Modifications
On Juillet 4, 2022, 15:01:41 (SAST),
wasim:
-
Moved Nigeria: High Resolution Population Density Maps + Demographic Estimates from organization HDX - The Humanitarian Data Exchange to organization Open Cities Lab
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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.", | ||||
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| 19 | "image_display_url": "", | 39 | "image_display_url": "", | ||
| 20 | "name": "population", | 40 | "name": "population", | ||
| 21 | "title": "Population " | 41 | "title": "Population " | ||
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| 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": "2021-06-03T14:01:21.364366", | 51 | "metadata_created": "2021-06-03T14:01:21.364366", | ||
| n | 32 | "metadata_modified": "2022-06-15T11:27:21.106689", | n | 52 | "metadata_modified": "2022-07-04T13:01:41.572802", |
| 33 | "name": | 53 | "name": | ||
| 34 | igeria-high-resolution-population-density-maps-demographic-estimates", | 54 | igeria-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 Nigeria: (1) Overall population density (2) Women (3) | 57 | populations in Nigeria: (1) Overall population density (2) Women (3) | ||
| 38 | 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 | ||
| 39 | 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### | ||
| 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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| 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 | ||
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| 478 | "name": "gender", | 492 | "name": "gender", | ||
| 479 | "state": "active", | 493 | "state": "active", | ||
| 480 | "vocabulary_id": null | 494 | "vocabulary_id": null | ||
| 481 | }, | 495 | }, | ||
| 482 | { | 496 | { | ||
| 483 | "display_name": "nigeria", | 497 | "display_name": "nigeria", | ||
| 484 | "id": "86671c58-04e8-4323-8904-b2b506740271", | 498 | "id": "86671c58-04e8-4323-8904-b2b506740271", | ||
| 485 | "name": "nigeria", | 499 | "name": "nigeria", | ||
| 486 | "state": "active", | 500 | "state": "active", | ||
| 487 | "vocabulary_id": null | 501 | "vocabulary_id": null | ||
| 488 | }, | 502 | }, | ||
| 489 | { | 503 | { | ||
| 490 | "display_name": "population", | 504 | "display_name": "population", | ||
| 491 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | 505 | "id": "0020415a-6815-440e-96c9-b4e4a7b58464", | ||
| 492 | "name": "population", | 506 | "name": "population", | ||
| 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": "women", | 518 | "display_name": "women", | ||
| 505 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 519 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 506 | "name": "women", | 520 | "name": "women", | ||
| 507 | "state": "active", | 521 | "state": "active", | ||
| 508 | "vocabulary_id": null | 522 | "vocabulary_id": null | ||
| 509 | }, | 523 | }, | ||
| 510 | { | 524 | { | ||
| 511 | "display_name": "youth", | 525 | "display_name": "youth", | ||
| 512 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 526 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 513 | "name": "youth", | 527 | "name": "youth", | ||
| 514 | "state": "active", | 528 | "state": "active", | ||
| 515 | "vocabulary_id": null | 529 | "vocabulary_id": null | ||
| 516 | } | 530 | } | ||
| 517 | ], | 531 | ], | ||
| 518 | "title": "Nigeria: High Resolution Population Density Maps + | 532 | "title": "Nigeria: High Resolution Population Density Maps + | ||
| 519 | Demographic Estimates", | 533 | Demographic Estimates", | ||
| 520 | "type": "dataset", | 534 | "type": "dataset", | ||
| 521 | "url": | 535 | "url": | ||
| 522 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | 536 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-nga", | ||
| 523 | "version": "" | 537 | "version": "" | ||
| 524 | } | 538 | } |