Division of Energy Systems, KTH Royal Institute of Technology, Brinellvägen 68, 10044, Stockholm, Sweden.
The World Bank Group, Washington, DC, 20433, USA.
Sci Data. 2021 Apr 23;8(1):117. doi: 10.1038/s41597-021-00897-9.
Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response.
人类住区通常围绕着人造中心点或独特的自然特征集中形成，形成形状和大小各异的集群。然而，地理科学中的人口分布通常以像素化栅格的形式表示。栅格表示在预定义的空间分辨率下的人口密度，但无法捕捉到住区的实际形状或大小。在这里，我们提出了一种将高分辨率栅格人口数据转换为基于矢量的人口集群的方法。我们使用开源数据并开发了一种适用于存在数据稀缺问题的中低收入国家的开放获取算法。每个集群都包含表示人口、电气化率和城乡分类的独特特征。结果与世界银行提供的国家电气化率数据和来自选定的人口与健康调查（DHS）的数据进行了验证。我们发现，我们模型化的国家电气化率与世界银行报告的比率一致，而模型化的城乡分类准确率为 88%。通过划定住区，该数据集可以在能源规划、城市规划和疾病应对等研究中补充现有的栅格人口数据。
