Changes
On June 14, 2023, 11:34:55 (SAST),
Brian O'Leary:
-
Deleted resource nga_general_2020_csv.zip from Nigeria: High Resolution Population Density Maps + Demographic Estimates
| f | 1 | { | f | 1 | { |
| 2 | "author": "Data for Good at Meta", | 2 | "author": "Data for Good at Meta", | ||
| 3 | "author_email": "", | 3 | "author_email": "", | ||
| 4 | "creator_user_id": "34f7a594-935e-4ac6-b13c-8bb3b861b487", | 4 | "creator_user_id": "34f7a594-935e-4ac6-b13c-8bb3b861b487", | ||
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| 10 | "display_name": "A_Done", | ||||
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| 13 | "name": "a_done", | ||||
| 14 | "title": "A_Done" | ||||
| 15 | }, | ||||
| 16 | { | 7 | { | ||
| 17 | "description": "The Humanitarian Data Exchange (HDX) is an open | 8 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||
| 18 | platform for sharing data across crises and organisations. Launched in | 9 | 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 | 10 | 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 | 11 | 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 | 12 | 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 | 13 | 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 | 14 | The Hague. OCHA is part of the United Nations Secretariat and is | ||
| 24 | responsible for bringing together humanitarian actors to ensure a | 15 | responsible for bringing together humanitarian actors to ensure a | ||
| 25 | coherent response to emergencies. The HDX team includes OCHA staff and | 16 | coherent response to emergencies. The HDX team includes OCHA staff and | ||
| 26 | a number of consultants who are based in North America, Europe and | 17 | a number of consultants who are based in North America, Europe and | ||
| 27 | Africa.", | 18 | Africa.", | ||
| 28 | "display_name": "HDX: Humanitarian Data Exchange", | 19 | "display_name": "HDX: Humanitarian Data Exchange", | ||
| 29 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | 20 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | ||
| 30 | "image_display_url": | 21 | "image_display_url": | ||
| 31 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | 22 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | ||
| 32 | "name": "hdx-humanitarian-data-exchange", | 23 | "name": "hdx-humanitarian-data-exchange", | ||
| 33 | "title": "HDX: Humanitarian Data Exchange" | 24 | "title": "HDX: Humanitarian Data Exchange" | ||
| 34 | }, | 25 | }, | ||
| 35 | { | 26 | { | ||
| 36 | "description": "", | 27 | "description": "", | ||
| 37 | "display_name": "Population ", | 28 | "display_name": "Population ", | ||
| 38 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | 29 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | ||
| 39 | "image_display_url": "", | 30 | "image_display_url": "", | ||
| 40 | "name": "population", | 31 | "name": "population", | ||
| 41 | "title": "Population " | 32 | "title": "Population " | ||
| 42 | } | 33 | } | ||
| 43 | ], | 34 | ], | ||
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| 45 | "isopen": true, | 36 | "isopen": true, | ||
| 46 | "license_id": "cc-by", | 37 | "license_id": "cc-by", | ||
| 47 | "license_title": "Creative Commons Attribution", | 38 | "license_title": "Creative Commons Attribution", | ||
| 48 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 39 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| 49 | "maintainer": "Heiko Heilgendorff (OCL)", | 40 | "maintainer": "Heiko Heilgendorff (OCL)", | ||
| 50 | "maintainer_email": "[email protected]", | 41 | "maintainer_email": "[email protected]", | ||
| 51 | "metadata_created": "2021-06-03T14:01:21.364366", | 42 | "metadata_created": "2021-06-03T14:01:21.364366", | ||
| n | 52 | "metadata_modified": "2022-07-04T13:01:41.572802", | n | 43 | "metadata_modified": "2023-06-14T09:34:55.460159", |
| 53 | "name": | 44 | "name": | ||
| 54 | igeria-high-resolution-population-density-maps-demographic-estimates", | 45 | igeria-high-resolution-population-density-maps-demographic-estimates", | ||
| 55 | "notes": "VERSION 1.5. The world's most accurate population | 46 | "notes": "VERSION 1.5. The world's most accurate population | ||
| 56 | datasets. Seven maps/datasets for the distribution of various | 47 | datasets. Seven maps/datasets for the distribution of various | ||
| 57 | populations in Nigeria: (1) Overall population density (2) Women (3) | 48 | populations in Nigeria: (1) Overall population density (2) Women (3) | ||
| 58 | 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 | ||
| 59 | 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### | ||
| 60 | Methodology\r\n\r\nThese high-resolution maps are created using | 51 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
| 61 | machine learning techniques to identify buildings from commercially | 52 | machine learning techniques to identify buildings from commercially | ||
| 62 | available satellite images. This is then overlayed with general | 53 | available satellite images. This is then overlayed with general | ||
| 63 | population estimates based on publicly available census data and other | 54 | population estimates based on publicly available census data and other | ||
| 64 | population statistics at Columbia University. The resulting maps are | 55 | population statistics at Columbia University. The resulting maps are | ||
| 65 | the most detailed and actionable tools available for aid and research | 56 | the most detailed and actionable tools available for aid and research | ||
| 66 | organizations. For more information about the methodology used to | 57 | organizations. For more information about the methodology used to | ||
| 67 | create our high resolution population density maps and the demographic | 58 | create our high resolution population density maps and the demographic | ||
| 68 | distributions, click | 59 | distributions, click | ||
| 69 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | 60 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | ||
| 70 | information about how to use HDX to access these datasets, please | 61 | information about how to use HDX to access these datasets, please | ||
| 71 | visit: | 62 | visit: | ||
| 72 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 63 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
| 73 | 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 | ||
| 74 | the national level. The UN estimate for a given country (or | 65 | the national level. The UN estimate for a given country (or | ||
| 75 | state/territory) is divided by the total census estimate of population | 66 | state/territory) is divided by the total census estimate of population | ||
| 76 | for the given country. The resulting adjustment factor is multiplied | 67 | for the given country. The resulting adjustment factor is multiplied | ||
| 77 | by each administrative unit census value for the target year. This | 68 | by each administrative unit census value for the target year. This | ||
| 78 | preserves the relative population totals across administrative units | 69 | preserves the relative population totals across administrative units | ||
| 79 | while matching the UN total. More information can be found | 70 | while matching the UN total. More information can be found | ||
| 80 | ocs/census-information-for-high-resolution-population-density-maps/)", | 71 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
| n | 81 | "num_resources": 16, | n | 72 | "num_resources": 15, |
| 82 | "num_tags": 7, | 73 | "num_tags": 7, | ||
| 83 | "organization": { | 74 | "organization": { | ||
| 84 | "approval_status": "approved", | 75 | "approval_status": "approved", | ||
| 85 | "created": "2022-07-04T08:06:45.420882", | 76 | "created": "2022-07-04T08:06:45.420882", | ||
| 86 | "description": "We work to build inclusion and participatory | 77 | "description": "We work to build inclusion and participatory | ||
| 87 | democracy in cities and urban spaces through empowering citizens, | 78 | democracy in cities and urban spaces through empowering citizens, | ||
| 88 | building trust and accountability in civic space, and capacitating | 79 | building trust and accountability in civic space, and capacitating | ||
| 89 | government. You can find our website | 80 | government. You can find our website | ||
| 90 | [here](https://opencitieslab.org/).", | 81 | [here](https://opencitieslab.org/).", | ||
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| 94 | "is_organization": true, | 85 | "is_organization": true, | ||
| 95 | "name": "open-cities-lab", | 86 | "name": "open-cities-lab", | ||
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| 97 | "state": "active", | 88 | "state": "active", | ||
| 98 | "title": "Open Cities Lab", | 89 | "title": "Open Cities Lab", | ||
| 99 | "type": "organization" | 90 | "type": "organization" | ||
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| n | 469 | "position": 15, | n | 437 | "position": 14, |
| 470 | "resource_type": null, | 438 | "resource_type": null, | ||
| t | 471 | "revision_id": "60c668ee-9387-49f5-ac18-0d484ab9a779", | t | 439 | "revision_id": "b1325a66-cb72-41b4-ad82-6b2cf770e681", |
| 472 | "size": null, | 440 | "size": null, | ||
| 473 | "state": "active", | 441 | "state": "active", | ||
| 474 | "url": | 442 | "url": | ||
| 475 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | 443 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | ||
| 476 | "url_type": null | 444 | "url_type": null | ||
| 477 | } | 445 | } | ||
| 478 | ], | 446 | ], | ||
| 479 | "revision_id": "9ca6a473-db23-4bf3-ad1d-cad6389f81e7", | 447 | "revision_id": "9ca6a473-db23-4bf3-ad1d-cad6389f81e7", | ||
| 480 | "state": "active", | 448 | "state": "active", | ||
| 481 | "tags": [ | 449 | "tags": [ | ||
| 482 | { | 450 | { | ||
| 483 | "display_name": "children", | 451 | "display_name": "children", | ||
| 484 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 452 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
| 485 | "name": "children", | 453 | "name": "children", | ||
| 486 | "state": "active", | 454 | "state": "active", | ||
| 487 | "vocabulary_id": null | 455 | "vocabulary_id": null | ||
| 488 | }, | 456 | }, | ||
| 489 | { | 457 | { | ||
| 490 | "display_name": "gender", | 458 | "display_name": "gender", | ||
| 491 | "id": "9923e71e-0c99-4abb-a0fa-5e7ccbc74956", | 459 | "id": "9923e71e-0c99-4abb-a0fa-5e7ccbc74956", | ||
| 492 | "name": "gender", | 460 | "name": "gender", | ||
| 493 | "state": "active", | 461 | "state": "active", | ||
| 494 | "vocabulary_id": null | 462 | "vocabulary_id": null | ||
| 495 | }, | 463 | }, | ||
| 496 | { | 464 | { | ||
| 497 | "display_name": "nigeria", | 465 | "display_name": "nigeria", | ||
| 498 | "id": "86671c58-04e8-4323-8904-b2b506740271", | 466 | "id": "86671c58-04e8-4323-8904-b2b506740271", | ||
| 499 | "name": "nigeria", | 467 | "name": "nigeria", | ||
| 500 | "state": "active", | 468 | "state": "active", | ||
| 501 | "vocabulary_id": null | 469 | "vocabulary_id": null | ||
| 502 | }, | 470 | }, | ||
| 503 | { | 471 | { | ||
| 504 | "display_name": "population", | 472 | "display_name": "population", | ||
| 505 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | 473 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | ||
| 506 | "name": "population", | 474 | "name": "population", | ||
| 507 | "state": "active", | 475 | "state": "active", | ||
| 508 | "vocabulary_id": null | 476 | "vocabulary_id": null | ||
| 509 | }, | 477 | }, | ||
| 510 | { | 478 | { | ||
| 511 | "display_name": "population density", | 479 | "display_name": "population density", | ||
| 512 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 480 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 513 | "name": "population density", | 481 | "name": "population density", | ||
| 514 | "state": "active", | 482 | "state": "active", | ||
| 515 | "vocabulary_id": null | 483 | "vocabulary_id": null | ||
| 516 | }, | 484 | }, | ||
| 517 | { | 485 | { | ||
| 518 | "display_name": "women", | 486 | "display_name": "women", | ||
| 519 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 487 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 520 | "name": "women", | 488 | "name": "women", | ||
| 521 | "state": "active", | 489 | "state": "active", | ||
| 522 | "vocabulary_id": null | 490 | "vocabulary_id": null | ||
| 523 | }, | 491 | }, | ||
| 524 | { | 492 | { | ||
| 525 | "display_name": "youth", | 493 | "display_name": "youth", | ||
| 526 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 494 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 527 | "name": "youth", | 495 | "name": "youth", | ||
| 528 | "state": "active", | 496 | "state": "active", | ||
| 529 | "vocabulary_id": null | 497 | "vocabulary_id": null | ||
| 530 | } | 498 | } | ||
| 531 | ], | 499 | ], | ||
| 532 | "title": "Nigeria: High Resolution Population Density Maps + | 500 | "title": "Nigeria: High Resolution Population Density Maps + | ||
| 533 | Demographic Estimates", | 501 | Demographic Estimates", | ||
| 534 | "type": "dataset", | 502 | "type": "dataset", | ||
| 535 | "url": | 503 | "url": | ||
| 536 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | 504 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | ||
| 537 | "version": "" | 505 | "version": "" | ||
| 538 | } | 506 | } |