German Aerospace Center (DLR), German Remote Sensing Data Center (DFD), Weßling, Germany.
Company for Remote Sensing and Environmental Research (SLU), München, Germany.
PLoS One. 2022 Sep 16;17(9):e0274504. doi: 10.1371/journal.pone.0274504. eCollection 2022.
High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping.
从危机管理决策到城市规划。地球观测极大地促进了利用更高分辨率数据将人口数据细分为精细人口地图的方法的发展。然而，基于可用数据，哪种方法最合适，以及空间单元和精度指标如何影响验证过程，这些都不完全清楚。我们旨在为试图使用遥感和地理空间信息在异质城市景观中生成高分辨率人口地图的研究人员提供建议。为此，我们针对 36 种不同场景的人口分解方法进行了全面的实验研究。我们将五种不同的自上而下的方法（从基本到复杂，即二进制和分类 dasymetric、统计和二进制和分类混合方法）应用于不同分辨率和可用性程度（较差、平均和较好）的数据子集上。然后，使用六种精度指标通过两种方法对生成的人口地图进行系统验证。我们发现，仅使用遥感数据时，统计和 dasymetric 方法的组合可以提供更好的结果，而高分辨率数据则需要更简单的方法。此外，强烈鼓励使用至少三种相对精度指标，因为验证取决于水平和方法。我们还分析了相对误差的行为及其如何受到城市景观异质性的影响。我们希望我们的建议能够为未来的人口制图节省额外的工作和时间。
