Modifications
On Juin 9, 2021, 16:31:40 (SAST),
-
Added resource zaf_children_under_five_geotiff.zip to South Africa: High Resolution Population Density Maps + Demographic Estimates
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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": "Facebook Data For Good", | 12 | "maintainer": "Facebook Data For Good", | ||
13 | "maintainer_email": "", | 13 | "maintainer_email": "", | ||
14 | "metadata_created": "2021-06-09T14:30:20.667813", | 14 | "metadata_created": "2021-06-09T14:30:20.667813", | ||
n | 15 | "metadata_modified": "2021-06-09T14:31:23.442829", | n | 15 | "metadata_modified": "2021-06-09T14:31:39.984412", |
16 | "name": | 16 | "name": | ||
17 | africa-high-resolution-population-density-maps-demographic-estimates", | 17 | africa-high-resolution-population-density-maps-demographic-estimates", | ||
18 | "notes": "VERSION 1.5. The world's most accurate population | 18 | "notes": "VERSION 1.5. The world's most accurate population | ||
19 | datasets. Seven maps/datasets for the distribution of various | 19 | datasets. Seven maps/datasets for the distribution of various | ||
20 | populations in Nigeria: (1) Overall population density (2) Women (3) | 20 | populations in Nigeria: (1) Overall population density (2) Women (3) | ||
21 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | 21 | Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages | ||
22 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 22 | 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
23 | Methodology\r\n\r\nThese high-resolution maps are created using | 23 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
24 | machine learning techniques to identify buildings from commercially | 24 | machine learning techniques to identify buildings from commercially | ||
25 | available satellite images. This is then overlayed with general | 25 | available satellite images. This is then overlayed with general | ||
26 | population estimates based on publicly available census data and other | 26 | population estimates based on publicly available census data and other | ||
27 | population statistics at Columbia University. The resulting maps are | 27 | population statistics at Columbia University. The resulting maps are | ||
28 | the most detailed and actionable tools available for aid and research | 28 | the most detailed and actionable tools available for aid and research | ||
29 | organizations. For more information about the methodology used to | 29 | organizations. For more information about the methodology used to | ||
30 | create our high resolution population density maps and the demographic | 30 | create our high resolution population density maps and the demographic | ||
31 | distributions, click | 31 | distributions, click | ||
32 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | 32 | h-resolution-population-density-maps-demographic-estimates/\r\n\r\nFor | ||
33 | information about how to use HDX to access these datasets, please | 33 | information about how to use HDX to access these datasets, please | ||
34 | visit: | 34 | visit: | ||
35 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 35 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
36 | to match the census population with the UN estimates are applied at | 36 | to match the census population with the UN estimates are applied at | ||
37 | the national level. The UN estimate for a given country (or | 37 | the national level. The UN estimate for a given country (or | ||
38 | state/territory) is divided by the total census estimate of population | 38 | state/territory) is divided by the total census estimate of population | ||
39 | for the given country. The resulting adjustment factor is multiplied | 39 | for the given country. The resulting adjustment factor is multiplied | ||
40 | by each administrative unit census value for the target year. This | 40 | by each administrative unit census value for the target year. This | ||
41 | preserves the relative population totals across administrative units | 41 | preserves the relative population totals across administrative units | ||
42 | while matching the UN total. More information can be found | 42 | while matching the UN total. More information can be found | ||
43 | ocs/census-information-for-high-resolution-population-density-maps/)", | 43 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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49 | "description": "Humanitarian Emergency Response Africa Datasets", | 49 | "description": "Humanitarian Emergency Response Africa Datasets", | ||
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57 | "title": "Humanitarian Emergency Response Africa", | 57 | "title": "Humanitarian Emergency Response Africa", | ||
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136 | "state": "draft", | 159 | "state": "draft", | ||
137 | "tags": [ | 160 | "tags": [ | ||
138 | { | 161 | { | ||
139 | "display_name": "children", | 162 | "display_name": "children", | ||
140 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 163 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
141 | "name": "children", | 164 | "name": "children", | ||
142 | "state": "active", | 165 | "state": "active", | ||
143 | "vocabulary_id": null | 166 | "vocabulary_id": null | ||
144 | }, | 167 | }, | ||
145 | { | 168 | { | ||
146 | "display_name": "deodata", | 169 | "display_name": "deodata", | ||
147 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 170 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
148 | "name": "deodata", | 171 | "name": "deodata", | ||
149 | "state": "active", | 172 | "state": "active", | ||
150 | "vocabulary_id": null | 173 | "vocabulary_id": null | ||
151 | }, | 174 | }, | ||
152 | { | 175 | { | ||
153 | "display_name": "elderly", | 176 | "display_name": "elderly", | ||
154 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 177 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
155 | "name": "elderly", | 178 | "name": "elderly", | ||
156 | "state": "active", | 179 | "state": "active", | ||
157 | "vocabulary_id": null | 180 | "vocabulary_id": null | ||
158 | }, | 181 | }, | ||
159 | { | 182 | { | ||
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161 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 184 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
162 | "name": "men", | 185 | "name": "men", | ||
163 | "state": "active", | 186 | "state": "active", | ||
164 | "vocabulary_id": null | 187 | "vocabulary_id": null | ||
165 | }, | 188 | }, | ||
166 | { | 189 | { | ||
167 | "display_name": "population density", | 190 | "display_name": "population density", | ||
168 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 191 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
169 | "name": "population density", | 192 | "name": "population density", | ||
170 | "state": "active", | 193 | "state": "active", | ||
171 | "vocabulary_id": null | 194 | "vocabulary_id": null | ||
172 | }, | 195 | }, | ||
173 | { | 196 | { | ||
174 | "display_name": "south africa", | 197 | "display_name": "south africa", | ||
175 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 198 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
176 | "name": "south africa", | 199 | "name": "south africa", | ||
177 | "state": "active", | 200 | "state": "active", | ||
178 | "vocabulary_id": null | 201 | "vocabulary_id": null | ||
179 | }, | 202 | }, | ||
180 | { | 203 | { | ||
181 | "display_name": "women", | 204 | "display_name": "women", | ||
182 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 205 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
183 | "name": "women", | 206 | "name": "women", | ||
184 | "state": "active", | 207 | "state": "active", | ||
185 | "vocabulary_id": null | 208 | "vocabulary_id": null | ||
186 | }, | 209 | }, | ||
187 | { | 210 | { | ||
188 | "display_name": "youth", | 211 | "display_name": "youth", | ||
189 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 212 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
190 | "name": "youth", | 213 | "name": "youth", | ||
191 | "state": "active", | 214 | "state": "active", | ||
192 | "vocabulary_id": null | 215 | "vocabulary_id": null | ||
193 | } | 216 | } | ||
194 | ], | 217 | ], | ||
195 | "title": "South Africa: High Resolution Population Density Maps + | 218 | "title": "South Africa: High Resolution Population Density Maps + | ||
196 | Demographic Estimates", | 219 | Demographic Estimates", | ||
197 | "type": "dataset", | 220 | "type": "dataset", | ||
198 | "url": | 221 | "url": | ||
199 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 222 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
200 | "version": "" | 223 | "version": "" | ||
201 | } | 224 | } |