LDU | Spain | 2020 Reachable Population Counts (by age and sex) within a 4 Hour timeframe by Truck | 72762 Origins

This is NOT a raw population dataset. We use our proprietary stack to combine detailed 'WorldPop' UN-adjusted, sex and age structured population data with a spatiotemporal OD matrix. The result is a dataset where each record indicates how many people can be reached in a fixed timeframe (4 Hours in this case) from that record's location. The dataset is broken down into sex and age bands at 5 year intervals, e.g - male 25-29 (m_25) and also contains a set of features detailing the representative percentage of the total that the count represents. The dataset provides 72762 records, one for each sampled location. These are labelled with a h3 index at resolution 7 - this allows easy plotting and filtering in Kepler.gl / Deck.gl / Mapbox, or easy conversion to a centroid (lat/lng) or the representative geometry of the hexagonal cell for integration with your geospatial applications and analyses. A h3 resolution of 7, is a hexagonal cell area equivalent to: - ~1.9928 sq miles - ~5.1613 sq km Higher resolutions or alternate geographies are available on request. More information on the h3 system is available here: https://eng.uber.com/h3/ WorldPop data provides for a population count using a grid of 1 arc second intervals and is available for every geography. More information on the WorldPop data is available here: https://www.worldpop.org/ One of the main use cases historically has been in prospecting for site selection, comparative analysis and network validation by asset investors and logistics companies. The data structure makes it very simple to filter out areas which do not meet requirements such as: - being able to access 70% of the Spanish population within 4 hours by Truck and show only the areas which do exhibit this characteristic. Clients often combine different datasets either for different timeframes of interest, or to understand different populations, such as that of the unemployed, or those with particular qualifications within areas reachable as a commute.

本数据集并非原始人口数据集。我们依托自研专有技术栈，将经联合国调整、按性别与年龄结构细分的详细WorldPop世界人口项目数据集（WorldPop），与时空起点-终点（OD）矩阵进行融合。最终生成的数据集中，每条记录均可表征其对应点位在固定时长（本案例中为4小时）内可覆盖的人口规模。
本数据集按性别与5年间隔的年龄组进行细分，例如男性25-29岁组（标注为m_25），同时附带一组特征字段，用于说明该统计值对应群体占总体人口的代表性占比。
本数据集共包含72762条记录，对应每一个采样点位。每条记录均标注有分辨率为7级的H3六边形网格索引（H3 index），可便捷地在Kepler.gl、Deck.gl及Mapbox平台中完成可视化绘图与数据筛选，也可轻松转换为质心坐标（经纬度）或该六边形单元格的代表性几何图形，以适配各类地理空间应用与分析场景。
分辨率为7级的H3六边形单元格面积约为：1.9928平方英里，或5.1613平方公里。可根据客户需求提供更高分辨率或适配其他地理区域的衍生数据集。有关H3系统的更多详情可访问：https://eng.uber.com/h3/
WorldPop世界人口项目数据集（WorldPop）采用1弧秒间隔的网格进行人口统计，覆盖全球所有地理区域。有关该数据集的更多详情可访问：https://www.worldpop.org/
该数据集过往的核心应用场景之一，是为资产投资者与物流企业提供选址勘探、对比分析及网络效能验证支持。该数据集的结构可大幅简化筛选流程，例如可快速剔除不符合预设条件的区域——如无法在4小时内通过卡车覆盖西班牙70%人口的区域，仅保留符合该特征的区域。
客户通常会将多份数据集组合使用，以适配不同的时间范围分析需求，或针对特定人群开展研究——例如通勤可达范围内的失业群体、具备特定职业资质群体的分布情况。

搜集汇总

背景与挑战

背景概述

该数据集基于西班牙2020年人口统计和时空OD矩阵，计算了从72762个采样点出发、4小时卡车行程内可达的人口数量，并按性别和5岁年龄组进行了细分。数据采用H3六边形网格系统组织，适用于地理空间分析、选址评估和物流网络验证等场景。