Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea.
Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea.
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DOI：
10.1371/journal.pone.0248646
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Dolgert AJ
中科院分区：
文献类型：
--
作者：
Fries B;Guerra CA;García GA;Wu SL;Smith JM;Oyono JNM;Donfack OT;Nfumu JOO;Hay SI;Smith DL;Dolgert AJ
人口的地理空间数据集在用于卫生政策的模型中越来越常见。公开的人口地图从不一致的人口普查数据中绘制出一致的图像，他们用来估算数据的技术使每张人口地图都是独一无二的。每个映射模型都解释了它的方法，但是很难知道哪个映射适合于哪个策略。如果有高质量的普查数据集，这是一个独特的机会，可以通过将地图与真实情况进行比较来确定地图的特征。我们使用人口普查数据从比奥科岛，赤道几内亚，比奥科岛疟疾消除计划作为黄金标准进行的蚊帐大规模分布运动，以评估LandScan（LS），WorldPop约束（WP-C）和WorldPop无约束（WP-U），网格化的世界人口（GPW），和高分辨率的结算层（HRSL）。每一层都与金标准进行比较，使用统计措施来评估分布，误差和偏差。我们调查了地图选择如何影响疟疾流行模型的负担估计。特定的人口层能够在不同的人口密度匹配的黄金标准分布。LandScan能够最准确地捕获高度城市分布，HRSL和WP-C在所有其他较低的人口密度下匹配得最好。GPW和WP-U在各地表现不佳。正确捕捉空像素是关键，更小的像素尺寸（100米对1公里）可以改善这一点。根据已知的地区人口标准化区域可以提高性能。在疟疾模型中使用不同的人口层显示，在地方性水平之间的过渡点周围的结果存在差异。本文中的指标，其中一些在这种情况下是新颖的，描述了这些人口地图与黄金标准人口普查以及彼此之间的差异。我们表明，这些指标有助于了解疟疾模型中人口地图的性能。与人口普查数据最接近的是将城市地区的联合收割机LandScan和农村地区的HRSL结合起来。如果健康计算很大程度上依赖于知道人们不在哪里，或者如果对城市内的密度变化进行分类很重要，那么研究人员应该更喜欢特定的地图。
Geospatial datasets of population are becoming more common in models used for health policy. Publicly-available maps of human population make a consistent picture from inconsistent census data, and the techniques they use to impute data makes each population map unique. Each mapping model explains its methods, but it can be difficult to know which map is appropriate for which policy work. High quality census datasets, where available, are a unique opportunity to characterize maps by comparing them with truth. We use census data from a bed-net mass-distribution campaign on Bioko Island, Equatorial Guinea, conducted by the Bioko Island Malaria Elimination Program as a gold standard to evaluate LandScan (LS), WorldPop Constrained (WP-C) and WorldPop Unconstrained (WP-U), Gridded Population of the World (GPW), and the High-Resolution Settlement Layer (HRSL). Each layer is compared to the gold-standard using statistical measures to evaluate distribution, error, and bias. We investigated how map choice affects burden estimates from a malaria prevalence model. Specific population layers were able to match the gold-standard distribution at different population densities. LandScan was able to most accurately capture highly urban distribution, HRSL and WP-C matched best at all other lower population densities. GPW and WP-U performed poorly everywhere. Correctly capturing empty pixels is key, and smaller pixel sizes (100 m vs 1 km) improve this. Normalizing areas based on known district populations increased performance. The use of differing population layers in a malaria model showed a disparity in results around transition points between endemicity levels. The metrics in this paper, some of them novel in this context, characterize how these population maps differ from the gold standard census and from each other. We show that the metrics help understand the performance of a population map within a malaria model. The closest match to the census data would combine LandScan within urban areas and the HRSL for rural areas. Researchers should prefer particular maps if health calculations have a strong dependency on knowing where people are not, or if it is important to categorize variation in density within a city.
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影响因子：
15.8
作者：
Stevens GA;Alkema L;Black RE;Boerma JT;Collins GS;Ezzati M;Grove JT;Hogan DR;Hogan MC;Horton R;Lawn JE;Marušić A;Mathers CD;Murray CJ;Rudan I;Salomon JA;Simpson PJ;Vos T;Welch V;GATHER Working Group
通讯作者：
GATHER Working Group
影响因子：
3
作者：
Cook J;Hergott D;Phiri W;Rivas MR;Bradley J;Segura L;Garcia G;Schwabe C;Kleinschmidt I
通讯作者：
Kleinschmidt I
影响因子：
3
作者：
Garcia, Guillermo A.;Hergott, Dianna E. B.;Guerra, Carlos A.
通讯作者：
Guerra, Carlos A.
影响因子：
16.6
作者：
Cooper, Laura;Kang, Su Yun;Smith, David L.
通讯作者：
Smith, David L.
影响因子：
16.6
作者：
Guerra, Carlos A.;Kang, Su Yun;Smith, David L.
通讯作者：
Smith, David L.