Changes
On June 14, 2023, 12:01:42 (SAST),
Brian O'Leary:
-
Deleted resource zaf_women_of_reproductive_age_15_49_geotiff.zip from South Africa: 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": "34f7a594-935e-4ac6-b13c-8bb3b861b487", | 4 | "creator_user_id": "34f7a594-935e-4ac6-b13c-8bb3b861b487", | ||
| 5 | "extras": [], | 5 | "extras": [], | ||
| 6 | "groups": [ | 6 | "groups": [ | ||
| 7 | { | 7 | { | ||
| 8 | "description": "The Humanitarian Data Exchange (HDX) is an open | 8 | "description": "The Humanitarian Data Exchange (HDX) is an open | ||
| 9 | platform for sharing data across crises and organisations. Launched in | 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 | 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 | 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 | 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 | 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 | 14 | The Hague. OCHA is part of the United Nations Secretariat and is | ||
| 15 | responsible for bringing together humanitarian actors to ensure a | 15 | responsible for bringing together humanitarian actors to ensure a | ||
| 16 | coherent response to emergencies. The HDX team includes OCHA staff and | 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 | 17 | a number of consultants who are based in North America, Europe and | ||
| 18 | Africa.", | 18 | Africa.", | ||
| 19 | "display_name": "HDX: Humanitarian Data Exchange", | 19 | "display_name": "HDX: Humanitarian Data Exchange", | ||
| 20 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | 20 | "id": "15791998-b8b5-41fa-841f-0393addadcbf", | ||
| 21 | "image_display_url": | 21 | "image_display_url": | ||
| 22 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | 22 | "https://data.humdata.org/images/homepage/logo-hdx.svg", | ||
| 23 | "name": "hdx-humanitarian-data-exchange", | 23 | "name": "hdx-humanitarian-data-exchange", | ||
| 24 | "title": "HDX: Humanitarian Data Exchange" | 24 | "title": "HDX: Humanitarian Data Exchange" | ||
| 25 | }, | 25 | }, | ||
| 26 | { | 26 | { | ||
| 27 | "description": "", | 27 | "description": "", | ||
| 28 | "display_name": "Population ", | 28 | "display_name": "Population ", | ||
| 29 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | 29 | "id": "ad455715-07fc-421f-aef4-780b25b9c67a", | ||
| 30 | "image_display_url": "", | 30 | "image_display_url": "", | ||
| 31 | "name": "population", | 31 | "name": "population", | ||
| 32 | "title": "Population " | 32 | "title": "Population " | ||
| 33 | } | 33 | } | ||
| 34 | ], | 34 | ], | ||
| 35 | "id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | 35 | "id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | ||
| 36 | "isopen": true, | 36 | "isopen": true, | ||
| 37 | "license_id": "cc-by", | 37 | "license_id": "cc-by", | ||
| 38 | "license_title": "Creative Commons Attribution", | 38 | "license_title": "Creative Commons Attribution", | ||
| 39 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | 39 | "license_url": "http://www.opendefinition.org/licenses/cc-by", | ||
| 40 | "maintainer": "Heiko Heilgendorff (OCL)", | 40 | "maintainer": "Heiko Heilgendorff (OCL)", | ||
| 41 | "maintainer_email": "[email protected]", | 41 | "maintainer_email": "[email protected]", | ||
| 42 | "metadata_created": "2021-06-09T14:30:20.667813", | 42 | "metadata_created": "2021-06-09T14:30:20.667813", | ||
| n | 43 | "metadata_modified": "2023-06-14T10:01:27.863509", | n | 43 | "metadata_modified": "2023-06-14T10:01:42.088906", |
| 44 | "name": | 44 | "name": | ||
| 45 | africa-high-resolution-population-density-maps-demographic-estimates", | 45 | africa-high-resolution-population-density-maps-demographic-estimates", | ||
| 46 | "notes": "VERSION 1.5. The world's most accurate population | 46 | "notes": "VERSION 1.5. The world's most accurate population | ||
| 47 | datasets. Seven maps/datasets for the distribution of various | 47 | datasets. Seven maps/datasets for the distribution of various | ||
| 48 | populations in South Africa: (1) Overall population density (2) Women | 48 | populations in South Africa: (1) Overall population density (2) Women | ||
| 49 | (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly | 49 | (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly | ||
| 50 | (ages 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | 50 | (ages 60+) (7) Women of reproductive age (ages 15-49).\r\n\r\n### | ||
| 51 | Methodology\r\n\r\nThese high-resolution maps are created using | 51 | Methodology\r\n\r\nThese high-resolution maps are created using | ||
| 52 | machine learning techniques to identify buildings from commercially | 52 | machine learning techniques to identify buildings from commercially | ||
| 53 | available satellite images. This is then overlayed with general | 53 | available satellite images. This is then overlayed with general | ||
| 54 | population estimates based on publicly available census data and other | 54 | population estimates based on publicly available census data and other | ||
| 55 | population statistics at Columbia University. The resulting maps are | 55 | population statistics at Columbia University. The resulting maps are | ||
| 56 | the most detailed and actionable tools available for aid and research | 56 | the most detailed and actionable tools available for aid and research | ||
| 57 | organizations. For more information about the methodology used to | 57 | organizations. For more information about the methodology used to | ||
| 58 | create our high resolution population density maps and the demographic | 58 | create our high resolution population density maps and the demographic | ||
| 59 | distributions, click | 59 | distributions, click | ||
| 60 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | 60 | resolution-population-density-maps-demographic-estimates/).\r\n\r\nFor | ||
| 61 | information about how to use HDX to access these datasets, please | 61 | information about how to use HDX to access these datasets, please | ||
| 62 | visit: | 62 | visit: | ||
| 63 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | 63 | n-density-maps-demographic-estimates-documentation/\r\n\r\nAdjustments | ||
| 64 | 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 | ||
| 65 | the national level. The UN estimate for a given country (or | 65 | the national level. The UN estimate for a given country (or | ||
| 66 | state/territory) is divided by the total census estimate of population | 66 | state/territory) is divided by the total census estimate of population | ||
| 67 | for the given country. The resulting adjustment factor is multiplied | 67 | for the given country. The resulting adjustment factor is multiplied | ||
| 68 | by each administrative unit census value for the target year. This | 68 | by each administrative unit census value for the target year. This | ||
| 69 | preserves the relative population totals across administrative units | 69 | preserves the relative population totals across administrative units | ||
| 70 | while matching the UN total. More information can be found | 70 | while matching the UN total. More information can be found | ||
| 71 | ocs/census-information-for-high-resolution-population-density-maps/)", | 71 | ocs/census-information-for-high-resolution-population-density-maps/)", | ||
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| 73 | "num_tags": 8, | 73 | "num_tags": 8, | ||
| 74 | "organization": { | 74 | "organization": { | ||
| 75 | "approval_status": "approved", | 75 | "approval_status": "approved", | ||
| 76 | "created": "2022-07-04T08:06:45.420882", | 76 | "created": "2022-07-04T08:06:45.420882", | ||
| 77 | "description": "We work to build inclusion and participatory | 77 | "description": "We work to build inclusion and participatory | ||
| 78 | democracy in cities and urban spaces through empowering citizens, | 78 | democracy in cities and urban spaces through empowering citizens, | ||
| 79 | building trust and accountability in civic space, and capacitating | 79 | building trust and accountability in civic space, and capacitating | ||
| 80 | government. You can find our website | 80 | government. You can find our website | ||
| 81 | [here](https://opencitieslab.org/).", | 81 | [here](https://opencitieslab.org/).", | ||
| 82 | "id": "8e526815-d516-4f6c-9ae0-a4d62555c30c", | 82 | "id": "8e526815-d516-4f6c-9ae0-a4d62555c30c", | ||
| 83 | "image_url": | 83 | "image_url": | ||
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| 85 | "is_organization": true, | 85 | "is_organization": true, | ||
| 86 | "name": "open-cities-lab", | 86 | "name": "open-cities-lab", | ||
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| 88 | "state": "active", | 88 | "state": "active", | ||
| 89 | "title": "Open Cities Lab", | 89 | "title": "Open Cities Lab", | ||
| 90 | "type": "organization" | 90 | "type": "organization" | ||
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| 194 | "description": "The world's most accurate population datasets. | 171 | "description": "The world's most accurate population datasets. | ||
| 195 | Seven maps/datasets for the distribution of various populations in | 172 | Seven maps/datasets for the distribution of various populations in | ||
| 196 | South Africa: (1) Overall population density (2) Women (3) Men (4) | 173 | South Africa: (1) Overall population density (2) Women (3) Men (4) | ||
| 197 | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | 174 | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | ||
| 198 | Women of reproductive age (ages 15-49).", | 175 | Women of reproductive age (ages 15-49).", | ||
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| 212 | "url": | 189 | "url": | ||
| 213 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 190 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
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| 218 | "state": "active", | 195 | "state": "active", | ||
| 219 | "tags": [ | 196 | "tags": [ | ||
| 220 | { | 197 | { | ||
| 221 | "display_name": "children", | 198 | "display_name": "children", | ||
| 222 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 199 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
| 223 | "name": "children", | 200 | "name": "children", | ||
| 224 | "state": "active", | 201 | "state": "active", | ||
| 225 | "vocabulary_id": null | 202 | "vocabulary_id": null | ||
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| 227 | { | 204 | { | ||
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| 234 | { | 211 | { | ||
| 235 | "display_name": "elderly", | 212 | "display_name": "elderly", | ||
| 236 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 213 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
| 237 | "name": "elderly", | 214 | "name": "elderly", | ||
| 238 | "state": "active", | 215 | "state": "active", | ||
| 239 | "vocabulary_id": null | 216 | "vocabulary_id": null | ||
| 240 | }, | 217 | }, | ||
| 241 | { | 218 | { | ||
| 242 | "display_name": "men", | 219 | "display_name": "men", | ||
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| 244 | "name": "men", | 221 | "name": "men", | ||
| 245 | "state": "active", | 222 | "state": "active", | ||
| 246 | "vocabulary_id": null | 223 | "vocabulary_id": null | ||
| 247 | }, | 224 | }, | ||
| 248 | { | 225 | { | ||
| 249 | "display_name": "population density", | 226 | "display_name": "population density", | ||
| 250 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 227 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 251 | "name": "population density", | 228 | "name": "population density", | ||
| 252 | "state": "active", | 229 | "state": "active", | ||
| 253 | "vocabulary_id": null | 230 | "vocabulary_id": null | ||
| 254 | }, | 231 | }, | ||
| 255 | { | 232 | { | ||
| 256 | "display_name": "south africa", | 233 | "display_name": "south africa", | ||
| 257 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 234 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
| 258 | "name": "south africa", | 235 | "name": "south africa", | ||
| 259 | "state": "active", | 236 | "state": "active", | ||
| 260 | "vocabulary_id": null | 237 | "vocabulary_id": null | ||
| 261 | }, | 238 | }, | ||
| 262 | { | 239 | { | ||
| 263 | "display_name": "women", | 240 | "display_name": "women", | ||
| 264 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 241 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 265 | "name": "women", | 242 | "name": "women", | ||
| 266 | "state": "active", | 243 | "state": "active", | ||
| 267 | "vocabulary_id": null | 244 | "vocabulary_id": null | ||
| 268 | }, | 245 | }, | ||
| 269 | { | 246 | { | ||
| 270 | "display_name": "youth", | 247 | "display_name": "youth", | ||
| 271 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 248 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 272 | "name": "youth", | 249 | "name": "youth", | ||
| 273 | "state": "active", | 250 | "state": "active", | ||
| 274 | "vocabulary_id": null | 251 | "vocabulary_id": null | ||
| 275 | } | 252 | } | ||
| 276 | ], | 253 | ], | ||
| 277 | "title": "South Africa: High Resolution Population Density Maps + | 254 | "title": "South Africa: High Resolution Population Density Maps + | ||
| 278 | Demographic Estimates", | 255 | Demographic Estimates", | ||
| 279 | "type": "dataset", | 256 | "type": "dataset", | ||
| 280 | "url": | 257 | "url": | ||
| 281 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 258 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 282 | "version": "" | 259 | "version": "" | ||
| 283 | } | 260 | } |