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On Június 30, 2022, 11:43:48 (SAST),
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Added resource Mali: High Resolution Population Density Maps + Demographic Estimates to Mali: 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": "77d79e63-5fb4-4320-94e4-51e5e9b8031c", | 4 | "creator_user_id": "77d79e63-5fb4-4320-94e4-51e5e9b8031c", | ||
5 | "extras": [], | 5 | "extras": [], | ||
6 | "groups": [], | 6 | "groups": [], | ||
7 | "id": "9ff27544-b476-4781-9251-8e9ca7677353", | 7 | "id": "9ff27544-b476-4781-9251-8e9ca7677353", | ||
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": "2022-06-30T09:42:30.484960", | 14 | "metadata_created": "2022-06-30T09:42:30.484960", | ||
n | 15 | "metadata_modified": "2022-06-30T09:42:30.484971", | n | 15 | "metadata_modified": "2022-06-30T09:43:47.959887", |
16 | "name": | 16 | "name": | ||
17 | "mali-high-resolution-population-density-maps-demographic-estimates", | 17 | "mali-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 Mali: (1) | 19 | maps/datasets for the distribution of various populations in Mali: (1) | ||
20 | Overall population density (2) Women (3) Men (4) Children (ages 0-5) | 20 | Overall population density (2) Women (3) Men (4) Children (ages 0-5) | ||
21 | (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | 21 | (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/)", | ||
n | 43 | "num_resources": 0, | n | 43 | "num_resources": 1, |
44 | "num_tags": 7, | 44 | "num_tags": 7, | ||
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": [], | ||
t | 73 | "resources": [], | t | 73 | "resources": [ |
74 | { | ||||
75 | "cache_last_updated": null, | ||||
76 | "cache_url": null, | ||||
77 | "created": "2022-06-30T09:43:47.985993", | ||||
78 | "datastore_active": false, | ||||
79 | "description": "The world's most accurate population datasets. | ||||
80 | Seven maps/datasets for the distribution of various populations in | ||||
81 | Mali: (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 | ||||
83 | reproductive age (ages 15-49).", | ||||
84 | "format": "", | ||||
85 | "hash": "", | ||||
86 | "id": "5bc35519-deab-4bc7-bc25-6ae507b56458", | ||||
87 | "last_modified": null, | ||||
88 | "mimetype": null, | ||||
89 | "mimetype_inner": null, | ||||
90 | "name": "Mali: High Resolution Population Density Maps + | ||||
91 | Demographic Estimates", | ||||
92 | "package_id": "9ff27544-b476-4781-9251-8e9ca7677353", | ||||
93 | "position": 0, | ||||
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96 | "size": null, | ||||
97 | "state": "active", | ||||
98 | "url": | ||||
99 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-mli", | ||||
100 | "url_type": null | ||||
101 | } | ||||
102 | ], | ||||
74 | "revision_id": "adf4dad7-d4bc-4f34-ae31-12f8223fdd3f", | 103 | "revision_id": "adf4dad7-d4bc-4f34-ae31-12f8223fdd3f", | ||
75 | "state": "draft", | 104 | "state": "draft", | ||
76 | "tags": [ | 105 | "tags": [ | ||
77 | { | 106 | { | ||
78 | "display_name": "Mali", | 107 | "display_name": "Mali", | ||
79 | "id": "65d5de3b-c4b2-498a-84f1-0c7022af130e", | 108 | "id": "65d5de3b-c4b2-498a-84f1-0c7022af130e", | ||
80 | "name": "Mali", | 109 | "name": "Mali", | ||
81 | "state": "active", | 110 | "state": "active", | ||
82 | "vocabulary_id": null | 111 | "vocabulary_id": null | ||
83 | }, | 112 | }, | ||
84 | { | 113 | { | ||
85 | "display_name": "children", | 114 | "display_name": "children", | ||
86 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 115 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
87 | "name": "children", | 116 | "name": "children", | ||
88 | "state": "active", | 117 | "state": "active", | ||
89 | "vocabulary_id": null | 118 | "vocabulary_id": null | ||
90 | }, | 119 | }, | ||
91 | { | 120 | { | ||
92 | "display_name": "elderly", | 121 | "display_name": "elderly", | ||
93 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 122 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
94 | "name": "elderly", | 123 | "name": "elderly", | ||
95 | "state": "active", | 124 | "state": "active", | ||
96 | "vocabulary_id": null | 125 | "vocabulary_id": null | ||
97 | }, | 126 | }, | ||
98 | { | 127 | { | ||
99 | "display_name": "men", | 128 | "display_name": "men", | ||
100 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 129 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
101 | "name": "men", | 130 | "name": "men", | ||
102 | "state": "active", | 131 | "state": "active", | ||
103 | "vocabulary_id": null | 132 | "vocabulary_id": null | ||
104 | }, | 133 | }, | ||
105 | { | 134 | { | ||
106 | "display_name": "population density", | 135 | "display_name": "population density", | ||
107 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 136 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
108 | "name": "population density", | 137 | "name": "population density", | ||
109 | "state": "active", | 138 | "state": "active", | ||
110 | "vocabulary_id": null | 139 | "vocabulary_id": null | ||
111 | }, | 140 | }, | ||
112 | { | 141 | { | ||
113 | "display_name": "women", | 142 | "display_name": "women", | ||
114 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 143 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
115 | "name": "women", | 144 | "name": "women", | ||
116 | "state": "active", | 145 | "state": "active", | ||
117 | "vocabulary_id": null | 146 | "vocabulary_id": null | ||
118 | }, | 147 | }, | ||
119 | { | 148 | { | ||
120 | "display_name": "youth", | 149 | "display_name": "youth", | ||
121 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 150 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
122 | "name": "youth", | 151 | "name": "youth", | ||
123 | "state": "active", | 152 | "state": "active", | ||
124 | "vocabulary_id": null | 153 | "vocabulary_id": null | ||
125 | } | 154 | } | ||
126 | ], | 155 | ], | ||
127 | "title": "Mali: High Resolution Population Density Maps + | 156 | "title": "Mali: High Resolution Population Density Maps + | ||
128 | Demographic Estimates", | 157 | Demographic Estimates", | ||
129 | "type": "dataset", | 158 | "type": "dataset", | ||
130 | "url": | 159 | "url": | ||
131 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-mli", | 160 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-mli", | ||
132 | "version": "" | 161 | "version": "" | ||
133 | } | 162 | } |