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文章总结： WorldPop发布2015-2030年全球100米分辨率网格化人口数据集，采用随机森林机器学习模型将人口普查数据细分到网格单元，提供受限与无约束两种模型。数据集覆盖242个国家，包含总人口及按年龄性别分类数据，已集成至GoogleEarthEngine平台。文档提供了GEE下载代码示例及可视化方法，适用于城市规划、灾害响应、公共卫生等领域的人口空间分析需求。 综合评分： 55 文章分类： 其他

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WorldPop 2015-2030 年全球网格化人口数据集

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LSC6 LSC6

生态遥感监测笔记

2026年2月6日 08:01 河南

今日分享：

WorldPop 2015-2030 年全球网格化人口数据集

WorldPop 的 2015-2030 年全球网格化人口数据集采用“自上而下”的方法，将行政单位的人口数量细分到 100 米网格单元，从而提供高分辨率的人口估算数据。该数据集较以往的全球人口数据集有了显著改进，最新的数据纳入了最新的 2020 年左右的人口普查数据、更新的地理空间协变量、改进的居民点以及利用人工智能方法从卫星图像中提取的建筑物轮廓。

该数据集提供全球 242 个国家/地区 2015-2030 年期间的年度人口估算数据，分辨率为 100 米。估算预测方法采用随机森林 (RF) 面积加权法，利用基于综合地理空间协变量（包括居民点数据、环境因素和基础设施特征）训练的机器学习模型，将人口数量从行政边界空间细分到网格单元。

数据下载地址：

https://data.humdata.org/organization/worldpop#

WorldPop采用了两种主要的估算模型：

受限模型：人口估算仅限于已识别的人类居住区，这些居住区数据来自卫星图像生成的建筑群图层。这种方法假设人们仅居住在有建筑物或基础设施的区域，通过排除水体、保护区或陡峭地形等不适宜居住的区域，提供更真实的人口分布情况。

无约束模型：人口可以分配到行政边界内的任何土地区域，无论该区域是否存在居民点。虽然这可能导致人口被分配到不适宜居住的地区，但对于居民点边界不确定或临时人口可能存在于正式居民点之外的应用场景较为适用。

数据集介绍：

数据集属性介绍：

| | | | | | — | — | — | — | | 数据名称 | 描述 | 图层 | 单位 | | pop | 年度人口估算(2015-2030 年) | population | 每个网格单元的人数 |

数据增加了按年龄和性别的分类情况

具体数据相关内容如下：

|&nbsp;Band&nbsp;Name&nbsp;|&nbsp;Description&nbsp;||-----------|-------------||&nbsp;`f_00`/`m_00`&nbsp;|&nbsp;Female/male population under&nbsp;1&nbsp;year old ||&nbsp;`f_01`/`m_01`&nbsp;|&nbsp;Female/male population aged&nbsp;1-4&nbsp;years ||&nbsp;`f_05`/`m_05`&nbsp;|&nbsp;Female/male population aged&nbsp;5-9&nbsp;years ||&nbsp;`f_10`/`m_10`&nbsp;|&nbsp;Female/male population aged&nbsp;10-14&nbsp;years ||&nbsp;`f_15`/`m_15`&nbsp;|&nbsp;Female/male population aged&nbsp;15-19&nbsp;years ||&nbsp;`f_20`/`m_20`&nbsp;|&nbsp;Female/male population aged&nbsp;20-24&nbsp;years ||&nbsp;`f_25`/`m_25`&nbsp;|&nbsp;Female/male population aged&nbsp;25-29&nbsp;years ||&nbsp;`f_30`/`m_30`&nbsp;|&nbsp;Female/male population aged&nbsp;30-34&nbsp;years ||&nbsp;`f_35`/`m_35`&nbsp;|&nbsp;Female/male population aged&nbsp;35-39&nbsp;years ||&nbsp;`f_40`/`m_40`&nbsp;|&nbsp;Female/male population aged&nbsp;40-44&nbsp;years ||&nbsp;`f_45`/`m_45`&nbsp;|&nbsp;Female/male population aged&nbsp;45-49&nbsp;years ||&nbsp;`f_50`/`m_50`&nbsp;|&nbsp;Female/male population aged&nbsp;50-54&nbsp;years ||&nbsp;`f_55`/`m_55`&nbsp;|&nbsp;Female/male population aged&nbsp;55-59&nbsp;years ||&nbsp;`f_60`/`m_60`&nbsp;|&nbsp;Female/male population aged&nbsp;60-64&nbsp;years ||&nbsp;`f_65`/`m_65`&nbsp;|&nbsp;Female/male population aged&nbsp;65-69&nbsp;years ||&nbsp;`f_70`/`m_70`&nbsp;|&nbsp;Female/male population aged&nbsp;70-74&nbsp;years ||&nbsp;`f_75`/`m_75`&nbsp;|&nbsp;Female/male population aged&nbsp;75-79&nbsp;years ||&nbsp;`f_80`/`m_80`&nbsp;|&nbsp;Female/male population aged&nbsp;80-84&nbsp;years ||&nbsp;`f_85`/`m_85`&nbsp;|&nbsp;Female/male population aged&nbsp;85-89&nbsp;years ||&nbsp;`f_90`/`m_90`&nbsp;|&nbsp;Female/male population aged&nbsp;90&nbsp;years and over |

数据集引用方式：

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center&nbsp;for&nbsp;International Earth Science Information&nbsp;Network&nbsp;(CIESIN), Columbia&nbsp;University&nbsp;(2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates&nbsp;Foundation&nbsp;(OPP1134076).

总人口数据集：

Bondarenko&nbsp;M., Priyatikanto R., Tejedor-Garavito N., Zhang W., McKeen T., Cunningham A., Woods T., Hilton J., Cihan D., Nosatiuk B., Brinkhoff T., Tatem A., Sorichetta A..&nbsp;2025&nbsp;Constrained estimates of&nbsp;2015-&nbsp;2030&nbsp;total number of people per grid square at a resolution of&nbsp;3&nbsp;arc (approximately&nbsp;100m at the equator) R2024B version v1. Global Demographic Data Project - Funded by The Bill and Melinda Gates Foundation (INV-045237). WorldPop - School of Geography and Environmental Science, University of Southampton. DOI:10.5258/SOTON/WP00803

年龄性别数据集：

Bondarenko&nbsp;M., Priyatikanto R., Tejedor-Garavito N., Zhang W., McKeen T., Cunningham A., Woods T., Hilton J., Cihan D., Nosatiuk B., Brinkhoff T., Tatem A., Sorichetta A..&nbsp;2025. The spatial distribution of population broken down by gender and age groupings in&nbsp;2015-2030&nbsp;at a resolution of&nbsp;3&nbsp;arc (approximately&nbsp;100m at the equator) R2025A version v1. Global Demographic Data Project - Funded by The Bill and Melinda Gates Foundation (INV-045237). WorldPop - School of Geography and Environmental Science, University of Southampton.

数据可以在GEE上集合下载

接下来就是下载数据

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GEE部分下载代码

// Function to select random city and zoom to itfunction&nbsp;zoomToRandomCity()&nbsp;{&nbsp;&nbsp;var&nbsp;randomIndex&nbsp;=&nbsp;Math.floor(Math.random() * majorCities.length);&nbsp;&nbsp;var&nbsp;selectedCity&nbsp;=&nbsp;majorCities[randomIndex];
&nbsp;&nbsp;// Set map center and zoom&nbsp; Map.setCenter(selectedCity.lon, selectedCity.lat, selectedCity.zoom);
&nbsp;&nbsp;// Update info panel to show selected city&nbsp; updateInfoPanelWithCity(2025, selectedCity.name);
&nbsp;&nbsp;return&nbsp;selectedCity;}
// Create global mosaic for a specific yearfunction&nbsp;createGlobalMosaic(year)&nbsp;{&nbsp;&nbsp;var&nbsp;collection&nbsp;=&nbsp;ee.ImageCollection('projects/sat-io/open-datasets/WORLDPOP/pop')&nbsp; &nbsp; .filter(ee.Filter.stringContains('system:index',&nbsp;'_POP_'&nbsp;+ year +&nbsp;'_'))&nbsp; &nbsp; .select([0], ['population']);
&nbsp;&nbsp;return&nbsp;collection.mosaic();}// Main function to update global visualizationfunction&nbsp;updateGlobalVisualization(year)&nbsp;{&nbsp;&nbsp;// Remove current layer&nbsp;&nbsp;if&nbsp;(currentLayer) {&nbsp; &nbsp; Map.remove(currentLayer);&nbsp; &nbsp; currentLayer =&nbsp;null;&nbsp; }
&nbsp;&nbsp;try&nbsp;{&nbsp; &nbsp;&nbsp;// Create global mosaic&nbsp; &nbsp;&nbsp;var&nbsp;globalPopData&nbsp;=&nbsp;createGlobalMosaic(year);
&nbsp; &nbsp;&nbsp;// Use fixed visualization parameters for global view&nbsp; &nbsp;&nbsp;var&nbsp;vis&nbsp;=&nbsp;{&nbsp; &nbsp; &nbsp; min:&nbsp;0,&nbsp; &nbsp; &nbsp; max:&nbsp;50,&nbsp;// Adjusted for global scale&nbsp; &nbsp; &nbsp; palette: beautifulPalette&nbsp; &nbsp; };
&nbsp; &nbsp;&nbsp;// Add global layer&nbsp; &nbsp; currentLayer = ui.Map.Layer(globalPopData, vis,&nbsp;'Global Population '&nbsp;+ year);&nbsp; &nbsp; Map.layers().insert(0, currentLayer);
&nbsp; &nbsp;&nbsp;// Update info display&nbsp; &nbsp; updateInfoPanel(year);
&nbsp; }&nbsp;catch(error) {&nbsp; &nbsp; print('Error loading global data:', error);&nbsp; }}
// Function called by date sliderfunction&nbsp;showPopulationForYear(dateRange)&nbsp;{&nbsp;&nbsp;var&nbsp;startDate&nbsp;=&nbsp;ee.Date(dateRange.start());&nbsp;&nbsp;var&nbsp;year&nbsp;=&nbsp;startDate.get('year').getInfo();&nbsp; updateGlobalVisualization(year);}
// Update information panelfunction&nbsp;updateInfoPanel(year)&nbsp;{&nbsp; infoLabel.setValue([&nbsp; &nbsp;&nbsp;'Global Population Density',&nbsp; &nbsp;&nbsp;'Year: '&nbsp;+ year,&nbsp; &nbsp;&nbsp;'Automatically filtered global mosaic',&nbsp; &nbsp;&nbsp;'Scale: 0-50 people/pixel (100m resolution)',&nbsp; &nbsp;&nbsp;'',&nbsp; &nbsp;&nbsp;'Refresh page to zoom to a different city!'&nbsp; ].join('\n'));}
//年龄// Load and process age-sex data for a specific yearfunction&nbsp;loadAgeSexData(year)&nbsp;{&nbsp;&nbsp;// Load the full collection&nbsp;&nbsp;var&nbsp;collection&nbsp;=&nbsp;ee.ImageCollection('projects/sat-io/open-datasets/WORLDPOP/agesex');
&nbsp;&nbsp;// Filter by year property instead of date range&nbsp;&nbsp;var&nbsp;filtered&nbsp;=&nbsp;collection.filter(ee.Filter.eq('year', year));
&nbsp;&nbsp;// Mosaic all country images for this year&nbsp;&nbsp;var&nbsp;data&nbsp;=&nbsp;filtered.mosaic();
&nbsp;&nbsp;// Define band lists&nbsp;&nbsp;var&nbsp;femaleBands&nbsp;=&nbsp;['f_00',&nbsp;'f_01',&nbsp;'f_05',&nbsp;'f_10',&nbsp;'f_15',&nbsp;'f_20',&nbsp;'f_25',&nbsp;'f_30',&nbsp;'f_35',&nbsp;'f_40',&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'f_45',&nbsp;'f_50',&nbsp;'f_55',&nbsp;'f_60',&nbsp;'f_65',&nbsp;'f_70',&nbsp;'f_75',&nbsp;'f_80',&nbsp;'f_85',&nbsp;'f_90'];&nbsp;&nbsp;var&nbsp;maleBands&nbsp;=&nbsp;['m_00',&nbsp;'m_01',&nbsp;'m_05',&nbsp;'m_10',&nbsp;'m_15',&nbsp;'m_20',&nbsp;'m_25',&nbsp;'m_30',&nbsp;'m_35',&nbsp;'m_40',&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;'m_45',&nbsp;'m_50',&nbsp;'m_55',&nbsp;'m_60',&nbsp;'m_65',&nbsp;'m_70',&nbsp;'m_75',&nbsp;'m_80',&nbsp;'m_85',&nbsp;'m_90'];
&nbsp;&nbsp;// Calculate female population&nbsp;&nbsp;var&nbsp;femalePop&nbsp;=&nbsp;data.select(femaleBands).reduce(ee.Reducer.sum()).rename('female_population');
&nbsp;&nbsp;// Calculate male population&nbsp;&nbsp;var&nbsp;malePop&nbsp;=&nbsp;data.select(maleBands).reduce(ee.Reducer.sum()).rename('male_population');
&nbsp;&nbsp;// Calculate total population by adding female and male&nbsp;&nbsp;var&nbsp;totalPop&nbsp;=&nbsp;femalePop.add(malePop).rename('total_population');
&nbsp;&nbsp;// Youth population (0-24 years)&nbsp;&nbsp;var&nbsp;youthPop&nbsp;=&nbsp;data.select([&nbsp; &nbsp;&nbsp;'f_00',&nbsp;'f_01',&nbsp;'f_05',&nbsp;'f_10',&nbsp;'f_15',&nbsp;'f_20',&nbsp; &nbsp;&nbsp;'m_00',&nbsp;'m_01',&nbsp;'m_05',&nbsp;'m_10',&nbsp;'m_15',&nbsp;'m_20'&nbsp; ]).reduce(ee.Reducer.sum()).rename('youth_population');
&nbsp;&nbsp;// Working age population (25-64 years)&nbsp;&nbsp;var&nbsp;workingPop&nbsp;=&nbsp;data.select([&nbsp; &nbsp;&nbsp;'f_25',&nbsp;'f_30',&nbsp;'f_35',&nbsp;'f_40',&nbsp;'f_45',&nbsp;'f_50',&nbsp;'f_55',&nbsp;'f_60',&nbsp; &nbsp;&nbsp;'m_25',&nbsp;'m_30',&nbsp;'m_35',&nbsp;'m_40',&nbsp;'m_45',&nbsp;'m_50',&nbsp;'m_55',&nbsp;'m_60'&nbsp; ]).reduce(ee.Reducer.sum()).rename('working_population');
&nbsp;&nbsp;// Elderly population (65+ years)&nbsp;&nbsp;var&nbsp;elderlyPop&nbsp;=&nbsp;data.select([&nbsp; &nbsp;&nbsp;'f_65',&nbsp;'f_70',&nbsp;'f_75',&nbsp;'f_80',&nbsp;'f_85',&nbsp;'f_90',&nbsp; &nbsp;&nbsp;'m_65',&nbsp;'m_70',&nbsp;'m_75',&nbsp;'m_80',&nbsp;'m_85',&nbsp;'m_90'&nbsp; ]).reduce(ee.Reducer.sum()).rename('elderly_population');
&nbsp;&nbsp;// Children (0-14 years)&nbsp;&nbsp;var&nbsp;childrenPop&nbsp;=&nbsp;data.select([&nbsp; &nbsp;&nbsp;'f_00',&nbsp;'f_01',&nbsp;'f_05',&nbsp;'f_10',&nbsp; &nbsp;&nbsp;'m_00',&nbsp;'m_01',&nbsp;'m_05',&nbsp;'m_10'&nbsp; ]).reduce(ee.Reducer.sum()).rename('children_population');
&nbsp;&nbsp;// Gender ratio (males per 100 females)&nbsp;&nbsp;var&nbsp;genderRatio&nbsp;=&nbsp;malePop.divide(femalePop).multiply(100).rename('gender_ratio');
&nbsp;&nbsp;return&nbsp;{&nbsp; &nbsp; total: totalPop,&nbsp; &nbsp; female: femalePop,&nbsp; &nbsp; male: malePop,&nbsp; &nbsp; youth: youthPop,&nbsp; &nbsp; working: workingPop,&nbsp; &nbsp; elderly: elderlyPop,&nbsp; &nbsp; children: childrenPop,&nbsp; &nbsp; gender_ratio: genderRatio,&nbsp; &nbsp; raw: data&nbsp; };}

—

结果显示

部分区域人口分布1

部分区域人口分布2

部分区域人口分布3

部分区域人口分布4

某区域（0-24）人口分布

某区域女性人口分布

某区域男性人口分布

某区域（65+）人口分布

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WorldPop 2015-2030 年全球网格化人口数据集”

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本文转载自：生态遥感监测笔记 LSC6 LSC6《WorldPop 2015-2030 年全球网格化人口数据集》