f | { | f | { |
| "author": "Data for Good at Meta", | | "author": "Data for Good at Meta", |
| "author_email": "", | | "author_email": "", |
| "creator_user_id": "77d79e63-5fb4-4320-94e4-51e5e9b8031c", | | "creator_user_id": "77d79e63-5fb4-4320-94e4-51e5e9b8031c", |
| "extras": [], | | "extras": [], |
| "groups": [], | | "groups": [], |
| "id": "00a4270d-0a80-4d97-abf5-155148b2d8ca", | | "id": "00a4270d-0a80-4d97-abf5-155148b2d8ca", |
| "isopen": true, | | "isopen": true, |
| "license_id": "cc-by", | | "license_id": "cc-by", |
| "license_title": "Creative Commons Attribution", | | "license_title": "Creative Commons Attribution", |
| "license_url": "http://www.opendefinition.org/licenses/cc-by", | | "license_url": "http://www.opendefinition.org/licenses/cc-by", |
| "maintainer": "Brian O'Leary", | | "maintainer": "Brian O'Leary", |
| "maintainer_email": "[email protected]", | | "maintainer_email": "[email protected]", |
| "metadata_created": "2022-06-27T14:38:00.605004", | | "metadata_created": "2022-06-27T14:38:00.605004", |
n | "metadata_modified": "2022-06-27T14:39:15.378024", | n | "metadata_modified": "2022-06-27T14:45:42.531777", |
| "name": | | "name": |
| -verde-high-resolution-population-density-maps-demographic-estimates", | | -verde-high-resolution-population-density-maps-demographic-estimates", |
| "notes": "The world's most accurate population datasets. Seven | | "notes": "The world's most accurate population datasets. Seven |
n | maps/datasets for the distribution of various populations in South | n | maps/datasets for the distribution of various populations in Cabo |
| Africa: (1) Overall population density (2) Women (3) Men (4) Children | | Verde: (1) Overall population density (2) Women (3) Men (4) Children |
| (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of |
| reproductive age (ages 15-49).\r\n\r\n### Methodology\r\n\r\nThese | | reproductive age (ages 15-49).\r\n\r\n### Methodology\r\n\r\nThese |
| high-resolution maps are created using machine learning techniques to | | high-resolution maps are created using machine learning techniques to |
| identify buildings from commercially available satellite images. This | | identify buildings from commercially available satellite images. This |
| is then overlayed with general population estimates based on publicly | | is then overlayed with general population estimates based on publicly |
| available census data and other population statistics at Columbia | | available census data and other population statistics at Columbia |
| University. The resulting maps are the most detailed and actionable | | University. The resulting maps are the most detailed and actionable |
| tools available for aid and research organizations. For more | | tools available for aid and research organizations. For more |
| information about the methodology used to create our high resolution | | information about the methodology used to create our high resolution |
| population density maps and the demographic distributions, click | | population density maps and the demographic distributions, click |
| resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor |
| information about how to use HDX to access these datasets, please | | information about how to use HDX to access these datasets, please |
| visit: | | visit: |
| n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments |
| to match the census population with the UN estimates are applied at | | to match the census population with the UN estimates are applied at |
| the national level. The UN estimate for a given country (or | | the national level. The UN estimate for a given country (or |
| state/territory) is divided by the total census estimate of population | | state/territory) is divided by the total census estimate of population |
| for the given country. The resulting adjustment factor is multiplied | | for the given country. The resulting adjustment factor is multiplied |
| by each administrative unit census value for the target year. This | | by each administrative unit census value for the target year. This |
| preserves the relative population totals across administrative units | | preserves the relative population totals across administrative units |
| while matching the UN total. More information can be found | | while matching the UN total. More information can be found |
| ocs/census-information-for-high-resolution-population-density-maps/)", | | ocs/census-information-for-high-resolution-population-density-maps/)", |
| "num_resources": 1, | | "num_resources": 1, |
| "num_tags": 5, | | "num_tags": 5, |
| "organization": { | | "organization": { |
| "approval_status": "approved", | | "approval_status": "approved", |
| "created": "2022-06-15T09:44:22.871262", | | "created": "2022-06-15T09:44:22.871262", |
| "description": "The Humanitarian Data Exchange (HDX) is an open | | "description": "The Humanitarian Data Exchange (HDX) is an open |
| platform for sharing data across crises and organisations. Launched in | | platform for sharing data across crises and organisations. Launched in |
| July 2014, the goal of HDX is to make humanitarian data easy to find | | July 2014, the goal of HDX is to make humanitarian data easy to find |
| and use for analysis. Our growing collection of datasets has been | | and use for analysis. Our growing collection of datasets has been |
| accessed by users in over 200 countries and territories.\r\n\r\nHDX is | | accessed by users in over 200 countries and territories.\r\n\r\nHDX is |
| managed by OCHA's Centre for Humanitarian Data, which is located in | | managed by OCHA's Centre for Humanitarian Data, which is located in |
| The Hague. OCHA is part of the United Nations Secretariat and is | | The Hague. OCHA is part of the United Nations Secretariat and is |
| responsible for bringing together humanitarian actors to ensure a | | responsible for bringing together humanitarian actors to ensure a |
| coherent response to emergencies. The HDX team includes OCHA staff and | | coherent response to emergencies. The HDX team includes OCHA staff and |
| a number of consultants who are based in North America, Europe and | | a number of consultants who are based in North America, Europe and |
| Africa.", | | Africa.", |
| "id": "ad11db0c-27d3-433d-bc99-973c2ffae709", | | "id": "ad11db0c-27d3-433d-bc99-973c2ffae709", |
| "image_url": | | "image_url": |
| "https://data.humdata.org/images/homepage/logo-hdx.svg", | | "https://data.humdata.org/images/homepage/logo-hdx.svg", |
| "is_organization": true, | | "is_organization": true, |
| "name": "hdx-the-humanitarian-data-exchange", | | "name": "hdx-the-humanitarian-data-exchange", |
| "revision_id": "e6c349dd-82c3-4176-a1c7-a4d89d8eae93", | | "revision_id": "e6c349dd-82c3-4176-a1c7-a4d89d8eae93", |
| "state": "active", | | "state": "active", |
| "title": "HDX - The Humanitarian Data Exchange", | | "title": "HDX - The Humanitarian Data Exchange", |
| "type": "organization" | | "type": "organization" |
| }, | | }, |
| "owner_org": "ad11db0c-27d3-433d-bc99-973c2ffae709", | | "owner_org": "ad11db0c-27d3-433d-bc99-973c2ffae709", |
| "private": false, | | "private": false, |
| "relationships_as_object": [], | | "relationships_as_object": [], |
| "relationships_as_subject": [], | | "relationships_as_subject": [], |
| "resources": [ | | "resources": [ |
| { | | { |
| "cache_last_updated": null, | | "cache_last_updated": null, |
| "cache_url": null, | | "cache_url": null, |
| "created": "2022-06-27T14:39:15.217686", | | "created": "2022-06-27T14:39:15.217686", |
| "datastore_active": false, | | "datastore_active": false, |
| "description": "The world's most accurate population datasets. | | "description": "The world's most accurate population datasets. |
| Seven maps/datasets for the distribution of various populations in | | Seven maps/datasets for the distribution of various populations in |
| Cabo Verde: (1) Overall population density (2) Women (3) Men (4) | | Cabo Verde: (1) Overall population density (2) Women (3) Men (4) |
| Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) |
| Women of reproductive age (ages 15-49).", | | Women of reproductive age (ages 15-49).", |
| "format": "", | | "format": "", |
| "hash": "", | | "hash": "", |
| "id": "b61442a8-eb87-4cfd-982c-ffe6e01feb6d", | | "id": "b61442a8-eb87-4cfd-982c-ffe6e01feb6d", |
| "last_modified": null, | | "last_modified": null, |
| "mimetype": null, | | "mimetype": null, |
| "mimetype_inner": null, | | "mimetype_inner": null, |
| "name": "Cabo Verde: High Resolution Population Density Maps + | | "name": "Cabo Verde: High Resolution Population Density Maps + |
| Demographic Estimates", | | Demographic Estimates", |
| "package_id": "00a4270d-0a80-4d97-abf5-155148b2d8ca", | | "package_id": "00a4270d-0a80-4d97-abf5-155148b2d8ca", |
| "position": 0, | | "position": 0, |
| "resource_type": null, | | "resource_type": null, |
| "revision_id": "2e48a504-d391-4274-8485-43d7f9b4ea88", | | "revision_id": "2e48a504-d391-4274-8485-43d7f9b4ea88", |
| "size": null, | | "size": null, |
| "state": "active", | | "state": "active", |
| "url": | | "url": |
| s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-cpv", | | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-cpv", |
| "url_type": null | | "url_type": null |
| } | | } |
| ], | | ], |
t | "revision_id": "2e48a504-d391-4274-8485-43d7f9b4ea88", | t | "revision_id": "a37d23b3-976c-4458-a2e3-cd4e058323dc", |
| "state": "active", | | "state": "active", |
| "tags": [ | | "tags": [ |
| { | | { |
| "display_name": "Cabo Verde", | | "display_name": "Cabo Verde", |
| "id": "b04a35c7-2c20-4b86-bcd8-38cae58146c1", | | "id": "b04a35c7-2c20-4b86-bcd8-38cae58146c1", |
| "name": "Cabo Verde", | | "name": "Cabo Verde", |
| "state": "active", | | "state": "active", |
| "vocabulary_id": null | | "vocabulary_id": null |
| }, | | }, |
| { | | { |
| "display_name": "Sudan", | | "display_name": "Sudan", |
| "id": "6942cfd8-178c-43ce-b3e4-07993c79824c", | | "id": "6942cfd8-178c-43ce-b3e4-07993c79824c", |
| "name": "Sudan", | | "name": "Sudan", |
| "state": "active", | | "state": "active", |
| "vocabulary_id": null | | "vocabulary_id": null |
| }, | | }, |
| { | | { |
| "display_name": "population count", | | "display_name": "population count", |
| "id": "f7228262-21a4-4fe5-a713-f43c29367f23", | | "id": "f7228262-21a4-4fe5-a713-f43c29367f23", |
| "name": "population count", | | "name": "population count", |
| "state": "active", | | "state": "active", |
| "vocabulary_id": null | | "vocabulary_id": null |
| }, | | }, |
| { | | { |
| "display_name": "population density", | | "display_name": "population density", |
| "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", |
| "name": "population density", | | "name": "population density", |
| "state": "active", | | "state": "active", |
| "vocabulary_id": null | | "vocabulary_id": null |
| }, | | }, |
| { | | { |
| "display_name": "population mapping", | | "display_name": "population mapping", |
| "id": "5a75e952-d82b-44e3-8152-e0408a3cb3e3", | | "id": "5a75e952-d82b-44e3-8152-e0408a3cb3e3", |
| "name": "population mapping", | | "name": "population mapping", |
| "state": "active", | | "state": "active", |
| "vocabulary_id": null | | "vocabulary_id": null |
| } | | } |
| ], | | ], |
| "title": "Cabo Verde: High Resolution Population Density Maps + | | "title": "Cabo Verde: High Resolution Population Density Maps + |
| Demographic Estimates", | | Demographic Estimates", |
| "type": "dataset", | | "type": "dataset", |
| "url": | | "url": |
| s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-cpv", | | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-cpv", |
| "version": "" | | "version": "" |
| } | | } |