Oak Ridge Ulusal Laboratuvarı (ORNL) tarafından sağlanan LandScan veri kümesi, çok çeşitli uygulamalar için değerli bir kaynak olarak hizmet veren kapsamlı ve yüksek çözünürlüklü bir küresel nüfus dağılımı veri kümesi sunar. En yeni mekansal modelleme tekniklerinden ve gelişmiş coğrafi veri kaynaklarından yararlanan LandScan, 30 yay saniyesi çözünürlükte nüfus sayıları ve yoğunluğu hakkında ayrıntılı bilgiler sunarak dünya genelindeki yerleşim düzenleri hakkında hassas ve güncel analizler sağlar. LandScan, doğruluk ve ayrıntı düzeyi sayesinde şehir planlaması, afet müdahalesi, epidemiyoloji ve çevre araştırmaları gibi çeşitli alanları destekler. Bu nedenle, küresel ölçekte çeşitli toplumsal ve çevresel sorunları anlamak ve ele almak isteyen karar vericiler ve araştırmacılar için vazgeçilmez bir araçtır.
Bantlar
Piksel Boyutu 1.000 metre
Bantlar
Ad
Min.
Maks.
Piksel Boyutu
Açıklama
b1
0*
21171*
metre
Tahmini nüfus sayısı
* tahmini minimum veya maksimum değer
Kullanım Şartları
Kullanım Şartları
Landscan veri kümeleri, Creative Commons Attribution 4.0 Uluslararası Lisansı ile lisanslanmıştır. Kullanıcılar, kaynağın açıkça belirtilmesi koşuluyla, ticari ve ticari olmayan amaçlarla eseri kısıtlama olmaksızın kullanabilir, kopyalayabilir, dağıtabilir, iletebilir ve uyarlayabilir.
Alıntılar
Alıntılar:
Sims, K., Reith, A., Bright, E., Kaufman, J., Pyle, J., Epting, J., Gonzales, J., Adams, D., Powell, E., Urban, M., & Rose, A. (2023). LandScan Global 2022 [Veri kümesi]. Oak Ridge National Laboratory. https://doi.org/10.48690/1529167
Oak Ridge Ulusal Laboratuvarı (ORNL) tarafından sağlanan LandScan veri kümesi, çok çeşitli uygulamalar için değerli bir kaynak olarak hizmet veren kapsamlı ve yüksek çözünürlüklü bir küresel nüfus dağılımı veri kümesi sunar. LandScan, en yeni mekansal modelleme tekniklerinden ve gelişmiş coğrafi veri kaynaklarından yararlanarak nüfus sayıları ve … hakkında ayrıntılı bilgiler sağlar.
[null,null,[],[[["\u003cp\u003eThe LandScan dataset provides high-resolution global population distribution data from 2000 to 2022.\u003c/p\u003e\n"],["\u003cp\u003eDeveloped by Oak Ridge National Laboratory (ORNL), it offers population counts and density at a 30 arc-second resolution (approximately 1km).\u003c/p\u003e\n"],["\u003cp\u003eLandScan data is valuable for applications like urban planning, disaster response, and epidemiology.\u003c/p\u003e\n"],["\u003cp\u003eThe dataset is licensed under Creative Commons Attribution 4.0 International, allowing for free use with attribution.\u003c/p\u003e\n"],["\u003cp\u003eUsers can access and analyze the LandScan dataset within Google Earth Engine.\u003c/p\u003e\n"]]],["The LandScan dataset, from Oak Ridge National Laboratory (ORNL), provides global population distribution data from 2000 to 2023. It uses spatial modeling to offer population counts and density at a 30 arc-second resolution, with a 1000-meter pixel size. The data is accessible in the Earth Engine via an ImageCollection, and the band 'b1' details estimated population counts, varying from 0 to 21171. Users can utilize it for free under a Creative Commons license.\n"],null,["# LandScan Population Data Global 1km\n\ninfo\n\n\nThis dataset is part of a Community Catalog, and not managed by Google Earth Engine.\n\nContact gee-community-catalog@googlegroups.com\n\nfor bugs or [view more datasets](https://developers.google.com/earth-engine/datasets/community/sat-io)\nfrom the Awesome GEE Community Catalog Catalog. [Learn more about Community datasets](/earth-engine/datasets/community). \n[](https://gee-community-catalog.org/) \n\nCatalog Owner\n: Awesome GEE Community Catalog\n\nDataset Availability\n: 2000-01-01T00:00:00Z--2023-12-31T00:00:00Z\n\nDataset Provider\n:\n\n\n [Oak Ridge National Laboratory](https://www.ornl.gov/)\n\nTags\n:\n[community-dataset](/earth-engine/datasets/tags/community-dataset) [demography](/earth-engine/datasets/tags/demography) [landscan](/earth-engine/datasets/tags/landscan) [population](/earth-engine/datasets/tags/population) [sat-io](/earth-engine/datasets/tags/sat-io) \n\n#### Description\n\nThe LandScan dataset, provided by the Oak Ridge National Laboratory (ORNL), offers a comprehensive and high-resolution global population distribution dataset that serves as a valuable resource for a wide range of applications. Leveraging state-of-the-art spatial modeling techniques and advanced geospatial data sources, LandScan provides detailed information on population counts and density at a 30 arc-second resolution, enabling precise and up-to-date insights into human settlement patterns across the globe. With its accuracy and granularity, LandScan supports diverse fields such as urban planning, disaster response, epidemiology, and environmental research, making it an essential tool for decision-makers and researchers seeking to understand and address various societal and environmental challenges on a global scale.\n\n### Bands\n\n\n**Pixel Size**\n\n1000 meters\n\n**Bands**\n\n| Name | Min | Max | Pixel Size | Description |\n|------|-----|---------|------------|----------------------------|\n| `b1` | 0\\* | 21171\\* | meters | Estimated Population count |\n\n\\* estimated min or max value\n\n### Terms of Use\n\n**Terms of Use**\n\nLandscan datasets are licensed under the Creative Commons Attribution 4.0\nInternational License. Users are free to use, copy, distribute, transmit,\nand adapt the work for commercial and non-commercial purposes, without\nrestriction, as long as clear attribution of the source is provided.\n\n### Citations\n\nCitations:\n\n- Sims, K., Reith, A., Bright, E., Kaufman, J., Pyle, J., Epting, J., Gonzales, J., Adams, D., Powell, E., Urban, M., \\& Rose, A. (2023). LandScan Global 2022 \\[Data set\\]. Oak Ridge National Laboratory. https://doi.org/10.48690/1529167\n\n### DOIs\n\n- \u003chttps://doi.org/10.48690/1529167\u003e\n\n### Explore with Earth Engine\n\n| **Important:** Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users. Earth Engine is free to use for research, education, and nonprofit use. To get started, please [register for Earth Engine access.](https://console.cloud.google.com/earth-engine)\n\n### Code Editor (JavaScript)\n\n```javascript\nvar landscan_global =\n ee.ImageCollection('projects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL');\nvar popcount_intervals = '\u003cRasterSymbolizer\u003e' +\n ' \u003cColorMap type=\"intervals\" extended=\"false\" \u003e' +\n '\u003cColorMapEntry color=\"#CCCCCC\" quantity=\"0\" label=\"No Data\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFFFBE\" quantity=\"5\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEFF73\" quantity=\"25\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#FEFF2C\" quantity=\"50\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#FFAA27\" quantity=\"100\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#FF6625\" quantity=\"500\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#FF0023\" quantity=\"2500\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#CC001A\" quantity=\"5000\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003cColorMapEntry color=\"#730009\" quantity=\"185000\" label=\"Population Count (Estimate)\"/\u003e' +\n '\u003c/ColorMap\u003e' +\n '\u003c/RasterSymbolizer\u003e';\n\n// Define a dictionary which will be used to make legend and visualize image on\n// map\nvar dict = {\n 'names': [\n '0', '1-5', '6-25', '26-50', '51-100', '101-500', '501-2500', '2501-5000',\n '5001-185000'\n ],\n 'colors': [\n '#CCCCCC', '#FFFFBE', '#FEFF73', '#FEFF2C', '#FFAA27', '#FF6625', '#FF0023',\n '#CC001A', '#730009'\n ]\n};\n\n// Create a panel to hold the legend widget\nvar legend = ui.Panel({style: {position: 'bottom-left', padding: '8px 15px'}});\n\n// Function to generate the legend\nfunction addCategoricalLegend(panel, dict, title) {\n // Create and add the legend title.\n var legendTitle = ui.Label({\n value: title,\n style: {\n fontWeight: 'bold',\n fontSize: '18px',\n margin: '0 0 4px 0',\n padding: '0'\n }\n });\n panel.add(legendTitle);\n\n var loading = ui.Label('Loading legend...', {margin: '2px 0 4px 0'});\n panel.add(loading);\n\n // Creates and styles 1 row of the legend.\n var makeRow = function(color, name) {\n // Create the label that is actually the colored box.\n var colorBox = ui.Label({\n style: {\n backgroundColor: color,\n // Use padding to give the box height and width.\n padding: '8px',\n margin: '0 0 4px 0'\n }\n });\n\n // Create the label filled with the description text.\n var description = ui.Label({value: name, style: {margin: '0 0 4px 6px'}});\n\n return ui.Panel({\n widgets: [colorBox, description],\n layout: ui.Panel.Layout.Flow('horizontal')\n });\n };\n\n // Get the list of palette colors and class names from the image.\n var palette = dict['colors'];\n var names = dict['names'];\n loading.style().set('shown', false);\n\n for (var i = 0; i \u003c names.length; i++) {\n panel.add(makeRow(palette[i], names[i]));\n }\n\n Map.add(panel);\n}\n\naddCategoricalLegend(legend, dict, 'Population Count(estimate)');\n\nMap.addLayer(\n landscan_global.sort('system:time_start')\n .first()\n .sldStyle(popcount_intervals),\n {}, 'Population Count Estimate 2000');\nMap.addLayer(\n landscan_global.sort('system:time_start', false)\n .first()\n .sldStyle(popcount_intervals),\n {}, 'Population Count Estimate 2022');\n```\n[Open in Code Editor](https://code.earthengine.google.com/?scriptPath=Examples:Datasets/sat-io/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL) \n[LandScan Population Data Global 1km](/earth-engine/datasets/catalog/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL) \nThe LandScan dataset, provided by the Oak Ridge National Laboratory (ORNL), offers a comprehensive and high-resolution global population distribution dataset that serves as a valuable resource for a wide range of applications. Leveraging state-of-the-art spatial modeling techniques and advanced geospatial data sources, LandScan provides detailed information on population counts and ... \nprojects/sat-io/open-datasets/ORNL/LANDSCAN_GLOBAL, community-dataset,demography,landscan,population,sat-io \n2000-01-01T00:00:00Z/2023-12-31T00:00:00Z \n-90 -180 90 180 \nGoogle Earth Engine \nhttps://developers.google.com/earth-engine/datasets\n\n- [https://doi.org/10.48690/1529167](https://doi.org/https://www.ornl.gov/)\n- [https://doi.org/10.48690/1529167](https://doi.org/https://developers.google.com/earth-engine/datasets/catalog/projects_sat-io_open-datasets_ORNL_LANDSCAN_GLOBAL)"]]
