Over a billion people live in slums in settlements that are often located in ecologically sensitive areas and hence highly vulnerable. This is a problem in many parts of the world, but it is more prominent in low-income countries, where in 2014 on average 65% of the urban population lived in slums. As a result, building resilient communities requires quantifying the population living in these deprived areas and improving their living conditions. However, most of the data about slums comes from census data, which is only available at aggregate levels and often excludes these settlements. Consequently, researchers have looked at alternative approaches. These approaches, however, commonly rely on expensive high-resolution satellite imagery and field-surveys, which hinders their large-scale applicability. In this paper, we investigate a cost-effective methodology to estimate the slum population by assessing the quality of gridded population data. We evaluate the accuracy of the WorldPOP and LandScan population layers against ground-truth data composed of 1,703 georeferenced polygons that were mapped as deprived areas and which had their population surveyed during the 2010 Brazilian census. While the LandScan data did not produce satisfactory results for most polygons, the WorldPOP estimates were less than 20% off for 67% of the polygons and the overall error for the totality of the studied area was only -5.9%. This small error margin demonstrates that population layers with a resolution of at least a 100m, such as WorldPOP's, can be useful tools to estimate the population living in slums.

中文翻译：

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评估网格化人口数据的质量以量化生活在贫困社区中的人口

超过十亿人居住在定居点的贫民窟中，这些定居点通常位于生态敏感地区，因此非常脆弱。这在世界许多地方都是一个问题，但在低收入国家尤为突出，2014年，这些国家的平均城市人口中有65％生活在贫民窟中。结果，建设有复原力的社区需要量化生活在这些贫困地区的人口并改善其生活条件。但是，有关贫民窟的大多数数据来自人口普查数据，而人口普查数据仅在汇总水平上可用，并且通常不包括这些定居点。因此，研究人员研究了替代方法。然而，这些方法通常依赖于昂贵的高分辨率卫星图像和野外测量，这阻碍了它们的大规模应用。在本文中，我们研究了一种经济有效的方法，通过评估网格化人口数据的质量来估计贫民窟人口。我们根据由1,703个地理参考的多边形构成的地面真实数据，对WorldPOP和LandScan人口图层的准确性进行了评估，这些多边形被映射为贫困区域，并且在2010年巴西人口普查中对其人口进行了调查。尽管对于大多数多边形，LandScan数据均无法获得令人满意的结果，但对于67％的多边形，WorldPOP估算值却不足20％，并且整个研究区域的总体误差仅为-5.9％。这个很小的误差幅度表明，分辨率至少为100m的人口层次（例如WorldPOP）可以作为估算居住在贫民窟中人口的有用工具。