Changes
On July 4, 2022, 14:55:31 (SAST),
-
Moved Niger: High Resolution Population Density Maps + Demographic Estimates from organization HDX - The Humanitarian Data Exchange to organization Open Cities Lab
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": [ | ||
n | n | 7 | { | ||
8 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||||
9 | platform for sharing data across crises and organisations. Launched in | ||||
10 | July 2014, the goal of HDX is to make humanitarian data easy to find | ||||
11 | and use for analysis. Our growing collection of datasets has been | ||||
12 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | ||||
13 | managed by OCHA's Centre for Humanitarian Data, which is located in | ||||
14 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
15 | responsible for bringing together humanitarian actors to ensure a | ||||
16 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
17 | a number of consultants who are based in North America, Europe and | ||||
18 | Africa.", | ||||
19 | "display_name": "HDX: Humanitarian Data Exchange", | ||||
20 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | ||||
21 | "image_display_url": | ||||
22 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | ||||
23 | "name": "hdx-humanitarian-data-exchange", | ||||
24 | "title": "HDX: Humanitarian Data Exchange" | ||||
25 | }, | ||||
7 | { | 26 | { | ||
8 | "description": "", | 27 | "description": "", | ||
9 | "display_name": "Population ", | 28 | "display_name": "Population ", | ||
10 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | 29 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | ||
11 | "image_display_url": "", | 30 | "image_display_url": "", | ||
12 | "name": "population", | 31 | "name": "population", | ||
13 | "title": "Population " | 32 | "title": "Population " | ||
14 | } | 33 | } | ||
15 | ], | 34 | ], | ||
16 | "id": "b3e563cc-5596-4778-9ed5-ff6ece7bc459", | 35 | "id": "b3e563cc-5596-4778-9ed5-ff6ece7bc459", | ||
17 | "isopen": true, | 36 | "isopen": true, | ||
18 | "license_id": "cc-by", | 37 | "license_id": "cc-by", | ||
19 | "license_title": "Creative Commons Attribution", | 38 | "license_title": "Creative Commons Attribution", | ||
20 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 39 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
21 | "maintainer": "Brian O'Leary", | 40 | "maintainer": "Brian O'Leary", | ||
22 | "maintainer_email": "[email protected]", | 41 | "maintainer_email": "[email protected]", | ||
23 | "metadata_created": "2022-06-21T10:41:59.274190", | 42 | "metadata_created": "2022-06-21T10:41:59.274190", | ||
n | 24 | "metadata_modified": "2022-06-23T10:31:32.091891", | n | 43 | "metadata_modified": "2022-07-04T12:55:31.506138", |
25 | "name": | 44 | "name": | ||
26 | "niger-high-resolution-population-density-maps-demographic-estimates", | 45 | "niger-high-resolution-population-density-maps-demographic-estimates", | ||
27 | "notes": "VERSION 1.5. The world's most accurate population | 46 | "notes": "VERSION 1.5. The world's most accurate population | ||
28 | datasets. Seven maps/datasets for the distribution of various | 47 | datasets. Seven maps/datasets for the distribution of various | ||
29 | populations in Niger: (1) Overall population density (2) Women (3) Men | 48 | populations in Niger: (1) Overall population density (2) Women (3) Men | ||
30 | (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) | 49 | (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) | ||
31 | (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 50 | (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
32 | Methodology\r\n\r\nThese high-resolution maps are created using | 51 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
33 | machine learning techniques to identify buildings from commercially | 52 | machine learning techniques to identify buildings from commercially | ||
34 | available satellite images. This is then overlayed with general | 53 | available satellite images. This is then overlayed with general | ||
35 | population estimates based on publicly available census data and other | 54 | population estimates based on publicly available census data and other | ||
36 | population statistics at Columbia University. The resulting maps are | 55 | population statistics at Columbia University. The resulting maps are | ||
37 | the most detailed and actionable tools available for aid and research | 56 | the most detailed and actionable tools available for aid and research | ||
38 | organizations. For more information about the methodology used to | 57 | organizations. For more information about the methodology used to | ||
39 | create our high resolution population density maps and the demographic | 58 | create our high resolution population density maps and the demographic | ||
40 | distributions, click | 59 | distributions, click | ||
41 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | 60 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | ||
42 | information about how to use HDX to access these datasets, please | 61 | information about how to use HDX to access these datasets, please | ||
43 | visit: | 62 | visit: | ||
44 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 63 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
45 | 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 | ||
46 | the national level. The UN estimate for a given country (or | 65 | the national level. The UN estimate for a given country (or | ||
47 | state/territory) is divided by the total census estimate of population | 66 | state/territory) is divided by the total census estimate of population | ||
48 | for the given country. The resulting adjustment factor is multiplied | 67 | for the given country. The resulting adjustment factor is multiplied | ||
49 | by each administrative unit census value for the target year. This | 68 | by each administrative unit census value for the target year. This | ||
50 | preserves the relative population totals across administrative units | 69 | preserves the relative population totals across administrative units | ||
51 | while matching the UN total. More information can be found | 70 | while matching the UN total. More information can be found | ||
52 | ocs/census-information-for-high-resolution-population-density-maps/)", | 71 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
53 | "num_resources": 1, | 72 | "num_resources": 1, | ||
54 | "num_tags": 7, | 73 | "num_tags": 7, | ||
55 | "organization": { | 74 | "organization": { | ||
56 | "approval_status": "approved", | 75 | "approval_status": "approved", | ||
n | 57 | "created": "2022-06-15T09:44:22.871262", | n | 76 | "created": "2022-07-04T08:06:45.420882", |
58 | "description": "The Humanitarian Data Exchange (HDX) is an open | 77 | "description": "We work to build inclusion and participatory | ||
59 | platform for sharing data across crises and organisations. Launched in | 78 | democracy in cities and urban spaces through empowering citizens, | ||
60 | July 2014, the goal of HDX is to make humanitarian data easy to find | 79 | building trust and accountability in civic space, and capacitating | ||
61 | and use for analysis. Our growing collection of datasets has been | 80 | government. You can find our website | ||
62 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | 81 | [here](https://opencitieslab.org/).", | ||
63 | managed by OCHA's Centre for Humanitarian Data, which is located in | 82 | "id": "8e526815-d516-4f6c-9ae0-a4d62555c30c", | ||
64 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
65 | responsible for bringing together humanitarian actors to ensure a | ||||
66 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
67 | a number of consultants who are based in North America, Europe and | ||||
68 | Africa.", | ||||
69 | "id": "ad11db0c-27d3-433d-bc99-973c2ffae709", | ||||
70 | "image_url": | 83 | "image_url": | ||
n | 71 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | n | 84 | ttps://opencitieslab.org/odd/static/dist/img/ocl_logo_header-nav.png", |
72 | "is_organization": true, | 85 | "is_organization": true, | ||
n | 73 | "name": "hdx-the-humanitarian-data-exchange", | n | 86 | "name": "open-cities-lab", |
74 | "revision_id": "e6c349dd-82c3-4176-a1c7-a4d89d8eae93", | 87 | "revision_id": "35e0d462-e6b8-4c18-8743-e9361e136009", | ||
75 | "state": "active", | 88 | "state": "active", | ||
n | 76 | "title": "HDX - The Humanitarian Data Exchange", | n | 89 | "title": "Open Cities Lab", |
77 | "type": "organization" | 90 | "type": "organization" | ||
78 | }, | 91 | }, | ||
n | 79 | "owner_org": "ad11db0c-27d3-433d-bc99-973c2ffae709", | n | 92 | "owner_org": "8e526815-d516-4f6c-9ae0-a4d62555c30c", |
80 | "private": false, | 93 | "private": false, | ||
81 | "relationships_as_object": [], | 94 | "relationships_as_object": [], | ||
82 | "relationships_as_subject": [], | 95 | "relationships_as_subject": [], | ||
83 | "resources": [ | 96 | "resources": [ | ||
84 | { | 97 | { | ||
85 | "cache_last_updated": null, | 98 | "cache_last_updated": null, | ||
86 | "cache_url": null, | 99 | "cache_url": null, | ||
87 | "created": "2022-06-21T10:43:06.693090", | 100 | "created": "2022-06-21T10:43:06.693090", | ||
88 | "datastore_active": false, | 101 | "datastore_active": false, | ||
89 | "description": "The world's most accurate population datasets. | 102 | "description": "The world's most accurate population datasets. | ||
90 | Seven maps/datasets for the distribution of various populations in | 103 | Seven maps/datasets for the distribution of various populations in | ||
91 | Niger: (1) Overall population density (2) Women (3) Men (4) Children | 104 | Niger: (1) Overall population density (2) Women (3) Men (4) Children | ||
92 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | 105 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | ||
93 | reproductive age (ages 15-49).", | 106 | reproductive age (ages 15-49).", | ||
94 | "format": "", | 107 | "format": "", | ||
95 | "hash": "", | 108 | "hash": "", | ||
96 | "id": "edf4c9e1-6eaf-4007-9e37-0e7d26f43bf2", | 109 | "id": "edf4c9e1-6eaf-4007-9e37-0e7d26f43bf2", | ||
97 | "last_modified": null, | 110 | "last_modified": null, | ||
98 | "mimetype": null, | 111 | "mimetype": null, | ||
99 | "mimetype_inner": null, | 112 | "mimetype_inner": null, | ||
100 | "name": "Niger: High Resolution Population Density Maps + | 113 | "name": "Niger: High Resolution Population Density Maps + | ||
101 | Demographic Estimates", | 114 | Demographic Estimates", | ||
102 | "package_id": "b3e563cc-5596-4778-9ed5-ff6ece7bc459", | 115 | "package_id": "b3e563cc-5596-4778-9ed5-ff6ece7bc459", | ||
103 | "position": 0, | 116 | "position": 0, | ||
104 | "resource_type": null, | 117 | "resource_type": null, | ||
105 | "revision_id": "682a6c47-c855-42b7-ae20-130724008a28", | 118 | "revision_id": "682a6c47-c855-42b7-ae20-130724008a28", | ||
106 | "size": null, | 119 | "size": null, | ||
107 | "state": "active", | 120 | "state": "active", | ||
108 | "url": | 121 | "url": | ||
109 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-ner", | 122 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-ner", | ||
110 | "url_type": null | 123 | "url_type": null | ||
111 | } | 124 | } | ||
112 | ], | 125 | ], | ||
t | 113 | "revision_id": "2128d89a-1f8a-48c2-8fe8-c914cc32f2f9", | t | 126 | "revision_id": "9eb5bb26-0e05-49ec-b301-4149bb0b6181", |
114 | "state": "active", | 127 | "state": "active", | ||
115 | "tags": [ | 128 | "tags": [ | ||
116 | { | 129 | { | ||
117 | "display_name": "Niger", | 130 | "display_name": "Niger", | ||
118 | "id": "5aec6ddc-d418-4c13-b72b-df65ace782f0", | 131 | "id": "5aec6ddc-d418-4c13-b72b-df65ace782f0", | ||
119 | "name": "Niger", | 132 | "name": "Niger", | ||
120 | "state": "active", | 133 | "state": "active", | ||
121 | "vocabulary_id": null | 134 | "vocabulary_id": null | ||
122 | }, | 135 | }, | ||
123 | { | 136 | { | ||
124 | "display_name": "children", | 137 | "display_name": "children", | ||
125 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 138 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
126 | "name": "children", | 139 | "name": "children", | ||
127 | "state": "active", | 140 | "state": "active", | ||
128 | "vocabulary_id": null | 141 | "vocabulary_id": null | ||
129 | }, | 142 | }, | ||
130 | { | 143 | { | ||
131 | "display_name": "elderly", | 144 | "display_name": "elderly", | ||
132 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 145 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
133 | "name": "elderly", | 146 | "name": "elderly", | ||
134 | "state": "active", | 147 | "state": "active", | ||
135 | "vocabulary_id": null | 148 | "vocabulary_id": null | ||
136 | }, | 149 | }, | ||
137 | { | 150 | { | ||
138 | "display_name": "men", | 151 | "display_name": "men", | ||
139 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 152 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
140 | "name": "men", | 153 | "name": "men", | ||
141 | "state": "active", | 154 | "state": "active", | ||
142 | "vocabulary_id": null | 155 | "vocabulary_id": null | ||
143 | }, | 156 | }, | ||
144 | { | 157 | { | ||
145 | "display_name": "population density", | 158 | "display_name": "population density", | ||
146 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 159 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
147 | "name": "population density", | 160 | "name": "population density", | ||
148 | "state": "active", | 161 | "state": "active", | ||
149 | "vocabulary_id": null | 162 | "vocabulary_id": null | ||
150 | }, | 163 | }, | ||
151 | { | 164 | { | ||
152 | "display_name": "women", | 165 | "display_name": "women", | ||
153 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 166 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
154 | "name": "women", | 167 | "name": "women", | ||
155 | "state": "active", | 168 | "state": "active", | ||
156 | "vocabulary_id": null | 169 | "vocabulary_id": null | ||
157 | }, | 170 | }, | ||
158 | { | 171 | { | ||
159 | "display_name": "youth", | 172 | "display_name": "youth", | ||
160 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 173 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
161 | "name": "youth", | 174 | "name": "youth", | ||
162 | "state": "active", | 175 | "state": "active", | ||
163 | "vocabulary_id": null | 176 | "vocabulary_id": null | ||
164 | } | 177 | } | ||
165 | ], | 178 | ], | ||
166 | "title": "Niger: High Resolution Population Density Maps + | 179 | "title": "Niger: High Resolution Population Density Maps + | ||
167 | Demographic Estimates", | 180 | Demographic Estimates", | ||
168 | "type": "dataset", | 181 | "type": "dataset", | ||
169 | "url": | 182 | "url": | ||
170 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-ner", | 183 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-ner", | ||
171 | "version": "" | 184 | "version": "" | ||
172 | } | 185 | } |