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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": "77d79e63-5fb4-4320-94e4-51e5e9b8031c", | 4 | "creator_user_id": "77d79e63-5fb4-4320-94e4-51e5e9b8031c", | ||
5 | "extras": [], | 5 | "extras": [], | ||
6 | "groups": [], | 6 | "groups": [], | ||
7 | "id": "ac4df45c-c38f-4eac-bc33-983386ecb6fb", | 7 | "id": "ac4df45c-c38f-4eac-bc33-983386ecb6fb", | ||
8 | "isopen": true, | 8 | "isopen": true, | ||
9 | "license_id": "cc-by", | 9 | "license_id": "cc-by", | ||
10 | "license_title": "Creative Commons Attribution", | 10 | "license_title": "Creative Commons Attribution", | ||
11 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 11 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
12 | "maintainer": "Brian O'Leary", | 12 | "maintainer": "Brian O'Leary", | ||
13 | "maintainer_email": "[email protected]", | 13 | "maintainer_email": "[email protected]", | ||
14 | "metadata_created": "2022-06-27T15:07:32.210118", | 14 | "metadata_created": "2022-06-27T15:07:32.210118", | ||
n | 15 | "metadata_modified": "2022-06-27T15:08:25.966931", | n | 15 | "metadata_modified": "2022-06-27T15:08:26.192908", |
16 | "name": | 16 | "name": | ||
17 | omoros-high-resolution-population-density-maps-demographic-estimates", | 17 | omoros-high-resolution-population-density-maps-demographic-estimates", | ||
18 | "notes": "The world's most accurate population datasets. Seven | 18 | "notes": "The world's most accurate population datasets. Seven | ||
19 | maps/datasets for the distribution of various populations in South | 19 | maps/datasets for the distribution of various populations in South | ||
20 | Africa: (1) Overall population density (2) Women (3) Men (4) Children | 20 | Africa: (1) Overall population density (2) Women (3) Men (4) Children | ||
21 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | 21 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | ||
22 | reproductive age (ages 15-49).\r\n\r\n### Methodology\r\n\r\nThese | 22 | reproductive age (ages 15-49).\r\n\r\n### Methodology\r\n\r\nThese | ||
23 | high-resolution maps are created using machine learning techniques to | 23 | high-resolution maps are created using machine learning techniques to | ||
24 | identify buildings from commercially available satellite images. This | 24 | identify buildings from commercially available satellite images. This | ||
25 | is then overlayed with general population estimates based on publicly | 25 | is then overlayed with general population estimates based on publicly | ||
26 | available census data and other population statistics at Columbia | 26 | available census data and other population statistics at Columbia | ||
27 | University. The resulting maps are the most detailed and actionable | 27 | University. The resulting maps are the most detailed and actionable | ||
28 | tools available for aid and research organizations. For more | 28 | tools available for aid and research organizations. For more | ||
29 | information about the methodology used to create our high resolution | 29 | information about the methodology used to create our high resolution | ||
30 | population density maps and the demographic distributions, click | 30 | population density maps and the demographic distributions, click | ||
31 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | 31 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | ||
32 | information about how to use HDX to access these datasets, please | 32 | information about how to use HDX to access these datasets, please | ||
33 | visit: | 33 | visit: | ||
34 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 34 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
35 | to match the census population with the UN estimates are applied at | 35 | to match the census population with the UN estimates are applied at | ||
36 | the national level. The UN estimate for a given country (or | 36 | the national level. The UN estimate for a given country (or | ||
37 | state/territory) is divided by the total census estimate of population | 37 | state/territory) is divided by the total census estimate of population | ||
38 | for the given country. The resulting adjustment factor is multiplied | 38 | for the given country. The resulting adjustment factor is multiplied | ||
39 | by each administrative unit census value for the target year. This | 39 | by each administrative unit census value for the target year. This | ||
40 | preserves the relative population totals across administrative units | 40 | preserves the relative population totals across administrative units | ||
41 | while matching the UN total. More information can be found | 41 | while matching the UN total. More information can be found | ||
42 | ocs/census-information-for-high-resolution-population-density-maps/)", | 42 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
43 | "num_resources": 1, | 43 | "num_resources": 1, | ||
44 | "num_tags": 4, | 44 | "num_tags": 4, | ||
45 | "organization": { | 45 | "organization": { | ||
46 | "approval_status": "approved", | 46 | "approval_status": "approved", | ||
47 | "created": "2022-06-15T09:44:22.871262", | 47 | "created": "2022-06-15T09:44:22.871262", | ||
48 | "description": "The Humanitarian Data Exchange (HDX) is an open | 48 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||
49 | platform for sharing data across crises and organisations. Launched in | 49 | platform for sharing data across crises and organisations. Launched in | ||
50 | July 2014, the goal of HDX is to make humanitarian data easy to find | 50 | July 2014, the goal of HDX is to make humanitarian data easy to find | ||
51 | and use for analysis. Our growing collection of datasets has been | 51 | and use for analysis. Our growing collection of datasets has been | ||
52 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | 52 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | ||
53 | managed by OCHA's Centre for Humanitarian Data, which is located in | 53 | managed by OCHA's Centre for Humanitarian Data, which is located in | ||
54 | The Hague. OCHA is part of the United Nations Secretariat and is | 54 | The Hague. OCHA is part of the United Nations Secretariat and is | ||
55 | responsible for bringing together humanitarian actors to ensure a | 55 | responsible for bringing together humanitarian actors to ensure a | ||
56 | coherent response to emergencies. The HDX team includes OCHA staff and | 56 | coherent response to emergencies. The HDX team includes OCHA staff and | ||
57 | a number of consultants who are based in North America, Europe and | 57 | a number of consultants who are based in North America, Europe and | ||
58 | Africa.", | 58 | Africa.", | ||
59 | "id": "ad11db0c-27d3-433d-bc99-973c2ffae709", | 59 | "id": "ad11db0c-27d3-433d-bc99-973c2ffae709", | ||
60 | "image_url": | 60 | "image_url": | ||
61 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | 61 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | ||
62 | "is_organization": true, | 62 | "is_organization": true, | ||
63 | "name": "hdx-the-humanitarian-data-exchange", | 63 | "name": "hdx-the-humanitarian-data-exchange", | ||
64 | "revision_id": "e6c349dd-82c3-4176-a1c7-a4d89d8eae93", | 64 | "revision_id": "e6c349dd-82c3-4176-a1c7-a4d89d8eae93", | ||
65 | "state": "active", | 65 | "state": "active", | ||
66 | "title": "HDX - The Humanitarian Data Exchange", | 66 | "title": "HDX - The Humanitarian Data Exchange", | ||
67 | "type": "organization" | 67 | "type": "organization" | ||
68 | }, | 68 | }, | ||
69 | "owner_org": "ad11db0c-27d3-433d-bc99-973c2ffae709", | 69 | "owner_org": "ad11db0c-27d3-433d-bc99-973c2ffae709", | ||
70 | "private": false, | 70 | "private": false, | ||
71 | "relationships_as_object": [], | 71 | "relationships_as_object": [], | ||
72 | "relationships_as_subject": [], | 72 | "relationships_as_subject": [], | ||
73 | "resources": [ | 73 | "resources": [ | ||
74 | { | 74 | { | ||
75 | "cache_last_updated": null, | 75 | "cache_last_updated": null, | ||
76 | "cache_url": null, | 76 | "cache_url": null, | ||
77 | "created": "2022-06-27T15:08:25.990980", | 77 | "created": "2022-06-27T15:08:25.990980", | ||
78 | "datastore_active": false, | 78 | "datastore_active": false, | ||
79 | "description": "The world's most accurate population datasets. | 79 | "description": "The world's most accurate population datasets. | ||
80 | Seven maps/datasets for the distribution of various populations in | 80 | Seven maps/datasets for the distribution of various populations in | ||
81 | Comoros: (1) Overall population density (2) Women (3) Men (4) Children | 81 | Comoros: (1) Overall population density (2) Women (3) Men (4) Children | ||
82 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | 82 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | ||
83 | reproductive age (ages 15-49).", | 83 | reproductive age (ages 15-49).", | ||
84 | "format": "", | 84 | "format": "", | ||
85 | "hash": "", | 85 | "hash": "", | ||
86 | "id": "94924848-929e-47b1-97b3-5af83f98c742", | 86 | "id": "94924848-929e-47b1-97b3-5af83f98c742", | ||
87 | "last_modified": null, | 87 | "last_modified": null, | ||
88 | "mimetype": null, | 88 | "mimetype": null, | ||
89 | "mimetype_inner": null, | 89 | "mimetype_inner": null, | ||
90 | "name": "Comoros: High Resolution Population Density Maps + | 90 | "name": "Comoros: High Resolution Population Density Maps + | ||
91 | Demographic Estimates", | 91 | Demographic Estimates", | ||
92 | "package_id": "ac4df45c-c38f-4eac-bc33-983386ecb6fb", | 92 | "package_id": "ac4df45c-c38f-4eac-bc33-983386ecb6fb", | ||
93 | "position": 0, | 93 | "position": 0, | ||
94 | "resource_type": null, | 94 | "resource_type": null, | ||
n | 95 | "revision_id": "fb150243-3045-42ef-b653-ff40939911dc", | n | 95 | "revision_id": "2ffca3ab-7abf-47f8-8397-661ab8ccbd10", |
96 | "size": null, | 96 | "size": null, | ||
97 | "state": "active", | 97 | "state": "active", | ||
98 | "url": | 98 | "url": | ||
99 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-com", | 99 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-com", | ||
100 | "url_type": null | 100 | "url_type": null | ||
101 | } | 101 | } | ||
102 | ], | 102 | ], | ||
t | 103 | "revision_id": "190fb64f-18f6-4018-8f57-da10aa119f9c", | t | 103 | "revision_id": "2ffca3ab-7abf-47f8-8397-661ab8ccbd10", |
104 | "state": "draft", | 104 | "state": "active", | ||
105 | "tags": [ | 105 | "tags": [ | ||
106 | { | 106 | { | ||
107 | "display_name": "Comoros", | 107 | "display_name": "Comoros", | ||
108 | "id": "67718117-1b29-4de6-bf38-284e21ac7739", | 108 | "id": "67718117-1b29-4de6-bf38-284e21ac7739", | ||
109 | "name": "Comoros", | 109 | "name": "Comoros", | ||
110 | "state": "active", | 110 | "state": "active", | ||
111 | "vocabulary_id": null | 111 | "vocabulary_id": null | ||
112 | }, | 112 | }, | ||
113 | { | 113 | { | ||
114 | "display_name": "population count", | 114 | "display_name": "population count", | ||
115 | "id": "f7228262-21a4-4fe5-a713-f43c29367f23", | 115 | "id": "f7228262-21a4-4fe5-a713-f43c29367f23", | ||
116 | "name": "population count", | 116 | "name": "population count", | ||
117 | "state": "active", | 117 | "state": "active", | ||
118 | "vocabulary_id": null | 118 | "vocabulary_id": null | ||
119 | }, | 119 | }, | ||
120 | { | 120 | { | ||
121 | "display_name": "population density", | 121 | "display_name": "population density", | ||
122 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 122 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
123 | "name": "population density", | 123 | "name": "population density", | ||
124 | "state": "active", | 124 | "state": "active", | ||
125 | "vocabulary_id": null | 125 | "vocabulary_id": null | ||
126 | }, | 126 | }, | ||
127 | { | 127 | { | ||
128 | "display_name": "population mapping", | 128 | "display_name": "population mapping", | ||
129 | "id": "5a75e952-d82b-44e3-8152-e0408a3cb3e3", | 129 | "id": "5a75e952-d82b-44e3-8152-e0408a3cb3e3", | ||
130 | "name": "population mapping", | 130 | "name": "population mapping", | ||
131 | "state": "active", | 131 | "state": "active", | ||
132 | "vocabulary_id": null | 132 | "vocabulary_id": null | ||
133 | } | 133 | } | ||
134 | ], | 134 | ], | ||
135 | "title": "Comoros: High Resolution Population Density Maps + | 135 | "title": "Comoros: High Resolution Population Density Maps + | ||
136 | Demographic Estimates", | 136 | Demographic Estimates", | ||
137 | "type": "dataset", | 137 | "type": "dataset", | ||
138 | "url": | 138 | "url": | ||
139 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-com", | 139 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-com", | ||
140 | "version": "" | 140 | "version": "" | ||
141 | } | 141 | } |