Changes
On June 14, 2023, 12:01:28 (SAST),
-
Deleted resource zaf_women_of_reproductive_age_15_49_csv.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 | ], | ||
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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": "2023-06-14T10:01:13.256303", | n | 43 | "metadata_modified": "2023-06-14T10:01:27.863509", |
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/)", | ||
n | 72 | "num_resources": 6, | n | 72 | "num_resources": 5, |
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/).", | ||
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89 | "title": "Open Cities Lab", | 89 | "title": "Open Cities Lab", | ||
90 | "type": "organization" | 90 | "type": "organization" | ||
91 | }, | 91 | }, | ||
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218 | Seven maps/datasets for the distribution of various populations in | 195 | Seven maps/datasets for the distribution of various populations in | ||
219 | South Africa: (1) Overall population density (2) Women (3) Men (4) | 196 | South Africa: (1) Overall population density (2) Women (3) Men (4) | ||
220 | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | 197 | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | ||
221 | Women of reproductive age (ages 15-49).", | 198 | Women of reproductive age (ages 15-49).", | ||
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250 | { | 227 | { | ||
251 | "display_name": "deodata", | 228 | "display_name": "deodata", | ||
252 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 229 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
253 | "name": "deodata", | 230 | "name": "deodata", | ||
254 | "state": "active", | 231 | "state": "active", | ||
255 | "vocabulary_id": null | 232 | "vocabulary_id": null | ||
256 | }, | 233 | }, | ||
257 | { | 234 | { | ||
258 | "display_name": "elderly", | 235 | "display_name": "elderly", | ||
259 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 236 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
260 | "name": "elderly", | 237 | "name": "elderly", | ||
261 | "state": "active", | 238 | "state": "active", | ||
262 | "vocabulary_id": null | 239 | "vocabulary_id": null | ||
263 | }, | 240 | }, | ||
264 | { | 241 | { | ||
265 | "display_name": "men", | 242 | "display_name": "men", | ||
266 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 243 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
267 | "name": "men", | 244 | "name": "men", | ||
268 | "state": "active", | 245 | "state": "active", | ||
269 | "vocabulary_id": null | 246 | "vocabulary_id": null | ||
270 | }, | 247 | }, | ||
271 | { | 248 | { | ||
272 | "display_name": "population density", | 249 | "display_name": "population density", | ||
273 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 250 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
274 | "name": "population density", | 251 | "name": "population density", | ||
275 | "state": "active", | 252 | "state": "active", | ||
276 | "vocabulary_id": null | 253 | "vocabulary_id": null | ||
277 | }, | 254 | }, | ||
278 | { | 255 | { | ||
279 | "display_name": "south africa", | 256 | "display_name": "south africa", | ||
280 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 257 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
281 | "name": "south africa", | 258 | "name": "south africa", | ||
282 | "state": "active", | 259 | "state": "active", | ||
283 | "vocabulary_id": null | 260 | "vocabulary_id": null | ||
284 | }, | 261 | }, | ||
285 | { | 262 | { | ||
286 | "display_name": "women", | 263 | "display_name": "women", | ||
287 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 264 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
288 | "name": "women", | 265 | "name": "women", | ||
289 | "state": "active", | 266 | "state": "active", | ||
290 | "vocabulary_id": null | 267 | "vocabulary_id": null | ||
291 | }, | 268 | }, | ||
292 | { | 269 | { | ||
293 | "display_name": "youth", | 270 | "display_name": "youth", | ||
294 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 271 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
295 | "name": "youth", | 272 | "name": "youth", | ||
296 | "state": "active", | 273 | "state": "active", | ||
297 | "vocabulary_id": null | 274 | "vocabulary_id": null | ||
298 | } | 275 | } | ||
299 | ], | 276 | ], | ||
300 | "title": "South Africa: High Resolution Population Density Maps + | 277 | "title": "South Africa: High Resolution Population Density Maps + | ||
301 | Demographic Estimates", | 278 | Demographic Estimates", | ||
302 | "type": "dataset", | 279 | "type": "dataset", | ||
303 | "url": | 280 | "url": | ||
304 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 281 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
305 | "version": "" | 282 | "version": "" | ||
306 | } | 283 | } |