이 데이터 세트는 커뮤니티 카탈로그에 속하며 Google Earth Engine에서 관리하지 않습니다.
버그에 관해 문의하거나 Awesome GEE Community Catalog 카탈로그에서 더 많은 데이터 세트 보기를 보려면 gee-community-catalog@googlegroups.com으로 문의하세요. 커뮤니티 데이터 세트 자세히 알아보기
오크리지 국립 연구소 (ORNL)에서 제공하는 LandScan 데이터 세트는 다양한 애플리케이션에 유용한 리소스로 활용되는 포괄적이고 고해상도의 전 세계 인구 분포 데이터 세트를 제공합니다. 최첨단 공간 모델링 기술과 고급 지리 공간 데이터 소스를 활용하는 LandScan은 30초 해상도로 인구수와 밀도에 관한 세부정보를 제공하여 전 세계 인구 밀집 패턴에 관한 정확하고 최신 통계를 제공합니다. LandScan은 정확성과 세부성을 바탕으로 도시 계획, 재해 대응, 역학, 환경 연구 등 다양한 분야를 지원하므로 전 세계적으로 다양한 사회적 및 환경적 문제를 이해하고 해결하려는 의사결정자와 연구자에게 필수적인 도구입니다.
대역
픽셀 크기 1,000미터
대역
이름
최소
최대
픽셀 크기
설명
b1
0*
21171*
미터
예상 인구수
* 예상 최솟값 또는 최댓값
이용약관
이용약관
Landscan 데이터 세트는 크리에이티브 커먼즈 저작자 표시 4.0 국제 라이선스에 따라 라이선스가 부여됩니다. 사용자는 출처를 명확하게 표시하는 한 상업적 및 비상업적 목적으로 제한 없이 저작물을 사용, 복사, 배포, 전송, 각색할 수 있습니다.
인용
인용:
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[데이터 세트]. Oak Ridge National Laboratory. https://doi.org/10.48690/1529167
오크리지 국립 연구소 (ORNL)에서 제공하는 LandScan 데이터 세트는 다양한 애플리케이션에 유용한 리소스로 활용되는 포괄적이고 고해상도의 전 세계 인구 분포 데이터 세트를 제공합니다. 최첨단 공간 모델링 기술과 고급 지리 공간 데이터 소스를 활용하는 LandScan은 인구수와 …에 관한 자세한 정보를 제공합니다.
[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)"]]
