ለውጦች
On ሃምሌ 4, 2022, 14:58:24 (SAST),
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Moved Algeria: 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", | ||
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6 | "groups": [ | 6 | "groups": [ | ||
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12 | "name": "not-useful", | 12 | "name": "not-useful", | ||
13 | "title": "A_Sort_Later" | 13 | "title": "A_Sort_Later" | ||
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15 | { | ||||
16 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||||
17 | platform for sharing data across crises and organisations. Launched in | ||||
18 | July 2014, the goal of HDX is to make humanitarian data easy to find | ||||
19 | and use for analysis. Our growing collection of datasets has been | ||||
20 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | ||||
21 | managed by OCHA's Centre for Humanitarian Data, which is located in | ||||
22 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
23 | responsible for bringing together humanitarian actors to ensure a | ||||
24 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
25 | a number of consultants who are based in North America, Europe and | ||||
26 | Africa.", | ||||
27 | "display_name": "HDX: Humanitarian Data Exchange", | ||||
28 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | ||||
29 | "image_display_url": | ||||
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31 | "name": "hdx-humanitarian-data-exchange", | ||||
32 | "title": "HDX: Humanitarian Data Exchange" | ||||
14 | }, | 33 | }, | ||
15 | { | 34 | { | ||
16 | "description": "", | 35 | "description": "", | ||
17 | "display_name": "Population ", | 36 | "display_name": "Population ", | ||
18 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | 37 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | ||
19 | "image_display_url": "", | 38 | "image_display_url": "", | ||
20 | "name": "population", | 39 | "name": "population", | ||
21 | "title": "Population " | 40 | "title": "Population " | ||
22 | } | 41 | } | ||
23 | ], | 42 | ], | ||
24 | "id": "48ab51c0-3ad1-4596-bf5a-11360a42f6ce", | 43 | "id": "48ab51c0-3ad1-4596-bf5a-11360a42f6ce", | ||
25 | "isopen": true, | 44 | "isopen": true, | ||
26 | "license_id": "cc-by", | 45 | "license_id": "cc-by", | ||
27 | "license_title": "Creative Commons Attribution", | 46 | "license_title": "Creative Commons Attribution", | ||
28 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 47 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
29 | "maintainer": "Brian O'Leary", | 48 | "maintainer": "Brian O'Leary", | ||
30 | "maintainer_email": "[email protected]", | 49 | "maintainer_email": "[email protected]", | ||
31 | "metadata_created": "2022-06-20T13:40:17.836591", | 50 | "metadata_created": "2022-06-20T13:40:17.836591", | ||
n | 32 | "metadata_modified": "2022-06-22T09:00:03.796442", | n | 51 | "metadata_modified": "2022-07-04T12:58:24.488511", |
33 | "name": | 52 | "name": | ||
34 | tps-data-humdata-org-dataset-highresolutionpopulationdensitymaps-dza", | 53 | tps-data-humdata-org-dataset-highresolutionpopulationdensitymaps-dza", | ||
35 | "notes": "The world's most accurate population datasets. Seven | 54 | "notes": "The world's most accurate population datasets. Seven | ||
36 | maps/datasets for the distribution of various populations in Algeria: | 55 | maps/datasets for the distribution of various populations in Algeria: | ||
37 | (1) Overall population density (2) Women (3) Men (4) Children (ages | 56 | (1) Overall population density (2) Women (3) Men (4) Children (ages | ||
38 | 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | 57 | 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | ||
39 | reproductive age (ages 15-49).\r\n\r\n__Methodology__\r\n\r\nThese | 58 | reproductive age (ages 15-49).\r\n\r\n__Methodology__\r\n\r\nThese | ||
40 | high-resolution maps are created using machine learning techniques to | 59 | high-resolution maps are created using machine learning techniques to | ||
41 | identify buildings from commercially available satellite images. This | 60 | identify buildings from commercially available satellite images. This | ||
42 | is then overlayed with general population estimates based on publicly | 61 | is then overlayed with general population estimates based on publicly | ||
43 | available census data and other population statistics at Columbia | 62 | available census data and other population statistics at Columbia | ||
44 | University. The resulting maps are the most detailed and actionable | 63 | University. The resulting maps are the most detailed and actionable | ||
45 | tools available for aid and research organizations. For more | 64 | tools available for aid and research organizations. For more | ||
46 | information about the methodology used to create our high resolution | 65 | information about the methodology used to create our high resolution | ||
47 | population density maps and the demographic distributions, click | 66 | population density maps and the demographic distributions, click | ||
48 | here.\r\n\r\nFor information about how to use HDX to access these | 67 | here.\r\n\r\nFor information about how to use HDX to access these | ||
49 | datasets, please visit: | 68 | datasets, please visit: | ||
50 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 69 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
51 | to match the census population with the UN estimates are applied at | 70 | to match the census population with the UN estimates are applied at | ||
52 | the national level. The UN estimate for a given country (or | 71 | the national level. The UN estimate for a given country (or | ||
53 | state/territory) is divided by the total census estimate of population | 72 | state/territory) is divided by the total census estimate of population | ||
54 | for the given country. The resulting adjustment factor is multiplied | 73 | for the given country. The resulting adjustment factor is multiplied | ||
55 | by each administrative unit census value for the target year. This | 74 | by each administrative unit census value for the target year. This | ||
56 | preserves the relative population totals across administrative units | 75 | preserves the relative population totals across administrative units | ||
57 | while matching the UN total. More information can be found here", | 76 | while matching the UN total. More information can be found here", | ||
58 | "num_resources": 1, | 77 | "num_resources": 1, | ||
59 | "num_tags": 7, | 78 | "num_tags": 7, | ||
60 | "organization": { | 79 | "organization": { | ||
61 | "approval_status": "approved", | 80 | "approval_status": "approved", | ||
n | 62 | "created": "2022-06-15T09:44:22.871262", | n | 81 | "created": "2022-07-04T08:06:45.420882", |
63 | "description": "The Humanitarian Data Exchange (HDX) is an open | 82 | "description": "We work to build inclusion and participatory | ||
64 | platform for sharing data across crises and organisations. Launched in | 83 | democracy in cities and urban spaces through empowering citizens, | ||
65 | July 2014, the goal of HDX is to make humanitarian data easy to find | 84 | building trust and accountability in civic space, and capacitating | ||
66 | and use for analysis. Our growing collection of datasets has been | 85 | government. You can find our website | ||
67 | accessed by users in over 200 countries and territories.\r\n\r\nHDX is | 86 | [here](https://opencitieslab.org/).", | ||
68 | managed by OCHA's Centre for Humanitarian Data, which is located in | 87 | "id": "8e526815-d516-4f6c-9ae0-a4d62555c30c", | ||
69 | The Hague. OCHA is part of the United Nations Secretariat and is | ||||
70 | responsible for bringing together humanitarian actors to ensure a | ||||
71 | coherent response to emergencies. The HDX team includes OCHA staff and | ||||
72 | a number of consultants who are based in North America, Europe and | ||||
73 | Africa.", | ||||
74 | "id": "ad11db0c-27d3-433d-bc99-973c2ffae709", | ||||
75 | "image_url": | 88 | "image_url": | ||
n | 76 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | n | 89 | ttps://opencitieslab.org/odd/static/dist/img/ocl_logo_header-nav.png", |
77 | "is_organization": true, | 90 | "is_organization": true, | ||
n | 78 | "name": "hdx-the-humanitarian-data-exchange", | n | 91 | "name": "open-cities-lab", |
79 | "revision_id": "e6c349dd-82c3-4176-a1c7-a4d89d8eae93", | 92 | "revision_id": "35e0d462-e6b8-4c18-8743-e9361e136009", | ||
80 | "state": "active", | 93 | "state": "active", | ||
n | 81 | "title": "HDX - The Humanitarian Data Exchange", | n | 94 | "title": "Open Cities Lab", |
82 | "type": "organization" | 95 | "type": "organization" | ||
83 | }, | 96 | }, | ||
n | 84 | "owner_org": "ad11db0c-27d3-433d-bc99-973c2ffae709", | n | 97 | "owner_org": "8e526815-d516-4f6c-9ae0-a4d62555c30c", |
85 | "private": false, | 98 | "private": false, | ||
86 | "relationships_as_object": [], | 99 | "relationships_as_object": [], | ||
87 | "relationships_as_subject": [], | 100 | "relationships_as_subject": [], | ||
88 | "resources": [ | 101 | "resources": [ | ||
89 | { | 102 | { | ||
90 | "cache_last_updated": null, | 103 | "cache_last_updated": null, | ||
91 | "cache_url": null, | 104 | "cache_url": null, | ||
92 | "created": "2022-06-20T13:41:58.621647", | 105 | "created": "2022-06-20T13:41:58.621647", | ||
93 | "datastore_active": false, | 106 | "datastore_active": false, | ||
94 | "description": "The world's most accurate population datasets. | 107 | "description": "The world's most accurate population datasets. | ||
95 | Seven maps/datasets for the distribution of various populations in | 108 | Seven maps/datasets for the distribution of various populations in | ||
96 | Algeria: (1) Overall population density (2) Women (3) Men (4) Children | 109 | Algeria: (1) Overall population density (2) Women (3) Men (4) Children | ||
97 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | 110 | (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of | ||
98 | reproductive age (ages 15-49).", | 111 | reproductive age (ages 15-49).", | ||
99 | "format": "", | 112 | "format": "", | ||
100 | "hash": "", | 113 | "hash": "", | ||
101 | "id": "3c6adddb-8383-405f-bfd3-69e05881c980", | 114 | "id": "3c6adddb-8383-405f-bfd3-69e05881c980", | ||
102 | "last_modified": null, | 115 | "last_modified": null, | ||
103 | "mimetype": null, | 116 | "mimetype": null, | ||
104 | "mimetype_inner": null, | 117 | "mimetype_inner": null, | ||
105 | "name": "Algeria: High Resolution Population Density Maps + | 118 | "name": "Algeria: High Resolution Population Density Maps + | ||
106 | Demographic Estimates", | 119 | Demographic Estimates", | ||
107 | "package_id": "48ab51c0-3ad1-4596-bf5a-11360a42f6ce", | 120 | "package_id": "48ab51c0-3ad1-4596-bf5a-11360a42f6ce", | ||
108 | "position": 0, | 121 | "position": 0, | ||
109 | "resource_type": null, | 122 | "resource_type": null, | ||
110 | "revision_id": "269f989c-86eb-45ab-b2b7-74e4dc0e347a", | 123 | "revision_id": "269f989c-86eb-45ab-b2b7-74e4dc0e347a", | ||
111 | "size": null, | 124 | "size": null, | ||
112 | "state": "active", | 125 | "state": "active", | ||
113 | "url": | 126 | "url": | ||
114 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-dza", | 127 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-dza", | ||
115 | "url_type": null | 128 | "url_type": null | ||
116 | } | 129 | } | ||
117 | ], | 130 | ], | ||
t | 118 | "revision_id": "8c87f37d-1494-4c17-9851-e1f3721a910c", | t | 131 | "revision_id": "76b72f1b-87f3-4d5d-a0cc-13eac4006dec", |
119 | "state": "active", | 132 | "state": "active", | ||
120 | "tags": [ | 133 | "tags": [ | ||
121 | { | 134 | { | ||
122 | "display_name": "Children ages 0_5", | 135 | "display_name": "Children ages 0_5", | ||
123 | "id": "0da45917-926a-46ca-bf26-61ae3ea953df", | 136 | "id": "0da45917-926a-46ca-bf26-61ae3ea953df", | ||
124 | "name": "Children ages 0_5", | 137 | "name": "Children ages 0_5", | ||
125 | "state": "active", | 138 | "state": "active", | ||
126 | "vocabulary_id": null | 139 | "vocabulary_id": null | ||
127 | }, | 140 | }, | ||
128 | { | 141 | { | ||
129 | "display_name": "Elderly ages 60 and older", | 142 | "display_name": "Elderly ages 60 and older", | ||
130 | "id": "4f8fde8a-5f44-4089-9426-d2f2478beab9", | 143 | "id": "4f8fde8a-5f44-4089-9426-d2f2478beab9", | ||
131 | "name": "Elderly ages 60 and older", | 144 | "name": "Elderly ages 60 and older", | ||
132 | "state": "active", | 145 | "state": "active", | ||
133 | "vocabulary_id": null | 146 | "vocabulary_id": null | ||
134 | }, | 147 | }, | ||
135 | { | 148 | { | ||
136 | "display_name": "Men", | 149 | "display_name": "Men", | ||
137 | "id": "b3644bfa-4d89-4577-a291-6368dc248b6f", | 150 | "id": "b3644bfa-4d89-4577-a291-6368dc248b6f", | ||
138 | "name": "Men", | 151 | "name": "Men", | ||
139 | "state": "active", | 152 | "state": "active", | ||
140 | "vocabulary_id": null | 153 | "vocabulary_id": null | ||
141 | }, | 154 | }, | ||
142 | { | 155 | { | ||
143 | "display_name": "Women", | 156 | "display_name": "Women", | ||
144 | "id": "7107a093-75a3-4dea-a616-a878c70d80a0", | 157 | "id": "7107a093-75a3-4dea-a616-a878c70d80a0", | ||
145 | "name": "Women", | 158 | "name": "Women", | ||
146 | "state": "active", | 159 | "state": "active", | ||
147 | "vocabulary_id": null | 160 | "vocabulary_id": null | ||
148 | }, | 161 | }, | ||
149 | { | 162 | { | ||
150 | "display_name": "Women of reproductive age_ ages 15_49", | 163 | "display_name": "Women of reproductive age_ ages 15_49", | ||
151 | "id": "162320c4-cd27-4752-91c0-7ca6872ec81c", | 164 | "id": "162320c4-cd27-4752-91c0-7ca6872ec81c", | ||
152 | "name": "Women of reproductive age_ ages 15_49", | 165 | "name": "Women of reproductive age_ ages 15_49", | ||
153 | "state": "active", | 166 | "state": "active", | ||
154 | "vocabulary_id": null | 167 | "vocabulary_id": null | ||
155 | }, | 168 | }, | ||
156 | { | 169 | { | ||
157 | "display_name": "Youth ages 15_24", | 170 | "display_name": "Youth ages 15_24", | ||
158 | "id": "68184dc0-c3e8-4451-b086-f85b459a4896", | 171 | "id": "68184dc0-c3e8-4451-b086-f85b459a4896", | ||
159 | "name": "Youth ages 15_24", | 172 | "name": "Youth ages 15_24", | ||
160 | "state": "active", | 173 | "state": "active", | ||
161 | "vocabulary_id": null | 174 | "vocabulary_id": null | ||
162 | }, | 175 | }, | ||
163 | { | 176 | { | ||
164 | "display_name": "population density", | 177 | "display_name": "population density", | ||
165 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 178 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
166 | "name": "population density", | 179 | "name": "population density", | ||
167 | "state": "active", | 180 | "state": "active", | ||
168 | "vocabulary_id": null | 181 | "vocabulary_id": null | ||
169 | } | 182 | } | ||
170 | ], | 183 | ], | ||
171 | "title": "Algeria: High Resolution Population Density Maps + | 184 | "title": "Algeria: High Resolution Population Density Maps + | ||
172 | Demographic Estimates", | 185 | Demographic Estimates", | ||
173 | "type": "dataset", | 186 | "type": "dataset", | ||
174 | "url": | 187 | "url": | ||
175 | tps-data-humdata-org-dataset-highresolutionpopulationdensitymaps-dza", | 188 | tps-data-humdata-org-dataset-highresolutionpopulationdensitymaps-dza", | ||
176 | "version": "" | 189 | "version": "" | ||
177 | } | 190 | } |