maps + | 539 | "title": "South Africa: High Resolution Population Density Maps + | ||
| 526 | Demographic Estimates", | 540 | Demographic Estimates", | ||
| 527 | "type": "dataset", | 541 | "type": "dataset", | ||
| 528 | "url": | 542 | "url": | ||
| 529 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 543 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 530 | "version": "" | 544 | "version": "" | ||
| 531 | } | 545 | } |