Veranderingen
On juni 14, 2023, 12:02:25 (SAST),
Brian O'Leary:
-
Deleted resource population-zaf.png 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": "2023-06-14T10:02:09.851714", | n | 43 | "metadata_modified": "2023-06-14T10:02:25.484947", |
| 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": 2, | n | 72 | "num_resources": 1, |
| 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": | ||
| 84 | ttps://opencitieslab.org/odd/static/dist/img/ocl_logo_header-nav.png", | 84 | ttps://opencitieslab.org/odd/static/dist/img/ocl_logo_header-nav.png", | ||
| 85 | "is_organization": true, | 85 | "is_organization": true, | ||
| 86 | "name": "open-cities-lab", | 86 | "name": "open-cities-lab", | ||
| 87 | "revision_id": "35e0d462-e6b8-4c18-8743-e9361e136009", | 87 | "revision_id": "35e0d462-e6b8-4c18-8743-e9361e136009", | ||
| 88 | "state": "active", | 88 | "state": "active", | ||
| 89 | "title": "Open Cities Lab", | 89 | "title": "Open Cities Lab", | ||
| 90 | "type": "organization" | 90 | "type": "organization" | ||
| 91 | }, | 91 | }, | ||
| 92 | "owner_org": "8e526815-d516-4f6c-9ae0-a4d62555c30c", | 92 | "owner_org": "8e526815-d516-4f6c-9ae0-a4d62555c30c", | ||
| 93 | "private": false, | 93 | "private": false, | ||
| 94 | "relationships_as_object": [], | 94 | "relationships_as_object": [], | ||
| 95 | "relationships_as_subject": [], | 95 | "relationships_as_subject": [], | ||
| 96 | "resources": [ | 96 | "resources": [ | ||
| 97 | { | 97 | { | ||
| 98 | "cache_last_updated": null, | 98 | "cache_last_updated": null, | ||
| 99 | "cache_url": null, | 99 | "cache_url": null, | ||
| n | 100 | "created": "2021-06-09T23:56:59.288437", | n | ||
| 101 | "datastore_active": false, | ||||
| 102 | "description": "", | ||||
| 103 | "format": "PNG", | ||||
| 104 | "hash": "", | ||||
| 105 | "id": "2b71922a-0a94-44cb-a6b4-7551b746f0de", | ||||
| 106 | "last_modified": "2021-06-09T23:56:59.237469", | ||||
| 107 | "mimetype": null, | ||||
| 108 | "mimetype_inner": null, | ||||
| 109 | "name": "population-zaf.png", | ||||
| 110 | "package_id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | ||||
| 111 | "position": 0, | ||||
| 112 | "resource_type": null, | ||||
| 113 | "revision_id": "9c00136f-cb3b-48dc-bc7d-b2469fcd7591", | ||||
| 114 | "size": null, | ||||
| 115 | "state": "active", | ||||
| 116 | "url": | ||||
| 117 | rce/2b71922a-0a94-44cb-a6b4-7551b746f0de/download/population-zaf.png", | ||||
| 118 | "url_type": "upload" | ||||
| 119 | }, | ||||
| 120 | { | ||||
| 121 | "cache_last_updated": null, | ||||
| 122 | "cache_url": null, | ||||
| 123 | "created": "2022-06-15T17:34:47.117245", | 100 | "created": "2022-06-15T17:34:47.117245", | ||
| 124 | "datastore_active": false, | 101 | "datastore_active": false, | ||
| 125 | "description": "The world's most accurate population datasets. | 102 | "description": "The world's most accurate population datasets. | ||
| 126 | Seven maps/datasets for the distribution of various populations in | 103 | Seven maps/datasets for the distribution of various populations in | ||
| 127 | South Africa: (1) Overall population density (2) Women (3) Men (4) | 104 | South Africa: (1) Overall population density (2) Women (3) Men (4) | ||
| 128 | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | 105 | Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) | ||
| 129 | Women of reproductive age (ages 15-49).", | 106 | Women of reproductive age (ages 15-49).", | ||
| 130 | "format": "", | 107 | "format": "", | ||
| 131 | "hash": "", | 108 | "hash": "", | ||
| 132 | "id": "33f646dc-91c5-48dd-a91e-c4656e054d4f", | 109 | "id": "33f646dc-91c5-48dd-a91e-c4656e054d4f", | ||
| 133 | "last_modified": null, | 110 | "last_modified": null, | ||
| 134 | "mimetype": null, | 111 | "mimetype": null, | ||
| 135 | "mimetype_inner": null, | 112 | "mimetype_inner": null, | ||
| 136 | "name": "highresolutionpopulationdensitymaps-zaf", | 113 | "name": "highresolutionpopulationdensitymaps-zaf", | ||
| 137 | "package_id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | 114 | "package_id": "f116ce54-41d1-4ebb-82ab-85a265d50adc", | ||
| n | 138 | "position": 1, | n | 115 | "position": 0, |
| 139 | "resource_type": null, | 116 | "resource_type": null, | ||
| t | 140 | "revision_id": "9c00136f-cb3b-48dc-bc7d-b2469fcd7591", | t | 117 | "revision_id": "e53e7924-7d59-4ef6-bad5-8d8ecadf90a4", |
| 141 | "size": null, | 118 | "size": null, | ||
| 142 | "state": "active", | 119 | "state": "active", | ||
| 143 | "url": | 120 | "url": | ||
| 144 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 121 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 145 | "url_type": null | 122 | "url_type": null | ||
| 146 | } | 123 | } | ||
| 147 | ], | 124 | ], | ||
| 148 | "revision_id": "05740a1d-bed7-4b03-b57f-1c9368aba145", | 125 | "revision_id": "05740a1d-bed7-4b03-b57f-1c9368aba145", | ||
| 149 | "state": "active", | 126 | "state": "active", | ||
| 150 | "tags": [ | 127 | "tags": [ | ||
| 151 | { | 128 | { | ||
| 152 | "display_name": "children", | 129 | "display_name": "children", | ||
| 153 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | 130 | "id": "a31aa94e-cdbe-470b-b953-602bfc01eeab", | ||
| 154 | "name": "children", | 131 | "name": "children", | ||
| 155 | "state": "active", | 132 | "state": "active", | ||
| 156 | "vocabulary_id": null | 133 | "vocabulary_id": null | ||
| 157 | }, | 134 | }, | ||
| 158 | { | 135 | { | ||
| 159 | "display_name": "deodata", | 136 | "display_name": "deodata", | ||
| 160 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | 137 | "id": "6e432f96-22f4-4aa9-9777-96774158a52b", | ||
| 161 | "name": "deodata", | 138 | "name": "deodata", | ||
| 162 | "state": "active", | 139 | "state": "active", | ||
| 163 | "vocabulary_id": null | 140 | "vocabulary_id": null | ||
| 164 | }, | 141 | }, | ||
| 165 | { | 142 | { | ||
| 166 | "display_name": "elderly", | 143 | "display_name": "elderly", | ||
| 167 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | 144 | "id": "9c11b077-a76b-4a95-b6ee-afb6c112f48c", | ||
| 168 | "name": "elderly", | 145 | "name": "elderly", | ||
| 169 | "state": "active", | 146 | "state": "active", | ||
| 170 | "vocabulary_id": null | 147 | "vocabulary_id": null | ||
| 171 | }, | 148 | }, | ||
| 172 | { | 149 | { | ||
| 173 | "display_name": "men", | 150 | "display_name": "men", | ||
| 174 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | 151 | "id": "887bf1dd-dcf9-42ab-9e75-7ee55de689a2", | ||
| 175 | "name": "men", | 152 | "name": "men", | ||
| 176 | "state": "active", | 153 | "state": "active", | ||
| 177 | "vocabulary_id": null | 154 | "vocabulary_id": null | ||
| 178 | }, | 155 | }, | ||
| 179 | { | 156 | { | ||
| 180 | "display_name": "population density", | 157 | "display_name": "population density", | ||
| 181 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | 158 | "id": "98311ff5-1cea-4f34-92fb-c0757c6ce5ca", | ||
| 182 | "name": "population density", | 159 | "name": "population density", | ||
| 183 | "state": "active", | 160 | "state": "active", | ||
| 184 | "vocabulary_id": null | 161 | "vocabulary_id": null | ||
| 185 | }, | 162 | }, | ||
| 186 | { | 163 | { | ||
| 187 | "display_name": "south africa", | 164 | "display_name": "south africa", | ||
| 188 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | 165 | "id": "6c1d99aa-1164-481a-b7ef-c05974b84af7", | ||
| 189 | "name": "south africa", | 166 | "name": "south africa", | ||
| 190 | "state": "active", | 167 | "state": "active", | ||
| 191 | "vocabulary_id": null | 168 | "vocabulary_id": null | ||
| 192 | }, | 169 | }, | ||
| 193 | { | 170 | { | ||
| 194 | "display_name": "women", | 171 | "display_name": "women", | ||
| 195 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | 172 | "id": "d8f22a99-abe8-41cb-ae9c-4b543eee7cdf", | ||
| 196 | "name": "women", | 173 | "name": "women", | ||
| 197 | "state": "active", | 174 | "state": "active", | ||
| 198 | "vocabulary_id": null | 175 | "vocabulary_id": null | ||
| 199 | }, | 176 | }, | ||
| 200 | { | 177 | { | ||
| 201 | "display_name": "youth", | 178 | "display_name": "youth", | ||
| 202 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | 179 | "id": "98ff881a-a277-4d3a-9641-7657ad12e6a7", | ||
| 203 | "name": "youth", | 180 | "name": "youth", | ||
| 204 | "state": "active", | 181 | "state": "active", | ||
| 205 | "vocabulary_id": null | 182 | "vocabulary_id": null | ||
| 206 | } | 183 | } | ||
| 207 | ], | 184 | ], | ||
| 208 | "title": "South Africa: High Resolution Population Density Maps + | 185 | "title": "South Africa: High Resolution Population Density Maps + | ||
| 209 | Demographic Estimates", | 186 | Demographic Estimates", | ||
| 210 | "type": "dataset", | 187 | "type": "dataset", | ||
| 211 | "url": | 188 | "url": | ||
| 212 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | 189 | s://data.humdata.org/dataset/highresolutionpopulationdensitymaps-zaf", | ||
| 213 | "version": "" | 190 | "version": "" | ||
| 214 | } | 191 | } |